diff --git a/CHANGELOG.md b/CHANGELOG.md index 67471a4..cdb030c 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -5,6 +5,11 @@ All notable changes to this project will be documented in this file. The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/), and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html). +## [0.0.4] - 2023-10-27 02:00:00 + +### Added +- Updates the Python and Git chapters as well as the corresponding data, images, and code + ## [0.0.3] - 2023-10-23 03:00:00 ### Added @@ -33,6 +38,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 +[0.0.4]: https://github.com/OpenSourceEcon/CompMethods/compare/v0.0.3...v0.0.4 [0.0.3]: https://github.com/OpenSourceEcon/CompMethods/compare/v0.0.2...v0.0.3 [0.0.2]: https://github.com/OpenSourceEcon/CompMethods/compare/v0.0.1...v0.0.2 [0.0.1]: https://github.com/OpenSourceEcon/CompMethods/compare/v0.0.0...v0.0.1 diff --git a/README.md b/README.md index 3475c00..074a78d 100644 --- a/README.md +++ b/README.md @@ -20,5 +20,8 @@ Please use the following citation form for this book. General citation to the book: * Evans, Richard W., *Computational Methods for Economists using Python*, Open access Jupyter Book, v#.#.#, 2023, https://opensourceecon.github.io/CompMethods/. -Citation to a chapter in the book: -* Evans, Richard W., "[insert chapter name]", in *Computational Methods for Economists using Python*, Open access Jupyter Book, v#.#.#, 2023, https://opensourceecon.github.io/CompMethods/ + [chapter path]. +Citation to a chapter in the book only authored by Evans: +* Evans, Richard W., "[insert chapter name]", in *Computational Methods for Economists using Python*, Open access Jupyter Book, v#.#.#, 2023, https://opensourceecon.github.io/CompMethods + [chapter path]. + +Citation to a chapter in the book only authored by multiple authors: +* DeBacker, Jason and Richard W. Evans, "[insert chapter name]", in *Computational Methods for Economists using Python*, Open access Jupyter Book, v#.#.#, 2023, https://opensourceecon.github.io/CompMethods + [chapter path]. diff --git a/code/AdvancedNumPy/advanced_numpy.py b/code/AdvancedNumPy/advanced_numpy.py new file mode 100644 index 0000000..0641aac --- /dev/null +++ b/code/AdvancedNumPy/advanced_numpy.py @@ -0,0 +1,143 @@ +# advanced_numpy.py +"""Python Essentials: Advanced NumPy. + + + +""" +import numpy as np +from sympy import isprime +from matplotlib import pyplot as plt + + +def prob1(A): + """Make a copy of 'A' and set all negative entries of the copy to 0. + Return the copy. + + Example: + >>> A = np.array([-3,-1,3]) + >>> prob4(A) + array([0, 0, 3]) + """ + raise NotImplementedError("Problem 1 Incomplete") + + +def prob2(arr_list): + """return all arrays in arr_list as one 3-dimensional array + where the arrays are padded with zeros appropriately.""" + raise NotImplementedError("Problem 2 Incomplete") + + +def prob3(func, A): + """Time how long it takes to run func on the array A in two different ways, + where func is a universal function. + First, use array broadcasting to operate on the entire array element-wise. + Second, use a nested for loop, operating on each element individually. + Return the ratio showing how many times faster array broadcasting is than + using a nested for loop, averaged over 10 trials. + + Parameters: + func -- array broadcast-able numpy function + A -- nxn array to operate on + Returns: + num_times_faster -- float + """ + raise NotImplementedError("Problem 3 Incomplete") + + +def prob4(A): + """Divide each row of 'A' by the row sum and return the resulting array. + + Example: + >>> A = np.array([[1,1,0],[0,1,0],[1,1,1]]) + >>> prob6(A) + array([[ 0.5 , 0.5 , 0. ], + [ 0. , 1. , 0. ], + [ 0.33333333, 0.33333333, 0.33333333]]) + """ + raise NotImplementedError("Problem 4 Incomplete") + + +# this is provided for problem 5 +def LargestPrime(x, show_factorization=False): + # account for edge cases. + if x == 0 or x == 1: + return np.nan + + # create needed variables + forced_break = False + prime_factors = [] # place to store factors of number + factor_test_arr = np.arange(1, 11) + + while True: + # a factor is never more than half the number + if np.min(factor_test_arr) > (x // 2) + 1: + forced_break = True + break + if isprime(x): # if the checked number is prime itself, stop + prime_factors.append(x) + break + + # check if anythin gin the factor_test_arr are factors + div_arr = x / factor_test_arr + factor_mask = div_arr - div_arr.astype(int) == 0 + divisors = factor_test_arr[factor_mask] + if divisors.size > 0: # if divisors exist... + if ( + divisors[0] == 1 and divisors.size > 1 + ): # make sure not to select 1 + i = 1 + elif ( + divisors[0] == 1 and divisors.size == 1 + ): # if one is the only one don't pick it + factor_test_arr = factor_test_arr + 10 + continue + else: # othewise take the smallest divisor + i = 0 + + # if divisor was found divide number by it and + # repeat the process + x = int(x / divisors[i]) + prime_factors.append(divisors[i]) + factor_test_arr = np.arange(1, 11) + else: # if no number was found increase the test_arr + # and keep looking for factors + factor_test_arr = factor_test_arr + 10 + continue + + if show_factorization: # show entire factorization if desired + print(prime_factors) + if forced_break: # if too many iterations break + print(f"Something wrong, exceeded iteration threshold for value: {x}") + return 0 + return max(prime_factors) + + +def prob5(arr, naive=False): + """Return an array where every number is replaced be the largest prime + in its factorization. Implement two methods. Switching between the two + is determined by a bool. + + Example: + >>> A = np.array([15, 41, 49, 1077]) + >>> prob4(A) + array([5,41,7,359]) + """ + raise NotImplementedError("Problem 5 Incomplete") + + +def prob6(x, y, z, A, optimize=False, split=True): + """takes three vectors and a matrix and performs + (np.outer(x,y)*z.reshape(-1,1))@A on them using einsum.""" + raise NotImplementedError("Problem 6 part 1 Incomplete") + + +def naive6(x, y, z, A): + """uses normal numpy functions to do what prob5 does""" + raise NotImplementedError("Problem 6 part 2 Incomplete") + + +def prob7(): + """Times and creates plots that generate the difference in + speeds between einsum and normal numpy functions + """ + raise NotImplementedError("Problem 7 Incomplete") diff --git a/code/Exceptions_FileIO/cf_example1.txt b/code/Exceptions_FileIO/cf_example1.txt new file mode 100644 index 0000000..8129203 --- /dev/null +++ b/code/Exceptions_FileIO/cf_example1.txt @@ -0,0 +1,2 @@ +A b C +d E f diff --git a/code/Exceptions_FileIO/cf_example2.txt b/code/Exceptions_FileIO/cf_example2.txt new file mode 100644 index 0000000..d203a8b --- /dev/null +++ b/code/Exceptions_FileIO/cf_example2.txt @@ -0,0 +1,3 @@ +Ab Cd +Ef Gh +Ij Kl diff --git a/code/Exceptions_FileIO/exceptions_fileIO.py b/code/Exceptions_FileIO/exceptions_fileIO.py new file mode 100644 index 0000000..ea24e7b --- /dev/null +++ b/code/Exceptions_FileIO/exceptions_fileIO.py @@ -0,0 +1,108 @@ +# exceptions_fileIO.py +"""Python Essentials: Exceptions and File Input/Output. + + + +""" + +from random import choice + + +# Problem 1 +def arithmagic(): + """ + Takes in user input to perform a magic trick and prints the result. + Verifies the user's input at each step and raises a + ValueError with an informative error message if any of the following occur: + + The first number step_1 is not a 3-digit number. + The first number's first and last digits differ by less than $2$. + The second number step_2 is not the reverse of the first number. + The third number step_3 is not the positive difference of the first two numbers. + The fourth number step_4 is not the reverse of the third number. + """ + + step_1 = input( + "Enter a 3-digit number where the first and last " + "digits differ by 2 or more: " + ) + step_2 = input( + "Enter the reverse of the first number, obtained " + "by reading it backwards: " + ) + step_3 = input("Enter the positive difference of these numbers: ") + step_4 = input("Enter the reverse of the previous result: ") + print(str(step_3), "+", str(step_4), "= 1089 (ta-da!)") + + +# Problem 2 +def random_walk(max_iters=1e12): + """ + If the user raises a KeyboardInterrupt by pressing ctrl+c while the + program is running, the function should catch the exception and + print "Process interrupted at iteration $i$". + If no KeyboardInterrupt is raised, print "Process completed". + + Return walk. + """ + + walk = 0 + directions = [1, -1] + for i in range(int(max_iters)): + walk += choice(directions) + return walk + + +# Problems 3 and 4: Write a 'ContentFilter' class. +class ContentFilter(object): + """Class for reading in file + + Attributes: + filename (str): The name of the file + contents (str): the contents of the file + + """ + + # Problem 3 + def __init__(self, filename): + """Read from the specified file. If the filename is invalid, prompt + the user until a valid filename is given. + """ + + # Problem 4 --------------------------------------------------------------- + def check_mode(self, mode): + """Raise a ValueError if the mode is invalid.""" + + def uniform(self, outfile, mode="w", case="upper"): + """Write the data to the outfile with uniform case. Include an additional + keyword argument case that defaults to "upper". If case="upper", write + the data in upper case. If case="lower", write the data in lower case. + If case is not one of these two values, raise a ValueError.""" + + def reverse(self, outfile, mode="w", unit="word"): + """Write the data to the outfile in reverse order. Include an additional + keyword argument unit that defaults to "line". If unit="word", reverse + the ordering of the words in each line, but write the lines in the same + order as the original file. If units="line", reverse the ordering of the + lines, but do not change the ordering of the words on each individual + line. If unit is not one of these two values, raise a ValueError.""" + + def transpose(self, outfile, mode="w"): + """Write a transposed version of the data to the outfile. That is, write + the first word of each line of the data to the first line of the new file, + the second word of each line of the data to the second line of the new + file, and so on. Viewed as a matrix of words, the rows of the input file + then become the columns of the output file, and viceversa. You may assume + that there are an equal number of words on each line of the input file. + """ + + def __str__(self): + """Printing a ContentFilter object yields the following output: + + Source file: + Total characters: + Alphabetic characters: + Numerical characters: + Whitespace characters: + Number of lines: + """ diff --git a/code/Exceptions_FileIO/hello_world.txt b/code/Exceptions_FileIO/hello_world.txt new file mode 100644 index 0000000..7d60d01 --- /dev/null +++ b/code/Exceptions_FileIO/hello_world.txt @@ -0,0 +1,2 @@ +Hello, +World! diff --git a/code/Matplotlib1/FARS.npy b/code/Matplotlib1/FARS.npy new file mode 100644 index 0000000..98d19aa Binary files /dev/null and b/code/Matplotlib1/FARS.npy differ diff --git a/code/Matplotlib1/matplotlib_intro.py b/code/Matplotlib1/matplotlib_intro.py new file mode 100644 index 0000000..7539cf6 --- /dev/null +++ b/code/Matplotlib1/matplotlib_intro.py @@ -0,0 +1,92 @@ +# matplotlib_intro.py +"""Python Essentials: Intro to Matplotlib. + + + +""" + + +# Problem 1 +def var_of_means(n): + """Create an (n x n) array of values randomly sampled from the standard + normal distribution. Compute the mean of each row of the array. Return the + variance of these means. + + Parameters: + n (int): The number of rows and columns in the matrix. + + Returns: + (float) The variance of the means of each row. + """ + raise NotImplementedError("Problem 1 Incomplete") + + +def prob1(): + """Create an array of the results of var_of_means() with inputs + n = 100, 200, ..., 1000. Plot and show the resulting array. + """ + raise NotImplementedError("Problem 1 Incomplete") + + +# Problem 2 +def prob2(): + """Plot the functions sin(x), cos(x), and arctan(x) on the domain + [-2pi, 2pi]. Make sure the domain is refined enough to produce a figure + with good resolution. + """ + raise NotImplementedError("Problem 2 Incomplete") + + +# Problem 3 +def prob3(): + """Plot the curve f(x) = 1/(x-1) on the domain [-2,6]. + 1. Split the domain so that the curve looks discontinuous. + 2. Plot both curves with a thick, dashed magenta line. + 3. Set the range of the x-axis to [-2,6] and the range of the + y-axis to [-6,6]. + """ + raise NotImplementedError("Problem 3 Incomplete") + + +# Problem 4 +def prob4(): + """Plot the functions sin(x), sin(2x), 2sin(x), and 2sin(2x) on the + domain [0, 2pi], each in a separate subplot of a single figure. + 1. Arrange the plots in a 2 x 2 grid of subplots. + 2. Set the limits of each subplot to [0, 2pi]x[-2, 2]. + 3. Give each subplot an appropriate title. + 4. Give the overall figure a title. + 5. Use the following line colors and styles. + sin(x): green solid line. + sin(2x): red dashed line. + 2sin(x): blue dashed line. + 2sin(2x): magenta dotted line. + """ + raise NotImplementedError("Problem 4 Incomplete") + + +# Problem 5 +def prob5(): + """Visualize the data in FARS.npy. Use np.load() to load the data, then + create a single figure with two subplots: + 1. A scatter plot of longitudes against latitudes. Because of the + large number of data points, use black pixel markers (use "k," + as the third argument to plt.plot()). Label both axes. + 2. A histogram of the hours of the day, with one bin per hour. + Label and set the limits of the x-axis. + """ + raise NotImplementedError("Problem 5 Incomplete") + + +# Problem 6 +def prob6(): + """Plot the function g(x,y) = sin(x)sin(y)/xy on the domain + [-2pi, 2pi]x[-2pi, 2pi]. + 1. Create 2 subplots: one with a heat map of g, and one with a contour + map of g. Choose an appropriate number of level curves, or specify + the curves yourself. + 2. Set the limits of each subplot to [-2pi, 2pi]x[-2pi, 2pi]. + 3. Choose a non-default color scheme. + 4. Include a color scale bar for each subplot. + """ + raise NotImplementedError("Problem 6 Incomplete") diff --git a/code/Matplotlib2/matplotlib2.ipynb b/code/Matplotlib2/matplotlib2.ipynb new file mode 100644 index 0000000..940e8e5 --- /dev/null +++ b/code/Matplotlib2/matplotlib2.ipynb @@ -0,0 +1,220 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "-2R2EklVnwxF" + }, + "source": [ + "# Pandas 2\n", + "\n", + "## Name\n", + "\n", + "## Class\n", + "\n", + "## Date" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "B4EqVNKaPFma" + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Problem 1" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "nmDeeo7kP9L8" + }, + "outputs": [], + "source": [ + "def prob1():\n", + " \"\"\"\n", + " Create 3 visualizations of the crime data set.\n", + " One of the visualizations should be a histogram.\n", + " The visualizations should be clearly labelled and easy to understand.\n", + " \"\"\"\n", + " raise NotImplementedError(\"Problem 1 Incomplete\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "prob1()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "uyGF9cEjsyFi" + }, + "source": [ + "# Problem 2" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "DJD2o3UxQbYQ" + }, + "outputs": [], + "source": [ + " def prob2():\n", + " \"\"\"\n", + " Use crime_data.csv to plot the trends between Larceny and\n", + " 1. Violent\n", + " 2. Burglary\n", + " 3. Aggravated Assault\n", + " Each graph should be clearly labelled and readable.\n", + " One of these variables does not have a linear trend with Larceny.\n", + " Return a string identifying this variable.\n", + " \"\"\"\n", + " raise NotImplementedError(\"Problem 2 Incomplete\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "prob2()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "UUuNrP5UszZ6" + }, + "source": [ + "# Problem 3" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "lWPH4Ay2Ux3f" + }, + "outputs": [], + "source": [ + "def prob3():\n", + " \"\"\"\n", + " Use crime_data.csv to display the following distributions.\n", + " 1. The distributions of Burglary, Violent, and Vehicle Theft \n", + " as box plots\n", + " 2. The distributions of Vehicle Thefts against Robberies as\n", + " a hexbin plot.\n", + " All plots should be labelled and easy to read.\n", + " \"\"\"\n", + " raise NotImplementedError(\"Problem 3 Incomplete\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "prob3()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "mhJbAS1ts0q5" + }, + "source": [ + "# Problem 4" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "OVF_p0fyZkXy" + }, + "outputs": [], + "source": [ + "def prob4():\n", + " \"\"\"\n", + " Create 3 plots that compare variables or universities.\n", + " These plots should answer questions about the data, \n", + " (e.g. What is the distribution of graduation rates? Do schools \n", + " with lower student to faculty ratios have higher tuition costs? \n", + " etc.). These plots should be easy to understand and have clear \n", + " titles, variable names, and citations.\n", + " \"\"\"\n", + " raise NotImplementedError(\"Problem 4 Incomplete\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "prob4()" + ] + } + ], + "metadata": { + "colab": { + "name": "pandas2.ipynb", + "provenance": [], + "version": "0.3.2" + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.4" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/code/Matplotlib3/animation.ipynb b/code/Matplotlib3/animation.ipynb new file mode 100644 index 0000000..217e0e8 --- /dev/null +++ b/code/Matplotlib3/animation.ipynb @@ -0,0 +1,156 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.animation as animation\n", + "from IPython.display import HTML" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib notebook" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Problem 1\n", + "\n", + "Use the FuncAnimation class to animate the function $y = \\sin(x + 3t)$ where $x \\in [0, 2\\pi]$, and t ranges from 0 to 10 seconds.\n", + "Save your animation to a file and embed the created file." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Problem 2\n", + "\n", + "The orbits for Mercury, Venus, Earth, and Mars are stored in the file `orbits.npz`. The file contains four NumPy arrays: `mercury`, `venus`, `earth`, and `mars`. The first column of each array contains the x-coordinates, the second column contains the y-coordinates, and the third column contians the z-coordinates, all relative to the Sun, and expressed in AU (astronomical units, the average distance between Earth and the Sun, approximately 150 million\n", + "kilometers).\n", + "\n", + "Use `np.load('orbits.npz')` to load the data for the four planets' orbits. Create a 3D plot of the orbits, and compare your results with Figure 1.1." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Problem 3\n", + "\n", + "Each row of the arrays in `orbits.npz` gives the position of the planets at a\n", + "particular time. The arrays have 1400 points in time over a 700 day period (beginning on 2018-5-30). Create a 3D animation of the planet orbits. Display lines for the trajectories of the orbits and points for the current positions of the planets at each point in time. Your update() function will need to return a list of `Line3D` objects, one for each orbit trajectory and one for each planet position marker. Using `animation.save()`, save your animated plot, and embed you animated plot." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Problem 4\n", + "\n", + "Make a surface plot of the bivariate normal density function given by:\n", + "\n", + "$$f(\\mathbf{x}) = \\frac{1}{\\sqrt{\\det(2\\pi\\Sigma)}}\\exp[-\\frac{1}{2}(\\mathbf{x}-\\mathbf{\\mu})^{T}\\Sigma^{-1}(\\mathbf{x}-\\mathbf{\\mu})]$$\n", + "\n", + "Where $\\mathbf{x} = [x,y]^T \\in \\mathbb{R}^2$, $\\mathbf{\\mu} = [0,0]^T$ is the mean vector, and: $$\\Sigma = \\begin{bmatrix} 1 & 3/5 \\\\ 3/5 & 2 \\end{bmatrix}$$ is the covariance matrix." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Problem 5\n", + "\n", + "Use the data in vibration.npz to produce a surface animation of the solution\n", + "to the wave equation for an elastic rectangular membrane. The file contains three NumPy arrays: `X`, `Y`, `Z`. `X` and `Y` are meshgrids of shape `(300,200)` corresponding to 300 points in\n", + "the y-direction and 200 points in the x-direction, all corresponding to a 2x3 rectangle with one corner at the origin. `Z` is of shape `(150,300,200)`, giving the height of the vibrating membrane at each (x,y) point for 150 values of time. In the language of partial differential equations, this is the solution to the following intital/boundary value problem for the wave equation:\n", + "\n", + "$$u_{tt} = 6^2(u_{xx}+u_{yy})$$\n", + "$$(x,y) \\in [0,2]\\times[0,3],t \\in [0,5]$$\n", + "$$u(t,0,y)=u(t,2,y)=u(t,x,0)=u(t,x,3) = 0$$\n", + "$$u(0,x,y) = xy(2-x)(3-y)$$\n", + "\n", + "Load the data with `np.load('vibration.npz')`. Create a 3D surface animation of the vibrating membrane. Save the animation and embed it in the notebook." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/code/NumPyIntro/grid.npy b/code/NumPyIntro/grid.npy new file mode 100644 index 0000000..6f1273a Binary files /dev/null and b/code/NumPyIntro/grid.npy differ diff --git a/code/NumPyIntro/numpy_intro.py b/code/NumPyIntro/numpy_intro.py new file mode 100644 index 0000000..4dd21f2 --- /dev/null +++ b/code/NumPyIntro/numpy_intro.py @@ -0,0 +1,70 @@ +# numpy_intro.py +"""Python Essentials: Intro to NumPy. + + + +""" + + +def prob1(): + """Define the matrices A and B as arrays. Return the matrix product AB.""" + raise NotImplementedError("Problem 1 Incomplete") + + +def prob2(): + """Define the matrix A as an array. Return the matrix -A^3 + 9A^2 - 15A.""" + raise NotImplementedError("Problem 2 Incomplete") + + +def prob3(): + """Define the matrices A and B as arrays using the functions presented in + this section of the manual (not np.array()). Calculate the matrix product ABA, + change its data type to np.int64, and return it. + """ + raise NotImplementedError("Problem 3 Incomplete") + + +def prob4(A): + """Make a copy of 'A' and use fancy indexing to set all negative entries of + the copy to 0. Return the resulting array. + + Example: + >>> A = np.array([-3,-1,3]) + >>> prob4(A) + array([0, 0, 3]) + """ + raise NotImplementedError("Problem 4 Incomplete") + + +def prob5(): + """Define the matrices A, B, and C as arrays. Use NumPy's stacking functions + to create and return the block matrix: + | 0 A^T I | + | A 0 0 | + | B 0 C | + where I is the 3x3 identity matrix and each 0 is a matrix of all zeros + of the appropriate size. + """ + raise NotImplementedError("Problem 5 Incomplete") + + +def prob6(A): + """Divide each row of 'A' by the row sum and return the resulting array. + Use array broadcasting and the axis argument instead of a loop. + + Example: + >>> A = np.array([[1,1,0],[0,1,0],[1,1,1]]) + >>> prob6(A) + array([[ 0.5 , 0.5 , 0. ], + [ 0. , 1. , 0. ], + [ 0.33333333, 0.33333333, 0.33333333]]) + """ + raise NotImplementedError("Problem 6 Incomplete") + + +def prob7(): + """Given the array stored in grid.npy, return the greatest product of four + adjacent numbers in the same direction (up, down, left, right, or + diagonally) in the grid. Use slicing, as specified in the manual. + """ + raise NotImplementedError("Problem 7 Incomplete") diff --git a/code/ObjectOriented/object_oriented.py b/code/ObjectOriented/object_oriented.py new file mode 100644 index 0000000..89de631 --- /dev/null +++ b/code/ObjectOriented/object_oriented.py @@ -0,0 +1,98 @@ +# object_oriented.py +"""Python Essentials: Object Oriented Programming. + + + +""" + + +class Backpack: + """A Backpack object class. Has a name and a list of contents. + + Attributes: + name (str): the name of the backpack's owner. + contents (list): the contents of the backpack. + """ + + # Problem 1: Modify __init__() and put(), and write dump(). + def __init__(self, name): + """Set the name and initialize an empty list of contents. + + Parameters: + name (str): the name of the backpack's owner. + """ + self.name = name + self.contents = [] + + def put(self, item): + """Add an item to the backpack's list of contents.""" + self.contents.append(item) + + def take(self, item): + """Remove an item from the backpack's list of contents.""" + self.contents.remove(item) + + # Magic Methods ----------------------------------------------------------- + + # Problem 3: Write __eq__() and __str__(). + def __add__(self, other): + """Add the number of contents of each Backpack.""" + return len(self.contents) + len(other.contents) + + def __lt__(self, other): + """Compare two backpacks. If 'self' has fewer contents + than 'other', return True. Otherwise, return False. + """ + return len(self.contents) < len(other.contents) + + +# An example of inheritance. You are not required to modify this class. +class Knapsack(Backpack): + """A Knapsack object class. Inherits from the Backpack class. + A knapsack is smaller than a backpack and can be tied closed. + + Attributes: + name (str): the name of the knapsack's owner. + color (str): the color of the knapsack. + max_size (int): the maximum number of items that can fit inside. + contents (list): the contents of the backpack. + closed (bool): whether or not the knapsack is tied shut. + """ + + def __init__(self, name, color): + """Use the Backpack constructor to initialize the name, color, + and max_size attributes. A knapsack only holds 3 item by default. + + Parameters: + name (str): the name of the knapsack's owner. + color (str): the color of the knapsack. + max_size (int): the maximum number of items that can fit inside. + """ + Backpack.__init__(self, name, color, max_size=3) + self.closed = True + + def put(self, item): + """If the knapsack is untied, use the Backpack.put() method.""" + if self.closed: + print("I'm closed!") + else: + Backpack.put(self, item) + + def take(self, item): + """If the knapsack is untied, use the Backpack.take() method.""" + if self.closed: + print("I'm closed!") + else: + Backpack.take(self, item) + + def weight(self): + """Calculate the weight of the knapsack by counting the length of the + string representations of each item in the contents list. + """ + return sum(len(str(item)) for item in self.contents) + + +# Problem 2: Write a 'Jetpack' class that inherits from the 'Backpack' class. + + +# Problem 4: Write a 'ComplexNumber' class. diff --git a/code/Pandas1/pandas1.ipynb b/code/Pandas1/pandas1.ipynb new file mode 100644 index 0000000..052ed3f --- /dev/null +++ b/code/Pandas1/pandas1.ipynb @@ -0,0 +1,292 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "hr6QvWC1sVno" + }, + "source": [ + "# Pandas 1\n", + "\n", + "## Name\n", + "\n", + "## Class\n", + "\n", + "## Date" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "D1pxi6sWEcmJ" + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "Y8nzrZCaE4bn" + }, + "source": [ + "# Problem 1" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Prob 1\n", + "def prob1(file='budget.csv'):\n", + " \"\"\"\"\n", + " Read in budget.csv as a DataFrame with the index as column 0 and perform each of these operations on the DataFrame in order. \n", + " \n", + " 1) Reindex the columns such that amount spent on groceries is the first column and all other columns maintain the same ordering.\n", + " 2) Sort the DataFrame in descending order based on how much money was spent on Groceries.\n", + " 3) Reset all values in the 'Rent' column to 800.0.\n", + " 4) Reset all values in the first 5 data points to 0.0\n", + " \n", + " Return the values of the updated DataFrame as a NumPy array.\n", + " \n", + " Parameters:\n", + " file (str): name of datafile\n", + " \n", + " Return:\n", + " values (ndarray): values of DataFrame\n", + " \"\"\"\n", + " raise NotImplementedError(\"Problem 1 Incomplete\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "FcGE9Qq5scpv" + }, + "source": [ + "# Problem 2" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "bZIdjL74RuuO" + }, + "outputs": [], + "source": [ + "# Prob 2\n", + "def prob2(file='budget.csv'):\n", + " \"\"\"\n", + " Read in file as DataFrame.\n", + " Fill all NaN values with 0.0.\n", + " Create two new columns, 'Living Expenses' and 'Other'. \n", + " Sum the columns 'Rent', 'Groceries', 'Gas' and 'Utilities' and set it as the value of 'Living Expenses'.\n", + " Sum the columns 'Dining Out', 'Out With Friends' and 'Netflix' and set as the value of 'Other'.\n", + " Identify which column, other than 'Living Expenses' correlates most with 'Living Expenses'\n", + " and which column other than 'Other' correlates most with 'Other'.\n", + "\n", + " Return the names of each of those columns as a tuple.\n", + " The first should be of the column corresponding to \\li{'Living Expenses'} and the second to \\li{'Other'}.\n", + " \n", + " Parameters:\n", + " file (str): name of datafile\n", + " \n", + " Return:\n", + " values (tuple): (name of column that most relates to Living Expenses, name of column that most relates to Other)\n", + " \"\"\"\n", + " raise NotImplementedError(\"Problem 2 Incomplete\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "qVHAwFRRseXh" + }, + "source": [ + "# Problem 3" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "35VAshdqZhVD" + }, + "outputs": [], + "source": [ + "def prob3(file='crime_data.csv'):\n", + " \"\"\"\n", + " Read in crime data and use pandas to answer the following questions.\n", + " \n", + " Set the index as the column 'Year', and return the answers to each question as a tuple.\n", + " \n", + " 1) Identify the three crimes that have a mean over 1,500,000. \n", + " Of these three crimes, which two are very correlated? \n", + " Which of these two crimes has a greater maximum value?\n", + " Save the title of this column as a variable to return as the answer.\n", + " \n", + " 2) Examine the data since 2000.\n", + " Sort this data (in ascending order) according to number of murders.\n", + " Find the years where Aggravated Assault is greater than 850,000.\n", + " Save the indices (the years) of the masked and reordered DataFrame as a NumPy array to return as the answer.\n", + " \n", + " 3) What year had the highest crime rate? \n", + " In this year, which crime was committed the most? \n", + " What percentage of the total crime that year was it? \n", + " Save this value as a float.\n", + " \n", + " \n", + " Parameters:\n", + " file (str): data\n", + " \n", + " Return:\n", + " ans_1 (string): answer to Question 1\n", + " ans_2 (ndarray): answer to Question 2\n", + " ans_3 (float): answer to Question 3\n", + " \"\"\"\n", + " raise NotImplementedError(\"Problem 3 Incomplete\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "4pfN6PbxsgC3" + }, + "source": [ + "# Problem 4" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "TAavKLA17LsN" + }, + "outputs": [], + "source": [ + "def prob4(file='DJIA.csv'):\n", + " \"\"\"\n", + "\n", + " Read the data with a DatetimeIndex as the index.\n", + " Drop rows any rows without numerical values, cast the \"VALUE\" column to floats, then return the updated DataFrame.\n", + "\n", + " Parameters:\n", + " file (str): data file\n", + " Returns:\n", + " df (DataFrame): updated DataFrame of stock market data\n", + " \"\"\"\n", + " raise NotImplementedError(\"Problem 4 Incomplete\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "I663KesNsjMK" + }, + "source": [ + "# Problem 5" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def prob5(file='paychecks.csv'):\n", + " \"\"\"\n", + "\n", + " Create data_range for index of paycheck data.\n", + "\n", + " Parameters:\n", + " file (str): data file\n", + " Returns:\n", + " df (DataFrame): DataFrame of paycheck data\n", + " \"\"\"\n", + " raise NotImplementedError(\"Problem 5 Incomplete\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "I663KesNsjMK" + }, + "source": [ + "# Problem 6" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "KGxh0mpSDLDD" + }, + "outputs": [], + "source": [ + "def prob6(file='DJIA.csv'):\n", + " \"\"\"\n", + " Compute the following information about the DJIA dataset\n", + " 1. The single day with the largest gain\n", + " 2. The single day with the largest loss\n", + "\n", + " Parameters:\n", + " file (str): data file\n", + " Returns:\n", + " max_day ( + + +""" + + +# Problem 1 (write code below) + + +# Problem 2 +def sphere_volume(r): + """Return the volume of the sphere of radius 'r'. + Use 3.14159 for pi in your computation. + """ + raise NotImplementedError("Problem 2 Incomplete") + + +# Problem 3 +def isolate(a, b, c, d, e): + """Print the arguments separated by spaces, but print 5 spaces on either + side of b. + """ + raise NotImplementedError("Problem 3 Incomplete") + + +# Problem 4 +def first_half(my_string): + """Return the first half of the string 'my_string'. Exclude the + middle character if there are an odd number of characters. + + Examples: + >>> first_half("python") + 'pyt' + >>> first_half("ipython") + 'ipy' + """ + raise NotImplementedError("Problem 4 Incomplete") + + +def backward(my_string): + """Return the reverse of the string 'my_string'. + + Examples: + >>> backward("python") + 'nohtyp' + >>> backward("ipython") + 'nohtypi' + """ + raise NotImplementedError("Problem 4 Incomplete") + + +# Problem 5 +def list_ops(): + """Define a list with the entries "bear", "ant", "cat", and "dog". + Perform the following operations on the list: + - Append "eagle". + - Replace the entry at index 2 with "fox". + - Remove (or pop) the entry at index 1. + - Sort the list in reverse alphabetical order. + - Replace "eagle" with "hawk". + - Add the string "hunter" to the last entry in the list. + Return the resulting list. + + Examples: + >>> list_ops() + ['fox', 'hawk', 'dog', 'bearhunter'] + """ + raise NotImplementedError("Problem 5 Incomplete") + + +# Problem 6 +def pig_latin(word): + """Translate the string 'word' into Pig Latin, and return the new word. + + Examples: + >>> pig_latin("apple") + 'applehay' + >>> pig_latin("banana") + 'ananabay' + """ + raise NotImplementedError("Problem 6 Incomplete") + + +# Problem 7 +def palindrome(): + """Find and retun the largest panindromic number made from the product + of two 3-digit numbers. + """ + raise NotImplementedError("Problem 7 Incomplete") + + +# Problem 8 +def alt_harmonic(n): + """Return the partial sum of the first n terms of the alternating + harmonic series, which approximates ln(2). + """ + raise NotImplementedError("Problem 8 Incomplete") diff --git a/code/StandardLibrary/box.py b/code/StandardLibrary/box.py new file mode 100644 index 0000000..4b3da72 --- /dev/null +++ b/code/StandardLibrary/box.py @@ -0,0 +1,52 @@ +# box.py +"""Python Essentials: The Standard Library. Auxiliary file (do not modify).""" + +from itertools import combinations + + +def isvalid(roll, remaining): + """Check to see whether or not a roll is valid. That is, check if there + exists a combination of the entries of 'remaining' that sum up to 'roll'. + + Parameters: + roll (int): The value of a dice roll, between 1 and 12 (inclusive). + This can either be a roll of one die or a roll of two dice + depending on the remaining values. + remaining (list): The list of the numbers that still need to be + removed before the box can be shut. + + Returns: + True if the roll is valid. + False if the roll is invalid. + """ + if roll not in range(1, 13): + return False + for i in range(1, len(remaining) + 1): + if any([sum(combo) == roll for combo in combinations(remaining, i)]): + return True + return False + + +def parse_input(player_input, remaining): + """Convert a string of numbers into a list of unique integers, if possible. + Then check that each of those integers is an entry in the other list. + + Parameters: + player_input (str): A string of integers, separated by spaces. + The player's choices for which numbers to remove. + remaining (list): The list of the numbers that still need to be + removed before the box can be shut. + + Returns: + A list of the integers if the input was valid. + An empty list if the input was invalid. + """ + try: + choices = [int(i) for i in player_input.split()] + if len(set(choices)) != len(choices): + raise ValueError + if any([number not in remaining for number in choices]): + raise ValueError + return choices + except ValueError: + return [] diff --git a/code/StandardLibrary/standard_library.py b/code/StandardLibrary/standard_library.py new file mode 100644 index 0000000..8d262db --- /dev/null +++ b/code/StandardLibrary/standard_library.py @@ -0,0 +1,56 @@ +# standard_library.py +"""Python Essentials: The Standard Library. + + + +""" + + +# Problem 1 +def prob1(L): + """Return the minimum, maximum, and average of the entries of L + (in that order, separated by a comma). + """ + raise NotImplementedError("Problem 1 Incomplete") + + +# Problem 2 +def prob2(): + """Determine which Python objects are mutable and which are immutable. + Test integers, strings, lists, tuples, and sets. Print your results. + """ + raise NotImplementedError("Problem 2 Incomplete") + + +# Problem 3 +def hypot(a, b): + """Calculate and return the length of the hypotenuse of a right triangle. + Do not use any functions other than sum(), product() and sqrt() that are + imported from your 'calculator' module. + + Parameters: + a: the length one of the sides of the triangle. + b: the length the other non-hypotenuse side of the triangle. + Returns: + The length of the triangle's hypotenuse. + """ + raise NotImplementedError("Problem 3 Incomplete") + + +# Problem 4 +def power_set(A): + """Use itertools to compute the power set of A. + + Parameters: + A (iterable): a str, list, set, tuple, or other iterable collection. + + Returns: + (list(sets)): The power set of A as a list of sets. + """ + raise NotImplementedError("Problem 4 Incomplete") + + +# Problem 5: Implement shut the box. +def shut_the_box(player, timelimit): + """Play a single game of shut the box.""" + raise NotImplementedError("Problem 5 Incomplete") diff --git a/code/UnitTest/specs.py b/code/UnitTest/specs.py new file mode 100644 index 0000000..e1deabc --- /dev/null +++ b/code/UnitTest/specs.py @@ -0,0 +1,159 @@ +# specs.py +"""Python Essentials: Unit Testing. + + + +""" + + +def add(a, b): + """Add two numbers.""" + return a + b + + +def divide(a, b): + """Divide two numbers, raising an error if the second number is zero.""" + if b == 0: + raise ZeroDivisionError("second input cannot be zero") + return a / b + + +# Problem 1 +def smallest_factor(n): + """Return the smallest prime factor of the positive integer n.""" + if n == 1: + return 1 + for i in range(2, int(n**0.5)): + if n % i == 0: + return i + return n + + +# Problem 2 +def month_length(month, leap_year=False): + """Return the number of days in the given month.""" + if month in {"September", "April", "June", "November"}: + return 30 + elif month in { + "January", + "March", + "May", + "July", + "August", + "October", + "December", + }: + return 31 + if month == "February": + if not leap_year: + return 28 + else: + return 29 + else: + return None + + +# Problem 3 +def operate(a, b, oper): + """Apply an arithmetic operation to a and b.""" + if type(oper) is not str: + raise TypeError("oper must be a string") + elif oper == "+": + return a + b + elif oper == "-": + return a - b + elif oper == "*": + return a * b + elif oper == "/": + if b == 0: + raise ZeroDivisionError("division by zero is undefined") + return a / b + raise ValueError("oper must be one of '+', '/', '-', or '*'") + + +# Problem 4 +class Fraction(object): + """Reduced fraction class with integer numerator and denominator.""" + + def __init__(self, numerator, denominator): + if denominator == 0: + raise ZeroDivisionError("denominator cannot be zero") + elif type(numerator) is not int or type(denominator) is not int: + raise TypeError("numerator and denominator must be integers") + + def gcd(a, b): + while b != 0: + a, b = b, a % b + return a + + common_factor = gcd(numerator, denominator) + self.numer = numerator // common_factor + self.denom = denominator // common_factor + + def __str__(self): + if self.denom != 1: + return "{}/{}".format(self.numer, self.denom) + else: + return str(self.numer) + + def __float__(self): + return self.numer / self.denom + + def __eq__(self, other): + if type(other) is Fraction: + return self.numer == other.numer and self.denom == other.denom + else: + return float(self) == other + + def __add__(self, other): + return Fraction( + self.numer * other.numer + self.denom * other.denom, + self.denom * other.denom, + ) + + def __sub__(self, other): + return Fraction( + self.numer * other.numer - self.denom * other.denom, + self.denom * other.denom, + ) + + def __mul__(self, other): + return Fraction(self.numer * other.numer, self.denom * other.denom) + + def __truediv__(self, other): + if self.denom * other.numer == 0: + raise ZeroDivisionError("cannot divide by zero") + return Fraction(self.numer * other.denom, self.denom * other.numer) + + +# Problem 6 +def count_sets(cards): + """Return the number of sets in the provided Set hand. + + Parameters: + cards (list(str)) a list of twelve cards as 4-bit integers in + base 3 as strings, such as ["1022", "1122", ..., "1020"]. + Returns: + (int) The number of sets in the hand. + Raises: + ValueError: if the list does not contain a valid Set hand, meaning + - there are not exactly 12 cards, + - the cards are not all unique, + - one or more cards does not have exactly 4 digits, or + - one or more cards has a character other than 0, 1, or 2. + """ + raise NotImplementedError("Problem 6 Incomplete") + + +def is_set(a, b, c): + """Determine if the cards a, b, and c constitute a set. + + Parameters: + a, b, c (str): string representations of 4-bit integers in base 3. + For example, "1022", "1122", and "1020" (which is not a set). + Returns: + True if a, b, and c form a set, meaning the ith digit of a, b, + and c are either the same or all different for i=1,2,3,4. + False if a, b, and c do not form a set. + """ + raise NotImplementedError("Problem 6 Incomplete") diff --git a/code/UnitTest/test_specs.py b/code/UnitTest/test_specs.py new file mode 100644 index 0000000..4f7348d --- /dev/null +++ b/code/UnitTest/test_specs.py @@ -0,0 +1,75 @@ +# test_specs.py +"""Python Essentials: Unit Testing. + + + +""" + +import specs +import pytest + + +def test_add(): + assert specs.add(1, 3) == 4, "failed on positive integers" + assert specs.add(-5, -7) == -12, "failed on negative integers" + assert specs.add(-6, 14) == 8 + + +def test_divide(): + assert specs.divide(4, 2) == 2, "integer division" + assert specs.divide(5, 4) == 1.25, "float division" + with pytest.raises(ZeroDivisionError) as excinfo: + specs.divide(4, 0) + assert excinfo.value.args[0] == "second input cannot be zero" + + +# Problem 1: write a unit test for specs.smallest_factor(), then correct it. + + +# Problem 2: write a unit test for specs.month_length(). + + +# Problem 3: write a unit test for specs.operate(). + + +# Problem 4: write unit tests for specs.Fraction, then correct it. +@pytest.fixture +def set_up_fractions(): + frac_1_3 = specs.Fraction(1, 3) + frac_1_2 = specs.Fraction(1, 2) + frac_n2_3 = specs.Fraction(-2, 3) + return frac_1_3, frac_1_2, frac_n2_3 + + +def test_fraction_init(set_up_fractions): + frac_1_3, frac_1_2, frac_n2_3 = set_up_fractions + assert frac_1_3.numer == 1 + assert frac_1_2.denom == 2 + assert frac_n2_3.numer == -2 + frac = specs.Fraction(30, 42) + assert frac.numer == 5 + assert frac.denom == 7 + + +def test_fraction_str(set_up_fractions): + frac_1_3, frac_1_2, frac_n2_3 = set_up_fractions + assert str(frac_1_3) == "1/3" + assert str(frac_1_2) == "1/2" + assert str(frac_n2_3) == "-2/3" + + +def test_fraction_float(set_up_fractions): + frac_1_3, frac_1_2, frac_n2_3 = set_up_fractions + assert float(frac_1_3) == 1 / 3.0 + assert float(frac_1_2) == 0.5 + assert float(frac_n2_3) == -2 / 3.0 + + +def test_fraction_eq(set_up_fractions): + frac_1_3, frac_1_2, frac_n2_3 = set_up_fractions + assert frac_1_2 == specs.Fraction(1, 2) + assert frac_1_3 == specs.Fraction(2, 6) + assert frac_n2_3 == specs.Fraction(8, -12) + + +# Problem 5: Write test cases for Set. diff --git a/code/UnixShell1/Shell1.zip b/code/UnixShell1/Shell1.zip new file mode 100644 index 0000000..142ac84 Binary files /dev/null and b/code/UnixShell1/Shell1.zip differ diff --git a/code/UnixShell1/unixshell1.sh b/code/UnixShell1/unixshell1.sh new file mode 100644 index 0000000..e2bbe2d --- /dev/null +++ b/code/UnixShell1/unixshell1.sh @@ -0,0 +1,65 @@ +#!/bin/bash + +# remove any previously unzipped copies of Shell1/ +if [ -d Shell1 ]; +then + echo "Removing old copies of Shell1/..." + rm -r Shell1 + echo "Done" +fi + +# unzip a fresh copy of Shell1/ +echo "Unzipping Shell1.zip..." +unzip -q Shell1 +echo "Done" + +: ' Problem 1: In the space below, write commands to change into the +Shell1/ directory and print a string telling you the current working +directory. ' + + + +: ' Problem 2: Use ls with flags to print one list of the contents of +Shell1/, including hidden files and folders, listing contents in long +format, and sorting output by file size. ' + + +: ' Problem 3: Inside the Shell1/ directory, delete the Audio/ folder +along with all its contents. Create Documents/, Photos/, and +Python/ directories. Rename the Random/ folder as Files/. ' + + + +: ' Problem 4: Using wildcards, move all the .jpg files to the Photos/ +directory, all the .txt files to the Documents/ directory, and all the +.py files to the Python/ directory. ' + + + +: ' Problem 5: Move organize_photos.sh to Scripts/, add executable +permissions to the script, and run the script. ' + + + +: ' Problem 6: Copy img_649.jpg from UnixShell1/ to Shell1/Photos, making +sure to leave a copy of the file in UnixShell1/.' + + + +# remove any old copies of UnixShell1.tar.gz +if [ ! -d Shell1 ]; +then + cd .. +fi + +if [ -f UnixShell1.tar.gz ]; +then + echo "Removing old copies of UnixShell1.tar.gz..." + rm -v UnixShell1.tar.gz + echo "Done" +fi + +# archive and compress the Shell1/ directory +echo "Compressing Shell1/ Directory..." +tar -zcpf UnixShell1.tar.gz Shell1/* +echo "Done" diff --git a/code/junk_funcs.py b/code/junk_funcs.py new file mode 100644 index 0000000..4a7ec6a --- /dev/null +++ b/code/junk_funcs.py @@ -0,0 +1,15 @@ +""" +------------------------------------------------------------------------ +This module contains a junk placeholder function for testing purposes. +------------------------------------------------------------------------ +""" + + +def junk_func_add(arg1, arg2): + """ + This is just a junk function in this junk module in the `/code/` + directory. We can delete this as soon as we have some real functions. + """ + junk_sum = arg1 + arg2 + + return junk_sum diff --git a/code/placeholder.txt b/code/placeholder.txt deleted file mode 100644 index eed293e..0000000 --- a/code/placeholder.txt +++ /dev/null @@ -1 +0,0 @@ -placeholder.txt diff --git a/data/Matplotlib3/orbits.npz b/data/Matplotlib3/orbits.npz new file mode 100644 index 0000000..cd4ca3b Binary files /dev/null and b/data/Matplotlib3/orbits.npz differ diff --git a/data/Pandas1/DJIA.csv b/data/Pandas1/DJIA.csv new file mode 100644 index 0000000..29dc8f4 --- /dev/null +++ b/data/Pandas1/DJIA.csv @@ -0,0 +1,2610 @@ +DATE,VALUE +2006-09-27,11689.24 +2006-09-28,11718.45 +2006-09-29,11679.07 +2006-10-02,11670.35 +2006-10-03,11727.34 +2006-10-04,11850.61 +2006-10-05,11866.69 +2006-10-06,11850.21 +2006-10-09,11857.81 +2006-10-10,11867.17 +2006-10-11,11852.13 +2006-10-12,11947.70 +2006-10-13,11960.51 +2006-10-16,11980.60 +2006-10-17,11950.02 +2006-10-18,11992.68 +2006-10-19,12011.73 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+0,40,46,39,1,1,50000 +0,40,38,39,1,1,27040 +0,40,29,41,1,1,30740 +0,40,37,40,1,2,16804 +0,40,36,39,1,1,38343 +0,40,30,39,1,1,27560 +0,48,43,39,1,1,41000 +0,40,61,39,1,1,30000 +0,40,33,40,1,1,35000 +0,70,50,39,1,1,26000 +0,40,78,39,1,2,1100 +0,46,45,39,1,2,24000 +0,50,44,39,1,1,29500 \ No newline at end of file diff --git a/data/Pandas3/college.csv b/data/Pandas3/college.csv new file mode 100644 index 0000000..5ad7ba0 --- /dev/null +++ b/data/Pandas3/college.csv @@ -0,0 +1,778 @@ +"","Private","Apps","Accept","Enroll","Top10perc","Top25perc","F.Undergrad","P.Undergrad","Outstate","Room.Board","Books","Personal","PhD","Terminal","S.F.Ratio","perc.alumni","Expend","Grad.Rate" +"Abilene Christian University","Yes",1660,1232,721,23,52,2885,537,7440,3300,450,2200,70,78,18.1,12,7041,60 +"Adelphi University","Yes",2186,1924,512,16,29,2683,1227,12280,6450,750,1500,29,30,12.2,16,10527,56 +"Adrian College","Yes",1428,1097,336,22,50,1036,99,11250,3750,400,1165,53,66,12.9,30,8735,54 +"Agnes Scott College","Yes",417,349,137,60,89,510,63,12960,5450,450,875,92,97,7.7,37,19016,59 +"Alaska Pacific University","Yes",193,146,55,16,44,249,869,7560,4120,800,1500,76,72,11.9,2,10922,15 +"Albertson College","Yes",587,479,158,38,62,678,41,13500,3335,500,675,67,73,9.4,11,9727,55 +"Albertus Magnus College","Yes",353,340,103,17,45,416,230,13290,5720,500,1500,90,93,11.5,26,8861,63 +"Albion College","Yes",1899,1720,489,37,68,1594,32,13868,4826,450,850,89,100,13.7,37,11487,73 +"Albright College","Yes",1038,839,227,30,63,973,306,15595,4400,300,500,79,84,11.3,23,11644,80 +"Alderson-Broaddus College","Yes",582,498,172,21,44,799,78,10468,3380,660,1800,40,41,11.5,15,8991,52 +"Alfred University","Yes",1732,1425,472,37,75,1830,110,16548,5406,500,600,82,88,11.3,31,10932,73 +"Allegheny College","Yes",2652,1900,484,44,77,1707,44,17080,4440,400,600,73,91,9.9,41,11711,76 +"Allentown Coll. of St. Francis de Sales","Yes",1179,780,290,38,64,1130,638,9690,4785,600,1000,60,84,13.3,21,7940,74 +"Alma College","Yes",1267,1080,385,44,73,1306,28,12572,4552,400,400,79,87,15.3,32,9305,68 +"Alverno College","Yes",494,313,157,23,46,1317,1235,8352,3640,650,2449,36,69,11.1,26,8127,55 +"American International College","Yes",1420,1093,220,9,22,1018,287,8700,4780,450,1400,78,84,14.7,19,7355,69 +"Amherst College","Yes",4302,992,418,83,96,1593,5,19760,5300,660,1598,93,98,8.4,63,21424,100 +"Anderson University","Yes",1216,908,423,19,40,1819,281,10100,3520,550,1100,48,61,12.1,14,7994,59 +"Andrews University","Yes",1130,704,322,14,23,1586,326,9996,3090,900,1320,62,66,11.5,18,10908,46 +"Angelo State University","No",3540,2001,1016,24,54,4190,1512,5130,3592,500,2000,60,62,23.1,5,4010,34 +"Antioch University","Yes",713,661,252,25,44,712,23,15476,3336,400,1100,69,82,11.3,35,42926,48 +"Appalachian State University","No",7313,4664,1910,20,63,9940,1035,6806,2540,96,2000,83,96,18.3,14,5854,70 +"Aquinas College","Yes",619,516,219,20,51,1251,767,11208,4124,350,1615,55,65,12.7,25,6584,65 +"Arizona State University Main campus","No",12809,10308,3761,24,49,22593,7585,7434,4850,700,2100,88,93,18.9,5,4602,48 +"Arkansas College (Lyon College)","Yes",708,334,166,46,74,530,182,8644,3922,500,800,79,88,12.6,24,14579,54 +"Arkansas Tech University","No",1734,1729,951,12,52,3602,939,3460,2650,450,1000,57,60,19.6,5,4739,48 +"Assumption College","Yes",2135,1700,491,23,59,1708,689,12000,5920,500,500,93,93,13.8,30,7100,88 +"Auburn University-Main Campus","No",7548,6791,3070,25,57,16262,1716,6300,3933,600,1908,85,91,16.7,18,6642,69 +"Augsburg College","Yes",662,513,257,12,30,2074,726,11902,4372,540,950,65,65,12.8,31,7836,58 +"Augustana College IL","Yes",1879,1658,497,36,69,1950,38,13353,4173,540,821,78,83,12.7,40,9220,71 +"Augustana College","Yes",761,725,306,21,58,1337,300,10990,3244,600,1021,66,70,10.4,30,6871,69 +"Austin College","Yes",948,798,295,42,74,1120,15,11280,4342,400,1150,81,95,13,33,11361,71 +"Averett College","Yes",627,556,172,16,40,777,538,9925,4135,750,1350,59,67,22.4,11,6523,48 +"Baker University","Yes",602,483,206,21,47,958,466,8620,4100,400,2250,58,68,11,21,6136,65 +"Baldwin-Wallace College","Yes",1690,1366,662,30,61,2718,1460,10995,4410,1000,1000,68,74,17.6,20,8086,85 +"Barat College","Yes",261,192,111,15,36,453,266,9690,4300,500,500,57,77,9.7,35,9337,71 +"Bard College","Yes",1910,838,285,50,85,1004,15,19264,6206,750,750,98,98,10.4,30,13894,79 +"Barnard College","Yes",2496,1402,531,53,95,2121,69,17926,8124,600,850,83,93,10.3,33,12580,91 +"Barry University","Yes",990,784,279,18,45,1811,3144,11290,5360,600,1800,76,78,12.6,11,9084,72 +"Baylor University","Yes",6075,5349,2367,34,66,9919,484,6450,3920,600,1346,71,76,18.5,38,7503,72 +"Beaver College","Yes",1163,850,348,23,56,878,519,12850,5400,400,800,78,89,12.2,30,8954,73 +"Bellarmine College","Yes",807,707,308,39,63,1198,605,8840,2950,750,1290,74,82,13.1,31,6668,84 +"Belmont Abbey College","Yes",632,494,129,17,36,709,131,9000,4850,300,2480,78,85,13.2,10,7550,52 +"Belmont University","Yes",1220,974,481,28,67,1964,623,7800,3664,650,900,61,61,11.1,19,7614,49 +"Beloit College","Yes",1320,923,284,26,54,1085,81,16304,3616,355,715,87,95,11.1,26,12957,69 +"Bemidji State University","No",1208,877,546,12,36,3796,824,4425,2700,660,1800,57,62,19.6,16,3752,46 +"Benedictine College","Yes",632,620,222,14,24,702,501,9550,3850,350,250,64,84,14.1,18,5922,58 +"Bennington College","Yes",519,327,114,25,53,457,2,21700,4100,600,500,35,59,10.1,33,16364,55 +"Bentley College","Yes",3466,2330,640,20,60,3095,1533,13800,5510,630,850,87,87,17.5,20,10941,82 +"Berry College","Yes",1858,1221,480,37,68,1620,49,8050,3940,350,2375,80,80,16.3,17,10511,63 +"Bethany College","Yes",878,816,200,16,41,706,62,8740,3363,550,1700,62,68,11.6,29,7718,48 +"Bethel College KS","Yes",202,184,122,19,42,537,101,8540,3580,500,1400,61,80,8.8,32,8324,56 +"Bethel College","Yes",502,384,104,11,28,347,74,6200,2900,600,800,63,63,11.7,13,7623,35 +"Bethune Cookman College","Yes",1646,1150,542,12,30,2128,82,5188,3396,650,2500,48,48,13.8,9,6817,58 +"Birmingham-Southern College","Yes",805,588,287,67,88,1376,207,11660,4325,400,900,74,79,14,34,8649,72 +"Blackburn College","Yes",500,336,156,25,55,421,27,6500,2700,500,1000,76,76,14.3,53,8377,51 +"Bloomsburg Univ. of Pennsylvania","No",6773,3028,1025,15,55,5847,946,7844,2948,500,1680,66,68,18,19,7041,75 +"Bluefield College","Yes",377,358,181,15,30,653,129,7150,4350,450,1500,61,67,17.8,3,6259,53 +"Bluffton College","Yes",692,514,209,20,50,760,81,9900,3990,400,900,76,71,13.3,19,9073,58 +"Boston University","Yes",20192,13007,3810,45,80,14971,3113,18420,6810,475,1025,80,81,11.9,16,16836,72 +"Bowdoin College","Yes",3356,1019,418,76,100,1490,8,19030,5885,1495,875,93,96,11.2,52,20447,96 +"Bowling Green State University","No",9251,7333,3076,14,45,13699,1213,7452,3352,600,1700,81,89,21.1,14,6918,67 +"Bradford College","Yes",443,330,151,5,36,453,42,14080,6270,500,900,57,80,10.2,21,15387,46 +"Bradley University","Yes",3767,3414,1061,30,58,4531,643,10870,4440,2000,1522,75,81,14.4,21,7671,85 +"Brandeis University","Yes",4186,2743,740,48,77,2819,62,19380,6750,410,1000,90,97,9.8,24,17150,84 +"Brenau University","Yes",367,274,158,12,41,917,479,9592,5879,500,700,71,80,13.7,12,5935,49 +"Brewton-Parker College","Yes",1436,1228,1202,10,26,1320,822,4371,2370,500,2000,62,62,12.6,10,4900,18 +"Briar Cliff College","Yes",392,351,155,16,44,738,430,10260,3597,600,1500,39,66,13.1,26,8355,58 +"Bridgewater College","Yes",838,673,292,22,53,881,55,10265,4725,560,875,68,73,13.2,24,8655,82 +"Brigham Young University at Provo","Yes",7365,5402,4615,48,82,27378,1253,2340,3580,860,1220,76,76,20.5,40,7916,33 +"Brown University","Yes",12586,3239,1462,87,95,5643,349,19528,5926,720,1100,99,100,7.6,39,20440,97 +"Bryn Mawr College","Yes",1465,810,313,71,95,1088,16,18165,6750,500,1200,100,100,12.3,49,17449,89 +"Bucknell University","Yes",6548,3813,862,49,85,3316,31,18550,4750,800,1200,95,97,14.2,36,13675,93 +"Buena Vista College","Yes",860,688,285,32,70,1928,442,13306,3797,450,950,62,69,8.8,10,6333,78 +"Butler University","Yes",2362,2037,700,40,68,2607,148,13130,4650,500,1600,77,81,10.9,29,9511,83 +"Cabrini College","Yes",599,494,224,8,28,1035,446,10518,6250,300,300,59,76,16.5,36,7117,71 +"Caldwell College","Yes",1011,604,213,17,42,693,868,8900,4600,425,1000,87,96,13.9,25,7922,55 +"California Lutheran University","Yes",563,247,247,23,52,1427,432,12950,5300,612,576,72,74,12.4,17,8985,60 +"California Polytechnic-San Luis","No",7811,3817,1650,47,73,12911,1404,7380,4877,612,2091,72,81,19.8,13,8453,59 +"California State University at Fresno","No",4540,3294,1483,5,60,13494,1254,7706,4368,600,1926,90,90,21.2,8,7268,61 +"Calvin College","Yes",1784,1512,913,29,56,3401,136,10230,3710,400,1210,75,81,14.8,41,7786,81 +"Campbell University","Yes",2087,1339,657,20,54,3191,1204,7550,2790,600,500,77,77,21.8,34,3739,63 +"Campbellsville College","Yes",848,587,298,25,55,935,184,6060,3070,600,1300,62,66,17.7,13,5391,49 +"Canisius College","Yes",2853,2193,753,16,34,2978,434,10750,5340,400,1130,90,92,14.6,26,7972,64 +"Capital University","Yes",1747,1382,449,34,66,1662,960,13050,4000,500,800,64,69,12.1,27,9557,83 +"Capitol College","Yes",100,90,35,10,52,282,331,8400,2812,300,2134,10,50,12.1,24,7976,52 +"Carleton College","Yes",2694,1579,489,75,93,1870,12,19292,3957,550,550,81,93,10.4,60,17960,91 +"Carnegie Mellon University","Yes",8728,5201,1191,60,89,4265,291,17900,5690,450,1250,86,93,9.2,31,24386,74 +"Carroll College","Yes",1160,991,352,19,55,1357,737,12200,3880,480,930,74,81,17.8,25,7666,79 +"Carson-Newman College","Yes",1096,951,464,27,62,1776,239,8150,3150,400,500,61,62,13.6,16,6716,67 +"Carthage College","Yes",1616,1427,434,20,43,1405,580,13125,3775,500,1300,74,89,15.9,22,7364,62 +"Case Western Reserve University","Yes",3877,3156,713,71,93,3051,513,15700,4730,525,1460,95,95,2.9,29,19733,67 +"Castleton State College","No",1257,940,363,9,22,1547,294,7656,4690,400,700,89,91,14.7,8,6318,79 +"Catawba College","Yes",1083,880,291,13,34,915,80,9270,4100,600,1860,75,82,13.5,27,8425,55 +"Catholic University of America","Yes",1754,1465,505,24,49,2159,211,13712,6408,526,1100,90,96,9.3,18,12751,75 +"Cazenovia College","Yes",3847,3433,527,9,35,1010,12,9384,4840,600,500,22,47,14.3,20,7697,118 +"Cedar Crest College","Yes",776,607,198,25,58,791,764,14340,5285,500,1000,58,83,11.7,39,10961,74 +"Cedarville College","Yes",1307,1090,616,25,55,2196,82,7344,4410,570,1000,50,52,15.3,34,6897,64 +"Centenary College","Yes",369,312,90,12,46,396,526,11400,5400,500,760,41,85,9.5,20,9583,24 +"Centenary College of Louisiana","Yes",495,434,210,35,55,775,44,8950,3490,600,1900,86,92,11.3,25,9685,66 +"Center for Creative Studies","Yes",601,396,203,1,20,525,323,11230,6643,2340,620,8,58,6.8,4,13025,47 +"Central College","Yes",1283,1113,401,31,65,1355,40,10938,3660,650,600,76,90,13.5,29,8444,67 +"Central Connecticut State University","No",4158,2532,902,6,24,6394,3881,5962,4444,500,985,69,73,16.7,4,4900,49 +"Central Missouri State University","No",4681,4101,1436,10,35,8094,1596,4620,3288,300,2250,69,80,19.7,4,5501,50 +"Central Washington University","No",2785,2011,1007,8,65,6507,898,7242,3603,654,1416,67,89,18.1,0,6413,51 +"Central Wesleyan College","Yes",174,146,88,8,29,1047,33,8300,3080,600,600,62,62,15.2,18,3365,58 +"Centre College","Yes",1013,888,288,55,82,943,7,11850,4270,600,900,95,99,11.4,60,13118,74 +"Chapman University","Yes",959,771,351,23,48,1662,209,16624,5895,600,1100,72,80,12.8,6,12692,47 +"Chatham College","Yes",212,197,91,28,56,471,148,13500,5230,400,850,95,98,9.3,37,16095,52 +"Chestnut Hill College","Yes",342,254,126,25,64,518,232,10335,5015,700,850,71,71,8.3,29,7729,73 +"Christendom College","Yes",81,72,51,33,71,139,3,8730,3600,400,800,92,92,9.3,17,10922,58 +"Christian Brothers University","Yes",880,520,224,16,42,1068,364,9300,3260,600,900,81,81,11.1,24,8129,63 +"Christopher Newport University","No",883,766,428,3,37,2910,1749,7860,4750,525,1889,80,82,21.2,16,4639,48 +"Claflin College","Yes",1196,697,499,21,47,959,13,4412,2460,500,1000,69,69,16.9,31,7083,21 +"Claremont McKenna College","Yes",1860,767,227,71,93,887,1,17000,6010,500,850,99,99,9.6,52,18443,87 +"Clark University","Yes",2887,2059,457,30,61,1928,296,17500,4200,500,950,94,95,10.5,35,11951,79 +"Clarke College","Yes",460,340,167,14,45,604,350,10740,3676,350,900,67,71,11,27,7963,74 +"Clarkson University","Yes",2174,1953,557,35,68,2332,53,15960,5580,700,1300,95,95,15.8,32,11659,77 +"Clemson University","No",8065,5257,2301,37,65,11755,770,8116,3610,800,1618,82,88,18,17,7597,73 +"Clinch Valley Coll. of the Univ. of Virginia","No",689,561,250,15,30,1125,422,7168,3689,600,1900,67,67,18.1,9,4417,46 +"Coe College","Yes",1006,742,275,29,60,1127,205,13925,4390,500,2200,73,86,12.7,32,10141,67 +"Coker College","Yes",604,452,295,15,47,690,222,9888,4502,400,1000,64,77,12.1,39,8741,75 +"Colby College","Yes",2848,1319,456,58,84,1720,35,18930,5590,500,1000,83,94,10.2,41,15954,91 +"Colgate University","Yes",4856,2492,727,46,75,2649,25,19510,5565,500,750,95,98,10.5,45,15494,93 +"College Misericordia","Yes",1432,888,317,29,58,1121,493,10860,5760,550,900,56,62,12.9,23,8604,96 +"College of Charleston","No",4772,3140,1265,22,55,6851,1200,6120,3460,666,2316,73,78,17.2,18,4776,51 +"College of Mount St. Joseph","Yes",798,620,238,14,41,1165,1232,9800,4430,400,1150,46,46,11.1,35,6889,100 +"College of Mount St. Vincent","Yes",946,648,177,23,46,707,432,11790,5770,500,1000,75,77,11.9,35,10015,83 +"College of Notre Dame","Yes",344,264,97,11,42,500,331,12600,5520,630,2250,77,80,10.4,7,9773,43 +"College of Notre Dame of Maryland","Yes",457,356,177,35,61,667,1983,11180,5620,600,700,64,64,11.5,32,7477,75 +"College of Saint Benedict","Yes",938,864,511,29,62,1715,103,12247,4221,500,600,70,88,13.1,26,8847,72 +"College of Saint Catherine","Yes",511,411,186,23,51,1692,562,12224,4440,450,1000,63,87,11.5,32,7315,77 +"College of Saint Elizabeth","Yes",444,359,122,34,53,493,968,10900,5250,380,1000,68,70,11.4,23,9447,78 +"College of Saint Rose","Yes",983,664,249,23,57,1698,894,9990,5666,800,1500,66,71,14.3,28,6084,64 +"College of Santa Fe","Yes",546,447,189,16,42,873,683,11138,4138,600,1200,40,74,14,7,8820,80 +"College of St. Joseph","Yes",141,118,55,12,21,201,173,8300,4850,450,1300,53,53,9.5,19,6936,76 +"College of St. Scholastica","Yes",672,596,278,29,60,1350,275,11844,3696,450,1146,54,76,11.6,33,8996,72 +"College of the Holy Cross","Yes",2994,1691,659,70,95,2675,22,18000,6300,400,900,92,96,11.3,55,12138,95 +"College of William and Mary","No",7117,3106,1217,68,88,5186,134,11720,4298,600,800,89,92,12.1,31,9534,93 +"College of Wooster","Yes",2100,1883,553,29,65,1704,1,16240,4690,500,500,84,96,11.1,43,14140,69 +"Colorado College","Yes",3207,1577,490,56,87,1892,7,17142,4190,450,1200,85,97,11.3,51,14664,84 +"Colorado State University","No",9478,6312,2194,29,65,15646,1829,8412,4180,470,1800,87,89,19.2,10,7850,59 +"Columbia College MO","Yes",314,158,132,10,28,690,5346,8294,3700,400,900,87,87,15.3,2,5015,37 +"Columbia College","Yes",737,614,242,21,67,968,237,10425,3975,500,1500,61,77,14.7,34,8693,76 +"Columbia University","Yes",6756,1930,871,78,96,3376,55,18624,6664,550,300,97,98,5.9,21,30639,99 +"Concordia College at St. Paul","Yes",281,266,139,13,29,1049,181,10500,3750,450,950,69,75,12.8,18,6955,45 +"Concordia Lutheran College","Yes",232,216,106,16,34,534,172,6900,3800,450,1825,67,76,12.1,9,6875,42 +"Concordia University CA","Yes",688,497,144,30,75,641,101,10800,4440,570,1515,55,60,13.1,13,8415,55 +"Concordia University","Yes",528,403,186,22,56,1168,145,9216,4191,400,1000,56,64,12.1,13,7309,75 +"Connecticut College","Yes",3035,1546,438,42,93,1630,232,18740,6300,600,500,86,95,10.7,40,14773,91 +"Converse College","Yes",440,407,149,35,70,643,80,12050,3700,500,900,63,76,10.2,31,10965,75 +"Cornell College","Yes",1538,1329,383,33,68,1140,10,15248,4323,550,800,71,76,12.2,31,10340,64 +"Creighton University","Yes",2967,2836,876,30,60,3450,644,10628,4372,650,2055,85,90,6.5,32,22906,85 +"Culver-Stockton College","Yes",1576,1110,274,24,55,992,112,8000,3700,400,500,51,52,14.1,28,5807,51 +"Cumberland College","Yes",995,789,398,26,47,1306,122,6230,3526,400,600,42,44,13,4,8189,63 +"D'Youville College","Yes",866,619,157,18,47,1074,336,8920,4310,680,1320,68,68,14.6,42,6898,46 +"Dana College","Yes",504,482,185,10,36,550,84,9130,3322,450,1450,46,51,12.6,25,8686,54 +"Daniel Webster College","Yes",585,508,153,12,30,460,536,12292,4934,500,500,61,61,22.2,10,8643,72 +"Dartmouth College","Yes",8587,2273,1087,87,99,3918,32,19545,6070,550,1100,95,99,4.7,49,29619,98 +"Davidson College","Yes",2373,956,452,77,96,1601,6,17295,5070,600,1011,95,97,12,46,17581,94 +"Defiance College","Yes",571,461,174,10,26,645,283,10850,3670,400,1159,58,60,12.8,19,7505,56 +"Delta State University","No",967,945,459,15,48,2806,538,4528,1880,500,1200,49,63,17.1,16,5113,58 +"Denison University","Yes",2762,2279,533,32,60,1835,14,16900,4720,500,600,88,97,11.6,45,12423,81 +"DePauw University","Yes",1994,1656,495,50,80,1983,36,14300,5020,550,950,78,94,11.1,31,11525,82 +"Dickinson College","Yes",3014,2539,487,31,68,1889,62,18700,5000,595,1250,87,94,11.2,39,13861,87 +"Dickinson State University","No",434,412,319,10,30,1376,237,4486,2146,600,2000,50,64,16.5,28,4525,46 +"Dillard University","Yes",1998,1376,651,41,88,1539,45,6700,3650,500,2307,52,52,14.1,12,7566,61 +"Doane College","Yes",793,709,244,20,47,1022,411,9570,3000,400,1000,67,72,15.1,42,6852,60 +"Dominican College of Blauvelt","Yes",360,329,108,4,19,756,863,8310,5500,600,1800,43,43,12.7,5,5480,54 +"Dordt College","Yes",604,562,328,25,50,1048,56,9800,2650,450,2800,61,60,12.5,17,7325,87 +"Dowling College","Yes",1011,829,410,9,33,1059,2458,9000,3100,450,1413,77,78,12.4,7,11178,42 +"Drake University","Yes",2799,2573,839,34,65,3322,726,13420,4770,560,1675,88,93,15,24,9473,77 +"Drew University","Yes",2153,1580,321,56,84,1192,87,18432,5616,520,660,93,97,10.2,28,14907,83 +"Drury College","Yes",700,650,314,33,66,1065,48,8730,3523,500,750,82,92,13.2,35,9303,67 +"Duke University","Yes",13789,3893,1583,90,98,6188,53,18590,5950,625,1162,95,96,5,44,27206,97 +"Earlham College","Yes",1358,1006,274,35,63,1028,13,15036,4056,600,600,90,94,10.6,46,14634,78 +"East Carolina University","No",9274,6362,2435,14,44,13171,1687,7248,3240,500,1700,74,78,13.2,18,9002,58 +"East Tennessee State University","No",3330,2730,1303,15,36,6706,2640,5800,3000,600,2200,73,75,14,9,9825,42 +"East Texas Baptist University","Yes",379,341,265,10,36,1050,151,4950,2780,530,1500,62,62,15.7,7,5619,38 +"Eastern College","Yes",458,369,165,16,42,1057,355,11190,4800,450,1230,60,60,13.6,22,8135,54 +"Eastern Connecticut State University","No",2172,1493,564,14,50,2766,1531,5962,4316,650,500,71,76,16.9,14,5719,50 +"Eastern Illinois University","No",5597,4253,1565,12,38,9161,845,5710,3066,120,1730,62,71,16.2,5,5682,76 +"Eastern Mennonite College","Yes",486,440,227,19,48,903,59,9650,3800,600,1300,46,65,11.4,29,10188,82 +"Eastern Nazarene College","Yes",516,409,200,17,40,1238,30,8770,3500,450,700,58,58,17.3,17,6430,70 +"Eckerd College","Yes",1422,1109,366,33,65,1363,23,15360,4080,600,1000,82,89,12.8,26,15003,59 +"Elizabethtown College","Yes",2417,1843,426,36,70,1476,299,14190,4400,500,750,65,68,12.8,25,9815,81 +"Elmira College","Yes",1457,1045,345,27,50,1109,502,14990,4980,450,550,77,98,21.5,21,7502,64 +"Elms College","Yes",245,208,125,23,46,544,436,11800,4765,450,1700,71,71,11.3,21,8952,86 +"Elon College","Yes",3624,2786,858,11,39,2933,334,9100,3883,490,1777,70,74,18.9,34,6329,63 +"Embry Riddle Aeronautical University","Yes",3151,2584,958,14,40,4772,856,7800,3750,570,3020,37,43,16.5,4,12878,44 +"Emory & Henry College","Yes",765,646,226,30,60,809,32,8578,4408,700,1600,79,88,13.9,51,8061,82 +"Emory University","Yes",8506,4168,1236,76,97,5544,192,17600,6000,600,870,97,98,5,28,28457,96 +"Emporia State University","No",1256,1256,853,43,79,3957,588,5401,3144,450,1888,72,75,19.3,4,5527,50 +"Erskine College","Yes",659,557,167,47,74,532,35,10485,3840,475,1246,76,80,13.5,47,7527,67 +"Eureka College","Yes",560,454,113,36,56,484,16,10955,3450,330,670,62,87,10.6,31,9552,53 +"Evergreen State College","No",1801,1101,438,14,50,3065,363,6297,4600,600,1323,75,78,18.1,14,8355,68 +"Fairfield University","Yes",4784,3346,781,30,66,2984,1037,15000,6200,700,1100,86,90,15.1,30,11220,94 +"Fayetteville State University","No",1455,1064,452,1,16,2632,617,6806,2550,350,766,75,75,15.1,10,6972,24 +"Ferrum College","Yes",1339,1107,336,12,36,1051,82,9400,4200,500,1600,53,58,12.5,9,7967,22 +"Flagler College","Yes",1415,714,338,18,52,1345,44,5120,3200,500,2140,52,60,18.1,9,3930,69 +"Florida Institute of Technology","Yes",1947,1580,523,39,74,1863,233,13900,4140,750,1500,90,90,10.6,7,8923,57 +"Florida International University","No",3306,2079,1071,42,89,10208,9310,6597,2494,800,3028,81,96,13.9,20,6722,66 +"Florida Southern College","Yes",1381,1040,374,20,44,1506,970,8025,4865,400,650,65,74,17.4,10,6339,68 +"Florida State University","No",11651,8683,3023,50,90,18906,3242,6680,4060,600,1020,80,89,23.1,15,7250,58 +"Fontbonne College","Yes",291,245,126,16,49,981,337,8390,4100,350,1500,45,55,21.5,24,4607,62 +"Fordham University","Yes",4200,2874,942,30,55,4740,1646,14235,6965,600,1735,86,97,14.4,14,10864,80 +"Fort Lewis College","No",3440,2823,1123,16,35,3793,486,6198,3320,500,2500,89,97,19.1,6,4362,46 +"Francis Marion University","No",1801,1655,819,13,38,3224,436,5840,3138,400,2430,76,76,19.1,8,5039,43 +"Franciscan University of Steubenville","Yes",553,452,228,22,49,1301,242,9650,4400,600,1000,57,69,14.9,8,6336,83 +"Franklin College","Yes",804,632,281,29,72,840,68,10390,4040,525,1345,54,78,12.5,37,11751,60 +"Franklin Pierce College","Yes",5187,4471,446,3,14,1818,1197,13320,4630,500,800,50,56,17.6,16,6418,51 +"Freed-Hardeman University","Yes",895,548,314,20,54,1174,50,5500,3340,600,1600,68,76,16.1,13,6078,62 +"Fresno Pacific College","Yes",346,274,146,51,87,704,63,9900,3670,630,1818,59,59,10.5,14,8095,54 +"Furman University","Yes",2161,1951,685,56,82,2371,175,13440,4048,600,1250,92,95,13.5,28,12940,82 +"Gannon University","Yes",2464,1908,678,24,57,2693,691,10970,4280,500,1380,47,51,13.3,18,7711,65 +"Gardner Webb University","Yes",1110,930,332,18,36,1603,374,8180,4270,500,500,65,58,15.2,12,5664,29 +"Geneva College","Yes",668,534,237,19,39,1306,258,9476,4820,500,1100,67,67,20.1,26,6786,74 +"George Fox College","Yes",809,726,294,27,52,1271,43,12500,4130,400,1050,53,53,13.5,22,7136,52 +"George Mason University","No",5653,4326,1727,17,29,9528,3822,10800,4840,580,1050,93,96,19.3,7,6751,46 +"George Washington University","Yes",7875,5062,1492,38,71,5471,1470,17450,6328,700,950,92,93,7.6,15,14745,72 +"Georgetown College","Yes",727,693,286,30,55,1063,48,8100,3950,550,550,73,76,13.3,28,7508,55 +"Georgetown University","Yes",11115,2881,1390,71,93,5881,406,18300,7131,670,1700,91,92,7.2,27,19635,95 +"Georgia Institute of Technology","No",7837,4527,2276,89,99,8528,654,6489,4438,795,1164,92,92,19.3,33,11271,70 +"Georgia State University","No",3793,2341,1238,9,24,7732,9054,6744,2655,720,3450,87,89,19,10,7762,34 +"Georgian Court College","Yes",348,281,127,12,52,1095,785,9150,3950,500,800,56,59,12.2,27,7348,76 +"Gettysburg College","Yes",3596,2466,575,42,78,1944,46,19964,4328,500,500,94,95,12.1,32,14720,83 +"Goldey Beacom College","Yes",633,468,284,10,27,823,963,6120,2985,531,1830,25,25,27.6,4,6081,36 +"Gonzaga University","Yes",1886,1524,526,31,67,2523,296,13000,4450,600,2400,78,90,14.7,32,9553,69 +"Gordon College","Yes",674,565,282,25,54,1151,39,12200,4070,400,1200,73,82,14.2,32,9226,66 +"Goshen College","Yes",440,396,221,26,51,910,166,9420,3730,600,1230,51,56,9.9,46,10270,72 +"Goucher College","Yes",1151,813,248,40,64,850,80,15588,6174,500,1200,78,90,9.2,34,16623,77 +"Grace College and Seminary","Yes",548,428,167,18,46,618,113,8958,3670,300,1000,53,59,15.3,26,9798,64 +"Graceland College","Yes",555,414,242,14,41,996,2281,9100,3100,550,880,51,61,23.6,24,5609,47 +"Grand Valley State University","No",5165,3887,1561,20,60,8234,2619,6108,3800,500,1000,64,66,20.6,9,5063,57 +"Green Mountain College","Yes",780,628,198,7,20,545,42,11750,2700,400,850,77,83,14,24,6475,76 +"Greensboro College","Yes",608,494,176,10,31,649,314,8330,3770,550,1300,64,80,13,31,7949,39 +"Greenville College","Yes",510,387,194,20,46,771,53,10310,4530,400,800,57,61,14.3,16,8222,60 +"Grinnell College","Yes",2039,1389,432,56,91,1333,30,15688,4618,400,400,88,92,9.5,54,18979,83 +"Grove City College","Yes",2491,1110,573,57,88,2213,35,5224,3048,525,350,65,65,18.4,18,4957,100 +"Guilford College","Yes",1202,1054,326,18,44,1410,299,13404,5160,450,1050,78,86,15.6,30,9114,65 +"Gustavus Adolphus College","Yes",1709,1385,634,36,72,2281,50,14125,3600,400,700,79,89,12.5,58,9907,80 +"Gwynedd Mercy College","Yes",380,237,104,30,56,716,1108,11000,5550,500,500,36,41,7.8,22,7483,96 +"Hamilton College","Yes",3140,1783,454,40,82,1646,24,19700,5050,300,800,91,96,9.6,60,17761,91 +"Hamline University","Yes",1006,825,328,34,73,1362,102,13252,4194,450,550,89,93,13,33,10296,65 +"Hampden - Sydney College","Yes",817,644,307,20,40,945,1,13218,4773,660,600,95,97,13.3,53,12263,69 +"Hampton University","Yes",7178,3755,1433,25,63,4623,740,7161,3518,600,2000,60,64,14,9,6791,70 +"Hanover College","Yes",1006,837,317,33,65,1024,15,8200,3485,500,1200,84,84,10.6,26,9248,64 +"Hardin-Simmons University","Yes",467,424,350,16,40,1365,334,6300,2980,700,2140,75,79,13.7,10,7054,38 +"Harding University","Yes",1721,1068,806,35,75,3128,213,5504,3528,700,910,71,77,17.7,37,6466,73 +"Hartwick College","Yes",2083,1725,430,22,49,1464,67,17480,4780,500,700,75,87,12.3,32,11625,73 +"Harvard University","Yes",13865,2165,1606,90,100,6862,320,18485,6410,500,1920,97,97,9.9,52,37219,100 +"Harvey Mudd College","Yes",1377,572,178,95,100,654,5,17230,6690,700,900,100,100,8.2,46,21569,100 +"Hastings College","Yes",817,708,262,22,52,935,37,9376,3272,500,1902,57,63,13,17,7335,52 +"Hendrix College","Yes",823,721,274,52,87,954,6,8800,3195,500,1200,82,99,13.1,26,8588,63 +"Hillsdale College","Yes",920,745,347,35,66,1133,42,11090,4700,400,750,80,80,12,31,12639,79 +"Hiram College","Yes",922,729,244,37,66,1000,275,14067,4560,400,1000,75,95,10.6,34,12165,79 +"Hobart and William Smith Colleges","Yes",2688,2081,500,25,53,1792,5,19029,5841,600,600,99,99,12.1,37,13040,79 +"Hofstra University","Yes",7428,5860,1349,25,63,6534,1350,11600,5920,1000,1000,81,90,13.9,10,10093,60 +"Hollins College","Yes",602,498,215,26,58,795,74,13470,5515,500,850,78,91,11.1,48,13957,72 +"Hood College","Yes",699,565,176,36,64,710,399,13960,6040,450,690,82,88,14.4,34,12434,72 +"Hope College","Yes",1712,1483,624,37,69,2505,208,12275,4341,465,1100,72,81,12.5,40,9284,72 +"Houghton College","Yes",949,786,302,30,70,1210,26,9990,3550,500,1500,85,90,15,24,8187,67 +"Huntingdon College","Yes",608,520,127,26,47,538,126,8080,3920,500,1100,63,72,11.4,9,7703,44 +"Huntington College","Yes",450,430,125,20,46,488,43,9950,3920,300,1300,76,76,11.8,25,9466,47 +"Huron University","Yes",600,197,124,3,9,392,69,7260,3090,600,1840,31,35,12.9,4,9249,21 +"Husson College","Yes",723,652,361,10,30,951,706,7800,4000,350,1500,36,44,22,4,4923,84 +"Illinois Benedictine College","Yes",607,558,269,22,47,1222,519,10500,4348,650,1500,81,91,11.6,29,8324,75 +"Illinois College","Yes",894,787,262,28,63,909,28,8050,3850,600,1000,75,75,15.6,30,7348,52 +"Illinois Institute of Technology","Yes",1756,1360,478,42,77,1911,626,14550,4620,500,700,80,88,12.3,26,12851,56 +"Illinois State University","No",8681,6695,2408,10,35,15701,1823,7799,3403,537,2605,77,84,21,16,5569,54 +"Illinois Wesleyan University","Yes",3050,1342,471,55,86,1818,23,14360,4090,400,650,77,92,12.9,34,9605,83 +"Immaculata College","Yes",268,253,103,16,44,494,1305,10000,5364,500,1000,56,64,11.2,33,7305,69 +"Incarnate Word College","Yes",1163,927,386,16,49,1685,556,8840,4689,750,2775,67,69,11.4,21,6095,95 +"Indiana State University","No",5659,4761,3147,10,31,8596,1949,6892,3706,600,2500,72,76,16.6,8,6996,40 +"Indiana University at Bloomington","No",16587,13243,5873,25,72,24763,2717,9766,3990,600,2000,77,88,21.3,24,8686,68 +"Indiana Wesleyan University","Yes",735,423,366,20,48,2448,707,9210,3782,700,1000,49,51,39.8,15,6562,34 +"Iona College","Yes",4892,3530,913,13,33,3906,1446,10690,6790,570,1150,66,83,16,14,8107,66 +"Iowa State University","No",8427,7424,3441,26,59,18676,1715,7550,3224,640,2055,81,88,19.2,22,8420,65 +"Ithaca College","Yes",7259,5526,1368,23,52,5612,166,14424,6192,634,1000,58,79,11.5,25,9812,75 +"James Madison University","No",11223,5285,2082,32,72,9652,742,7994,4544,500,732,77,81,17.9,29,5212,98 +"Jamestown College","Yes",472,410,262,14,41,9950,71,7620,3050,400,400,51,53,17,21,3186,54 +"Jersey City State College","No",2957,1423,691,10,30,3817,1394,3946,4800,400,1500,63,67,14.9,10,8367,26 +"John Brown University","Yes",605,405,284,24,53,961,99,6398,3672,400,1350,68,68,13.3,19,8118,75 +"John Carroll University","Yes",2421,2109,820,27,57,3168,392,11700,5550,600,450,89,90,14.5,28,7738,89 +"Johns Hopkins University","Yes",8474,3446,911,75,94,3566,1569,18800,6740,500,1040,96,97,3.3,38,56233,90 +"Johnson State College","No",833,669,279,3,13,1224,345,7656,4690,500,624,80,91,14.4,15,6564,36 +"Judson College","Yes",313,228,137,10,30,552,67,9414,4554,500,1700,34,55,10.6,30,7840,56 +"Juniata College","Yes",1005,859,298,36,55,1075,43,14850,4460,450,420,97,97,12.7,37,12067,80 +"Kansas State University","No",5880,4075,2833,25,55,14914,2246,6995,3120,600,2000,76,86,18.5,22,6122,54 +"Kansas Wesleyan University","Yes",589,575,148,16,40,474,258,8400,3250,500,1400,63,55,12.4,14,6535,68 +"Keene State College","No",3121,2446,822,5,19,3480,776,7870,4157,500,1150,73,73,16.1,13,6195,61 +"Kentucky Wesleyan College","Yes",584,497,175,20,49,662,121,8000,4150,500,1300,57,65,11.3,32,7058,62 +"Kenyon College","Yes",2212,1538,408,44,75,1445,1,19240,3690,750,480,95,95,11.1,46,14067,88 +"Keuka College","Yes",461,381,174,10,43,738,55,9600,4550,600,750,55,94,13.3,43,7863,51 +"King's College","Yes",1456,1053,381,20,45,500,541,10910,5160,400,1795,66,72,15.6,37,7649,87 +"King College","Yes",355,300,142,34,65,509,44,8664,3350,600,3000,65,68,10.7,25,8954,65 +"Knox College","Yes",1040,845,286,48,77,967,24,15747,4062,400,800,88,95,12.7,33,13224,79 +"La Roche College","Yes",361,321,185,10,41,650,819,8842,4782,600,1100,57,73,14.2,14,7022,52 +"La Salle University","Yes",2929,1834,622,20,56,2738,1662,12600,5610,450,3160,90,90,15.1,9,9084,84 +"Lafayette College","Yes",4010,2402,572,36,59,2018,226,18730,5740,600,1000,93,96,10.5,38,15365,92 +"LaGrange College","Yes",544,399,177,15,35,600,363,6987,3585,750,1500,77,83,12.5,12,9067,75 +"Lake Forest College","Yes",979,638,271,31,70,968,20,16880,3970,920,1320,91,94,10.7,19,15687,77 +"Lakeland College","Yes",497,452,231,24,47,887,1957,9400,4005,500,1000,49,65,17.2,25,4054,57 +"Lamar University","No",2336,1725,1043,10,27,5438,4058,4752,3040,508,1463,48,82,18.4,12,5879,26 +"Lambuth University","Yes",831,538,224,15,35,840,325,5170,3430,600,1590,61,61,16.1,10,5531,60 +"Lander University","No",1166,1009,510,9,33,2074,341,4938,2987,528,1702,67,77,17,11,6119,51 +"Lawrence University","Yes",1243,947,324,50,77,1129,74,17163,3891,525,975,76,92,10.1,57,13965,77 +"Le Moyne College","Yes",1470,1199,425,21,76,1820,558,11040,4840,400,900,89,92,13.3,28,8118,94 +"Lebanon Valley College","Yes",1386,1060,320,28,56,965,502,13850,4755,400,1125,84,84,12.3,30,8196,85 +"Lehigh University","Yes",6397,4304,1092,40,84,4298,132,18700,5580,750,1130,96,99,12.5,43,14665,91 +"Lenoir-Rhyne College","Yes",979,743,259,25,46,1188,166,10100,4000,400,1000,88,92,12,20,8539,66 +"Lesley College","Yes",244,198,82,12,33,1134,336,11700,5300,550,805,71,88,27.8,18,8694,58 +"LeTourneau University","Yes",477,417,204,29,54,1532,77,8840,4240,600,1400,58,70,20.8,23,6863,56 +"Lewis and Clark College","Yes",2774,2092,482,35,64,1763,59,15800,4790,450,950,97,98,12.3,21,12999,69 +"Lewis University","Yes",1154,1050,395,12,31,2192,1423,10560,4520,500,1200,36,48,14.3,10,7701,61 +"Lincoln Memorial University","Yes",787,562,363,21,55,925,605,5950,2890,600,1300,67,72,14.6,35,5177,53 +"Lincoln University","No",1660,1091,326,15,41,1196,33,4818,3400,350,1400,71,72,12.6,8,10912,45 +"Lindenwood College","Yes",810,484,356,6,33,2155,191,9200,4800,1000,4200,65,85,24.1,9,3480,100 +"Linfield College","Yes",1561,1188,458,48,72,1548,840,13380,4210,500,900,89,91,17.8,34,8747,81 +"Livingstone College","Yes",900,473,217,22,47,621,11,4400,3400,800,900,53,93,10.4,16,9268,92 +"Lock Haven University of Pennsylvania","No",3570,2215,651,17,41,3390,325,7352,3620,225,500,47,55,16.1,14,6374,63 +"Longwood College","No",2747,1870,724,12,47,2874,118,7920,3962,550,2200,74,80,18.4,23,5553,62 +"Loras College","Yes",1641,1283,527,20,39,1663,170,11200,4000,500,1200,61,62,14.2,24,7578,70 +"Louisiana College","Yes",2013,1053,212,33,61,912,158,5150,3036,500,1655,64,74,10.5,11,7547,59 +"Louisiana State University at Baton Rouge","No",5996,4993,3079,29,57,16269,3757,5925,2980,600,2242,83,87,15.9,11,6741,37 +"Louisiana Tech University","No",2397,2144,1525,22,45,6720,1822,3957,2325,618,1656,66,77,20,13,4546,45 +"Loyola College","Yes",4076,3137,738,25,54,3010,184,12990,6300,600,900,86,88,14.7,27,9448,80 +"Loyola Marymount University","Yes",3768,2662,753,42,64,3558,436,13592,5916,545,1328,84,88,14.2,10,11677,84 +"Loyola University","Yes",1891,1698,719,24,80,2740,761,11100,5870,600,750,77,88,11.7,14,9456,53 +"Loyola University Chicago","Yes",3579,2959,868,25,55,5244,3417,11500,5330,700,2000,94,95,6.2,15,13009,65 +"Luther College","Yes",1549,1392,587,38,72,2269,85,13240,3560,600,400,73,85,13.8,38,8949,77 +"Lycoming College","Yes",1286,1005,363,16,37,1363,74,13900,4300,500,900,75,81,14,32,8024,72 +"Lynchburg College","Yes",1756,1500,366,3,21,1524,280,12450,5400,450,870,62,66,12.4,24,8832,70 +"Lyndon State College","No",535,502,223,6,20,959,150,7320,4640,500,600,48,65,12.6,15,7114,51 +"Macalester College","Yes",2939,1496,452,56,86,1723,113,15909,4772,500,700,85,91,11.9,37,14213,77 +"MacMurray College","Yes",740,558,177,12,29,628,63,9620,3750,550,950,49,55,10.8,33,10642,59 +"Malone College","Yes",874,758,428,21,46,1605,246,9858,3700,450,1200,42,45,17.6,16,4796,55 +"Manchester College","Yes",1004,802,239,23,63,909,51,10440,3850,525,1450,63,72,11.8,20,7940,64 +"Manhattan College","Yes",2432,1730,563,20,63,2578,254,12370,6800,500,1800,92,92,13.6,25,10062,79 +"Manhattanville College","Yes",962,750,212,21,54,830,150,14700,6550,450,400,97,97,11.3,24,11291,70 +"Mankato State University","No",3073,2672,1547,9,29,9649,1792,4300,2643,450,1660,57,68,19,11,5801,68 +"Marian College of Fond du Lac","Yes",824,670,337,15,41,1160,653,9400,3400,500,1100,37,37,8.4,21,5352,59 +"Marietta College","Yes",1611,960,342,27,60,1089,210,13850,3920,470,810,80,97,13.2,30,10223,96 +"Marist College","Yes",4731,3171,830,12,31,3557,658,10700,5925,550,1200,74,81,17.6,34,8408,69 +"Marquette University","Yes",5152,4600,1685,36,71,7016,804,11610,4760,600,1950,86,94,13.5,25,9982,77 +"Marshall University","Yes",4226,3666,2007,14,60,7703,2339,5094,4010,700,1560,77,86,16.6,10,6203,50 +"Mary Baldwin College","Yes",499,441,199,26,52,846,377,11200,7400,600,1300,66,79,6.8,50,10819,90 +"Mary Washington College","No",4350,2178,756,39,78,2997,736,6490,4942,650,2102,75,80,17.6,30,5358,84 +"Marymount College Tarrytown","Yes",478,327,117,9,34,731,370,11510,6450,575,1075,71,93,10.3,30,10502,77 +"Marymount Manhattan College","Yes",695,535,239,21,30,988,785,10200,7000,350,1100,63,76,11.7,20,10622,68 +"Marymount University","Yes",941,772,214,10,30,1247,776,11390,5280,500,750,77,82,10.6,17,8575,55 +"Maryville College","Yes",1464,888,176,26,52,624,128,11200,4208,500,1642,80,90,11.1,43,8317,51 +"Maryville University","Yes",549,397,169,26,51,1343,1751,9250,4550,425,1350,52,58,13.1,13,5925,61 +"Marywood College","Yes",1107,859,323,13,51,1452,402,11040,4500,600,700,65,76,11.8,30,9034,66 +"Massachusetts Institute of Technology","Yes",6411,2140,1078,96,99,4481,28,20100,5975,725,1600,99,99,10.1,35,33541,94 +"Mayville State University","No",233,233,153,5,12,658,58,4486,2516,600,1900,68,68,15.7,11,6971,51 +"McKendree College","Yes",1002,555,119,16,43,836,684,7680,3740,500,800,70,74,17.7,21,6652,52 +"McMurry University","Yes",578,411,187,25,50,880,477,6930,3452,400,1525,57,64,11,11,6383,32 +"McPherson College","Yes",420,293,93,11,32,336,80,7950,3750,600,2740,54,54,9.8,45,9754,48 +"Mercer University","Yes",2286,1668,564,37,70,2943,1260,11985,4081,400,1500,93,95,9.2,15,8995,91 +"Mercyhurst College","Yes",1557,1074,397,15,40,1805,433,9813,4050,425,1000,45,63,16.7,29,7307,78 +"Meredith College","Yes",857,772,376,25,58,1721,470,6720,3250,450,1520,77,82,13.9,33,6881,82 +"Merrimack College","Yes",1981,1541,514,18,36,1927,1084,12500,6200,375,1000,73,75,16.8,22,8707,80 +"Mesa State College","No",1584,1456,891,6,18,3471,911,5016,3798,540,2256,48,48,28.8,12,3871,59 +"Messiah College","Yes",1742,1382,607,30,64,2258,53,10300,5080,475,1200,68,75,14.1,30,7762,89 +"Miami University at Oxford","No",9239,7788,3290,35,39,13606,807,8856,3960,500,1382,81,89,17.6,20,7846,85 +"Michigan State University","No",18114,15096,6180,23,57,26640,4120,10658,3734,504,600,93,95,14,9,10520,71 +"Michigan Technological University","No",2618,2288,1032,42,77,5524,414,8127,3978,900,1200,82,82,17,25,7473,65 +"MidAmerica Nazarene College","Yes",331,331,225,15,36,1100,166,6840,3720,1100,4913,33,33,15.4,20,5524,49 +"Millersville University of Penn.","No",6011,3075,960,22,60,5146,1532,7844,3830,450,1258,72,74,16.8,20,7832,71 +"Milligan College","Yes",610,461,189,26,52,685,49,8200,3300,550,1000,63,69,12,16,8128,64 +"Millikin University","Yes",1444,1261,456,29,62,1788,95,11910,4378,450,965,60,77,11.4,25,8149,75 +"Millsaps College","Yes",905,834,319,32,61,1073,179,11320,4402,550,1350,82,89,12.7,38,11218,58 +"Milwaukee School of Engineering","Yes",1217,1088,496,36,69,1773,884,11505,3255,1000,2075,35,46,16.7,23,7140,67 +"Mississippi College","Yes",594,385,307,36,57,1695,721,5580,2830,600,700,77,79,16.5,18,6170,61 +"Mississippi State University","No",4255,3277,1609,18,57,10094,1621,9866,3084,480,1479,77,77,15.9,20,6223,53 +"Mississippi University for Women","No",480,405,380,19,46,1673,1014,4386,2217,600,1500,49,54,15.8,8,5704,63 +"Missouri Southern State College","No",1576,1326,913,13,50,3689,2200,3840,2852,200,400,52,54,20.3,9,4172,100 +"Missouri Valley College","Yes",1310,983,316,5,35,1057,175,8550,5050,400,900,35,67,17.4,16,4333,27 +"Monmouth College IL","Yes",601,503,204,28,57,671,11,13000,4100,400,460,91,91,11.6,43,11087,56 +"Monmouth College","Yes",2707,1881,478,14,34,1893,847,12480,5290,530,1740,70,85,14.2,15,9492,54 +"Montana College of Mineral Sci. & Tech.","No",572,544,320,45,72,1470,416,6073,3400,550,1400,71,71,16.4,31,6112,74 +"Montana State University","No",3500,2836,1779,15,42,8730,993,5552,3710,550,2300,75,83,17.6,8,6324,37 +"Montclair State University","No",5220,2128,865,19,53,6411,3186,3648,4834,700,950,82,87,21.5,9,6717,58 +"Montreat-Anderson College","Yes",263,223,103,10,24,316,20,8438,3372,500,2958,42,50,11.1,4,11989,15 +"Moorhead State University","No",2442,2164,1189,12,37,5983,1075,4426,2664,600,1000,76,81,18.1,19,4795,60 +"Moravian College","Yes",1232,955,303,23,58,1241,485,14990,4730,550,1250,86,92,15.2,28,9566,74 +"Morehouse College","Yes",3708,1678,722,41,66,2852,153,7050,5490,250,600,71,74,17.8,10,8122,83 +"Morningside College","Yes",586,533,239,16,36,950,228,10520,3678,500,1000,48,68,13,32,8111,56 +"Morris College","Yes",882,730,330,2,13,926,12,4515,2550,850,2100,53,60,18.6,34,6990,60 +"Mount Holyoke College","Yes",1800,1314,526,47,79,1891,40,19300,5700,750,750,79,91,9,51,18359,84 +"Mount Marty College","Yes",279,276,126,17,37,600,435,6844,2980,500,500,45,55,11.7,38,5073,44 +"Mount Mary College","Yes",235,217,121,12,32,931,487,8950,3119,550,1125,51,51,10.7,26,7016,78 +"Mount Mercy College","Yes",368,317,159,20,49,806,542,10500,3555,500,2285,44,50,11.3,30,6695,64 +"Mount Saint Clare College","Yes",325,284,95,16,33,364,88,9900,3650,500,1200,32,37,13.6,43,6525,21 +"Mount Saint Mary's College","Yes",1321,1159,328,15,36,1243,79,12850,6200,550,900,77,82,12.8,36,8536,80 +"Mount Saint Mary College","Yes",1170,695,238,14,48,1170,429,7470,4600,250,1400,74,75,15.3,23,6898,88 +"Mount St. Mary's College","Yes",657,537,113,37,90,1039,466,12474,5678,630,1278,53,71,11.9,19,10613,72 +"Mount Union College","Yes",1310,1086,458,26,61,1365,144,12250,3530,400,1150,85,87,16.7,35,7215,81 +"Mount Vernon Nazarene College","Yes",510,485,334,18,36,1114,94,7400,3346,600,600,57,57,19.8,7,6869,58 +"Muhlenberg College","Yes",2519,1836,462,30,61,1656,352,16975,4565,600,850,76,86,12.8,39,10888,83 +"Murray State University","No",2225,1910,1190,29,55,5968,955,4738,3110,700,940,72,76,20.2,27,5972,52 +"Muskingum College","Yes",1109,922,375,24,46,1115,70,13240,3914,600,800,73,85,13.4,27,9333,73 +"National-Louis University","Yes",513,347,279,23,48,2508,505,9090,4500,650,500,62,65,18.3,2,7905,71 +"Nazareth College of Rochester","Yes",947,798,266,36,68,1274,471,10850,5150,550,800,77,93,13.6,24,8797,61 +"New Jersey Institute of Technology","No",1879,1216,483,27,62,3311,1646,8832,5376,700,1850,92,98,13.5,19,12529,72 +"New Mexico Institute of Mining and Tech.","No",787,601,233,40,73,1017,411,5376,3214,600,1100,99,100,13.7,11,9241,34 +"New York University","Yes",13594,7244,2505,70,86,12408,2814,17748,7262,450,1000,87,98,7.8,16,21227,71 +"Newberry College","Yes",872,722,154,14,36,601,36,10194,2600,500,1500,57,63,11.4,32,5788,83 +"Niagara University","Yes",2220,1796,467,65,99,1919,334,10320,4762,450,650,68,100,14.2,20,7788,65 +"North Adams State College","No",1563,1005,240,1,19,1380,136,5542,4330,500,1000,65,71,14.2,17,6562,57 +"North Carolina A. & T. State University","No",4809,3089,1429,12,33,6162,871,6806,1780,600,1651,72,72,16.7,9,7090,44 +"North Carolina State University at Raleigh","No",10634,7064,3176,39,78,16505,5481,8400,6540,600,1300,92,98,17.5,21,9670,62 +"North Carolina Wesleyan College","Yes",812,689,195,7,24,646,84,8242,4230,600,1295,77,77,12.7,11,10090,52 +"North Central College","Yes",1127,884,308,30,64,1310,766,11718,7398,450,1800,73,87,16.4,33,8871,76 +"North Dakota State University","No",2968,2297,1610,13,47,7368,1128,5834,2744,600,2000,79,83,17,24,6310,42 +"North Park College","Yes",465,361,176,19,39,879,156,12580,4345,400,970,76,79,13.1,24,10889,74 +"Northeast Missouri State University","No",6040,4577,1620,36,72,5640,266,4856,3416,400,1100,69,72,15.7,13,6601,76 +"Northeastern University","Yes",11901,8492,2517,16,42,11160,10221,13380,7425,600,1750,73,82,12.9,17,9563,46 +"Northern Arizona University","No",5891,4931,1973,23,48,11249,2682,6746,3728,620,2342,78,83,21.7,7,6157,41 +"Northern Illinois University","No",10706,7219,2397,12,37,14826,1979,7799,3296,470,1750,73,78,17.3,11,6086,56 +"Northwest Missouri State University","No",2729,2535,1257,8,29,4787,472,3735,3136,250,1630,62,65,21.7,23,5284,54 +"Northwest Nazarene College","Yes",616,514,385,29,52,1115,60,9840,2820,450,822,59,59,14.8,20,6261,58 +"Northwestern College","Yes",860,811,366,22,56,1040,52,9900,3075,300,1800,68,68,14.9,34,6357,68 +"Northwestern University","Yes",12289,5200,1902,85,98,7450,45,16404,5520,759,1585,96,100,6.8,25,26385,92 +"Norwich University","Yes",1743,1625,626,8,29,1862,382,14134,5270,500,800,71,74,13.1,22,9209,63 +"Notre Dame College","Yes",379,324,107,15,37,500,311,9990,4900,400,600,44,47,12.1,26,4948,33 +"Oakland University","No",3041,2581,1173,16,56,6441,3982,9114,4030,400,650,88,90,19.7,13,6637,53 +"Oberlin College","Yes",4778,2767,678,50,89,2587,120,19670,5820,575,1119,77,96,10.1,47,16593,83 +"Occidental College","Yes",2324,1319,370,52,81,1686,35,16560,5140,558,1152,91,93,10.5,30,16196,79 +"Oglethorpe University","Yes",792,649,186,56,87,769,377,12900,4340,600,4110,91,95,13.1,27,8568,67 +"Ohio Northern University","Yes",2936,2342,669,35,62,2502,66,15990,4080,600,825,73,78,14.5,31,9979,83 +"Ohio University","No",11023,8298,3183,21,54,14861,1310,7629,4095,550,2300,79,87,20.4,13,8811,64 +"Ohio Wesleyan University","Yes",2190,1700,458,36,65,1780,48,16732,5650,550,550,93,93,12.1,32,12011,75 +"Oklahoma Baptist University","Yes",758,681,484,35,59,1707,705,5390,3140,515,1290,63,71,15.1,18,5511,50 +"Oklahoma Christian University","Yes",776,765,351,22,44,1419,228,6400,3150,500,1900,58,64,16.2,8,6578,45 +"Oklahoma State University","No",4522,3913,2181,29,57,12830,1658,5336,3344,800,3100,84,92,15.3,14,6433,48 +"Otterbein College","Yes",1496,1205,428,26,57,1648,936,12888,4440,420,840,62,68,13.9,30,8802,87 +"Ouachita Baptist University","Yes",910,773,450,31,73,1310,61,6530,2800,500,1500,63,67,13.3,10,6413,65 +"Our Lady of the Lake University","Yes",2308,1336,295,22,46,1202,942,8530,3644,616,1576,56,64,14.9,25,7114,37 +"Pace University","Yes",8256,3750,1522,37,70,5809,4379,11000,5160,660,1115,90,95,13.8,10,10059,62 +"Pacific Lutheran University","Yes",1603,1392,504,31,68,2580,302,13312,4488,600,1516,78,78,11,23,9431,83 +"Pacific Union College","Yes",940,668,385,20,48,1316,139,11925,3825,630,1926,48,87,12.3,12,9157,69 +"Pacific University","Yes",943,849,288,41,71,1041,35,14210,3994,450,1100,76,76,10.9,22,11216,42 +"Pembroke State University","No",944,774,440,14,34,2174,529,6360,2760,550,1498,77,77,15,5,6443,48 +"Pennsylvania State Univ. Main Campus","No",19315,10344,3450,48,93,28938,2025,10645,4060,512,2394,77,96,18.1,19,8992,63 +"Pepperdine University","Yes",3821,2037,680,86,96,2488,625,18200,6770,500,700,95,98,11.6,13,16185,66 +"Peru State College","No",701,501,458,10,40,959,457,2580,2624,500,900,48,100,20.1,24,4870,44 +"Pfeiffer College","Yes",838,651,159,11,25,654,162,8640,3700,400,1915,62,62,12.2,13,7634,48 +"Philadelphia Coll. of Textiles and Sci.","Yes",1538,1259,468,19,42,1664,1042,11690,5062,600,1664,48,80,12.9,15,8028,68 +"Phillips University","Yes",692,576,174,19,50,597,83,10500,3860,600,940,58,64,11.6,19,8990,39 +"Piedmont College","Yes",663,562,127,20,40,641,63,5640,3620,600,750,89,89,13.2,17,7309,31 +"Pikeville College","Yes",404,400,169,28,48,797,100,6000,3000,500,500,48,57,13.4,14,5557,61 +"Pitzer College","Yes",1133,630,220,37,73,750,30,17688,5900,650,850,100,100,10.4,11,14820,73 +"Point Loma Nazarene College","Yes",809,687,428,20,43,1889,217,10178,4190,800,750,71,71,16.1,19,7895,54 +"Point Park College","Yes",875,744,207,7,38,1173,1402,9700,4830,400,1200,45,90,14.5,10,7652,66 +"Polytechnic University","Yes",1132,847,302,58,89,1379,214,16200,4200,436,2486,90,90,10.4,14,14329,62 +"Prairie View A. and M. University","No",2405,2234,1061,10,22,4564,448,4290,3500,598,1582,55,93,19.4,1,5967,35 +"Presbyterian College","Yes",1082,832,302,34,63,1133,30,11859,3635,554,1429,80,85,13.4,42,8354,85 +"Princeton University","Yes",13218,2042,1153,90,98,4540,146,19900,5910,675,1575,91,96,8.4,54,28320,99 +"Providence College","Yes",5139,3346,973,20,55,3717,1358,14400,6200,450,1100,66,74,18.4,35,8135,96 +"Purdue University at West Lafayette","No",21804,18744,5874,29,60,26213,4065,9556,3990,570,1060,86,86,18.2,15,8604,67 +"Queens College","Yes",516,392,154,32,62,630,549,11020,4970,610,1900,73,75,14,36,9315,58 +"Quincy University","Yes",1025,707,297,22,66,1070,72,10100,4140,450,1080,69,71,16.3,32,6880,80 +"Quinnipiac College","Yes",3712,2153,806,17,45,2677,714,12030,6140,1000,500,63,73,12,33,8847,86 +"Radford University","No",5702,4894,1742,15,37,8077,472,6684,4110,500,900,73,83,19.6,9,4519,62 +"Ramapo College of New Jersey","No",2088,957,362,6,29,2745,1938,4449,4860,600,1655,74,95,17.8,8,7333,47 +"Randolph-Macon College","Yes",1771,1325,306,21,46,1071,27,13840,3735,400,900,77,80,10.7,38,11080,74 +"Randolph-Macon Woman's College","Yes",696,616,169,35,66,653,56,13970,6110,370,920,88,97,9.2,24,16358,68 +"Reed College","Yes",1966,1436,327,47,80,1199,61,19960,5490,500,450,90,90,11.8,37,15886,68 +"Regis College","Yes",427,385,143,18,38,581,533,12700,5800,450,700,81,85,10.3,37,11758,84 +"Rensselaer Polytechnic Institute","Yes",4996,4165,936,53,82,4291,16,17475,5976,1230,1100,94,98,15.4,21,15605,70 +"Rhodes College","Yes",2302,1831,391,58,82,1345,59,15200,4768,550,1500,90,96,10.8,47,13388,77 +"Rider University","Yes",3586,2424,730,16,31,2748,1309,13250,5420,700,3100,84,92,12.3,23,11299,70 +"Ripon College","Yes",587,501,211,28,52,735,28,15200,4100,350,650,87,90,9.4,49,12472,64 +"Rivier College","Yes",484,386,141,6,28,590,1196,9870,4860,600,1100,59,59,12.2,19,6744,81 +"Roanoke College","Yes",2227,1790,437,27,54,1460,239,13425,4425,450,1200,85,89,13,26,9405,72 +"Rockhurst College","Yes",935,858,345,22,50,1127,754,9490,4100,500,1500,60,79,10.7,21,7519,79 +"Rocky Mountain College","Yes",560,392,270,11,31,743,118,8734,3362,600,625,56,78,11.3,27,6422,68 +"Roger Williams University","Yes",3304,2804,679,10,20,2111,1489,12520,6050,500,730,44,54,16.4,8,7957,61 +"Rollins College","Yes",1777,1151,382,31,55,1668,1052,16425,5220,955,750,81,85,13.3,23,11561,90 +"Rosary College","Yes",434,321,141,28,53,624,269,10950,4600,550,950,79,82,12.9,30,9264,81 +"Rowan College of New Jersey","No",3820,1431,695,21,70,5303,3942,4356,4830,800,800,76,81,22.1,6,7252,51 +"Rutgers at New Brunswick","No",48094,26330,4520,36,79,21401,3712,7410,4748,690,2009,90,95,19.5,19,10474,77 +"Rutgers State University at Camden","No",3366,1752,232,27,79,2585,1300,7411,4748,690,2009,90,95,18.6,12,10134,57 +"Rutgers State University at Newark","No",5785,2690,499,26,62,4005,1886,7410,4748,690,2009,90,95,17.4,16,11878,58 +"Sacred Heart University","Yes",2307,1896,509,19,51,1707,1889,11070,5780,400,600,71,73,14.8,16,7120,82 +"Saint Ambrose University","Yes",897,718,276,12,48,1345,390,10450,4020,500,1500,56,56,14.1,16,7444,70 +"Saint Anselm College","Yes",2095,1553,514,15,40,1873,94,12950,5400,450,1120,70,82,14.5,29,6719,97 +"Saint Cloud State University","No",3971,3306,1921,10,34,11493,2206,4259,2625,350,1884,70,75,18.9,10,4629,58 +"Saint Francis College IN","Yes",213,166,85,13,36,513,247,8670,3820,450,1000,43,78,12.5,4,7440,48 +"Saint Francis College","Yes",1046,824,284,21,45,1223,451,10880,5050,400,1235,64,64,19.3,24,7344,69 +"Saint John's University","Yes",933,800,444,18,45,1691,72,12247,4081,500,600,76,85,12,38,9853,70 +"Saint Joseph's College IN","Yes",920,684,225,24,42,815,222,11200,4250,600,950,55,60,14.8,19,7360,67 +"Saint Joseph's College","Yes",833,682,217,12,33,716,2196,9985,5180,500,800,53,89,27.2,8,4322,85 +"Saint Joseph's University","Yes",2519,2003,776,39,71,2473,1314,12750,6350,350,1690,84,90,17.4,13,8243,83 +"Saint Joseph College","Yes",292,241,96,20,52,543,712,12200,4600,650,950,87,90,11.2,32,8680,76 +"Saint Louis University","Yes",3294,2855,956,44,67,4576,1140,11690,4730,800,6800,84,94,4.6,19,18367,67 +"Saint Mary's College","Yes",888,734,393,26,60,1433,27,12730,4514,500,1525,74,95,9.9,31,11165,98 +"Saint Mary's College of Minnesota","Yes",876,802,367,14,35,1263,118,10800,3600,400,820,68,74,18.8,19,5081,78 +"Saint Mary-of-the-Woods College","Yes",150,130,88,23,50,341,768,10300,4130,500,1700,44,58,10.2,37,9678,75 +"Saint Michael's College","Yes",1910,1380,463,16,64,1715,106,13030,5860,500,750,79,88,14.5,34,10190,84 +"Saint Olaf College","Yes",2248,1673,745,38,73,2888,105,14350,3750,550,550,82,88,10,31,12502,83 +"Saint Peter's College","Yes",1606,1413,530,23,38,1921,1154,9408,5520,500,450,78,78,12.1,22,7669,53 +"Saint Vincent College","Yes",700,595,278,19,35,1035,182,10850,3936,500,900,62,64,12.3,31,8534,88 +"Saint Xavier University","Yes",785,647,295,15,65,1670,726,10860,4624,600,794,87,100,13.7,15,8953,55 +"Salem-Teikyo University","Yes",489,384,120,23,52,700,45,10575,3952,400,620,46,24,13,9,8946,98 +"Salem College","Yes",335,284,132,28,69,534,216,10475,6300,500,2000,68,68,11.2,46,9599,60 +"Salisbury State University","No",4216,2290,736,20,52,4296,1027,5130,4690,600,1450,73,75,17.9,18,5125,56 +"Samford University","Yes",1680,1395,691,34,76,2959,402,8236,3700,569,1650,74,75,14.7,17,9533,61 +"San Diego State University","No",9402,7020,2151,20,70,16407,5550,8384,5110,612,2400,87,93,19.5,7,7930,41 +"Santa Clara University","Yes",4019,2779,888,40,73,3891,128,13584,5928,630,1278,88,92,13.9,19,10872,100 +"Sarah Lawrence College","Yes",1380,768,263,57,82,1000,105,19300,6694,600,700,89,93,6.1,18,14779,83 +"Savannah Coll. of Art and Design","Yes",1109,688,386,20,65,1897,208,8325,5000,1200,1600,14,98,16.1,26,6874,55 +"Schreiner College","Yes",584,413,131,19,51,521,99,8955,5900,500,1488,51,56,11.8,23,8545,52 +"Scripps College","Yes",855,632,139,60,83,569,7,17238,7350,600,800,95,100,8.2,41,18372,73 +"Seattle Pacific University","Yes",1183,1016,411,42,82,1922,704,12669,4875,600,1250,83,85,16.8,20,10368,66 +"Seattle University","Yes",2115,1540,494,28,72,2993,347,12825,4375,500,1500,85,85,12.2,16,10175,89 +"Seton Hall University","Yes",4576,3565,1000,16,36,4384,1530,12000,6484,650,1000,81,84,14.4,15,10080,64 +"Seton Hill College","Yes",936,794,197,24,56,752,210,11240,4180,350,2000,71,71,11.2,37,10065,71 +"Shippensburg University of Penn.","No",5818,3281,1116,14,53,5268,300,7844,3504,450,1700,80,83,18.8,13,6719,72 +"Shorter College","Yes",540,445,165,23,70,1115,111,7210,3600,500,2000,62,65,13.2,18,7356,58 +"Siena College","Yes",2961,1932,628,24,68,2669,616,10800,5100,575,1090,71,82,14.1,42,8189,100 +"Siena Heights College","Yes",464,419,183,10,31,686,287,9240,3880,475,1090,29,49,7.2,17,9431,47 +"Simmons College","Yes",1003,782,295,23,53,1144,160,16160,6950,500,1200,74,81,8.9,33,14086,79 +"Simpson College","Yes",1016,872,300,27,57,1116,602,11250,4980,550,1400,66,73,15.8,36,7411,70 +"Sioux Falls College","Yes",437,400,211,13,35,614,271,8990,3064,500,1700,73,73,14.8,7,7881,48 +"Skidmore College","Yes",4293,2728,591,25,62,2322,263,18710,5970,500,700,87,92,12.7,29,14837,81 +"Smith College","Yes",2925,1598,632,51,88,2479,95,18820,6390,500,1050,85,97,10.3,44,21199,90 +"South Dakota State University","No",2807,2589,1701,13,37,7000,1103,3811,2190,500,1970,62,65,15,29,5084,67 +"Southeast Missouri State University","No",2281,1870,1408,18,43,6553,1246,4680,3540,200,2150,75,76,17.1,8,5916,45 +"Southeastern Oklahoma State Univ.","No",818,700,447,20,50,2962,651,3738,2619,450,1022,55,59,19.6,9,4444,53 +"Southern California College","Yes",385,340,193,18,38,784,127,9520,4124,630,1818,63,65,18.6,11,8219,43 +"Southern Illinois University at Edwardsville","No",2540,2195,994,13,40,6063,2550,5472,3598,221,2216,76,81,16.5,8,7498,43 +"Southern Methodist University","Yes",4301,3455,1166,41,69,4892,387,12772,5078,576,1802,74,88,13.5,17,12726,72 +"Southwest Baptist University","Yes",1093,1093,642,12,32,1770,967,7070,2500,400,1000,52,54,15.9,13,4718,71 +"Southwest Missouri State University","No",6118,5254,3204,15,37,13131,3374,4740,2590,500,1360,70,75,19.9,11,4632,56 +"Southwest State University","No",1047,938,511,13,33,2091,546,4285,2750,600,1800,58,75,16.5,31,6591,51 +"Southwestern Adventist College","Yes",321,318,172,11,27,620,280,7536,3736,430,1651,44,77,13,12,5309,36 +"Southwestern College","Yes",213,155,75,28,66,504,147,7200,3532,550,1500,56,56,11.8,12,7818,52 +"Southwestern University","Yes",1244,912,352,44,77,1177,43,11850,4675,600,1050,83,89,11.3,35,12995,67 +"Spalding University","Yes",283,201,97,10,45,589,263,8400,2800,600,900,50,56,10.6,40,6860,89 +"Spelman College","Yes",3713,1237,443,47,83,1971,107,7000,5565,660,2400,73,80,12.5,18,9988,65 +"Spring Arbor College","Yes",372,362,181,15,32,1501,353,8600,3550,385,665,48,48,15.4,9,10938,49 +"St. Bonaventure University","Yes",1489,1313,375,13,45,1688,131,10456,4927,500,1050,91,91,17.7,32,9828,78 +"St. John's College","Yes",323,278,122,31,51,393,4,16150,5450,275,800,63,72,7.2,26,15622,64 +"St. John Fisher College","Yes",1368,1064,354,19,51,1687,677,10570,5600,400,800,86,81,14.5,29,7908,66 +"St. Lawrence University","Yes",2753,1820,505,31,56,1801,45,18720,5730,650,825,90,94,11.5,38,14980,85 +"St. Martin's College","Yes",191,165,63,5,25,494,574,11550,4270,300,500,43,77,14.5,8,9209,40 +"St. Mary's College of California","Yes",2643,1611,465,36,80,2615,248,13332,6354,630,1584,88,89,16.1,17,9619,78 +"St. Mary's College of Maryland","No",1340,695,285,42,73,1315,209,6800,4730,675,1250,84,89,11.6,23,10357,63 +"St. Mary's University of San Antonio","Yes",1243,1020,414,33,60,2149,418,8678,3858,700,1736,82,83,16.2,7,7651,72 +"St. Norbert College","Yes",1334,1243,568,30,56,1946,95,12140,4450,425,1100,74,78,15.1,36,8595,88 +"St. Paul's College","Yes",651,581,243,8,17,617,34,5000,3650,600,600,45,45,14,8,8426,45 +"St. Thomas Aquinas College","Yes",861,609,215,10,27,1117,815,8650,5700,500,1750,69,73,16.1,13,6534,67 +"Stephens College","Yes",450,405,194,17,34,614,388,13900,5200,450,2150,46,63,10.9,17,9995,59 +"Stetson University","Yes",1557,1227,489,37,69,1964,81,12315,4565,600,1365,85,90,12.5,24,10307,73 +"Stevens Institute of Technology","Yes",1768,1249,380,51,93,1263,11,16900,5680,450,750,89,89,19,33,12837,79 +"Stockton College of New Jersey","No",4019,1579,710,23,65,4365,765,3040,4351,711,1125,78,92,19.5,7,5599,64 +"Stonehill College","Yes",3646,2300,585,25,69,2022,926,12170,6172,480,800,79,79,13,30,7495,97 +"SUNY at Albany","No",13528,9198,1843,16,61,10168,1231,6550,4355,700,1560,93,96,17.4,16,9075,74 +"SUNY at Binghamton","No",14463,6166,1757,60,94,8544,671,6550,4598,700,1000,83,100,18,15,8055,80 +"SUNY at Buffalo","No",15039,9649,3087,36,100,13963,3124,6550,4731,708,957,90,97,13.6,15,11177,56 +"SUNY at Stony Brook","No",12512,6969,1724,27,66,9744,1351,6550,4712,600,1200,91,96,10.5,7,13705,57 +"SUNY College at Brockport","No",7294,3564,904,7,34,5758,1363,6550,4460,500,705,79,83,19,14,6632,49 +"SUNY College at Oswego","No",8000,4556,1464,17,70,6943,869,6550,4810,500,1500,69,85,22,21,5280,63 +"SUNY College at Buffalo","No",5318,3515,1025,8,29,7626,2091,6550,4040,550,1230,71,78,18.7,12,7511,42 +"SUNY College at Cortland","No",7888,3519,1036,6,40,5011,346,6550,4680,630,1274,82,85,17.8,17,5563,53 +"SUNY College at Fredonia","No",4877,2798,814,13,48,4123,298,6550,4420,620,1481,82,90,16.3,10,6442,66 +"SUNY College at Geneseo","No",8598,4562,1143,56,93,5060,146,6550,4170,600,650,79,84,19.1,25,5716,76 +"SUNY College at New Paltz","No",8399,3609,656,19,53,4658,1478,6550,4240,550,1500,85,93,15.3,8,6608,53 +"SUNY College at Plattsburgh","No",5549,3583,853,9,40,5004,475,6550,4176,600,1380,80,90,17.9,16,6174,65 +"SUNY College at Potsdam","No",3150,2289,650,16,51,3598,234,6840,4660,500,1000,71,75,15.1,17,6436,59 +"SUNY College at Purchase","No",2119,1264,390,5,33,2478,1441,6550,4760,1125,1362,80,100,14.9,8,8170,46 +"Susquehanna University","Yes",2096,1512,465,27,59,1442,166,16130,4710,400,800,83,86,13.9,37,10554,90 +"Sweet Briar College","Yes",462,402,146,36,68,527,41,14500,6000,500,600,91,99,6.5,48,18953,61 +"Syracuse University","Yes",10477,7260,2442,28,67,10142,117,15150,6870,635,960,73,84,11.3,13,14231,67 +"Tabor College","Yes",257,183,109,19,41,396,38,7850,3410,400,1500,55,70,10,15,7233,53 +"Talladega College","Yes",4414,1500,335,30,60,908,119,5666,2964,1000,1400,56,58,15.5,7,5970,46 +"Taylor University","Yes",1769,1092,437,41,80,1757,81,10965,4000,450,1250,60,61,14.2,32,8294,98 +"Tennessee Wesleyan College","Yes",232,182,99,7,29,402,237,7070,3640,400,3158,59,65,8.9,16,6286,36 +"Texas A&M Univ. at College Station","No",14474,10519,6392,49,85,31643,2798,5130,3412,600,2144,89,91,23.1,29,8471,69 +"Texas A&M University at Galveston","No",529,481,243,22,47,1206,134,4860,3122,600,650,103,88,17.4,16,6415,43 +"Texas Christian University","Yes",4095,3079,1195,33,64,5064,660,8490,3320,650,2400,81,93,14.8,23,9158,64 +"Texas Lutheran College","Yes",497,423,215,27,57,895,429,7850,3410,490,1700,54,58,13.8,24,7002,50 +"Texas Southern University","No",4345,3245,2604,15,85,5584,3101,7860,3360,600,1700,65,75,18.2,21,3605,10 +"Texas Wesleyan University","Yes",592,501,279,19,44,1204,392,6400,3484,600,1800,80,83,14.5,10,7936,43 +"The Citadel","No",1500,1242,611,12,36,2024,292,7070,2439,400,779,95,94,17.1,17,7744,84 +"Thiel College","Yes",1154,951,253,15,31,791,140,11172,4958,700,1350,68,76,11.6,16,9186,60 +"Tiffin University","Yes",845,734,254,5,21,662,351,7600,3800,600,1200,59,74,19,40,5096,39 +"Transylvania University","Yes",759,729,244,57,81,867,51,10900,4450,500,1000,81,91,12.1,41,10219,70 +"Trenton State College","No",5042,2312,944,55,94,5167,902,5391,5411,700,1000,81,87,14.4,6,8504,81 +"Tri-State University","Yes",1262,1102,276,14,40,978,98,9456,4350,468,1323,53,53,12.8,24,7603,65 +"Trinity College CT","Yes",3058,1798,478,46,84,1737,244,18810,5690,500,680,91,96,10.4,48,18034,91 +"Trinity College DC","Yes",247,189,100,19,49,309,639,11412,6430,500,900,89,93,8.3,37,11806,96 +"Trinity College VT","Yes",222,185,91,16,41,484,541,11010,5208,550,500,58,78,10.4,26,9586,78 +"Trinity University","Yes",2425,1818,601,62,93,2110,95,12240,5150,500,490,94,96,9.6,20,14703,93 +"Tulane University","Yes",7033,5125,1223,47,75,4941,1534,19040,5950,350,800,98,98,9.1,21,16920,74 +"Tusculum College","Yes",626,372,145,12,34,983,40,7700,3400,450,800,70,70,21.9,28,4933,52 +"Tuskegee University","Yes",2267,1827,611,20,59,2825,144,6735,3395,600,1425,70,74,12.2,7,10872,65 +"Union College KY","Yes",484,384,177,9,45,634,78,7800,2950,500,600,60,88,14.1,9,6864,64 +"Union College NY","Yes",3495,1712,528,49,84,1915,123,18732,6204,450,1024,94,96,11.5,49,15411,88 +"Univ. of Wisconsin at OshKosh","No",4800,2900,1515,14,48,7764,1472,6874,2394,518,1890,73,78,19.2,14,5901,56 +"University of Alabama at Birmingham","No",1797,1260,938,24,35,6960,4698,4440,5175,750,2200,96,96,6.7,16,16352,33 +"University of Arkansas at Fayetteville","No",3235,3108,2133,25,65,9978,1530,5028,3300,500,2000,73,89,14.8,10,6820,39 +"University of California at Berkeley","No",19873,8252,3215,95,100,19532,2061,11648,6246,636,1933,93,97,15.8,10,13919,78 +"University of California at Irvine","No",15698,10775,2478,85,100,12677,864,12024,5302,790,1818,96,96,16.1,11,15934,66 +"University of Central Florida","No",6986,2959,1918,25,60,12330,7152,6618,4234,700,1600,80,98,22.2,9,6742,46 +"University of Charleston","Yes",682,535,204,22,43,771,611,9500,3540,400,750,26,58,2.5,10,7683,57 +"University of Chicago","Yes",6348,2999,922,68,94,3340,39,18930,6380,500,1254,99,99,5.3,36,36854,90 +"University of Cincinnati","No",6855,5553,2408,26,57,11036,2011,8907,4697,556,1851,89,95,10.8,6,13889,54 +"University of Connecticut at Storrs","No",9735,7187,2064,23,63,12478,1660,11656,5072,700,2300,89,95,16,16,10178,71 +"University of Dallas","Yes",681,588,246,44,74,1058,73,10760,6230,500,1200,85,93,13.4,26,8731,63 +"University of Dayton","Yes",6361,5293,1507,26,51,5889,665,11380,4220,500,900,81,85,14.8,25,8894,93 +"University of Delaware","Yes",14446,10516,3252,22,57,14130,4522,10220,4230,530,1300,82,87,18.3,15,10650,75 +"University of Denver","Yes",2974,2001,580,29,60,2666,554,15192,4695,400,1350,84,91,15.9,21,11762,67 +"University of Detroit Mercy","Yes",927,731,415,24,50,2149,2217,11130,3996,600,2166,72,79,13.5,14,10891,51 +"University of Dubuque","Yes",576,558,137,11,39,662,131,10430,3620,400,1500,85,98,16.5,18,8767,45 +"University of Evansville","Yes",2096,1626,694,35,67,2551,407,11800,4340,700,960,60,81,15.8,26,7780,77 +"University of Florida","No",12445,8836,3623,54,85,24470,3286,7090,4180,630,1530,88,97,13.4,20,14737,66 +"University of Georgia","No",11220,7871,3320,43,79,19553,2748,5697,3600,525,1755,88,95,14.7,22,7881,63 +"University of Hartford","Yes",5081,4040,1194,11,26,3768,1415,14220,6000,500,1440,61,76,10.7,9,10625,66 +"University of Hawaii at Manoa","No",3580,2603,1627,36,69,11028,2411,4460,3038,687,1281,85,87,11.8,6,12833,54 +"University of Illinois - Urbana","No",14939,11652,5705,52,88,25422,911,7560,4574,500,1982,87,90,17.4,13,8559,81 +"University of Illinois at Chicago","No",8384,5727,2710,22,50,13518,2916,7230,5088,630,3228,82,84,10,6,13883,34 +"University of Indianapolis","Yes",1487,1276,388,26,51,1417,1646,11120,4080,525,1405,55,56,11.1,23,6735,69 +"University of Kansas","No",8579,5561,3681,25,50,17880,1673,6994,3384,700,2681,88,94,13.7,17,9657,57 +"University of La Verne","Yes",1597,969,226,16,38,1431,1522,13540,5050,630,2298,66,68,14.1,23,10139,47 +"University of Louisville","No",4777,3057,1823,16,33,9844,6198,6540,3600,530,2440,84,92,11.1,24,10207,31 +"University of Maine at Farmington","No",1208,803,438,20,48,1906,344,6810,3970,450,1647,67,75,15.9,26,5712,59 +"University of Maine at Machias","No",441,369,172,17,45,633,317,6600,3680,600,400,46,46,15.1,4,5935,64 +"University of Maine at Presque Isle","No",461,381,235,10,40,974,503,6600,3630,400,1675,67,67,15.2,11,6408,35 +"University of Maryland at Baltimore County","No",4269,2594,985,27,57,6476,2592,8594,4408,494,2768,82,88,18.4,6,7618,55 +"University of Maryland at College Park","No",14292,10315,3409,22,53,19340,3991,8723,5146,550,1550,89,92,18.1,12,9021,63 +"University of Massachusetts at Amherst","No",14438,12414,3816,12,39,16282,1940,8566,3897,500,1400,88,92,16.7,15,10276,68 +"University of Massachusetts at Dartmouth","No",3347,2597,1006,10,37,4664,1630,6919,4500,500,1250,74,90,15,20,7462,56 +"University of Miami","Yes",7122,5386,1643,42,69,7760,876,16500,6526,630,1985,82,94,5.9,17,17500,59 +"University of Michigan at Ann Arbor","No",19152,12940,4893,66,92,22045,1339,15732,4659,476,1600,90,98,11.5,26,14847,87 +"University of Minnesota at Duluth","No",4192,3126,1656,15,45,5887,1254,8828,3474,753,2610,79,91,19,11,6393,53 +"University of Minnesota at Morris","No",1458,874,588,56,86,1846,154,9843,3180,600,1500,74,78,14.6,16,6716,51 +"University of Minnesota Twin Cities","No",11054,6397,3524,26,55,16502,21836,8949,3744,714,2910,88,90,12.2,37,16122,45 +"University of Mississippi","No",3844,3383,1669,26,47,7524,804,4916,3810,600,550,81,86,20.3,14,6971,53 +"University of Missouri at Columbia","No",6574,4637,2940,32,62,14782,1583,9057,3485,600,1983,87,87,12.7,15,10145,58 +"University of Missouri at Rolla","No",1877,1826,823,49,77,3926,561,9057,3600,700,1435,88,88,14.4,23,9699,49 +"University of Missouri at Saint Louis","No",1618,1141,479,18,54,4793,4552,7246,3964,500,4288,71,73,13.4,15,6433,48 +"University of Mobile","Yes",452,331,269,17,54,1417,301,6150,3680,550,1200,59,63,16.6,4,5412,52 +"University of Montevallo","No",1351,892,570,18,78,2385,331,4440,3030,300,600,72,72,18.9,8,5883,51 +"University of Nebraska at Lincoln","No",6277,6003,3526,33,63,16454,3171,5595,3145,500,2070,86,92,15.1,48,6813,53 +"University of New England","Yes",1209,750,265,19,54,820,159,11450,5045,900,2500,72,75,11.4,13,9718,64 +"University of New Hampshire","No",9750,7640,2529,24,62,10358,1338,11180,3862,650,2450,89,87,17.5,16,7855,75 +"University of North Carolina at Asheville","No",1757,979,394,32,74,2033,1078,5972,3420,600,750,77,83,13,11,7011,37 +"University of North Carolina at Chapel Hill","No",14596,5985,3331,75,92,14609,1100,8400,4200,550,1200,88,93,8.9,23,15893,83 +"University of North Carolina at Charlotte","No",5803,4441,1730,19,62,10099,3255,7248,3109,600,1900,79,91,16.8,7,6227,62 +"University of North Carolina at Greensboro","No",5191,4134,1500,15,44,7532,1847,8677,3505,600,1300,75,94,15.5,17,7392,53 +"University of North Carolina at Wilmington","No",6071,3856,1449,15,67,6635,1145,7558,3680,500,1500,82,85,19.1,15,6005,55 +"University of North Dakota","No",2777,2249,1652,20,54,8334,1435,5634,2703,450,1200,97,97,15.9,16,9424,49 +"University of North Florida","No",1800,1253,560,44,85,3876,3588,6634,4360,600,2604,82,85,17.8,14,6104,47 +"University of North Texas","No",4418,2737,2049,23,51,14047,5134,4104,3579,450,1700,86,94,22.6,6,5657,35 +"University of Northern Colorado","No",5530,4007,1697,12,37,8463,1498,7731,4128,540,2286,75,75,21.5,8,6309,40 +"University of Northern Iowa","No",4144,3379,1853,18,52,10135,1448,6197,2930,595,2380,78,82,16.3,26,6333,77 +"University of Notre Dame","Yes",7700,3700,1906,79,96,7671,30,16850,4400,600,1350,96,92,13.1,46,13936,97 +"University of Oklahoma","No",4743,3970,2233,32,63,13436,2582,5173,3526,765,3176,86,90,11.5,11,10244,44 +"University of Oregon","No",8631,6732,2546,25,61,11669,1605,10602,3660,570,1530,79,87,19.7,13,8020,54 +"University of Pennsylvania","Yes",12394,5232,2464,85,100,9205,531,17020,7270,500,1544,95,96,6.3,38,25765,93 +"University of Pittsburgh-Main Campus","No",8586,6383,2503,25,59,13138,4289,10786,4560,400,900,93,93,7.8,10,13789,66 +"University of Portland","Yes",1758,1485,419,27,58,2041,174,12040,4100,600,1100,92,96,13.2,17,9060,72 +"University of Puget Sound","Yes",4044,2826,688,51,83,2738,138,16230,4500,630,1800,79,86,15,17,11217,63 +"University of Rhode Island","No",9643,7751,1968,12,40,8894,2456,10330,5558,500,1250,84,89,16.6,7,9158,63 +"University of Richmond","Yes",5892,2718,756,46,72,2854,594,14500,3285,700,1125,75,89,11.7,32,11984,100 +"University of Rochester","Yes",8766,5498,1243,56,75,5071,438,17840,6582,500,882,93,99,5.9,23,26037,80 +"University of San Diego","Yes",3934,2735,886,40,70,3698,217,13600,5940,630,1820,93,96,15.6,13,10813,66 +"University of San Francisco","Yes",2306,1721,538,23,48,4309,549,13226,6452,750,2450,86,86,13.6,8,10074,62 +"University of Sci. and Arts of Oklahoma","No",285,280,208,21,43,1140,473,3687,1920,600,1800,67,77,23.6,3,3864,43 +"University of Scranton","Yes",4471,2942,910,29,60,3674,493,11584,5986,650,800,83,83,14.1,41,9131,92 +"University of South Carolina at Aiken","No",848,560,377,14,24,1855,1412,5800,3066,500,1500,62,62,14.8,3,5035,48 +"University of South Carolina at Columbia","No",7693,5815,2328,30,66,12594,3661,8074,3522,495,2941,84,88,16.9,18,8246,63 +"University of South Florida","No",7589,4676,1876,29,63,14770,10962,6760,3776,500,2180,84,89,17,7,11020,47 +"University of Southern California","Yes",12229,8498,2477,45,71,13259,1429,17230,6482,600,2210,90,94,11.4,10,17007,68 +"University of Southern Colorado","No",1401,1239,605,10,34,3716,675,7100,4380,540,2948,63,88,19.4,0,5389,36 +"University of Southern Indiana","No",2379,2133,1292,8,25,4283,2973,4973,3192,500,1425,56,65,22,21,4078,38 +"University of Southern Mississippi","No",2850,2044,1046,20,50,9260,1387,4652,2470,500,500,78,99,18.7,23,5917,45 +"University of St. Thomas MN","Yes",2057,1807,828,26,53,4106,982,11712,4037,500,800,80,80,13.8,13,8546,89 +"University of St. Thomas TX","Yes",374,280,185,45,77,995,408,8550,4050,500,1344,75,75,12.6,17,7237,62 +"University of Tennessee at Knoxville","No",7473,5372,3013,27,53,15749,3237,5764,3262,750,3300,86,92,16.5,22,8612,53 +"University of Texas at Arlington","No",3281,2559,1448,19,43,10975,8431,4422,2780,500,2850,73,73,21,4,4696,29 +"University of Texas at Austin","No",14752,9572,5329,48,85,30017,5189,5130,3309,650,3140,91,99,19.7,11,7837,65 +"University of Texas at San Antonio","No",4217,3100,1686,17,46,9375,5457,4104,5376,452,1200,94,100,25.3,3,4329,50 +"University of the Arts","Yes",974,704,290,5,22,1145,39,12520,3860,1300,700,16,59,7.5,9,11641,57 +"University of the Pacific","Yes",2459,1997,582,36,66,2664,299,16320,5326,646,1171,87,94,11.2,14,13706,65 +"University of the South","Yes",1445,966,326,46,83,1129,24,15350,4080,450,810,89,93,10.3,52,18784,82 +"University of Tulsa","Yes",1712,1557,696,41,68,2936,433,11750,4160,1200,2350,94,96,11.5,10,11743,47 +"University of Utah","No",5095,4491,2400,27,53,13894,8374,6857,3975,858,3093,89,93,12.8,9,9275,37 +"University of Vermont","No",7663,6008,1735,18,51,7353,1674,15516,4928,500,990,87,90,9.9,10,12646,79 +"University of Virginia","No",15849,5384,2678,74,95,11278,114,12212,3792,500,1000,90,92,9.5,22,13597,95 +"University of Washington","No",12749,7025,3343,40,81,20356,4582,8199,4218,708,2172,96,94,9,10,16527,65 +"University of West Florida","No",1558,1254,472,20,57,3754,2477,6172,3994,541,1387,83,87,23.4,12,8488,53 +"University of Wisconsin-Stout","No",2593,1966,1030,9,32,6038,579,6704,2592,376,1750,78,78,21,17,6254,65 +"University of Wisconsin-Superior","No",910,910,342,14,53,1434,417,7032,2780,550,1960,75,81,15.2,15,6490,36 +"University of Wisconsin-Whitewater","No",4400,3719,1472,12,38,7804,1552,6950,2500,300,1200,90,95,23.1,16,5559,67 +"University of Wisconsin at Green Bay","No",2409,1939,759,17,50,3819,1347,6900,2800,475,1200,81,89,22.2,1,5968,46 +"University of Wisconsin at Madison","No",14901,10932,4631,36,80,23945,2200,9096,4290,535,1545,93,96,11.5,20,11006,72 +"University of Wisconsin at Milwaukee","No",5244,3782,1930,12,37,11561,7443,8786,2964,570,1980,79,87,15.9,8,8094,38 +"University of Wyoming","No",2029,1516,1073,23,46,7535,1488,5988,3422,600,1500,91,94,15.1,13,8745,45 +"Upper Iowa University","Yes",663,452,192,10,35,1481,1160,8840,3060,500,1000,69,75,17.4,19,3733,78 +"Ursinus College","Yes",1399,1026,308,44,77,1131,17,14900,5160,500,800,82,85,11.6,40,12082,79 +"Ursuline College","Yes",325,260,86,21,47,699,717,9600,4202,450,750,39,69,10.5,15,7164,68 +"Valley City State University","No",368,344,212,5,27,863,189,4286,2570,600,2000,39,41,14.9,25,4958,40 +"Valparaiso University","Yes",2075,1727,520,49,81,2501,198,11800,3260,500,800,87,89,14.2,23,9681,95 +"Vanderbilt University","Yes",7791,4690,1499,71,92,5500,90,17865,6525,630,952,93,98,5.8,26,23850,83 +"Vassar College","Yes",3550,1877,653,53,87,2164,77,18920,5950,600,800,90,98,9.7,39,17089,90 +"Villanova University","Yes",7759,5588,1477,30,68,6362,1292,15925,6507,400,300,89,90,13.4,24,10458,96 +"Virginia Commonwealth University","No",4963,3497,1567,18,45,10262,5065,10217,4182,500,3630,81,87,8.7,11,11183,45 +"Virginia State University","No",2996,2440,704,2,30,3006,338,5587,4845,500,600,61,63,16,11,5733,31 +"Virginia Tech","No",15712,11719,4277,29,53,18511,604,10260,3176,740,2200,85,89,13.8,20,8944,73 +"Virginia Union University","Yes",1847,1610,453,19,59,1298,67,7384,3494,500,1763,51,67,13.7,8,6757,30 +"Virginia Wesleyan College","Yes",1470,900,287,20,49,1130,417,10900,5100,500,550,70,81,15.7,14,7804,68 +"Viterbo College","Yes",647,518,271,17,43,1014,387,9140,3365,500,2245,51,65,10.7,31,8050,73 +"Voorhees College","Yes",1465,1006,188,10,30,703,20,4450,2522,500,1200,43,43,22.9,3,5861,58 +"Wabash College","Yes",800,623,256,41,76,801,5,12925,4195,500,635,78,85,9.9,55,14904,72 +"Wagner College","Yes",1416,1015,417,10,44,1324,117,13500,5800,585,1700,67,78,13.2,23,9006,75 +"Wake Forest University","Yes",5661,2392,903,75,88,3499,172,13850,4360,500,1250,95,97,4.3,37,41766,89 +"Walsh University","Yes",1092,890,477,27,92,847,497,8670,4180,500,1450,42,58,11.3,33,5738,68 +"Warren Wilson College","Yes",440,311,112,25,49,466,7,10000,3052,400,1100,65,75,11.4,20,9430,63 +"Wartburg College","Yes",1231,1074,345,34,66,1295,105,11600,3610,400,850,66,91,12.4,37,7735,67 +"Washington and Jefferson College","Yes",1305,1100,334,42,64,1098,151,16260,4005,300,500,91,91,12.1,40,10162,86 +"Washington and Lee University","Yes",3315,1096,425,68,93,1584,3,13750,4619,680,1115,81,96,9.6,45,15736,90 +"Washington College","Yes",1209,942,214,31,60,822,46,15276,5318,500,300,79,86,11.2,37,10830,65 +"Washington State University","No",6540,5839,2440,31,70,14445,1344,8200,4210,800,2719,84,87,16.9,30,10912,56 +"Washington University","Yes",7654,5259,1254,62,93,4879,1274,18350,5775,768,1512,91,98,3.9,31,45702,90 +"Wayne State College","No",1373,1373,724,6,21,2754,474,2700,2660,540,1660,60,68,20.3,29,4550,52 +"Waynesburg College","Yes",1190,978,324,12,30,1280,61,8840,3620,500,1200,57,58,16.2,26,6563,63 +"Webber College","Yes",280,143,79,5,27,327,110,5590,2900,650,1952,53,63,15.1,4,4839,90 +"Webster University","Yes",665,462,226,17,44,1739,1550,9160,4340,500,500,68,68,20.6,14,6951,48 +"Wellesley College","Yes",2895,1249,579,80,96,2195,156,18345,5995,500,700,94,98,10.6,51,21409,91 +"Wells College","Yes",318,240,130,40,85,416,19,14900,5550,600,500,93,98,8.3,42,13935,69 +"Wentworth Institute of Technology","Yes",1480,1257,452,6,25,2961,572,9850,6050,850,920,10,68,15.4,8,17858,64 +"Wesley College","Yes",980,807,350,10,25,872,448,9890,4674,500,1350,52,57,14.4,15,6243,84 +"Wesleyan University","Yes",4772,1973,712,60,86,2714,27,19130,5600,1400,1400,90,94,12.1,39,16262,92 +"West Chester University of Penn.","No",6502,3539,1372,11,51,7484,1904,7844,4108,400,2000,76,79,15.3,16,6773,52 +"West Liberty State College","No",1164,1062,478,12,25,2138,227,4470,2890,600,1210,33,33,16.3,10,4249,60 +"West Virginia Wesleyan College","Yes",1566,1400,483,28,55,1509,170,14200,3775,450,1100,58,81,16.4,42,8080,67 +"Western Carolina University","No",3224,2519,1057,11,31,5000,706,6390,2380,110,1622,67,78,14.6,9,6554,55 +"Western Maryland College","Yes",1205,984,278,31,50,1071,98,14510,5340,500,1400,84,91,12.5,39,10026,60 +"Western Michigan University","No",9167,7191,2738,24,53,15739,4278,6940,4100,500,1700,80,84,24.7,11,5983,55 +"Western New England College","Yes",1650,1471,409,7,21,1803,1116,8994,5500,498,2065,74,97,15.4,15,8409,59 +"Western State College of Colorado","No",2702,1623,604,7,24,2315,146,5918,3755,500,2050,76,79,19.4,4,4599,52 +"Western Washington University","No",5548,3563,1549,30,71,8909,506,8124,4144,639,2385,83,89,22.7,10,7203,61 +"Westfield State College","No",3100,2150,825,3,20,3234,941,5542,3788,500,1300,75,79,15.7,20,4222,65 +"Westminster College MO","Yes",662,553,184,20,43,665,37,10720,4050,600,1650,66,70,12.5,20,7925,62 +"Westminster College","Yes",996,866,377,29,58,1411,72,12065,3615,430,685,62,78,12.5,41,8596,80 +"Westminster College of Salt Lake City","Yes",917,720,213,21,60,979,743,8820,4050,600,2025,68,83,10.5,34,7170,50 +"Westmont College","No",950,713,351,42,72,1276,9,14320,5304,490,1410,77,77,14.9,17,8837,87 +"Wheaton College IL","Yes",1432,920,548,56,84,2200,56,11480,4200,530,1400,81,83,12.7,40,11916,85 +"Westminster College PA","Yes",1738,1373,417,21,55,1335,30,18460,5970,700,850,92,96,13.2,41,22704,71 +"Wheeling Jesuit College","Yes",903,755,213,15,49,971,305,10500,4545,600,600,66,71,14.1,27,7494,72 +"Whitman College","Yes",1861,998,359,45,77,1220,46,16670,4900,750,800,80,83,10.5,51,13198,72 +"Whittier College","Yes",1681,1069,344,35,63,1235,30,16249,5699,500,1998,84,92,13.6,29,11778,52 +"Whitworth College","Yes",1121,926,372,43,70,1270,160,12660,4500,678,2424,80,80,16.9,20,8328,80 +"Widener University","Yes",2139,1492,502,24,64,2186,2171,12350,5370,500,1350,88,86,12.6,19,9603,63 +"Wilkes University","Yes",1631,1431,434,15,36,1803,603,11150,5130,550,1260,78,92,13.3,24,8543,67 +"Willamette University","Yes",1658,1327,395,49,80,1595,159,14800,4620,400,790,91,94,13.3,37,10779,68 +"William Jewell College","Yes",663,547,315,32,67,1279,75,10060,2970,500,2600,74,80,11.2,19,7885,59 +"William Woods University","Yes",469,435,227,17,39,851,120,10535,4365,550,3700,39,66,12.9,16,7438,52 +"Williams College","Yes",4186,1245,526,81,96,1988,29,19629,5790,500,1200,94,99,9,64,22014,99 +"Wilson College","Yes",167,130,46,16,50,199,676,11428,5084,450,475,67,76,8.3,43,10291,67 +"Wingate College","Yes",1239,1017,383,10,34,1207,157,7820,3400,550,1550,69,81,13.9,8,7264,91 +"Winona State University","No",3325,2047,1301,20,45,5800,872,4200,2700,300,1200,53,60,20.2,18,5318,58 +"Winthrop University","No",2320,1805,769,24,61,3395,670,6400,3392,580,2150,71,80,12.8,26,6729,59 +"Wisconsin Lutheran College","Yes",152,128,75,17,41,282,22,9100,3700,500,1400,48,48,8.5,26,8960,50 +"Wittenberg University","Yes",1979,1739,575,42,68,1980,144,15948,4404,400,800,82,95,12.8,29,10414,78 +"Wofford College","Yes",1501,935,273,51,83,1059,34,12680,4150,605,1440,91,92,15.3,42,7875,75 +"Worcester Polytechnic Institute","Yes",2768,2314,682,49,86,2802,86,15884,5370,530,730,92,94,15.2,34,10774,82 +"Worcester State College","No",2197,1515,543,4,26,3089,2029,6797,3900,500,1200,60,60,21,14,4469,40 +"Xavier University","Yes",1959,1805,695,24,47,2849,1107,11520,4960,600,1250,73,75,13.3,31,9189,83 +"Xavier University of Louisiana","Yes",2097,1915,695,34,61,2793,166,6900,4200,617,781,67,75,14.4,20,8323,49 +"Yale University","Yes",10705,2453,1317,95,99,5217,83,19840,6510,630,2115,96,96,5.8,49,40386,99 +"York College of Pennsylvania","Yes",2989,1855,691,28,63,2988,1726,4990,3560,500,1250,75,75,18.1,28,4509,99 diff --git a/data/Pandas3/mammal_sleep.csv b/data/Pandas3/mammal_sleep.csv new file mode 100644 index 0000000..c847b99 --- /dev/null +++ b/data/Pandas3/mammal_sleep.csv @@ -0,0 +1,84 @@ +name,genus,vore,order,sleep_total,sleep_rem,sleep_cycle +Cheetah,Acinonyx,carni,Carnivora,12.1,, +Owl monkey,Aotus,omni,Primates,17.0,1.8, +Mountain beaver,Aplodontia,herbi,Rodentia,14.4,2.4, +Greater short-tailed shrew,Blarina,omni,Soricomorpha,14.9,2.3,0.133333333 +Cow,Bos,herbi,Artiodactyla,4.0,0.7,0.666666667 +Three-toed sloth,Bradypus,herbi,Pilosa,14.4,2.2,0.7666666670000001 +Northern fur seal,Callorhinus,carni,Carnivora,8.7,1.4,0.38333333299999994 +Vesper mouse,Calomys,,Rodentia,7.0,, +Dog,Canis,carni,Carnivora,10.1,2.9,0.333333333 +Roe deer,Capreolus,herbi,Artiodactyla,3.0,, +Goat,Capri,herbi,Artiodactyla,5.3,0.6, +Guinea pig,Cavis,herbi,Rodentia,9.4,0.8,0.21666666699999998 +Grivet,Cercopithecus,omni,Primates,10.0,0.7, +Chinchilla,Chinchilla,herbi,Rodentia,12.5,1.5,0.11666666699999999 +Star-nosed mole,Condylura,omni,Soricomorpha,10.3,2.2, +African giant pouched rat,Cricetomys,omni,Rodentia,8.3,2.0, +Lesser short-tailed shrew,Cryptotis,omni,Soricomorpha,9.1,1.4,0.15 +Long-nosed armadillo,Dasypus,carni,Cingulata,17.4,3.1,0.38333333299999994 +Tree hyrax,Dendrohyrax,herbi,Hyracoidea,5.3,0.5, +North American Opossum,Didelphis,omni,Didelphimorphia,18.0,4.9,0.333333333 +Asian elephant,Elephas,herbi,Proboscidea,3.9,, +Big brown bat,Eptesicus,insecti,Chiroptera,19.7,3.9,0.11666666699999999 +Horse,Equus,herbi,Perissodactyla,2.9,0.6,1.0 +Donkey,Equus,herbi,Perissodactyla,3.1,0.4, +European hedgehog,Erinaceus,omni,Erinaceomorpha,10.1,3.5,0.283333333 +Patas monkey,Erythrocebus,omni,Primates,10.9,1.1, +Western american chipmunk,Eutamias,herbi,Rodentia,14.9,, +Domestic cat,Felis,carni,Carnivora,12.5,3.2,0.41666666700000005 +Galago,Galago,omni,Primates,9.8,1.1,0.55 +Giraffe,Giraffa,herbi,Artiodactyla,1.9,0.4, +Pilot whale,Globicephalus,carni,Cetacea,2.7,0.1, +Gray seal,Haliochoerus,carni,Carnivora,6.2,1.5, +Gray hyrax,Heterohyrax,herbi,Hyracoidea,6.3,0.6, +Human,Homo,omni,Primates,8.0,1.9,1.5 +Mongoose lemur,Lemur,herbi,Primates,9.5,0.9, +African elephant,Loxodonta,herbi,Proboscidea,3.3,, +Thick-tailed opposum,Lutreolina,carni,Didelphimorphia,19.4,6.6, +Macaque,Macaca,omni,Primates,10.1,1.2,0.75 +Mongolian gerbil,Meriones,herbi,Rodentia,14.2,1.9, +Golden hamster,Mesocricetus,herbi,Rodentia,14.3,3.1,0.2 +Vole ,Microtus,herbi,Rodentia,12.8,, +House mouse,Mus,herbi,Rodentia,12.5,1.4,0.18333333300000001 +Little brown bat,Myotis,insecti,Chiroptera,19.9,2.0,0.2 +Round-tailed muskrat,Neofiber,herbi,Rodentia,14.6,, +Slow loris,Nyctibeus,carni,Primates,11.0,, +Degu,Octodon,herbi,Rodentia,7.7,0.9, +Northern grasshopper mouse,Onychomys,carni,Rodentia,14.5,, +Rabbit,Oryctolagus,herbi,Lagomorpha,8.4,0.9,0.41666666700000005 +Sheep,Ovis,herbi,Artiodactyla,3.8,0.6, +Chimpanzee,Pan,omni,Primates,9.7,1.4,1.4166666669999999 +Tiger,Panthera,carni,Carnivora,15.8,, +Jaguar,Panthera,carni,Carnivora,10.4,, +Lion,Panthera,carni,Carnivora,13.5,, +Baboon,Papio,omni,Primates,9.4,1.0,0.666666667 +Desert hedgehog,Paraechinus,,Erinaceomorpha,10.3,2.7, +Potto,Perodicticus,omni,Primates,11.0,, +Deer mouse,Peromyscus,,Rodentia,11.5,, +Phalanger,Phalanger,,Diprotodontia,13.7,1.8, +Caspian seal,Phoca,carni,Carnivora,3.5,0.4, +Common porpoise,Phocoena,carni,Cetacea,5.6,, +Potoroo,Potorous,herbi,Diprotodontia,11.1,1.5, +Giant armadillo,Priodontes,insecti,Cingulata,18.1,6.1, +Rock hyrax,Procavia,,Hyracoidea,5.4,0.5, +Laboratory rat,Rattus,herbi,Rodentia,13.0,2.4,0.18333333300000001 +African striped mouse,Rhabdomys,omni,Rodentia,8.7,, +Squirrel monkey,Saimiri,omni,Primates,9.6,1.4, +Eastern american mole,Scalopus,insecti,Soricomorpha,8.4,2.1,0.166666667 +Cotton rat,Sigmodon,herbi,Rodentia,11.3,1.1,0.15 +Mole rat,Spalax,,Rodentia,10.6,2.4, +Arctic ground squirrel,Spermophilus,herbi,Rodentia,16.6,, +Thirteen-lined ground squirrel,Spermophilus,herbi,Rodentia,13.8,3.4,0.21666666699999998 +Golden-mantled ground squirrel,Spermophilus,herbi,Rodentia,15.9,3.0, +Musk shrew,Suncus,,Soricomorpha,12.8,2.0,0.18333333300000001 +Pig,Sus,omni,Artiodactyla,9.1,2.4,0.5 +Short-nosed echidna,Tachyglossus,insecti,Monotremata,8.6,, +Eastern american chipmunk,Tamias,herbi,Rodentia,15.8,, +Brazilian tapir,Tapirus,herbi,Perissodactyla,4.4,1.0,0.9 +Tenrec,Tenrec,omni,Afrosoricida,15.6,2.3, +Tree shrew,Tupaia,omni,Scandentia,8.9,2.6,0.233333333 +Bottle-nosed dolphin,Tursiops,carni,Cetacea,5.2,, +Genet,Genetta,carni,Carnivora,6.3,1.3, +Arctic fox,Vulpes,carni,Carnivora,12.5,, +Red fox,Vulpes,carni,Carnivora,9.8,2.4,0.35 diff --git a/data/Pandas3/titanic.csv b/data/Pandas3/titanic.csv new file mode 100644 index 0000000..d16b058 --- /dev/null +++ b/data/Pandas3/titanic.csv @@ -0,0 +1,1311 @@ +Pclass,Survived,Name,Sex,Age,Sibsp,Parch,Ticket,Fare,Cabin,Embarked,Boat,Body,home.dest +1,1,"Allen, Miss. Elisabeth Walton",female,29,0,0,24160,211.3375,B5,S,2,,"St Louis, MO" +1,1,"Allison, Master. Hudson Trevor",male,0.9167,1,2,113781,151.5500,C22 C26,S,11,,"Montreal, PQ / Chesterville, ON" +1,0,"Allison, Miss. Helen Loraine",female,2,1,2,113781,151.5500,C22 C26,S,,,"Montreal, PQ / Chesterville, ON" +1,0,"Allison, Mr. Hudson Joshua Creighton",male,30,1,2,113781,151.5500,C22 C26,S,,135,"Montreal, PQ / Chesterville, ON" +1,0,"Allison, Mrs. Hudson J C (Bessie Waldo Daniels)",female,25,1,2,113781,151.5500,C22 C26,S,,,"Montreal, PQ / Chesterville, ON" +1,1,"Anderson, Mr. Harry",male,48,0,0,19952,26.5500,E12,S,3,,"New York, NY" +1,1,"Andrews, Miss. Kornelia Theodosia",female,63,1,0,13502,77.9583,D7,S,10,,"Hudson, NY" +1,0,"Andrews, Mr. Thomas Jr",male,39,0,0,112050,0.0000,A36,S,,,"Belfast, NI" +1,1,"Appleton, Mrs. Edward Dale (Charlotte Lamson)",female,53,2,0,11769,51.4792,C101,S,D,,"Bayside, Queens, NY" +1,0,"Artagaveytia, Mr. Ramon",male,71,0,0,PC 17609,49.5042,,C,,22,"Montevideo, Uruguay" +1,0,"Astor, Col. John Jacob",male,47,1,0,PC 17757,227.5250,C62 C64,C,,124,"New York, NY" +1,1,"Astor, Mrs. John Jacob (Madeleine Talmadge Force)",female,18,1,0,PC 17757,227.5250,C62 C64,C,4,,"New York, NY" +1,1,"Aubart, Mme. Leontine Pauline",female,24,0,0,PC 17477,69.3000,B35,C,9,,"Paris, France" +1,1,"Barber, Miss. Ellen ""Nellie""",female,26,0,0,19877,78.8500,,S,6,, +1,1,"Barkworth, Mr. Algernon Henry Wilson",male,80,0,0,27042,30.0000,A23,S,B,,"Hessle, Yorks" +1,0,"Baumann, Mr. John D",male,,0,0,PC 17318,25.9250,,S,,,"New York, NY" +1,0,"Baxter, Mr. Quigg Edmond",male,24,0,1,PC 17558,247.5208,B58 B60,C,,,"Montreal, PQ" +1,1,"Baxter, Mrs. James (Helene DeLaudeniere Chaput)",female,50,0,1,PC 17558,247.5208,B58 B60,C,6,,"Montreal, PQ" +1,1,"Bazzani, Miss. Albina",female,32,0,0,11813,76.2917,D15,C,8,, +1,0,"Beattie, Mr. Thomson",male,36,0,0,13050,75.2417,C6,C,A,,"Winnipeg, MN" +1,1,"Beckwith, Mr. Richard Leonard",male,37,1,1,11751,52.5542,D35,S,5,,"New York, NY" +1,1,"Beckwith, Mrs. Richard Leonard (Sallie Monypeny)",female,47,1,1,11751,52.5542,D35,S,5,,"New York, NY" +1,1,"Behr, Mr. Karl Howell",male,26,0,0,111369,30.0000,C148,C,5,,"New York, NY" +1,1,"Bidois, Miss. Rosalie",female,42,0,0,PC 17757,227.5250,,C,4,, +1,1,"Bird, Miss. 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Hulda Amanda Adolfina",female,14,0,0,350406,7.8542,,S,,, +3,0,"Vovk, Mr. Janko",male,22,0,0,349252,7.8958,,S,,, +3,0,"Waelens, Mr. Achille",male,22,0,0,345767,9.0000,,S,,,"Antwerp, Belgium / Stanton, OH" +3,0,"Ware, Mr. Frederick",male,,0,0,359309,8.0500,,S,,, +3,0,"Warren, Mr. Charles William",male,,0,0,C.A. 49867,7.5500,,S,,, +3,0,"Webber, Mr. James",male,,0,0,SOTON/OQ 3101316,8.0500,,S,,, +3,0,"Wenzel, Mr. Linhart",male,32.5,0,0,345775,9.5000,,S,,298, +3,1,"Whabee, Mrs. George Joseph (Shawneene Abi-Saab)",female,38,0,0,2688,7.2292,,C,C,, +3,0,"Widegren, Mr. Carl/Charles Peter",male,51,0,0,347064,7.7500,,S,,, +3,0,"Wiklund, Mr. Jakob Alfred",male,18,1,0,3101267,6.4958,,S,,314, +3,0,"Wiklund, Mr. Karl Johan",male,21,1,0,3101266,6.4958,,S,,, +3,1,"Wilkes, Mrs. James (Ellen Needs)",female,47,1,0,363272,7.0000,,S,,, +3,0,"Willer, Mr. Aaron (""Abi Weller"")",male,,0,0,3410,8.7125,,S,,, +3,0,"Willey, Mr. Edward",male,,0,0,S.O./P.P. 751,7.5500,,S,,, +3,0,"Williams, Mr. Howard Hugh ""Harry""",male,,0,0,A/5 2466,8.0500,,S,,, +3,0,"Williams, Mr. Leslie",male,28.5,0,0,54636,16.1000,,S,,14, +3,0,"Windelov, Mr. Einar",male,21,0,0,SOTON/OQ 3101317,7.2500,,S,,, +3,0,"Wirz, Mr. Albert",male,27,0,0,315154,8.6625,,S,,131, +3,0,"Wiseman, Mr. Phillippe",male,,0,0,A/4. 34244,7.2500,,S,,, +3,0,"Wittevrongel, Mr. Camille",male,36,0,0,345771,9.5000,,S,,, +3,0,"Yasbeck, Mr. Antoni",male,27,1,0,2659,14.4542,,C,C,, +3,1,"Yasbeck, Mrs. Antoni (Selini Alexander)",female,15,1,0,2659,14.4542,,C,,, +3,0,"Youseff, Mr. Gerious",male,45.5,0,0,2628,7.2250,,C,,312, +3,0,"Yousif, Mr. Wazli",male,,0,0,2647,7.2250,,C,,, +3,0,"Yousseff, Mr. Gerious",male,,0,0,2627,14.4583,,C,,, +3,0,"Zabour, Miss. Hileni",female,14.5,1,0,2665,14.4542,,C,,328, +3,0,"Zabour, Miss. Thamine",female,,1,0,2665,14.4542,,C,,, +3,0,"Zakarian, Mr. Mapriededer",male,26.5,0,0,2656,7.2250,,C,,304, +3,0,"Zakarian, Mr. Ortin",male,27,0,0,2670,7.2250,,C,,, +3,0,"Zimmerman, Mr. Leo",male,29,0,0,315082,7.8750,,S,,, +,,,,,,,,,,,,, diff --git a/docs/book/CompMethods_references.bib b/docs/book/CompMethods_references.bib index 22c7e09..b01bfb7 100755 --- a/docs/book/CompMethods_references.bib +++ b/docs/book/CompMethods_references.bib @@ -52,13 +52,147 @@ @ARTICLE{BrockMirman:1972 month = {June} } -@TECHREPORT{BYUACME_PythonIntro, - AUTHOR = {BYU ACME}, +@INCOLLECTION{BYUACME_ExceptIO, + AUTHOR = {{BYU ACME}}, + TITLE = {Exceptions and File Input/Output}, + BOOKTITLE = {BYU ACME 2023-2024 Incoming Junior Materials}, + INSTITUTION = {Brigham Young University Applied and Computational Mathematics Emphasis}, + YEAR = {2023}, + chapter = {7}, + type = {Open access labs}, + url = {https://acme.byu.edu/2023-2024-materials}, +} + +@INCOLLECTION{BYUACME_Matplotlib1, + AUTHOR = {{BYU ACME}}, + TITLE = {Introduction to Matplotlib}, + BOOKTITLE = {BYU ACME 2023-2024 Incoming Junior Materials}, + INSTITUTION = {Brigham Young University Applied and Computational Mathematics Emphasis}, + YEAR = {2023}, + chapter = {6}, + type = {Open access labs}, + url = {https://acme.byu.edu/2023-2024-materials}, +} + +@INCOLLECTION{BYUACME_Matplotlib2, + AUTHOR = {{BYU ACME}}, + TITLE = {Pandas 2: Plotting}, + BOOKTITLE = {BYU ACME 2023-2024 Incoming Senior Materials}, + INSTITUTION = {Brigham Young University Applied and Computational Mathematics Emphasis}, + YEAR = {2023}, + chapter = {3}, + type = {Open access labs}, + url = {https://acme.byu.edu/2023-2024-materials}, +} + +@INCOLLECTION{BYUACME_Matplotlib3, + AUTHOR = {{BYU ACME}}, + TITLE = {Animations and 3D Plotting in Matplotlib}, + BOOKTITLE = {BYU ACME 2023-2024 Incoming Senior Materials}, + INSTITUTION = {Brigham Young University Applied and Computational Mathematics Emphasis}, + YEAR = {2023}, + chapter = {5}, + type = {Open access labs}, + url = {https://acme.byu.edu/2023-2024-materials}, +} + +@INCOLLECTION{BYUACME_NumPy1, + AUTHOR = {{BYU ACME}}, + TITLE = {Introduction to NumPy}, + BOOKTITLE = {BYU ACME 2023-2024 Incoming Junior Materials}, + INSTITUTION = {Brigham Young University Applied and Computational Mathematics Emphasis}, + YEAR = {2023}, + chapter = {4}, + type = {Open access labs}, + url = {https://acme.byu.edu/2023-2024-materials}, +} + +@INCOLLECTION{BYUACME_NumPy2, + AUTHOR = {{BYU ACME}}, + TITLE = {Advanced NumPy}, + BOOKTITLE = {BYU ACME 2023-2024 Incoming Senior Materials}, + INSTITUTION = {Brigham Young University Applied and Computational Mathematics Emphasis}, + YEAR = {2023}, + chapter = {1}, + type = {Open access labs}, + url = {https://acme.byu.edu/2023-2024-materials}, +} + +@INCOLLECTION{BYUACME_OOP, + AUTHOR = {{BYU ACME}}, + TITLE = {Object-Oriented Programming}, + BOOKTITLE = {BYU ACME 2023-2024 Incoming Junior Materials}, + INSTITUTION = {Brigham Young University Applied and Computational Mathematics Emphasis}, + YEAR = {2023}, + chapter = {5}, + type = {Open access labs}, + url = {https://acme.byu.edu/2023-2024-materials}, +} + +@INCOLLECTION{BYUACME_Pandas1, + AUTHOR = {{BYU ACME}}, + TITLE = {Pandas 1: Introduction}, + BOOKTITLE = {BYU ACME 2023-2024 Incoming Senior Materials}, + INSTITUTION = {Brigham Young University Applied and Computational Mathematics Emphasis}, + YEAR = {2023}, + chapter = {2}, + type = {Open access labs}, + url = {https://acme.byu.edu/2023-2024-materials}, +} + +@INCOLLECTION{BYUACME_Pandas3, + AUTHOR = {{BYU ACME}}, + TITLE = {Pandas 3: Grouping}, + BOOKTITLE = {BYU ACME 2023-2024 Incoming Senior Materials}, + INSTITUTION = {Brigham Young University Applied and Computational Mathematics Emphasis}, + YEAR = {2023}, + chapter = {4}, + type = {Open access labs}, + url = {https://acme.byu.edu/2023-2024-materials}, +} + +@INCOLLECTION{BYUACME_PythonIntro, + AUTHOR = {{BYU ACME}}, TITLE = {Introduction to Python}, + BOOKTITLE = {BYU ACME 2023-2024 Incoming Junior Materials}, INSTITUTION = {Brigham Young University Applied and Computational Mathematics Emphasis}, - YEAR = {2021}, - type = {Open access lab}, - url = {https://acme.byu.edu/00000181-448a-d778-a18f-dfcae22f0001/intro-to-python}, + YEAR = {2023}, + chapter = {2}, + type = {Open access labs}, + url = {https://acme.byu.edu/2023-2024-materials}, +} + +@INCOLLECTION{BYUACME_StandardLibrary, + AUTHOR = {{BYU ACME}}, + TITLE = {Standard Library}, + BOOKTITLE = {BYU ACME 2023-2024 Incoming Junior Materials}, + INSTITUTION = {Brigham Young University Applied and Computational Mathematics Emphasis}, + YEAR = {2023}, + chapter = {3}, + type = {Open access labs}, + url = {https://acme.byu.edu/2023-2024-materials}, +} + +@INCOLLECTION{BYUACME_UnitTest, + AUTHOR = {{BYU ACME}}, + TITLE = {Unit Testing}, + BOOKTITLE = {BYU ACME 2023-2024 Incoming Junior Materials}, + INSTITUTION = {Brigham Young University Applied and Computational Mathematics Emphasis}, + YEAR = {2023}, + chapter = {8}, + type = {Open access labs}, + url = {https://acme.byu.edu/2023-2024-materials}, +} + +@INCOLLECTION{BYUACME_Unix1, + AUTHOR = {{BYU ACME}}, + TITLE = {Unix Shell 1: Introduction}, + BOOKTITLE = {BYU ACME 2023-2024 Incoming Junior Materials}, + INSTITUTION = {Brigham Young University Applied and Computational Mathematics Emphasis}, + YEAR = {2023}, + chapter = {1}, + type = {Open access labs}, + url = {https://acme.byu.edu/2023-2024-materials}, } @BOOK{DavidsonMacKinnon:2004, @@ -112,6 +246,38 @@ @ARTICLE{Eilers:2005 pages = {317-328}, } +@ARTICLE{EvansPhillips:2017, + AUTHOR = {Richard W. Evans and Kerk L. Phillips}, + TITLE = {Advantages of an Ellipse when Modeling Leisure Utility}, + JOURNAL = {Computational Economics}, + YEAR = {2017}, + volume = {51}, + number = {3}, + month = {March}, + pages = {513-533}, +} + +@TECHREPORT{Fridman:2021, + AUTHOR = {Lex Fridman}, + TITLE = {Travis Oliphant: NumPy, SciPy, Anaconda, Python and Scientific Programming}, + INSTITUTION = {Lex Fridman Podcast}, + YEAR = {2021}, + type = {Podcast}, + number = {\#224}, + month = {September 22}, + url = {https://youtu.be/gFEE3w7F0ww?si=XKcRlcw7FXkA9oxB}, +} + +@TECHREPORT{GitHub:2022, + AUTHOR = {GitHub}, + TITLE = {Octoverse 2022: The State of Open Source Software}, + INSTITUTION = {GitHub, Inc.}, + YEAR = {2022}, + type = {Report}, + month = {November 17}, + url = {https://octoverse.github.com/}, +} + @ARTICLE{GitWiki2020, AUTHOR = {{Wikipedia Contributors}}, TITLE = {"{G}it"}, @@ -130,6 +296,17 @@ @ARTICLE{GitIDE2020 url = {https://en.wikipedia.org/wiki/Integrated_development_environment}, } +@TECHREPORT{GoodgerVanRossum:2001, + AUTHOR = {David Goodger and Guido {van Rossum}}, + TITLE = {PEP 257--{D}ocstring Conventions}, + INSTITUTION = {Python Steering Council}, + YEAR = {2001}, + type = {Python Enhancement Proposal}, + number = {257}, + month = {May 29}, + url = {https://peps.python.org/pep-0257}, +} + @BOOK{GourierouxMonfort:1996, AUTHOR = {Christian Gourieroux and Alain Monfort}, TITLE = {Simulation-based Econometric Methods}, @@ -137,6 +314,21 @@ @BOOK{GourierouxMonfort:1996 YEAR = {1996}, } +@BOOK{HumpherysJarvis:2020, + AUTHOR = {Jeffrey Humpherys and Tyler J. Jarvis}, + TITLE = {Foundations of Applied Mathematics: Algorithms, Approximation, Optimization}, + PUBLISHER = {SIAM, Society for Industrial and Applied Mathematics}, + YEAR = {2020}, + volume = {2}, +} + +@BOOK{Judd:1998, + AUTHOR = {Kenneth L. Judd}, + TITLE = {Numerical Methods in Economics}, + PUBLISHER = {MIT Press}, + YEAR = {1998}, +} + @ARTICLE{Keane:2010, AUTHOR = {Michael P. Keane}, TITLE = {Structural vs. Atheoretic Approaches to Econometrics}, @@ -181,6 +373,16 @@ @ARTICLE{McFadden:1989 month = {September} } +@TECHREPORT{Mertz:2023, + AUTHOR = {James Mertz}, + TITLE = {Documenting Python Code: A Complete Guide}, + INSTITUTION = {RealPython}, + YEAR = {2023}, + type = {Tutorial}, + note = {[Online; accessed 08-October-2023]}, + url = {https://realpython.com/documenting-python-code/}, +} + @ARTICLE{NeweyWest:1987, title = {A Simple, Positive, Semi-definite, Heteroskedasticy and Autocorrelation Consistent Covariance Matrix}, author = {Whitney K. Newey and Kenneth D. West}, @@ -192,6 +394,24 @@ @ARTICLE{NeweyWest:1987 month = {May} } +@ARTICLE{OliphantWiki, + AUTHOR = {{Wikipedia Contributors}}, + TITLE = {"{T}ravis {O}liphant"}, + JOURNAL = {{W}ikipedia{,} The Free Encyclopedia}, + YEAR = {2023}, + note = {[Online; accessed 5-October-2023]}, + url = {https://en.wikipedia.org/wiki/Travis_Oliphant}, +} + +@ARTICLE{PandasWiki, + AUTHOR = {{Wikipedia Contributors}}, + TITLE = {"{p}andas (software)"}, + JOURNAL = {{W}ikipedia{,} The Free Encyclopedia}, + YEAR = {2023}, + note = {[Online; accessed 5-October-2023]}, + url = {https://en.wikipedia.org/wiki/Pandas_(software)}, +} + @ARTICLE{Rust:1987, AUTHOR = {John Rust}, TITLE = {Optimal Replacement of GMC Bus Engines: An Empirical Model of Harold Zurcher}, @@ -225,6 +445,16 @@ @INCOLLECTION{Smith:2020 url = {http://www.econ.yale.edu/smith/palgrave7.pdf}, } +@TECHREPORT{Stackscale:2023, + AUTHOR = {Stackscale}, + TITLE = {Most popular programming languages in 2023}, + INSTITUTION = {Stackscale Grupo Aire}, + YEAR = {2023}, + type = {Report}, + month = {September 13}, + url = {https://www.stackscale.com/blog/most-popular-programming-languages/#Top_10_programming_languages_in_2023}, +} + @ARTICLE{StraubWerning:2020, AUTHOR = {Ludwig Straub and Iv\'{a}n Werning}, TITLE = {Positive Long-run Capital Taxation: Chamley-Judd Revisited}, @@ -236,3 +466,32 @@ @ARTICLE{StraubWerning:2020 month = {January}, url = {https://pubs.aeaweb.org/doi/pdfplus/10.1257/aer.20150210}, } + +@TECHREPORT{VanRossumEtAl:2001, + AUTHOR = {Guido {van Rossum} and Barry Warsaw and Nick Coghlan}, + TITLE = {PEP 8--{S}tyle Guide for Python Code}, + INSTITUTION = {Python Steering Council}, + YEAR = {2001}, + type = {Python Enhancement Proposal}, + number = {8}, + month = {July 5}, + url = {https://peps.python.org/pep-0008}, +} + +@TECHREPORT{Wallen:2020, + AUTHOR = {Jack Wallen}, + TITLE = {GitHub to replace master with main starting in October: What developers need to do now}, + INSTITUTION = {TechRepublic}, + YEAR = {2020}, + month = {September 22}, + url = {https://www.techrepublic.com/article/github-to-replace-master-with-main-starting-in-october-what-developers-need-to-know/}, +} + +@ARTICLE{WikiTaxonomicRank, + AUTHOR = {{Wikipedia Contributors}}, + TITLE = {"{T}axonomic rank"}, + JOURNAL = {{W}ikipedia{,} The Free Encyclopedia}, + YEAR = {2023}, + note = {[Online; accessed 04-October-2023]}, + url = {https://en.wikipedia.org/wiki/Taxonomic_rank}, +} diff --git a/docs/book/_toc.yml b/docs/book/_toc.yml index ec269c0..deb7250 100644 --- a/docs/book/_toc.yml +++ b/docs/book/_toc.yml @@ -8,6 +8,7 @@ parts: chapters: - file: python/intro - file: python/StandardLibrary + - file: python/ExceptionsIO - file: python/OOP - file: python/NumPy - file: python/Pandas diff --git a/docs/book/git/intro.md b/docs/book/git/intro.md index b5e89e5..20bcfb8 100644 --- a/docs/book/git/intro.md +++ b/docs/book/git/intro.md @@ -1,10 +1,134 @@ (Chap_GitIntro)= # Git and GitHub -Put Git and GitHub intro here. +This chapter was coauthored by Jason DeBacker and Richard W. Evans. + +Two warnings that a seasoned Git and GitHub user should always give a new entrant to this type of version control and code collaboration are the following. +* The learning curve is steep. +* The workflow initially is not intuitive. + +These two obstacles seem to work together to make this form of collaboration harder than the sum of their parts initially. However, once you begin collaborating on open source projects or on large-group academic or research projects, you start to see the value of all the different steps, methods, and safeguards invoved with using Git and GitHub. {numref}`Figure %s ` below is a diagram of the main pieces and actions in the primary workflow that we advocate in this book. You will notice that a version of this figure is the main image for the book and is also the `favicon` for the tabs of the web pages of the online book. This figure of a Git and GitHub workflow diagram looks complicated, but these actions will become second nature. And following this workflow will save the collaborators time in the long-run. + +```{figure} ../images/Git/GitFlowDiag.png +:height: 500px +:align: center +:name: FigGitFlowDiag + +Flow diagram of Git and GitHub workflow +``` + + +## Brief definitions + +```{prf:definition} Repository +:label: DefRepository + +A {term}`repository` or "repo" is a directory containing files that are tracked by a version control system. A local repository resides on a local machine. A {term}`remote` repository resides in the cloud. +``` + +```{prf:definition} Git +:label: DefGit + +{term}`Git` is an {term}`open source` {term}`distributed version control system` (DVCS) software that resides on your local computer and tracks changes and the history of changes to all the files in a directory or {term}`repository`. See the Git website [https://git-scm.com/](https://git-scm.com/) and the [Git Wikipedia entry](https://en.wikipedia.org/wiki/Git) {cite}`GitWiki2020` for more information. +``` + +```{prf:definition} GitHub +:label: DefGitHub + +{term}`GitHub` or [*GitHub.com*](https://github.com/) is a {term}`cloud` {term}`source code management service` platform designed to enable scalable, efficient, and secure version controlled collaboration by linking {term}`local` {term}`Git` version controlled software development by users. *GitHub*'s main business footprint is hosting a collection of millions of version controlled code repositories. In addition to being a platform for {term}`distributed version control system` (DVCS), *GitHub*'s primary features include code review, project management, {term}`continuous integration` {term}`unit testing`, {term}`GitHub actions`, and associated web page (GitHub pages) and documentation hosting and deployment. +``` + +To be clear at the outset, Git is the version control software that resides on your local computer. It's main functionalities are to track changes in the files in specified directories. But Git also has some functionality to interact with remote repositories. The ineraction between Git and GitHub creates an ideal environment and platform for scaleable collaboration on code among large teams. + +## Wide usage +Every year in November, GitHub publishes are report entitled, "The State of the Octoverse", in which they detail the growth and developments in the GitHub community in the most recent year. The most recent [State of the Octoverse](https://github.blog/2022-11-17-octoverse-2022-10-years-of-tracking-open-source/) was published on November 17, 2022 and covered developments from October 1, 2021 to September 30, 2022. Some interesting statistics from that report are the following. + +* more than 94 million developers on GitHub +* 85.7 million new repositories in the last year for a total of about 517 million code repositories +* more than 413 million contributions were made to open source projects on GitHub in 2022 +* The two most widely used programming languages on GitHub are 1st JavaScript (the language of web dev) and 2nd Python +* more than 90% of Fortune 100 companies use GitHub +* Open source software is now the foundation of more than 90% of the world’s software + +Alternatives to GitHub include [GitLab](https://about.gitlab.com/), [Bitbucket](https://bitbucket.org/). Other alternatives are documented in [this June 2020 post](https://www.softwaretestinghelp.com/github-alternatives/) by Software Testing Help. But GitHub has the largest user base and largest number of repositories. + + +(SecGitBasics)= +## Git and GitHub basics + +Create, clone, fork, remote, branch, push, pull, pull request. + +Include a discussion of `git pull` vs. `git pull --ff-only` vs. `git pull --rebase`. A good blog post is "[Why You Should Use git pull –ff-only](https://blog.sffc.xyz/post/185195398930/why-you-should-use-git-pull-ff-only)" by Shane at ssfc's Tech Blog. + + +### Fork a repository and clone it to your local machine + +For this example, let the primary repository is [`OG-Core`](https://github.com/PSLmodels/OG-Core) which is in the [PSLmodels](https://github.com/PSLmodels) GitHub organization. This primary repository has a `master` branch that is the lead branch to which we want to contribute and stay up to date.[^MasterMain] If you wanted to contribute to or modify this repository, and you were following the workflow described in {numref}`Figure %s `, you would execute the following three steps. + +1. Fork the repository. In your internet browser, go to the main page of the GitHub repository you want to fork (https://github.com/PSLmodels/OG-Core). Click on the "Fork" button in the upper-right corner of the page. This will open a dialogue that confirms the repository owned by you to which you will create the forked copy. This will create an exact copy of the OG-Core repository on your GitHub account or GitHub organization. + +2. Clone the repository. In your terminal on your machine, navigate to the directory in which you want your Git repository to reside. Use the `git clone` command plus the URL of the repository on your GitHub account. In the case of my GitHub repository and the OG-Core repository, the command would be the following. Note that you are not cloning the primary repository. + +``` +DirectoryAboveRepo >> git clone https://github.com/rickecon/OG-Core.git +``` + +3. Add an `upstream` remote to your fork. Once you have cloned the repository to your local machine, change directories to the new repository on your machine by typing `cd OG-Core` in your terminal. If you type `git remote -v`, you'll see that there is automatically a remote named `origin`. That `origin` name is the name for all the branches on your GitHub account in the cloud associated with the repository. In {numref}`Figure %s `, `origin` represents boxes B and E. You want to add another remote called `upstream` that represents all the branches associated with the primary repository. + +``` +OG-Core >> git remote add upstream https://github.com/PSLmodels/OG-Core.git +``` + + +### Updating your main or master branch + +Let the primary repository is [`OG-Core`](https://github.com/PSLmodels/OG-Core) which is in the [PSLmodels](https://github.com/PSLmodels) GitHub organization. This primary repository has a `master` branch that is the lead branch to which we want to contribute and stay up to date. This repository is represented by box A in {numref}`Figure %s `. You have forked that repository, and your remote fork `master` branch is represented by box B in {numref}`Figure %s ` and your local `master` branch is represented by box C. + +Suppose that OG-Core has been updated with some pull requests (PRs) that have been merged in. You want to update your remote and local `master` branches (boxes B and C) with the new code from the primary branch (box A). + + +### Create a development branch to make changes + +``` +OG-Core >> git checkout -b DevBranchName +``` + + +### Adding, committing, pushing changes to remote repository + + +### Submit a pull request from your development branch + + +### Resolve merge conflicts + +(SecGitcheatsheet)= +## Git and GitHub Cheat Sheet + +About 99% of the commands you'll type in `git` are summarized in the table below: + + +| Functionality | Git Command | +|-------------------------------------------------------------|------------------------------------------------------------------| +| See active branch and uncommitted changes for tracked files | `git status -uno` | +| Change branch | `git checkout ` | +| Create new branch and change to it | `git checkout -b ` | +| Track file or latest changes to file | `git add ` | +| Commit changes to branch | `git commit -m "message describing changes" ` | +| Push committed changes to remote branch | `git push origin ` | +| Merge changes from master into development branch | `(change working branch to master, then…) git merge ` | +| Merge changes from development branch into master | (change to development branch, then…) `git merge master` | +| List current tags | `git tag` | +| Create a new tag | `git tag -a v -m "message with new tag"` | +| Pull changes from remote repo onto local machine | `git fetch upstream` | +| Merge changes from remote into active local branch | `git merge upstream/` | +| Clone a remote repository | `git clone ` | + (SecGitIntroFootnotes)= ## Footnotes - +The footnotes from this chapter. + +[^MasterMain]: Some primary branches of repositories are called `main` and some are called `master`. Starting in October 2020, GitHub stopped calling the primary branches of repositories `master` and started calling them main. This is due to the potentially offensive or divisive connotations of the term `master`. See {cite}`Wallen:2020`. Repositories with `master` are usually in repos that are older than 2020 and the maintainers have not taken the time to change them. diff --git a/docs/book/images/Git/GitFlowDiag.png b/docs/book/images/Git/GitFlowDiag.png new file mode 100644 index 0000000..56c90fa Binary files /dev/null and b/docs/book/images/Git/GitFlowDiag.png differ diff --git a/docs/book/images/SciPy/root_examp1.png b/docs/book/images/SciPy/root_examp1.png new file mode 100644 index 0000000..7404647 Binary files /dev/null and b/docs/book/images/SciPy/root_examp1.png differ diff --git a/docs/book/index.md b/docs/book/index.md index ec02d09..b34dbcd 100644 --- a/docs/book/index.md +++ b/docs/book/index.md @@ -38,5 +38,8 @@ Please use the following citation form for this book. General citation to the book: * Evans, Richard W., *Computational Methods for Economists using Python*, Open access Jupyter Book, v#.#.#, 2023, https://opensourceecon.github.io/CompMethods. -Citation to a chapter in the book: +Citation to a chapter in the book only authored by Evans: * Evans, Richard W., "[insert chapter name]", in *Computational Methods for Economists using Python*, Open access Jupyter Book, v#.#.#, 2023, https://opensourceecon.github.io/CompMethods + [chapter path]. + +Citation to a chapter in the book only authored by multiple authors: +* DeBacker, Jason and Richard W. Evans, "[insert chapter name]", in *Computational Methods for Economists using Python*, Open access Jupyter Book, v#.#.#, 2023, https://opensourceecon.github.io/CompMethods + [chapter path]. diff --git a/docs/book/python/DocStrings.md b/docs/book/python/DocStrings.md index 1b58db5..8f4c31e 100644 --- a/docs/book/python/DocStrings.md +++ b/docs/book/python/DocStrings.md @@ -1,20 +1,131 @@ (Chap_DocStrings)= +# Docstrings and Documentation +This chapter was coauthored by Jason DeBacker and Richard W. Evans. -# Docstrings and Documentation +```{prf:observation} Eagleson's Law of Programming +:label: ObsDocStrings_Eagleson +> "Any code of your own that you haven't looked at for six or more months might as well have been written by someone else."[^EaglesonsLaw] +``` + +```{prf:observation} Guido van Rossum on clear code +:label: ObsDocStrings_Guido +> "Code is more often read than written."[^Guido] +``` + +Good documentation is critical to the ability of yourself and others to understand and disseminate your work and to allow others to reproduce it. As Eagleson's Law of Programming implies in {prf:ref}`ObsDocStrings_Eagleson` above, one of the biggest benefits of good documentation might be to the core maintainers and original code writers of a project. Despite the aspiration that the Python programming language be easy and intuitive enough to be its own documentation, we have often found than any not-well-documented code written by ourselves that is only a few months old is more likely to require a full rewrite rather than incremental additions and improvements. + +Python scripts allow for two types of comments: inline comments (which are usually a line or two at a time) and docstrings, which are longer blocks set aside to document the source code. We further explore other more extensive types of documentation including README files, Jupyter notebooks, cloud notebooks, Jupyter Books, and published documentation forms. + +A good resource is the RealPython tutorial entitled, "[Documenting Python Code: A Complete Guide](https://realpython.com/documenting-python-code/)" {cite}`Mertz:2023`. + + +(SecDoc_comments)= +## Inline comments + +"[PEP 257--Docstring Conventions](https://peps.python.org/pep-0257/)" differentiates between inline comments, which use the `#` symbol, and one-line docstrings, which use the `"""..."""` format {cite}`GoodgerVanRossum:2001`. An block of code with inline comments might look like: + +```python +# imports +import numpy as np + +# create an array of zeros +zeros_array = np.zeros(10) +``` + +These types of comments are short and help to clarify what is to appear in the next few lines. They help to remind others (or the future you) why you did something. In this time of large language models, and [GitHub Copilot](https://github.com/features/copilot) they also provide valuable input for feed-forwrd models and make it much more likely the AI predicts your next line of code and writes it for you. + +(SecDoc_docstrings)= +## Docstrings + +Docstrings are longer blocks of comments that are set aside to document the source code. Docstrings are usually multi-line and are enclosed in triple quotes `"""..."""`. Docstrings are most often used at the top of a module to document what it does and the functions it containts and just after function or class definitions to document what they do. Docstrings can also be used to document variables and other objects. Docstrings can be accessed by the `help()` function and are used by the `pydoc` module to automatically generate documentation for your code. + +The following is an example of a docstring for a function: + +```python +def FOC_savings(c, r, beta, sigma): + r""" + Computes Euler errors for the first order condition for savings from + the household's problem. + + .. math:: + c_{t}^{-\sigma} = \beta (1 + r_{t+1}) c_{t+1}^{-\sigma} + + Args: + c (array_like): consumption in each period + r (array_like): the real interest rate in each period + beta (scalar): discount factor + sigma (scalar): coefficient of relative risk aversion + + Returns: + euler (Numpy array): Euler error from FOC for savings + + """ + if sigma == 1: + muc = 1 / c + else: + mu_c = c ** (-sigma) + euler_error = mu_c[:-1] - beta * (1 + r[1:]) * mu_c[1:] + + return euler_error +``` -Quote about yourself 6 months from now trying to climb back into code... +A few notes on this documentation of the `FOC_savings` function. First, see that the docstring starts of with a clear description of what the function does. Second, you can see the `:math` tags that allow you to write [LaTeX](https://www.latex-project.org) equations that will be rendered in the documentation. Docstrings written using [reStructuredText](https://docutils.sourceforge.io/rst.html) markup can be compiled through various packages to render equations and other formatting options. Third, the `Args` and `Returns` sections are used to document the arguments and return values of the function. -Good documentation is critical to the ability to disseminate your work and allow others to reproduce it - and that includes yourself several months from now when you may have forgotten why you did something a certain way. Python scripts allow for two types of comments: inline comments (which are usually a line or two at a time) and docstrings, which are longer blocks set aside to document the source code. +"[PEP 257--Docstring Conventions](https://peps.python.org/pep-0257/)" give suggested format and usage for docstrings in Python {cite}`GoodgerVanRossum:2001`. And there are two main styles for writing docstrings, the [Google style]*(https://sphinxcontrib-napoleon.readthedocs.io/en/latest/example_google.html) and the [NumPy style](https://sphinxcontrib-napoleon.readthedocs.io/en/latest/example_numpy.html). While there are other ways to write docstrings (even those that meet PEP 257 standards), these two styles are so commonly used and are compatible with the Sphinx documentation package that we recommend using one of these two styles. `OG-Core` used the Google style, so you might adopt that to be consistent. -Here we show the uses of each of these. More... -Docstrings also have the power to help with automatric documentation of your codes API. Talk about sphinx and docstring styles (e.g., google and Numpy)... +(SecDoc_README)= +## README file +`README` files are a common way to document software. These are typically plain text files that include file structures and instructions on running the software. +If your project is hosted on GitHub, it would make sense to write the `README` file in Markdown. Markdown is a lightweight markup language that is easy to read and write and can be rendered in HTML, a very nice feature when you have this file on the internet via GitHub. Markdown is used in many places including GitHub, Jupyter notebooks, and Jupyter Book documentation. See the [Markdown Guide](https://www.markdownguide.org) for more information on Markdown. And you can see an example of a `README` file written with Markdown in the `OG-Core` repository [here](https://github.com/PSLmodels/OG-Core/#readme). + + +(SecDoc_JupNote)= +## Jupyter notebooks + +As discussed in the {ref}`Chap_PythonIntro` Chapter, Jupyter notebooks are a great way to interactively work with Python. They are also a great way to document your work. You can write Markdown in cells of these notebooks to provide text around your blocks of code. This Markdown can then be compiled to render nice formatting. You can also use the code cells to document your code just as you would in any Python script. You can see an example of a Jupyter notebook in the Cost-of-Capital-Calculator` repository [here](https://github.com/PSLmodels/Cost-of-Capital-Calculator/blob/master/docs/book/content/examples/PSL_demo.ipynb). As you can see in that example, Jupyter notebooks are rendered as HTML on GitHub, making viewing them easy. + + +(SecDoc_CloudNote)= +## Cloud notebooks + +[Google Colab](https://colab.research.google.com) provides cloud-hosting for Jupyter notebooks. These have all the same functionality as locally hosted notebooks described above, but they are hosted on Google's servers. This allows you to share your work with others and to collaborate on projects. It also means you can run Python (or other languages) without the need to install any special software on your machine. You just need a browser, internet connection, and Google account. + + +(SecDoc_JupBook)= +## Jupyter Book documentation + +For long and detailed documentation, [Jupyter Books](https://jupyterbook.org/en/stable/intro.html) are a great option. Jupyter Books are a collection Markdown, ReStructuredText, [MyST](https://mystmd.org) files and Jupyter notebooks that are compiled into a book format. Jupyter Books can be compiled to HTML or PDF formats, making them easy to share. This training guide was created in Jupyter Book! + +[TODO: Show the slick rst interface between Sphinx and the OG-Core modules that automatically compile LaTeX documentation into the Jupyter Book API documentation. See this Jupyter Book API chapter on [Firms](https://pslmodels.github.io/OG-Core/content/api/firm.html) and the code that created it in [this folder](https://github.com/PSLmodels/OG-Core/tree/master/docs/book/content/api).] + + +(SecDoc_Other)= +## Other published documentaiton + +Put discusion of other forms of published documentation here such as white papers, peer-reviewed articles, websites (readthedocs by Sphinx). + + +(SecDocstringExercises)= ## Exercises -1. Inline comment excercise -2. Use Google docstring style to format a function that is the FOC_savings function (worked with in SciPy exercises) -3. Create auto documentation of (2) with Sphinx? -4. Other?? +```{exercise-start} +:label: ExerDoc-google +:class: green +``` +Take a function your wrote in your solution to {numref}`ExerScipy-BM72_ss`. Add a docstring to this function that uses the Google style. +```{exercise-end} +``` + + +(SecDocstringFootnotes)= +## Footnotes + +The footnotes from this chapter. + +[^EaglesonsLaw]: We could not find a proper citation for the source of this quote "Eagleson's Law of Programming". Some entries on this thread entitled "[Who is Eagleson and where did Eagleson's law originate?](https://ask.metafilter.com/200910/Who-is-Eagleson-and-where-did-Eaglesons-law-originate)" suggest that the quote is at least as old as 1987 and is likely from [Peter S. Eagleson](https://en.wikipedia.org/wiki/Peter_S._Eagleson), a member of the MIT faculty since 1952. However, neither the date, nor the author is confirmed. + +[^Guido]: This is a quote from Guido van Rossum, the original creator of the Python programming language, supposedly from an early PyCon conference. This quote is referenced in one of the early Python Enhancement Proposals, "[PEP 8--Style Guide for Python Code](https://peps.python.org/pep-0008/)" {cite}`VanRossumEtAl:2001`. diff --git a/docs/book/python/ExceptionsIO.md b/docs/book/python/ExceptionsIO.md new file mode 100644 index 0000000..eeecc44 --- /dev/null +++ b/docs/book/python/ExceptionsIO.md @@ -0,0 +1,31 @@ +(Chap_ExceptIO)= +# Exception Handling and File Input/Output + +This chapter was coauthored by Jason DeBacker and Richard W. Evans. + +Python stops the computation process when it encounters an error. Sometimes you want to describe certain errors with descriptive error messages. And sometimes you want your code to move past errors while saving them and including descriptive error messages. Other times, you want to ensure that no errors occur and that your program stops and informs you in the case of an error. + +Python's error handling, assertion functionality, traceback capability, and type hinting are powerful methods to make sure your code does what you expect it to do, breaks when you expect it to break, and moves past issues when you don't want the computation to stop. + +The iframe below contains a PDF of the BYU ACME open-access lab entitled, "Exceptions and File Input/Output". You can either scroll through the lab on this page using the iframe window, or you can download the PDF for use on your computer. See {cite}`BYUACME_ExceptIO`. {numref}`ExerExceptionIO` below has you work through the problems in this BYU ACME lab. The Python file ([`exceptions_fileIO.py`](https://github.com/OpenSourceEcon/CompMethods/tree/main/code/Exceptions_FileIO/exceptions_filIO.py)) and associated text files (`.txt`) associated with this lab are stored in the [`./code/Exceptions_FileIO/`](https://github.com/OpenSourceEcon/CompMethods/tree/main/code/Exceptions_FileIO) directory. + +
+ +
+ + +(SecExceptIOExercises)= +## Exercises + +```{exercise-start} +:label: ExerExceptionIO +:class: green +``` +Read the BYU ACME "[Exceptions and file input/output](https://drive.google.com/file/d/1gAam1i1Gy0YgULT92ul72DUqRCb4q2fA/view?usp=sharing)" lab and complete Problems 1 through 4 in the lab. {cite}`BYUACME_ExceptIO` +```{exercise-end} +``` diff --git a/docs/book/python/Matplotlib.md b/docs/book/python/Matplotlib.md index 3f4de0e..2448046 100644 --- a/docs/book/python/Matplotlib.md +++ b/docs/book/python/Matplotlib.md @@ -1,17 +1,97 @@ (Chap_Matplotlib)= +# Matplotlib +This chapter was coauthored by Jason DeBacker and Richard W. Evans. + +[Matplotlib](https://matplotlib.org) is Python's most widely used and most basic visualization package.[^Matplotlib1] Some of the other most popular Python visualization packages [Bokeh](http://bokeh.org/), [Plotly](https://plotly.com/), and [Seaborn](https://seaborn.pydata.org/). Of these, Matplotlib is the most general for static images and is what is used on `OG-Core`. Once you have a general idea of how to create plots in Python, that knowlege will generalize (to varying degrees) to the other plotting packages. + +The iframe below contains a PDF of the BYU ACME open-access lab entitled, "[Introduction to Matplotlib](https://drive.google.com/file/d/12dnf8tjXBExoQf6W3J5_b52AN27GoBTV/view?usp=sharing)". You can either scroll through the lab on this page using the iframe window, or you can download the PDF for use on your computer. See {cite}`BYUACME_Matplotlib1`. {numref}`ExerMatplot-acme1` below has you work through the problems in this BYU ACME lab. A Python file template ([`matplotlib_intro.py`](https://github.com/OpenSourceEcon/CompMethods/tree/main/code/Matplotlib1/matplotlib_intro.py)) and a data file ([`FARS.npy`](https://github.com/OpenSourceEcon/CompMethods/tree/main/code/Matplotlib1/FARS.npy)) used in the lab are stored in the [`./code/Matplotlib1/`](https://github.com/OpenSourceEcon/CompMethods/tree/main/code/Matplotlib1) directory. + +
+ +
+ +The iframe below contains a PDF of the BYU ACME open-access lab entitled, "[Pandas 2: Plotting](https://drive.google.com/file/d/1grhP5AcxR9uzvTHSmM4Q4kABM0XENH8r/view?usp=sharing)". In spite of having "Pandas" in the title, we include this lab here in this Matplotlib chapter because all of the plotting uses the Matplotlib package. You can either scroll through the lab on this page using the iframe window, or you can download the PDF for use on your computer. See {cite}`BYUACME_Matplotlib2`. {numref}`ExerMatplot-acme2` below has you work through the problems in this BYU ACME lab. A Jupyter notebook file template ([`matplotlib2.ipynb`](https://github.com/OpenSourceEcon/CompMethods/tree/main/code/Matplotlib2/matplotlib2.ipynb)) used in the lab is stored in the [`./code/Matplotlib2/`](https://github.com/OpenSourceEcon/CompMethods/tree/main/code/Matplotlib2) directory. The [`budget.csv`](https://github.com/OpenSourceEcon/CompMethods/tree/main/data/Pandas1/budget.csv) and [`crime_data.csv`](https://github.com/OpenSourceEcon/CompMethods/tree/main/data/Pandas1/crime_data.csv) data files are stored in the [`./data/Pandas1`](https://github.com/OpenSourceEcon/CompMethods/tree/main/data/Pandas1) directory, which were used in the "[Pandas 1: Introduction](https://drive.google.com/file/d/1t5fjjQXBSIYekZUZIDRvMQOcfCpy8edh/view?usp=sharing)" lab. And the other data file used in this lab [`college.csv`](https://github.com/OpenSourceEcon/CompMethods/tree/main/data/Pandas3/college.csv) is stored in the [`./data/Pandas3`](https://github.com/OpenSourceEcon/CompMethods/tree/main/data/Pandas3) directory, which was used in the "[Pandas 3: Grouping](https://drive.google.com/file/d/13DoapcC2whPxSzQQCRaOKv6jow4AkeuZ/view?usp=sharing)" lab. + +
+ +
+ + +(SecMatplotlibAnim3D)= +## (Optional): Animations and 3D + +This section with its accompanying BYU ACME lab and {numref}`ExerMatplot-acme3` is optional because these plotting skills are used less often and because other plotting packages do a better job of visualization dynamics. That said, this lab is a good one. And the 3D plotting in Matplotlib is fairly good. + +The iframe below contains a PDF of the BYU ACME open-access lab entitled, "[Animations and 3D Plotting in Matplotlib](https://drive.google.com/file/d/19y4Uhe4uckSx83duWyELrUz0K-tiYGFc/view?usp=sharing)". You can either scroll through the lab on this page using the iframe window, or you can download the PDF for use on your computer. See {cite}`BYUACME_Matplotlib3`. Optional {numref}`ExerMatplot-acme3` below has you work through the problems in this BYU ACME lab. A Jupyter notebook file template ([`animation.ipynb`](https://github.com/OpenSourceEcon/CompMethods/tree/main/code/Matplotlib3/animation.ipynb)) used in the lab is stored in the [`./code/Matplotlib3/`](https://github.com/OpenSourceEcon/CompMethods/tree/main/code/Matplotlib3) directory. And two data files used in the lab are stored in the [`./data/Matplotlib3`](https://github.com/OpenSourceEcon/CompMethods/tree/main/data/Matplotlib3) directory. + +
+ +
+ + +(SecMatplotlibExercises)= +## Exercises + +```{exercise-start} +:label: ExerMatplot-acme1 +:class: green +``` +Read the BYU ACME "[Introduction to Matplotlib](https://drive.google.com/file/d/12dnf8tjXBExoQf6W3J5_b52AN27GoBTV/view?usp=sharing)" lab and complete Problems 1 through 6 in the lab. {cite}`BYUACME_Matplotlib1` +```{exercise-end} +``` + +```{exercise-start} +:label: ExerMatplot-acme2 +:class: green +``` +Read the BYU ACME "[Pandas 2: Plotting](https://drive.google.com/file/d/1grhP5AcxR9uzvTHSmM4Q4kABM0XENH8r/view?usp=sharing)" lab and complete Problems 1 through 4 in the lab. {cite}`BYUACME_Matplotlib2` +```{exercise-end} +``` + +```{exercise-start} OPTIONAL: Animations nad 3D +:label: ExerMatplot-acme3 +:class: green +``` +Read the BYU ACME "[Animations and 3D Plotting in Matplotlib](https://drive.google.com/file/d/19y4Uhe4uckSx83duWyELrUz0K-tiYGFc/view?usp=sharing)" lab and complete Problems 1 through 5 in the lab. {cite}`BYUACME_Matplotlib3` +```{exercise-end} +``` -# Matplotlib -Many plotting packages for Python. Some of the most popular are [Matplotlib](https://matplotlib.org/), [Plotly](https://plotly.com/), [Bokeh](http://bokeh.org/), and [Seaborn](https://seaborn.pydata.org/). Of these, Matplotlib is the most general for static images and is what is used on `OG-Core`. Once you have a general idea of how to create plots in Python, that knowlege will generalize (to varying degrees) to the other plotting packages. +```{exercise-start} +:label: ExerMatplot-bar +:class: green +``` +Using the country GDP DataFrame you created in Exercise {numref}`ExerPandas-make_df`, collapse these data to find mean GDP per capita by country. Create a bar plot that shows the means for each of the four countries. +```{exercise-end} +``` -ACME materials link... +```{exercise-start} +:label: ExerMatplot-grouped_bar +:class: green +``` +Using same DataFrame as above, create a grouped bar plot that represents the full DataFrame and shows GDP per capita for each country and year. Group the bar plot so that there is a grouping for each decade and within each group, all four countries are represented. +```{exercise-end} +``` +(SecMatplotlibFootnotes)= +## Footnotes -# Exercises +The footnotes from this chapter. -1. Take UN pop data, create line plot -2. Format it - title, font, legened, etc -3. Collapse data and create bar plot -4. Plot mulitple series -5. Grouped bar plot \ No newline at end of file +[^Matplotlib1]: Matplotlib's website is https://matplotlib.org. diff --git a/docs/book/python/NumPy.md b/docs/book/python/NumPy.md index 77ece84..7234e46 100644 --- a/docs/book/python/NumPy.md +++ b/docs/book/python/NumPy.md @@ -1,25 +1,139 @@ (Chap_Numpy)= +# NumPy +This chapter was coauthored by Jason DeBacker and Richard W. Evans. -# NumPy +NumPy is Python's fundamantal numerical package (the name stands for "numerical Python"), and is at the basis of most computation using Python.[^NumPy] Our discussion of Python's NumPy package starts with Travis Oliphant, who was the primary creator of the NumPy package, a founding contributor to Python's SciPy package (covered in the {ref}`Chap_SciPy` chapter), founder of [Anaconda, Inc.](https://www.anaconda.com/) that maintains the most popular distribution of Python, and a co-founder of the [NumFOCUS](https://numfocus.org/) non-profit that fiscally supports some of the primary package projects in Python.[^Oliphant] + +Oliphant was a mathematics and electrical engineering student who came up through his masters degree using MATLAB with a focus primarily on signal processing. While working on a PhD, he needed to create custom code that could do signal processing operations that had never been done before. These operations required combinations of mathematical operations. Oliphant liked the ideas of network and collaboration in the open source software community, and Python was a language that felt intuitive and comfortable to him. However, Python had no established numerical matrix operations libraries. Oliphant created the NumPy package to be that numerical engine based on linear algebra array operations. + +The fundamental object of the NumPy package is the NumPy array [`numpy.array`](https://numpy.org/doc/stable/reference/generated/numpy.array.html). Python's native objects---such as lists, tuples, and dictionaries---can hold numbers and perform operations on those numbers. But the NumPy array allows for storing high-dimensional arrays of numbers on which linear algebra and tensor functions can be operated. These linear algebra operations are more effecient than working with lists and tuples, and they form the foundation of modern optimization and machine learning. Learning to use Python's NumPy package is an essential skill for many numerical computations and other operations. + +The iframe below contains a PDF of the BYU ACME open-access lab entitled, "Introduction to NumPy". You can either scroll through the lab on this page using the iframe window, or you can download the PDF for use on your computer. See {cite}`BYUACME_NumPy1`. {numref}`ExerNumPy-acme1` below has you work through the problems in this BYU ACME lab. A Python file template ([`numpy_intro.py`](https://github.com/OpenSourceEcon/CompMethods/tree/main/code/NumPyIntro/numpy_intro.py)) and a matrix data file ([`grid.npy`](https://github.com/OpenSourceEcon/CompMethods/tree/main/code/NumPyIntro/grid.npy)) used in the lab are stored in the [`./code/NumPyIntro/`](https://github.com/OpenSourceEcon/CompMethods/tree/main/code/NumPyIntro) directory. + +
+ +
+ +The following iframe contains a PDF of the BYU ACME open-access lab entitled, "Advanced NumPy", which contains content and exercises that build off of the previous BYU ACME NumPy lab. You can either scroll through the lab on this page using the iframe window, or you can download the PDF for use on your computer. See {cite}`BYUACME_NumPy2`. {numref}`ExerNumPy-acme2` below has you work through the problems in this BYU ACME lab. A Python file template ([`advanced_numpy.py`](https://github.com/OpenSourceEcon/CompMethods/tree/main/code/AdvancedNumPy/_advanced_numpy.py)) used in the lab are stored in the [`./code/AdvancedNumPy/`](https://github.com/OpenSourceEcon/CompMethods/tree/main/code/AdvancedNumPy) directory. + +
+ +
+ + +(SecNumPyExtensions)= +## Extensions and future paths + +One of the drawbacks to the degree to which NumPy arrays are fundamental to Python's numerical computing is that the format of those arrays is a requirement in Python's most highly used scientific computing and machine learning packages ({ref}`Chap_SciPy` and scikit-learn). However, advances in hardware, large data methods, and optimization algorithms now take much more advantage of parallel computing algorithms, hybrid architectures across multiple traditional processors and GPU's. All of these innovations have been difficult to incorporate into Python's scientific computing stack because NumPy arrays have been difficult to make flexible to these architectures. + +Below are three areas that have been working to make Python better on these dimentions. +* Dask arrays +* QuantSight development and support of array API's in SciPy and in scikit-learn. See here for the [scikit-learn blog post](https://labs.quansight.org/blog/array-api-support-scikit-learn). And see here for the [SciPy blog post](https://labs.quansight.org/blog/scipy-array-api). +* Modular's development of the Mojo programming language. + + +(SecNumPyExercises)= +## Exercises + +```{exercise-start} +:label: ExerNumPy-acme1 +:class: green +``` +Read the BYU ACME "[Introduction to NumPy](https://drive.google.com/file/d/1Hj3ok81gJAxcUTHh_8BrxX-B4belupPN/view?usp=sharing)" lab and complete Problems 1 through 7 in the lab. {cite}`BYUACME_NumPy1` +```{exercise-end} +``` + +```{exercise-start} +:label: ExerNumPy-acme2 +:class: green +``` +Read the BYU ACME "[Advanced NumPy](https://drive.google.com/file/d/15KxliSp0C_mLf7TrLQbnC0wO4YaK7ePi/view?usp=sharing)" lab and complete Problems 1 through 7 in the lab. {cite}`BYUACME_NumPy2` +```{exercise-end} +``` + +```{exercise-start} +:label: ExerNumpy-array +:class: green +``` +Create a Numpy array `b` (defined this as the savings of 2 agents (the rows) over 5 periods (the columns)): +\begin{equation*} + b= \begin{bmatrix} + 1.1 & 2.2 & 3.0 & 2.0 & 1.0 \\ + 3.3 & 4.4 & 5.0 & 3.7 & 2.0 + \end{bmatrix} +\end{equation*} +Use the `shape` method of NumPy arrays to print the shape of this matrix. Use array slicing to print the first row of `b`, which represents the lifecycle savings decisions of the first agent (i.e., the amount they choose to save in each of their 5 periods of life). Use array slicing to print the second column of `b`, which is the savings of both agents when they are in their second period of life. Finally, use array slicing to print the first two rows and the last three columns of `b` (i.e., the savings of both agents from middle age onwards). +```{exercise-end} +``` + +```{exercise-start} +:label: ExerNumpy-dotproduct +:class: green +``` +Now let's think about the matrix `b` as representing not two individual agents, but two types of agents who each live for five periods. In this way, we will interpret the values in `b` as the total savings of different cohorts of these two types of agents who are all living together at a point in time. Now, define a matrix `Omega`: +\begin{equation*} + \Omega= + \begin{bmatrix} + 0.05 & 0.05 & 0.08 & 0.06 & 0.2 \\ + 0.12 & 0.16 & 0.03 & 0.2 & 0.05 + \end{bmatrix} +\end{equation*} +`Omega` represents the fraction of agents in the economy of each type/cohort (Note that the elements of `Omega` sum to 1). Use matrix multiplication to find `B`, which is the dot product of `b` and the transpose of `Omega`. +\begin{equation*} + B = b\Omega^T +\end{equation*} +Print your matrix `B`. What is its shape? What does `B` represent? +```{exercise-end} +``` + +```{exercise-start} +:label: ExerNumpy-mult +:class: green +``` +Multiply element-wise (Hadamard product) the matrix `b` from {numref}`ExerNumpy-array` by the matrix `Omega` from {numref}`ExerNumpy-dotproduct`. Use the `numpy.array.sum()` method on the resulting matrix, with the appropriate `axis` argument in the parentheses to find the total savings of each cohort. +```{exercise-end} +``` + +```{exercise-start} +:label: ExerNumpy-zeros +:class: green +``` +In one line, create a matrix of zeros that is the same size as `b` from {numref}`ExerNumpy-array`. +```{exercise-end} +``` + +```{exercise-start} +:label: ExerNumpy-where +:class: green +``` +Use `numpy.where` to return the elements of `b` from {numref}`ExerNumpy-array` that are greater than 2.0 and zero elsewhere. +```{exercise-end} +``` -ACME materials link... +```{exercise-start} +:label: ExerNumpy-stack +:class: green +``` +Now suppose a third type of agent. This agent has savings $b_3 = \left[4.1, 5.1, 7.1, 4.5, 0.9\right]$. Use `numpy.vstack` to stack `b` from {numref}`ExerNumpy-array` on top of `b_3` to create a new $3\times 5$ matrix `b_new`. +```{exercise-end} +``` +(SecNumPyFootnotes)= +## Footnotes -# Exercises +The footnotes from this chapter. -TODO: update excerises to be more relevant to OG-Core. e.g., matrix represents savings. Then we do some array operations to multiple savings by the "population distribution" matrix omega. Then sum to get total savings, accounting for population weights. +[^NumPy]: The website for NumPy is https://numpy.org. -1. Create a Numpy array `b` (defined this as the savings of 2 types of agents over 5 periods): - $$ \[ -M= - \begin{bmatrix} - 1 & 2 & 3 & 4 & 5 \\ - 3 & 4 & 5 & 6 & 7 - \end{bmatrix} -\]. Use the `shape` method of Numpy arrays to print the shape of this matrix. Use array slicing to print the first row of `A`. Use array slicing to print the second column of `A`. Use array slicing to print the first two rows and the last three columns of `A`. -2. Reshape the matrix `A` unto a 5x2 matrix. Assign this new matrix to name `B`. Use matrix multiplication to find `C`, which is the dot product of `A` and `B`. -3. Multiply the matrix `A` by itself element-wise (Hadamard product). -4. In one line, create a matrix of zeros that is the same size as `A`. -5. Something with `np.where`. -6. Something with appending/stacking. \ No newline at end of file +[^Oliphant]: Travis Oliphant has a [Wikipedia page](https://en.wikipedia.org/wiki/Travis_Oliphant) {cite}`OliphantWiki`. We highly recommend [Oliphant's interview](https://youtu.be/gFEE3w7F0ww?si=XKcRlcw7FXkA9oxB) on the Lex Fridman Podcast from September 22, 2021 {cite}`Fridman:2021`. diff --git a/docs/book/python/OOP.md b/docs/book/python/OOP.md index bc7718a..6c85657 100644 --- a/docs/book/python/OOP.md +++ b/docs/book/python/OOP.md @@ -1,15 +1,73 @@ (Chap_OOP)= +# Object Oriented Programming +This chapter was coauthored by Jason DeBacker and Richard W. Evans. -# Object Oriented Programming +Python is literally a programming language built on objects. Objects are instances of classes. And classes are definitions of objects with their corresponding methods and attributes. Objects are a powerful way to group functionality and attributes in a class that has a limited and common set of characteristics. + +An analogy is how life forms are classified by [taxonomic rank](https://en.wikipedia.org/wiki/Taxonomic_rank) going from most general to most specific: domain, kingdom, phylum, class, order, family, genus, and species {cite}`WikiTaxonomicRank`. If you have a model of all the different types of cats, you would probably care about the taxonomic rank *family* of *felidae* or cats. If you were interested in modeling all the different types of mammals that live on land, you might need many different *orders*, with sub-class objects for each *family*, *genus*, and *species* within each order. + +Python objects defined as classes have a limited set of attributes that apply to that class in the same way the cat family *felidae* has different attributes than the dog family *canidae*. In the family of `OG-Core` macroeconomic model country calibrations, we have many custom objects defined by classes, the most important of which might be the `parameters` class. + +Using objects wisely and efficiently can make your code more readable, easier to modify and use, more scalable, and more interoperable. The iframe below contains a PDF of the BYU ACME open-access lab entitled, "Object-oriented Programming". You can either scroll through the lab on this page using the iframe window, or you can download the PDF for use on your computer. See {cite}`BYUACME_OOP`. {numref}`ExerOOP-acme` below has you work through the problems in this BYU ACME lab. A Python file ([`object_oriented.py`](https://github.com/OpenSourceEcon/CompMethods/tree/main/code/ObjectOriented/.py/object_oriented.py)) template for the problems in this lab is stored in the [`./code/ObjectOriented/`](https://github.com/OpenSourceEcon/CompMethods/tree/main/code/ObjectOriented) directory. + +
+ +
+ + +(SecOOPExercises)= +## Exercises + +```{exercise-start} +:label: ExerOOP-acme +:class: green +``` +Read the BYU ACME "[Object-oriented programming](https://drive.google.com/file/d/1dtDaHYhA_7_6vt_uh60CHIPlHf6CA3qf/view?usp=sharing)" lab and complete Problems 1 through 4 in the lab. {cite}`BYUACME_ExceptIO` +```{exercise-end} +``` + +```{exercise-start} +:label: ExerOOP-defclass +:class: green +``` +Define a class called `Specifications` with an attribute that is the rate of time preference `beta` (usually represented by the Greek letter $\beta$). Create two instances of this class, the first called `p1` for `beta=0.96` and the second called `p2` for `beta=0.99`. +```{exercise-end} +``` -ACME materials link... +```{exercise-start} +:label: ExerOOP-attr +:class: green +``` +Update the `Specifications` class from {numref}`ExerOOP-defclass` so that it not only allows one to specify the value of `beta` upon instantiation of the class but also checks that `beta` is between 0 and 1. +```{exercise-end} +``` +```{exercise-start} +:label: ExerOOP-method +:class: green +``` +Modify the `Specifications` class from {numref}`ExerOOP-attr` so that it has a method that prints the value of `beta`. +```{exercise-end} +``` -# Exercises +```{exercise-start} +:label: ExerOOP-adjust +:class: green +``` +Building off the `Specifications` class in {numref}`ExerOOP-method`, change the input of `beta` to the class so that it is input at an annual rate `beta_annual`. Allow another attribute of the class called `S` that is the number of periods in an economic agent's life. Include a method in the `Specifications` class that adjusts the value of `beta` to represent the discount rate applied per model period. Let each model period be `S/80` years, such that each model period equals one years when `S=80`. +```{exercise-end} +``` -1. Define a class called `Specifications` with an attribute that is the rate of time preference, $\beta$. Create instances of this class called `p` for $\beta=0.96$ and $\beta=0.99$. -2. Update the `Specifications` class so that allows one to specify the value of $\beta$ upon initialization of the class and checks that $\beta$ is between 0 and 1. -3. Modify the `Specifications` class so that it has a method that prints the value of $\beta$. -4. Change the input of $\beta$ to the class so that it is input at an annual rate. Allow another attribute of the class called `S` that is the number of periods in an economic agent's life. Include a method in the `Specifications` class that adjusts the value the value of $\beta$ to represent the discount rate applied per model period, which will be equivalent to `S/80` years. -5. Add a method to the `Specifications` class that allows one to update the values of the class attributes `S` and `beta_annual` by providing a dictionary of the form `{"S": 40, "beta_annual": 0.8}`. Ensure that when the instance is updated, the new `beta` attribute is consistent with the new `S` and `beta_annual`. \ No newline at end of file +```{exercise-start} +:label: ExerOOP-update +:class: green +``` +Add a method to the `Specifications` class in {numref}`ExerOOP-adjust` that allows one to update the values of the class attributes `S` and `beta_annual` by providing a dictionary of the form `{"S": 40, "beta_annual": 0.8}`. Ensure that when the instance is updated, the new `beta` attribute is consistent with the new `S` and `beta_annual`. +```{exercise-end} +``` diff --git a/docs/book/python/Pandas.md b/docs/book/python/Pandas.md index 02326d7..cb7eb84 100644 --- a/docs/book/python/Pandas.md +++ b/docs/book/python/Pandas.md @@ -1,21 +1,168 @@ (Chap_Pandas)= +# Pandas +This chapter was coauthored by Jason DeBacker and Richard W. Evans. -# Pandas +Pandas is to data wrangling and analysis in Python what {ref}`Chap_NumPy` is to numerical methods in Python. Pandas is Python's primary data analysis package.[^Pandas1] Its name is derived from the econometric term, "panel data". The Pandas package was originially developed in 2008 by Wes McKinney while at global investment firm AQR Capital Management.[^Pandas2] The Python Pandas package became open source in 2009, and Pandas became a NumFOCUS sponsored project in 2015. + +The primary Python object in Pandas is the DataFrame ([`pandas.DataFrame`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html)). A Pandas DataFrame is similar to the R programming language's dataframe.[^PandasR] The dataframe is a two-dimensional data object that often include rows that serve as observations, columns that serve as variables, advanced date functions, and rich multi-layered indexing capability. Pandas also includes rich functionality for reading in data, saving and exporting data, data cleaning and munging, data description, and data manipulation, selection, and grouping. + +Pandas also has a Series object ([`pandas.Series`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html)), that represents a single data series, often a single variable from a DataFrame. The operations on and attributes of a Pandas Series object are similar to those of the DataFrame. + +The iframe below contains a PDF of the BYU ACME open-access lab entitled, "Pandas 1: Introduction". You can either scroll through the lab on this page using the iframe window, or you can download the PDF for use on your computer. See {cite}`BYUACME_Pandas1`. {numref}`ExerPandas-acme1` below has you work through the problems in this BYU ACME lab. The data files used in this lab are stored in the [`./data/Pandas1/`](https://github.com/OpenSourceEcon/CompMethods/tree/main/data/Pandas1) directory. A Jupyter notebook file template ([`pandas1.ipynb`](https://github.com/OpenSourceEcon/CompMethods/tree/main/code/Pandas1/pandas1.ipynb)) used in the lab is stored in the [`./code/Pandas1/`](https://github.com/OpenSourceEcon/CompMethods/tree/main/code/Pandas1) directory. + +
+ +
+ +The iframe below contains a PDF of the BYU ACME open-access lab entitled, "Pandas 3: Grouping". You can either scroll through the lab on this page using the iframe window, or you can download the PDF for use on your computer. See {cite}`BYUACME_Pandas3`. {numref}`ExerPandas-acme2` below has you work through the problems in this BYU ACME lab. The data files used in this lab are stored in the [`./data/Pandas3/`](https://github.com/OpenSourceEcon/CompMethods/tree/main/data/Pandas3) directory. A Jupyter notebook file template ([`pandas3.ipynb`](https://github.com/OpenSourceEcon/CompMethods/tree/main/code/Pandas3/pandas3.ipynb)) used in the lab is stored in the [`./code/Pandas3/`](https://github.com/OpenSourceEcon/CompMethods/tree/main/code/Pandas3) directory. + +
+ +
+ + +(SecPandasExercises)= +## Exercises + +```{exercise-start} +:label: ExerPandas-acme1 +:class: green +``` +Read the BYU ACME "[Pandas 1: Introduction](https://drive.google.com/file/d/1t5fjjQXBSIYekZUZIDRvMQOcfCpy8edh/view?usp=sharing)" lab and complete Problems 1 through 6 in the lab. {cite}`BYUACME_Pandas1` +```{exercise-end} +``` + +```{exercise-start} +:label: ExerPandas-acme2 +:class: green +``` +Read the BYU ACME "[Pandas 3: Grouping](https://drive.google.com/file/d/13DoapcC2whPxSzQQCRaOKv6jow4AkeuZ/view?usp=sharing)" lab and complete Problems 1 through 5 in the lab. {cite}`BYUACME_Pandas3` +```{exercise-end} +``` + +```{exercise-start} +:label: ExerPandas-make_df +:class: green +``` +Consider the following GDP per capita data (in constant 2011$, source: [Maddison Project Database](https://www.rug.nl/ggdc/historicaldevelopment/maddison/releases/maddison-project-database-2020?lang=en)): +| | IND | MYS | USA | ZAF| +|----------------|----|----|----|----| +| 1990 | 2,087 | 8,179 | 36,982 | 6,111 | +| 2000 | 2,753 | 13,475 | 45,886 | 7,583 | +| 2010 | 4,526 | 18,574 | 49,267 | 11,319 | +| 2018 | 6,806 | 24,842 | 55,335 | 12,166 | + +Create a dictionary with keys `Year`, `IND`, `MYS`, `USA`, and `ZAF` and values that are lists of the GDP per capita data for each country. Create a DataFrame named `df` from this dictionary. Print the DataFrame. +```{exercise-end} +``` + +```{exercise-start} +:label: ExerPandas-inspect +:class: green +``` +Inspect this data frame. Print `df.head(3)`. Print `df.tail(3)`. Get a list of column names with the `keys` method. Finally, use the `describe` method to print descriptive statistics. +```{exercise-end} +``` + +```{exercise-start} +:label: ExerPandas-index +:class: green +``` +Pandas DataFrames use an index to keep track of rows. Note the default index in a DataFrame `df` are integers for each row. Change the index so the year is the index value. Print the updated DataFrame `df`. +```{exercise-end} +``` + +```{exercise-start} +:label: ExerPandas-reshape +:class: green +``` +In this exercise reshape your DataFrame `df` from {numref}`ExerPandas-index` into a long panel format with a [`MultiIndex`](https://pandas.pydata.org/docs/user_guide/advanced.html) for the columns. The first level of the `MultiIndex` should be the country name and the second level should be the year. The values should be the GDP per capita. To do this, use the [`pivot_table`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.pivot_table.html) or [`stack`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.stack.html) methods of the DataFrame class. Please print the resulting DataFrame. +```{exercise-end} +``` + +```{exercise-start} +:label: ExerPandas-groupby +:class: green +``` +Create a new variable that is the growth rate in GDP per capita from the prior period measure. To do this, use [`groupby`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.groupby.html) to find growth rate for each country over the sample. +```{exercise-end} +``` + +```{exercise-start} +:label: ExerPandas-print_tables +:class: green +``` +The DataFrame object has several methods to help output a formatted table suitable for reports or presentations. Use one of these methods to print a DataFrame formatted as a [markdown](https://www.markdownguide.org/basic-syntax/) table. +```{exercise-end} +``` + +```{exercise-start} +:label: ExerPandas-read +:class: green +``` +In most cases, you are likely to use a DataFrame as a container for a large dataset, not something simple that you can enter by manually as we did above. Pandas has [several methods](https://pandas.pydata.org/docs/user_guide/io.html) to read in data from files are various formats. Let's use one of these methods to read in some population data extracted from the [United Nations' World Population Prospects](https://population.un.org/wpp/). Note that Pandas will download these data for you if you have a URL to the data file. The URL for these data on South Africa's population is: [https://raw.githubusercontent.com/EAPD-DRB/OG-ZAF/main/ogzaf/data/demographic/un_zaf_pop.csv](https://raw.githubusercontent.com/EAPD-DRB/OG-ZAF/main/ogzaf/data/demographic/un_zaf_pop.csv). Please read in these data (Note: the separator is the verical bar ("|") and the header is on the second line (in Python this has index 1, so you'll want to use the argument `header=1`)). Print the first 5 rows of the DataFrame. +```{exercise-end} +``` + +```{exercise-start} +:label: ExerPandas-subset +:class: green +``` +Now we'll select a subset of this DataFrame. Please create a new DataFrame called `zaf_pop` that contains only the columns `AgeId`, `Value` and only rows where `SexId=3` (i.e., both sexes are included), `TimeLabel=2021` (i.e., only values for the year 2021). Print the first 5 rows of the DataFrame. +```{exercise-end} +``` + +```{exercise-start} +:label: ExerPandas-new_var +:class: green +``` +With your new `zaf_pop` DataFrame, rename the column `Value` to `Count`. Create a new variable in the DataFrame called `Density` that is the fraction of the total population for each age. Print the first 5 rows of the DataFrame. +```{exercise-end} +``` + +```{exercise-start} +:label: ExerPandas-plot +:class: green +``` +Use the Pandas DataFrame [`plot`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.plot.html) method to plot the population density across age for South Africa. +```{exercise-end} +``` + +```{exercise-start} +:label: ExerPandas-merge +:class: green +``` + It is often the case that we need to combine more than one dataset. Pandas offers a few options to do this, including the [`merge`] and [`join`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.join.html) methods of the DataFrame class. Let's test this, but reading in the original data again, and finding the population of women in 2021. Then use `merge` or `join` to combine the `zaf_pop` and `zaf_female_pop` DataFrames. Plot the density of women and the overall population together. +```{exercise-end} +``` + +```{exercise-start} +:label: ExerPandas-save +:class: green +``` +Save your final DataFrame to your hard drive as a comma separated values `.csv` format file. +```{exercise-end} +``` + + +(SecPandasFootnotes)= +## Footnotes -ACME materials link... +The footnotes from this chapter. +[^Pandas1]: The website for Pandas is https://pandas.pydata.org/. -# Exercises +[^Pandas2]: See the "About" page on the Pandas website (https://pandas.pydata.org/about/) as well as the Pandas Wikipedia article {cite}`PandasWiki`. -1. Creating a DataFrame from a dictionary. Simple example with GDP and inflation for a small set of countries over a few years. -2. Inspecting a DataFrame with `head` and `tail` and `describe` and `keys` -3. Something to illustrate indexing. Can turn dataframe into a panel with multindex. -4. Using `groupby` to aggregate data. -5. Printing a DataFrame as a table (e.g. to tex or json or md) -6. Read in a csv file as a DataFrame. Use UN population data as an example. Saving a DataFrame to a CSV file. -7. Creating a new variable in a DataFrame. -8. Selecting a subset of a DataFrame. Using `.loc` and subsetting columns etc -9. Using `merge` to join two DataFrames. Read other UN data (e.g., fertility) and merge with data from (4) -10. Using the `plot` method -11. Replacing values, dropping, renaming columns \ No newline at end of file +[^PandasR]: The Pandas online documentation has a page that gives a correspondence between Pandas Dataframe functionality and R dataframe functionality (https://pandas.pydata.org/pandas-docs/stable/getting_started/comparison/comparison_with_r.html). diff --git a/docs/book/python/SciPy.md b/docs/book/python/SciPy.md index e2b99e0..44b6dbf 100644 --- a/docs/book/python/SciPy.md +++ b/docs/book/python/SciPy.md @@ -1,15 +1,459 @@ -(Chap_Scipy)= +--- +jupytext: + formats: md:myst + text_representation: + extension: .md + format_name: myst +kernelspec: + display_name: Python 3 + language: python + name: python3 +--- +(Chap_SciPy)= +# SciPy: Root finding, minimizing, interpolation -# SciPy +This chapter was coauthored by Jason DeBacker and Richard W. Evans. -ACME materials link... +SciPy is Python's primary scientific computing package.[^SciPy] As described in the {ref}`Chap_NumPy` chapter, SciPy is built with NumPy as a core dependency. The SciPy website [homepage](https://scipy.org/) states that, "SciPy provides algorithms for optimization, integration, interpolation, eigenvalue problems, algebraic equations, differential equations, statistics and many other classes of problems." +The `OG-Core` model and its country calibrations use SciPy primarily for three functionalities, although there are some other smaller use cases. +* Finding the roots or zeros of functions ([`scipy.optimize.root`](https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.root.html)) +* Solving minimization problem ([`scipy.optimize.minimize`](https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.minimize.html)) +* Interpolation ([`scipy.interpolate`](https://docs.scipy.org/doc/scipy/tutorial/interpolate.html)) -# Exercises -1. Define household FOC for savings and use `scipy.optimize.root` to solve for the optimal savings rate for a household with CRRA utility -2. Define the household FOC for labor supply and use `scipy.optimize.root` to solve for the optimal labor supply for a household with ellipitcal utility -3. Use `scipy.optimize.minimize` to minimize the function $f(x) = x^2 + 2x + 1$ (or some other function). -4. Use `scipy.optimize.minimize` to minimize the function $f(x) = x^2 + 2x + 1$ (or some other function) subject to the constraint that $x \geq 2$. -5. Use `scipy.interpolate`... \ No newline at end of file +(SecSciPyRoot)= +## Root finding + + +(SecSciPyRoot_theory)= +### Root finding theory + +Root finding is equivalent to finding the solution to a system of equations. For example, observe the following quadratic equation. +```{math} + :label: EqSciPy_UnivarNonZeroFunc + ax^2 + bx + c = 12 +``` +We can always restate that equation as a function that equals zero. +```{math} + :label: EqSciPy_UnivarZeroFunc + ax^2 + bx + c - 12 = 0 +``` + +Let $f(x)$ be a vector of functions $f_r(x)$, each of which is a function of a vector of variables $x\equiv\left[x_1,x_2,...x_K\right]$. Without loss of generality, we can specify an arbitrary number of functions $f(x)$ as an equation equal to zero. +```{math} + :label: EqSciPy_ZeroFunc + f(x)=0 \quad\text{or}\quad + \begin{bmatrix} + f_1(x) \\ + f_2(x) \\ + \vdots \\ + f_R(x) \\ + \end{bmatrix} = + \begin{bmatrix} + 0 \\ + 0 \\ + \vdots \\ + 0 + \end{bmatrix} +``` +Examples of systems that fit this representation in {eq}`EqSciPy_ZeroFunc` include single equations like {eq}`EqSciPy_UnivarNonZeroFunc` and {eq}`EqSciPy_UnivarZeroFunc`, systems of linear equations, univariate and multivariate equations, and systems of nonlinear equations. + +```{prf:definition} System Rank +:label: DefSciPy_SysRank + +The **system rank** $R^*$ for the system of $R$ equations $f(x)$ with $K$ variables $x=[x_1, x_2,...x_K]$ is the number of equations in $f:\mathbb{R}^K\rightarrow\mathbb{R}^R$ that are independent of each other, such that $R^*\leq R$, where independence of two equations is defined as: +\begin{equation*} + f_r(x) \neq f_s(x) \quad\forall r\neq s +\end{equation*} +``` + +As an example of system rank in {prf:ref}`DefSciPy_SysRank`, the following system of equations has three equations $R=3$ but only has rank two $R^*=2$ because the first equation is equal to the second equation. The first equation is simply two times the first equation. The second equation gives no unique information once we know the first equation. Only two equations in this system give unique information. +\begin{equation*} + \begin{split} + 3x + y +10z = 0.5 \\ + 6x + 2y + 20z = 1 \\ + x + y - z = 7 + \end{split} +\end{equation*} + +System rank of $R^*K$ +* **under identified** if the number of independent equations is strictly less than the number of variables $R^* Nonlinear equation solving presents problems not present with linear equations or optimization. In particular, the existence problem is much more difficult for nonlinear systems. Unless one has an existence proof in had, a programmer must keep in mind that the absence of a solutino may explain a program's failure to converge. Ever if there exists a solution, all methods will do poorly if the problem is poorly conditioned near a solution. Transforming the problem will often improve performance.{cite}`Judd:1998` (p. 192) + +Because root finding in nonlinear systems can be so difficult, much research into the best methods has accumulated over the years. And the approaches to solving nonlinear systems can be an art as much as a science. This is also true of minimization problems discussed in the next section ({ref}`SecSciPyMin`). For this reason, the [`scipy.optimize.root`](https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.root.html) module has many different solution algorithms you can use to find the solution to a nonlinear system of equations (e.g., `hybr`, `lm`, `linearmixing`). + +All of the root finder methods in [`scipy.optimize.root`](https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.root.html) are iterative. They take an initial guess for the solution for the variable vector $x_i$, evaluate the functions $f(x_i)$ in {eq}`EqSciPy_ZeroFuncErr` at $x_i$, and guess a new value for the solution vector $x_{i+1}$ until the errors on the left-hand-side of the functions in {eq}`EqSciPy_ZeroFuncErr` get arbitrarily close to zero. +```{math} + :label: EqSciPy_ZeroFuncErr + \hat{x} = x:\quad + \begin{bmatrix} + f_1(x) \\ + f_2(x) \\ + \vdots \\ + f_R(x) \\ + \end{bmatrix} = + \begin{bmatrix} + \varepsilon_1 \\ + \varepsilon_2 \\ + \vdots \\ + \varepsilon_R + \end{bmatrix} \quad\text{and}\quad + || \left[\varepsilon_1, \varepsilon_2,...\varepsilon_R\right] || \leq \text{toler} +``` + + +Before we go through some root finding examples using [`scipy.optimize.root`](https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.root.html), we want to share some root finding wisdom in the following {prf:ref}`ObsSciPy_RootMinWisdom` that we have learned over the years. The wisdom in this definition also applies to minimization problems discussed in the following section. + +```{prf:observation} Root finding and minimization problem wisdom +:label: ObsSciPy_RootMinWisdom + +The following strategies for successfully finding the solution to systems of equations or finding the global minimum of an optimization problem come from long experience working with these problems. +1. Knowing and debugging the **underlying model theory** is often more effective and more important than finding the best, most advanced, or most robust root finder or minimizer. In most instances in which our optimizers have given non solutions or incorrect solutions, the adjustment that fixed the problem was most often going back to the underlying system of equations and understanding what they mean. +2. Choose an **intelligent initial guess**. Many optimization algorithms require an initial guess as an input. When the underlying system of equations or criterion function is highly nonlinear, a good initial guess is critical for the root finder or minimizer to converge. The theory or the underlying data often suggest a reasonable initial guess. In dynamic models, the steady-state or the previous period's solution might be a good initial guess. +3. Give the root finder or minimizer **as much information as possible** about the problem. Many root finders and minimizers can take as inputs contraints on the solution and theoretical derivatives. These save the algorithm computational calories and may engage components of the algorithm that are specifically designed to use those details. +``` + + +(SecSciPyRoot_examp)= +### Root finding examples + + +(SecSciPyRoot_examp1)= +#### Simple numerical example + +Assume that the system of equations we are trying to solve is a two-equation system $R=2$ of nonlinear independent equations in two variables $x$ and $y$. + +```{math} + :label: EqSciPyRootEx1a + + x^2 - 4x + 5 - y = 0 +``` +```{math} + :label: EqSciPyRootEx1b + + e^x - y = 0 +``` + +By plotting these two equations in {numref}`Figure %s `, we can see that there is only one solution. And just by looking at the plot, we can see that the solution is close to $(\hat{x},\hat{y})\approx (0.9, 2.2)$. + +```{code-cell} ipython3 +:tags: ["hide-input", "remove-output"] + +import numpy as np +import matplotlib.pyplot as plt + + +def eq1_y_SciPyRoot_examp1(x): + """ + This function uses the function of x and y in the first equation of example 1 + in the SciPy Chapter, Root finding section to take a value for x and deliver + the corresponding value for y + """ + y = (x ** 2) - (4 * x) + 5 + + return y + + +def eq2_y_SciPyRoot_examp1(x): + """ + This function uses the function of x and y in the second equation of example 1 + in the SciPy Chapter, Root finding section to take a value for x and deliver + the corresponding value for y + """ + y = np.exp(x) + + return y + + +xmin = -2 +xmax = 6 +xvals = np.linspace(xmin, xmax, 500) +y1vals = eq1_y_SciPyRoot_examp1(xvals) +y2vals = eq2_y_SciPyRoot_examp1(xvals) +plt.plot(xvals, y1vals, color='blue', label=r"equation 1: $y=x^2 - 4x + 5$") +plt.plot(xvals, y2vals, color='red', label=r"equation 2: $y=e^x$") +plt.hlines([0], -3, 7, colors=["black"], linestyles=["dashed"]) +plt.xlim(xmin, xmax) +plt.ylim(-0.5, 10) +plt.xlabel(r"$x$ values") +plt.ylabel(r"$y$ values") +plt.legend() + +plt.show() +``` + +```{figure} ../images/SciPy/root_examp1.png +:height: 500px +:name: FigScipyRoot_examp1 + +Solution to two nonlinear functions in $x$ and $y$ +``` + +We can now use SciPy's root finder [`scipy.optimize.root`](https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.root.html) to find the solution to equations {eq}`EqSciPyRootEx1a` and {eq}`EqSciPyRootEx1b`. + +Note first some properties of the theory or the functions in the system of equations. Although equation {eq}`EqSciPyRootEx1a` is defined for any $x$ in the real line $x\in(-\infty,\infty)$, it is only defined for $y$ weakly greater than one $y\geq 1$. However, the left-hand-side of {eq}`EqSciPyRootEx1a` is defined for any values of $x$ and $y$ on the real line. Similarly, equation {eq}`EqSciPyRootEx1b` is defined for any $x$ in the real line $x\in(-\infty,\infty)$, it is only defined for strictly positive $y>0$. But any values for $x$ and $y$ on the real line are defined for the left-hand-side of {eq}`EqSciPyRootEx1b`. + +The following Python code block executes a [`scipy.optimize.root`](https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.root.html) root finder to find the solution to equations {eq}`EqSciPyRootEx1a` and {eq}`EqSciPyRootEx1b`. The key components to a Scipy root finder are +* An error function (see `errfunc_SciPyRoot_examp1` function below) that takes an arbitrary vector of input variable values $x$ and outputs the corresponding right-hand-side errors associated with that vector as shown in the right-hand-side of {eq}`EqSciPy_ZeroFuncErr`. +* An initial guess $x_{init}$ (see `init_guess_xy` list below) that does not violate any of the properties of the equations of the problem. + +The root finder algorithm then iterates on values of the $x$ vector starting at the initial guess $x_{init}$ that reduce the error values that are right-hand-side of {eq}`EqSciPy_ZeroFuncErr` which is the the output of `errfunc_SciPyRoot_examp1` function below. + +```{code-cell} ipython3 +:tags: [] + +import scipy.optimize as opt + + +def f1_SciPyRoot_examp1(x, y): + """ + This is the evaluation of the right-hand-side of the first equation of example 1 + in the SciPy Chapter, Root finding section. We can interpret this value as an + error because it need not equal zero in general. + """ + error1 = (x ** 2) - (4 * x) + 5 - y + + return error1 + + +def f2_SciPyRoot_examp1(x, y): + """ + This is the evaluation of the right-hand-side of the second equation of example 1 + in the SciPy Chapter, Root finding section. We can interpret this value as an + error because it need not equal zero in general. + """ + error2 = np.exp(x) - y + + return error2 + + +def errfunc_SciPyRoot_examp1(xy_list): + """ + This function takes as arguments + """ + x, y = xy_list + error_func1 = f1_SciPyRoot_examp1(x, y) + error_func2 = f2_SciPyRoot_examp1(x, y) + errors_list = [error_func1, error_func2] + + return errors_list + + +init_guess_xy = [0, 0] +solution = opt.root(errfunc_SciPyRoot_examp1, init_guess_xy) + +print(solution) +print("") +print("The solution for (x, y) is:", solution.x) +print("") +print("The error values for eq1 and eq2 at the solution are:", solution.fun) +``` + +As we saw in {numref}`Figure %s `, the solution is $(\hat{x},\hat{y})=(0.846, 2.331)$ and the zero functions are solved to $1e-12$ precision. {numref}`ExerScipy-root-lin` has you test the linear algebra solution to a system of linear equations to the SciPy root finder solution. + + +(SecSciPyRoot_examp2)= +#### OG-Core equations example + +In the `OG-Core` macroeconomic model, every age-$s$ individual in the model chooses how much to consume $c_{s,t}$, save $b_{s+1,t+1}$, and work $n_{s,t}$ in each period $t$.[^OG-Core-Indiv] In this model, each individual's decision problem can be reduced to choosing consumption $c_{s,t}$ and labor supply $n_{s,t}$ each period. In {numref}`ExerScipy-root_labor` and {numref}`ExerScipy-root_save`, you will use SciPy's root finder to solve for optimal labor supply decisions for three different households and optimal consumption decisions over the lifetime of a household, respectively. + + +(SecSciPyMin)= +## Minimization + +Minimization problems are a more general type of problem than root finding problems. Any root finding problem can be reformulated as a minimization problem. But it is not the case that any minimization problem can be reformulated as a root finding problem. Furthermore, if a minimization problem can be reformulated as a root finding problem, it is often much faster to compute the root finding problem. But the minimization problem allows for more generality and often more robustness. + +{numref}`ExerSciPy-root-min` has you compute the solution to a problem using minimization and root finding, respectively, and to compare the corresponding computation times. One of our favorite books and resources on the mathematics behind minimization problems is {cite}`HumpherysJarvis:2020` (section IV, pp.519-760). + + +(SecSciPyInterp)= +## Interpolation + + +(SecSciPyExercises)= +## Exercises + +```{exercise-start} Linear algebra vs. root finder +:label: ExerScipy-root-lin +:class: green +``` +Define an exactly identified linear system of three equations and three unknown variables $R=R^*=3=K$. +\begin{equation*} + \begin{split} + 3x_1 + x_2 - 9x_3 &= 0.0 \\ + -4x_1 + 6x_2 + 2x_3 &= 0.5 \\ + 5x_1 - 8x_2 + 7x_3 &= -2.5 + \end{split} +\end{equation*} +or +\begin{equation*} + \begin{bmatrix} + 3 & 1 & -9 \\ + -4 & 6 & 2 \\ + 5 & -8 & 7 \\ + \end{bmatrix} + \begin{bmatrix} + x_1 \\ x_2 \\ x_3 + \end{bmatrix} = + \begin{bmatrix} + 0.0 \\ 0.5 \\ -2.5 + \end{bmatrix} +\end{equation*} +Use linear algebra matrix inversion to solve for the solution $\hat{x}\equiv[\hat{x}_1,\hat{x}_2,\hat{x}_3]^T$ to the equation (i.e., $\hat{x} = A^{-1}b$). Next, use `scipy.optimize.root` to solve for the same solution. Verify that both sets of answers are close to the nearest $1e-8$. +```{exercise-end} +``` + +```{exercise-start} +:label: ExerScipy-root_labor +:class: green +``` +In a three-period-lived agent overlapping generations model, let $s=\{1,2,3\}$ represent the age of an individual. In every period, a young agent $s=1$, a middle-aged agent $s=2$, and an old agent $s=3$ exist in the economy together. The consumption-labor Euler equation for each age-$s$ agent represents the optimal labor supply decision $n_s$ that balances benefit of extra consumption from labor income with the disutility of working, given the consumption amount $c_s$ and the current wage $w$.[^EvansPhillips] +\begin{equation*} + \frac{w}{c_s} = (n_s)^\frac{1}{2}\left[1 - (n_s)^\frac{3}{2}\right]^{-\frac{1}{3}} \quad\text{for}\quad s=1,2,3 +\end{equation*} + +Let the wage be one $w=1$ and let the consumption of each aged individual be $[c_1,c_2,c_3]=[1.0, 2.0, 1.5]$. The system of three equations and three unknowns $[n_1,n_2,n_3]$ is therefore the following. +\begin{equation*} + \begin{split} + 1 &= (n_1)^\frac{1}{2}\left[1 - (n_1)^\frac{3}{2}\right]^{-\frac{1}{3}} \\ + \frac{1}{2} &= (n_2)^\frac{1}{2}\left[1 - (n_2)^\frac{3}{2}\right]^{-\frac{1}{3}} \\ + \frac{1}{1.5} &= (n_3)^\frac{1}{2}\left[1 - (n_3)^\frac{3}{2}\right]^{-\frac{1}{3}} + \end{split} +\end{equation*} +Use SciPy's root finder to solve for each age agent's optimal labor supply decision $[\hat{n}_1,\hat{n}_2,\hat{n}_3]$. Each equation is independently identified in that each function $f_s(n_s)$ is only a function of one variable. But solve for all three variables simultaneously. +```{exercise-end} +``` + +```{exercise-start} +:label: ExerScipy-root_save +:class: green +``` +In a four-period-lived agent overlapping generations model, let $s=\{1,2,3,4\}$ represent the age of an individual. Assume that labor supply over the lifetime of an individual is exogenously supplied. Let $n_s$ be the amount of labor supplied by the age-$s$ individual in any period $t$. Then assume the lifetime labor supply of an individual is exogenously $(n_1,n_2,n_3,n_4)=(0.3, 0.5, 0.6, 0.2)$. The consumption-savings Euler equation for each of the youngest three age-$s$ agents represents the optimal savings decision $b_{s+1,t+1}$ that balances benefit of consumption in the current period $c_t$ with discounted consumption in the next period $c_{t+1}$, given preference parameter values and exogenous labor supply $n_s$. The oldest agent $s=4$ has no savings decision because they die at the end of the period. +\begin{equation*} + \begin{split} + &(c_{s,t})^{-1.5} = \beta\left(1 + r_{t+1}\right)(c_{s+1,t+1})^{-1.5} \quad\text{for}\quad s=1,2,3 \\ + \text{where}\quad &c_{s,t} = w_t n_s + (1 + r_t)b_{s,t} - b_{s+1,t+1} \quad\text{and}\quad b_{1,t}, b_{5,t}=0 + \end{split} +\end{equation*} +If we plug the budget constraint from the second line of the equation above into each of the Euler equations in the first line, and assume $\beta = 0.8$, constant wages $w_t=1$ for all $t$, constant interest rates $r_t=0.1$ for all $t$, and exogenous labor supply over the lifetime is $(n_1,n_2,n_3,n_4)=(0.3, 0.5, 0.6, 0.2)$, we get a system of three Euler equations in three unknown optimal savings amounts $(b_{2,t+1}, b_{3,t+2}, b_{4,t+3})$ over the lifetime of the individual. +\begin{equation*} + \begin{split} + \left[n_1 - b_{2,t+1}\right]^{-1.5} &= 0.8(1.1)\left[n_2 + 1.1b_{2,t+1} - b_{3,t+2}\right]^{-1.5} \\ + \left[n_2 + 1.1b_{2,t+1} - b_{3,t+2}\right]^{-1.5} &= 0.8(1.1)\left[n_3 + 1.1b_{3,t+2} - b_{4,t+3}\right]^{-1.5} \\ + \left[n_3 + 1.1b_{3,t+2} - b_{4,t+3}\right]^{-1.5} &= 0.8(1.1)\left[n_4 + 1.1b_{4,t+3}\right]^{-1.5} + \end{split} +\end{equation*} +Use SciPy's root finder to solve for the three optimal lifetime savings amounts $(\hat{b}_{2,t+1},\hat{b}_{3,t+2},\hat{b}_{4,t+3})$. Plug those values back into the budget constraint $c_{s,t}= w_t n_s + (1 + r_t)b_{s,t} - b_{s+1,t+1}$ given $b_{1,t}, b_{5,t}=0$ to solve for optimal consumption values $(\hat{c}_{1,t},\hat{c}_{2,t+1},\hat{c}_{3,t+2}, \hat{c}_{4,t+3})$. +```{exercise-end} +``` + +```{exercise-start} +:label: ExerScipy-BM72_ss +:class: green +``` +{cite}`BrockMirman:1972` is a simple two-period-lived overlapping generations model, the stochastic equilibrium of which is characterized by six dynamic equations (equations in which the variables are changing over time). The deterministic steady-state of the model is characterized by the variables reaching constant values that do not change over time. The deterministic steady state of the {cite}`BrockMirman:1972` is characterized by the following five equations and five unknown variables $(c, k, y, w, r)$, +\begin{equation*} + \begin{split} + \frac{1}{c} &= \beta\frac{r}{c} \\ + c &= (1+r)k + w \\ + w &= (1-\alpha)k^\alpha \\ + r &= \alpha k^{\alpha-1} \\ + y &= k^\alpha + \end{split} +\end{equation*} +where $c$ is consumption, $k$ is capital investment/savings, $y$ is GDP, $w$ is the wage, and $r$ is the interest rate. Assume $\beta=0.7$ and $\alpha=0.35$. Solve for the steady-state variables $(c, k, y, w, r)$ using the above five equations and SciPy's root finder. + +```{exercise-end} +``` + +```{exercise-start} Root finder vs. minimizer +:label: ExerScipy-root-min +:class: green +``` +Characterize a minimization problem that can also be solved using a root finder. Write code to solve the problem both ways. Record the respective computation times of both solution methods. How does the minimization method computation time compare to the root finder computation time? +```{exercise-end} +``` + +```{exercise-start} +:label: ExerScipy-min_constraint +:class: green +``` +Use `scipy.optimize.minimize` to minimize the function $f(x,y)=x^2y$ on the unit circle, i.e., subject to $x^2 + y^2 = 1$. Use the `constraints` keyword argument to specify the constraint. What is the minimum value of $f(x,y)$ subject to this constraint? Can you confirm this by doing the problem by hand using calculus? +```{exercise-end} +``` + +```{exercise-start} +:label: ExerScipy-interp +:class: green +``` +Consider the following `x` and `y` vectors, which represent some functional relationship, `y=f(x)`: + +```python +x = np.array([ + 5.15151515, 3.13131313, -6.36363636, 9.39393939, + -1.31313131, 0.50505051, -0.50505051, -2.12121212, + -7.37373737, -0.1010101 , 3.73737374, 2.52525253, + 2.12121212, -10. , -9.5959596 , 6.36363636, + 3.53535354, -5.75757576, -4.34343434, -8.18181818, + 8.18181818, -3.13131313, 2.92929293, 4.74747475, + -6.56565657, -0.3030303 , -2.32323232, 1.11111111, + -7.17171717, -5.55555556, -3.73737374, -4.14141414, + 8.38383838, 4.94949495, 0.70707071, -3.33333333, + 6.96969697, -2.72727273, 5.55555556, -7.77777778]) +``` + +```python +y = np.array([ + -0.90512352, 0.01027934, -0.0803643 , 0.03083368, -0.96698762, + 0.48385164, -0.48385164, -0.85230712, -0.8868821 , -0.10083842, + -0.56115544, 0.57805259, 0.85230712, 0.54402111, 0.17034683, + 0.0803643 , -0.38366419, 0.50174037, 0.93270486, -0.94674118, + 0.94674118, -0.01027934, 0.21070855, -0.99938456, -0.27872982, + -0.2984138 , -0.73002623, 0.8961922 , -0.77614685, 0.66510151, + 0.56115544, 0.84137452, 0.86287948, -0.97202182, 0.64960951, + 0.19056796, 0.63384295, -0.40256749, -0.66510151, -0.99709789]) +``` + +Create a scatter plot of `x` and `y` to see their relationship. Is it hard to tell what this function looks like? + +Now use `scipy.interpolate.interp1d` to interpolate the function $f(x)$ using `x` and `y`. Use the keyword argument `kind='cubic'` to specify that you want to use cubic splines to interpolate the function and `fill_value=extrapolate` to note that you want to extrapolate beyond the values in the original `x` vector. + +Create a plot of this interpolated function over the domain $x \in [-10, 10]$. Can you now see what this function is? +```{exercise-end} +``` + + +(SecSciPyFootnotes)= +## Footnotes + +The footnotes from this chapter. + +[^SciPy]: The website for Python's SciPy package is https://scipy.org. + +[^SciPyJuddMethods]: See {cite}`Judd:1998` (Chap. 5) for a discussion of solution methods to nonlinear equations. + +[^OG-Core-Indiv]: See `OG-Core` model documentation theory chapter "[Households](https://pslmodels.github.io/OG-Core/content/theory/households.html)". + +[^EvansPhillips]: This Euler equation corresponds to a simple model in which the coefficient of relative risk aversion is unity $\sigma=1$ in a CRRA utility function and the disutility of labor supply is characterized by the functional form proposed in {cite}`EvansPhillips:2017`, with $b=1$, $\nu=1.5$, and maximum labor supply $l=1$. diff --git a/docs/book/python/StandardLibrary.md b/docs/book/python/StandardLibrary.md index 37b8623..a1e1af3 100644 --- a/docs/book/python/StandardLibrary.md +++ b/docs/book/python/StandardLibrary.md @@ -1,15 +1,61 @@ (Chap_StdLib)= +# Python Standard Library +This chapter was coauthored by Jason DeBacker and Richard W. Evans. -# Python Standard Library +The **standard library** of Python is all the built-in functions of the programming language as well as the modules included with the most common Python distributions. The Python online documentation has an [excellent page](https://docs.python.org/3/library/index.html) describing the standard library. These functionalities include built-in [functions](https://docs.python.org/3/library/functions.html), [constants](https://docs.python.org/3/library/constants.html), and [object types](https://docs.python.org/3/library/stdtypes.html), and [data types](https://docs.python.org/3/library/datatypes.html). We recommend that you read these sections in the Python documentation. + +In addition, the iframe below contains a PDF of the BYU ACME open-access lab entitled, "The Standard Library". You can either scroll through the lab on this page using the iframe window, or you can download the PDF for use on your computer. See {cite}`BYUACME_StandardLibrary`. {numref}`ExerStandardLibrary` below has you work through the problems in this BYU ACME lab. The two Python files used in this lab are stored in the [`./code/StandardLibrary/`](https://github.com/OpenSourceEcon/CompMethods/tree/main/code/StandardLibrary) directory. + +
+ +
+ + +(SecStdLibExercises)= +## Exercises + +```{exercise-start} +:label: ExerStandardLibrary +:class: green +``` +Read the BYU ACME "[The Standard Library](https://drive.google.com/file/d/1JT2TolhLhyQBO2iyGoBZYVPgni0dc3x6/view?usp=sharing)" lab and complete Problems 1 through 5 in the lab. {cite}`BYUACME_StandardLibrary` +```{exercise-end} +``` -ACME materials link... +```{exercise-start} +:label: ExerStd-module_run +:class: green +``` +Create a python module that prints something (e.g. `Hello World!`) and run it from the command line using `python module_name.py`. +```{exercise-end} +``` +```{exercise-start} +:label: ExerStd-notebook_run +:class: green +``` +Create a Jupyter notebook (`.ipynb`) with your Python code from {numref}`ExerStd-module_run` and run it in the VS Code text editor. +```{exercise-end} +``` -# Exercises +```{exercise-start} +:label: ExerStd-def_function +:class: green +``` +Write a function that finds the Fibonacci sequence up to an integer `N` > 0 in the notebook. Now call this function for `N = 10` and `N=100`. +```{exercise-end} +``` -1. Determining which of Python's build in objects (string, dict, list, tuple, set, int) are mutable and which are immutable. -2. Create a python module and run it from the command line -3. Create a python notebook and run it from VS Code -4. Write a function in the notebook and run it -5. Use the `sys` module to create a relative path from a Python module, print that path \ No newline at end of file +```{exercise-start} +:label: ExerStd-sys +:class: green +``` +Use the `sys` module to create a relative path from a Python module, print that path. +```{exercise-end} +``` diff --git a/docs/book/python/UnitTesting.md b/docs/book/python/UnitTesting.md index 549c104..16c8778 100644 --- a/docs/book/python/UnitTesting.md +++ b/docs/book/python/UnitTesting.md @@ -1,14 +1,84 @@ (Chap_UnitTesting)= +# Unit Testing +This chapter was coauthored by Jason DeBacker and Richard W. Evans. -# Unit Testing +As a code base expands and the scripts and modules become more interdependent and interconnected, the probability increases that additions to the code will introduce bugs. And as the code base becomes bigger, the harder it can be to find bugs. One of the primary ways to protect the functionality of a code base from bugs is unit testing. + + +(SecUnitTestPytest)= +## PyTest + +Testing of your source code is important to ensure that the results of your code are accurate and to cut down on debugging time. Fortunately, `Python` has a nice suite of tools for unit testing. In this section, we will introduce the `pytest` package and show how to use it to test your code. + +The iframe below contains a PDF of the BYU ACME open-access lab entitled, "[Unit Testing](https://drive.google.com/file/d/1109ci_tqZz30C2ymf0Hs3UO66l865U0-/view?usp=sharing)". You can either scroll through the lab on this page using the iframe window, or you can download the PDF for use on your computer. See {cite}`BYUACME_UnitTest`. {numref}`ExerTest-acme` below has you work through the problems in this BYU ACME lab. Two Python scripts ([`specs.py`](https://github.com/OpenSourceEcon/CompMethods/tree/main/code/UnitTest/specs.py) and [`test_specs.py`](https://github.com/OpenSourceEcon/CompMethods/tree/main/code/UnitTest/test_specs.py)) used in the lab are stored in the [`./code/UnitTest/`](https://github.com/OpenSourceEcon/CompMethods/tree/main/code/UnitTest) directory. + +
+ +
+ + +(SecUnitTestCodecov)= +## Code coverage + +Ideally, one wants to make sure that all of their source code is tested, thereby ensuring it is producing expected results and reducing the potential that new contributions will introduce bugs. But for any significant code base, it is difficult to know which lines of code are tested and which are. To get an understanding of what is covered by unit tests, packages like [`coverage.py`](https://coverage.readthedocs.io/en/7.3.2/#) can be used to automatically generate a report of code coverage. The report will show which lines of code are covered by unit tests and which are not. This can be useful for identifying parts of the code that need more testing. + + +(SecUnitTestGHActions)= +## Continuous integration testing and GitHub Actions + +When using GitHub to collaborate with others on a code base, one can leverage the ability to use [GitHub Actions](https://github.com/features/actions) to automate unit testing and code coverage reports (as well as other checks on might want to run). GitHub actions are specified in yaml files and triggered by some set event (e.g., a push, or a pull request, or a chronological schedule). One of the most effective ways to ensure new contributions are not introducing bugs is to run unit tests and code coverage reports on every push to the repository. This can be done by creating a GitHub action that runs the unit tests and code coverage report on every push to the repository. [Codecov](https://about.codecov.io) provides some useful tools for reporting code coverage from unit tests in GitHub Actions. You can see the actions `OG-Core` uses [here](https://github.com/PSLmodels/OG-Core/tree/master/.github/workflows). These include unit tests and coverage reports, as well as checks that documentation builds and then is published upon a merge to the `master` branch. + + +(SecUnitTestExercises)= +## Exercises + +```{exercise-start} +:label: ExerTest-acme +:class: green +``` +Read the BYU ACME "[Unit Testing](https://drive.google.com/file/d/1109ci_tqZz30C2ymf0Hs3UO66l865U0-/view?usp=sharing)" lab and complete Problems 1 through 6 in the lab. {cite}`BYUACME_UnitTest` +```{exercise-end} +``` + +```{exercise-start} +:label: ExerTest-assert_value +:class: green +``` +In Chapter {ref}`Chap_SciPy`, {numref}`ExerScipy-root-lin`, you wrote wrote a function, and called `SciPy.optimize` to minimize that function. This function had an analytical solution so you could check that SciPy obtained the correct constrained minimum. Now, write a `test_min` function in a module named `test_exercises.py`. This function should end with an assert statement that the minimum value of the function is equal to the analytical solution. Then, run the test using `pytest` and make sure it passes. Note, if your wrote the original function for {numref}`ExerScipy-root-lin` in a notebook, copy it over to a module can save it as `exercises.py`. +```{exercise-end} +``` -Testing of your source code is important to ensure that the results of your code are accurate and to cut down on debugging time. Fortunately, `Python` has a nice suite of tools for unit testing. In this section, we will introduce the `pytest` package and show how to use it to test your code. +```{exercise-start} +:label: ExerTest-assert_type +:class: green +``` +Write another test in your `test_exercises.py` module that uses an assert statement to test that the type of the output of your `test_min` function is a NumPy `ndarray` object. Then, run the test using `pytest` and make sure it passes. +```{exercise-end} +``` -# Exercises +```{exercise-start} +:label: ExerTest-parameterize +:class: green +``` +Write a simple function that returns the sum of two digits: + ```python + def my_sum(a, b): + return a + b + ``` +Save this in a module called `exercises.py`. Now, use the `@pytest.mark.parametrize` decorator to test a function for multiple inputs of `a` and `b`. +```{exercise-end} +``` -1. Take simple function to minimize from SciPy and write a unit test for it (know analytic solutions so can test that SciPy is working correctly) -2. Another single test where assert that object a certain type -3. Use the `@pytest.mark.parametrize` decorator to test a function for multiple inputs -4. Use pytest markers to skip a test -5. ??? \ No newline at end of file +```{exercise-start} +:label: ExerTest-markers +:class: green +``` +Use the `@pytest.mark` decorator to mark one of your tests in `test_exercises.py`. Then, your tests using `pytest` but in a way that skips tests with the marker you just gave. +```{exercise-end} +``` diff --git a/docs/book/python/intro.md b/docs/book/python/intro.md index b138cb8..426396e 100644 --- a/docs/book/python/intro.md +++ b/docs/book/python/intro.md @@ -1,9 +1,11 @@ (Chap_PythonIntro)= # Introduction to Python -The `OG-Core` model, as well as the country-specific calibration packages, are written in the Python programming language. These training materials will provide you with sufficient background with Python and some of its most-used packages for data science that you will be able to understand and contribute to the source code underlying `OG-Core` and the calibration packages. We assume some basic background with mathematics, economics, and programming, but we do not assume any prior knowledge of Python. +This chapter was coauthored by Jason DeBacker and Richard W. Evans. -As we walk you through the basics of Python, we will leverage some excellent open source materials put together by [QuantEcon](https://quantecon.org/) and the [Applied and Computational Mathematics Emphasis at BYU (BYU ACME)](https://acme.byu.edu/). And while we will point you to their tutorials, we have customized all our excercises to be relevant to the `OG-Core` model and the calibration packages. +Many models are written in the Python programming language. Python is the 2nd most widely used language on all GitHub repository projects {cite}`GitHub:2022`, and Python is the 1st most used programming language according to the PYPL ranking of September 2023 {cite}`Stackscale:2023`. + +As these tutorials walk you through the basics of Python, they will leverage some excellent open source materials put together by [QuantEcon](https://quantecon.org/) and the [Applied and Computational Mathematics Emphasis at BYU (BYU ACME)](https://acme.byu.edu/2023-2024-materials). And while the tutorials will point you to those of these other organizations, we have customized all our excercises to be relevant to the work and research of economists. (SecPythonIntroOverview)= @@ -14,7 +16,7 @@ The Python.org site has documentation essays, one of which is entitled "[What is In addition to the description above, Python is an open source programming language that is freely available and customizable (see https://www.python.org/downloads/source/). -Python has some built in functionality with the standard library, but most of the functionality comes from packages that are developed by the open source community. The most important packages for data science are: NumPy, SciPy, Pandas, and Matplotlib. We will introduce each of these packages as we go through the training materials as they are used heavily in `OG-Core` and the corresponding country calibration packages. +Python has some built in functionality with the standard library, but most of the functionality comes from packages that are developed by the open source community. The most important packages for data science are: NumPy, SciPy, Pandas, and Matplotlib. We will introduce each of these packages as we go through the training materials as they are used heavily in economics applications. (SecPythonIntroInstall)= @@ -45,20 +47,24 @@ Some extensions that we recommend installing into your VS Code: In addition, [GitHub Copilot](https://github.com/features/copilot) is an amazing resource and can be added as an extension to VS Code. However, this service is not free of charge and does require an internet connection to work. -```{exercise-start} -:label: ExerPythonIntro -``` -Read the BYU ACME "[Introduction to Python](https://acme.byu.edu/00000181-448a-d778-a18f-dfcae22f0001/intro-to-python)" lab and complete Problems 1 through 8 in the lab. {cite}`BYUACME_PythonIntro` -```{exercise-end} -``` +In the iframe below is a PDF of the BYU ACME open-access lab entitled, "Python Intro". You can either scroll through the lab on this page using the iframe window, or you can download the PDF for use on your computer. See {cite}`BYUACME_PythonIntro`. {numref}`ExerPythonIntro` below has you work through the problems in this BYU ACME lab. The Python code file ([`python_intro.py`](https://github.com/OpenSourceEcon/CompMethods/tree/main/code/PythonIntro/python_intro.py)) used in the lab is stored in the [`./code/PythonIntro/`](https://github.com/OpenSourceEcon/CompMethods/tree/main/code/PythonIntro) directory. -[Put a review of Python data types here: string, byte, float, list, set, dict. Discuss mutability versus immutability as well as iterability. Also introduce NumPy arrays and Pandas DataFrames.] +
+ +
+ +We cover Python's built-in functions, constants, and data types and their properties in {numref}`ExerStandardLibrary` of the {ref}`Chap_StdLib` chapter. We also introduce different commonly used objects like Numpy arrays and operations in chapter {ref}`Chap_Numpy` and Pandas DataFrames and operations in chapter {ref}`Chap_Pandas`. (SecPythonIntroPackages)= ## Python Packages -When using `OG-Core` there are a handful of Python packages that will be useful and that these training materials will cover: +Economics applications heavily use a handful of Python packages that will be useful and that these training materials will cover: 1. The Standard Library 2. NumPy for numerical computing (e.g., arrays, linear algebra, etc.) @@ -66,23 +72,54 @@ When using `OG-Core` there are a handful of Python packages that will be useful 4. Matplotlib for plotting 5. SciPy for scientific computing (e.g., optimization, interpolation, etc.) -All of these will be included as part of your installation of Anaconda. Anaconda also includes a package manager called `conda` that will allow you to install additional packages and well help keep versions of packages consistent with each other. We will not cover this in these training materials, but you can find more information about `conda` [here](https://docs.conda.io/en/latest/) and you'll find references to `conda` as we install the `OG-Core` package in the latter part of these training materials. +All of these will be included as part of your installation of Anaconda. Anaconda also includes a package manager called `conda` that will allow you to install additional packages and well help keep versions of packages consistent with each other. We will not cover this in these training materials, but you can find more information about `conda` [here](https://docs.conda.io/en/latest/) and you'll find references to `conda` as we install packages throughout these training materials. (SecPythonIntroTopics)= ## Python Training Topics 1. [Python Standard Library](StandardLibrary.md) -2. [Object Oriented Programming](OOP.md) -3. [NumPy](NumPy.md) -4. [Pandas](Pandas.md) -5. [Matplotlib](Matplotlib.md) -6. [SciPy](SciPy.md) -7. [Doc strings and comments](DocStrings.md) -8. [Unit testing in Python](UnitTesting.md) +2. [Exception handling and file input/output](ExceptionsIO.md) +3. [Object Oriented Programming](OOP.md) +4. [NumPy](NumPy.md) +5. [Pandas](Pandas.md) +6. [Matplotlib](Matplotlib.md) +7. [SciPy](SciPy.md) +8. [Doc strings and documentation](DocStrings.md) +9. [Unit testing](UnitTesting.md) + + +(SecPythonIntroUnix)= +## (Optional): Using the Unix Shell + +Unix is an old operating system that is the basis for the Linux and Mac operating systems. Many Python users with Mac or Linux operating systems follow a workflow that includes working in the terminal and using Unix commands. This section is optional because Windows terminals do not have the same Unix commands. For those interested, feel free to work through the Unix lab below from BYU ACME. This lab features great examples and instruction, and also has seven good exercises for you to practice on. +In the iframe below is a PDF of the BYU ACME open-access lab entitled, "Unix Shell 1: Introduction". You can either scroll through the lab on this page using the iframe window, or you can download the PDF for use on your computer. See {cite}`BYUACME_Unix1`. {numref}`ExerUnix1` below has you work through the problems in this BYU ACME lab. The shell script file ([`unixshell1.sh`](https://github.com/OpenSourceEcon/CompMethods/tree/main/code/UnixShell1/unixshell1.sh)) used in the lab, along with the associated zip file ([`Shell1.zip`](https://github.com/OpenSourceEcon/CompMethods/tree/main/code/UnixShell1/Shell1.zip)), are stored in the [`./code/UnixShell1/`](https://github.com/OpenSourceEcon/CompMethods/tree/main/code/UnixShell1) directory. - +
+ +
- +(SecPythonIntroExercises)= +## Exercises + +```{exercise-start} Python introduction +:label: ExerPythonIntro +:class: green +``` +Read the BYU ACME "[Introduction to Python](https://drive.google.com/file/d/1CHl8C-QKgs8jHzsRfJSMWkVqq0elzP1F/view?usp=sharing)" lab and complete Problems 1 through 8 in the lab. {cite}`BYUACME_PythonIntro` +```{exercise-end} +``` + +```{exercise-start} OPTIONAL: Unix shell commands +:label: ExerUnix1 +:class: green +``` +Read the BYU ACME "[Unix Shell 1: Introduction](https://drive.google.com/file/d/18eTLp_FhWFYgAItIZnX6gesIvg91rXW5/view?usp=sharing)" lab and complete Problems 1 through 7 in the lab. {cite}`BYUACME_Unix1` +```{exercise-end} +``` diff --git a/setup.py b/setup.py index 4a1305b..d02e3a7 100644 --- a/setup.py +++ b/setup.py @@ -7,7 +7,7 @@ setup( name="CompMethods", - version="0.0.3", + version="0.0.4", author="Richard W. Evans", author_email="rickecon@gmail.com", long_description=readme,