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@@ -14,24 +14,35 @@ kernelspec: | |
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| # Week 00: Start here | ||
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||
| MATH 131: Introduction to Python is a 1 unit, 5 week course offered at Chico | ||
| State through the Department of Mathematics and Statistics. The course consists | ||
| Lessons and Assignments. We will learn to code in the programming language | ||
| Python using Google Colab notebooks. Lessons, Assignments, and Google Colab | ||
| notebooks are discussed in more detail below, and there is a link to a Google | ||
| Colab notebook that better introduces this programming environment. | ||
| MATH 131: Introduction to Python is a 1 unit, 5 week course offered at | ||
| Chico State through the Department of Mathematics and Statistics. The | ||
| course consists of Lessons, with accompanying Notes, and Assignments. | ||
| We will learn to code in the programming language Python using Google | ||
| Colab notebooks. | ||
|
|
||
| ### Lessons | ||
| Lessons are the webpages found on this website, named after each week | ||
| of the course. Lessons contain the explanations of what's to come in | ||
| the Assignments. You are currently reading the Lesson associated with | ||
| Week 00. There is one Lesson for each of the five weeks of this | ||
| course. | ||
|
|
||
| Lessons found on this website. Lessons contain the explanations of what's to | ||
| come in the assignments. Examples and all. | ||
| The Lessons have associated Notes. Each Note is a Google | ||
| Colab notebook, e.g. Week 01 Notes. Notes are par-baked Colab | ||
| notebooks that are meant to provide you with a space to follow along | ||
| with the programming in the Lesson. Google Colab notebooks are | ||
| discussed in more detail below. | ||
|
|
||
| ### Assignments | ||
| Each week has an Assignment, e.g. Week 02 Assignment, starting with | ||
| Week 01. You are responsible for completing the Assignments | ||
| throughout the course. Each Lesson will have a link to the associated | ||
| Assignment for you to complete. | ||
|
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||
| For assignments, we'll use Google Colab with your [email protected] | ||
| account (same password as your [email protected] account). | ||
| For assignments, we will use Google Colab with your | ||
| [email protected] account (same password as your | ||
| [email protected] account). | ||
|
|
||
| ### Google Colab | ||
|
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||
| ## Google Colab | ||
|
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||
| [Google Colab](https://colab.research.google.com) provides a notebook | ||
| environment where the user can develop a reproducible document that blends text | ||
|
|
@@ -41,14 +52,16 @@ programming. By combining text and code, you can walk (via text) your audience | |
| through an analysis (usually via code and/or math), showing the exact code you | ||
| used to draw any conclusions about the data or otherwise. | ||
|
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||
| We will use Google Colab for free, as part of your campus Google account | ||
| [email protected]. The free aspect means we'll have to deal with some | ||
| issues that don't otherwise come about if we were to install Python on our own | ||
| personal machines. I, Edward, have made the active choice to use Google Colab | ||
| instead of teaching you how to install Python on your personal machine, | ||
| because I believe we can get started faster this way. If you want to follow | ||
| along with this course using different tools, please see the details on the | ||
| page [Week 06 and beyond][and-beyond.md]. | ||
| We will use Google Colab for free, as part of your campus Google | ||
| account [email protected]. The free aspect means we'll have | ||
| to deal with some issues that don't otherwise come about if we were to | ||
| install Python on our own personal machines. I, Edward, have made the | ||
| active choice to try Google Colab instead of teaching you how to | ||
| install Python on your personal machine, because I believe we can get | ||
| started faster this way. If you want to follow along with this course | ||
| using different tools, and you understand the consequences you face | ||
| for doing so, please see your options on the page [Week 06 and | ||
| beyond][./and-beyond.md]. | ||
|
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| From here, there's really no better way to learn about Google Colab than to go | ||
| touch it. Here's a link to [the Colab notebook associated with Week 00: Start | ||
|
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||
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| name: python3 | ||
| --- | ||
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| <link rel="stylesheet" href="https://fonts.googleapis.com/css2?family=Material+Symbols+Outlined:opsz,wght,FILL,[email protected],100..700,0..1,-50..200" /> | ||
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| # Week 04: Filling in some details | ||
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| ## Learning outcomes | ||
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| ## Preparation | ||
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| To follow along with this Lesson, please open the Colab notebook | ||
| [Week-04-Notes](#TODO). The first code cell of this notebook calls to | ||
| [Week-04-Notes](https://colab.research.google.com/drive/1FvGDi4MQxNzVOJRk6w07RWy3nnGNNF2K?usp=sharing). The first code cell of this notebook calls to | ||
| the remote computer, on which the notebook is running, and installs | ||
| the necessary packages. For practice, you are repsonible for | ||
| importing the necessary packages. | ||
|
|
@@ -38,17 +40,18 @@ In this section, we cover some details about customizing a plotnine plot. This | |
| lesson will not explore all available options, nor are you expected to memorize | ||
| all the details we cover. This lesson is intended to be an introduction to the | ||
| types of things you can easily change on a plotnine plot. For details and/or | ||
| reference material on computer software, you should consult the plotnine | ||
| documentation. | ||
| reference material on computer software, you should consult the [plotnine | ||
| documentation](https://plotnine.readthedocs.io/en/stable/api.html). | ||
|
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||
| Let's continue developing the plot we started at the end of [Week | ||
| 03](../week-03.md). Our goal is to display information about the mean total | ||
| amount of sleep mamals (in our dataset) get, where the data are grouped by their | ||
| `vore` status. This is a task for aggregating: group by `vore` and summarize | ||
| with the function `np.mean`. Since the mean of any dataset is random, as part | ||
| of the random sampling of the data, we should also calculate and display the | ||
| error in our estimate associated with the mean. That is, we should display a | ||
| confidence interval for the mean. | ||
| 03](aggregating). Our goal is to display information about the mean | ||
| total amount of sleep mamals (in our dataset) get, where the data are | ||
| grouped by their `vore` status. This is a task for aggregating: group | ||
| by `vore` and summarize `sleep_total` with the function `np.mean`. | ||
| Since the mean of any dataset is random, as part of the random | ||
| sampling of the data, we should also calculate and display the error | ||
| in our estimate associated with the mean. That is, we should display | ||
| a confidence interval for the mean. | ||
|
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||
| ```{code-cell} | ||
| import numpy as np | ||
|
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@@ -91,7 +94,7 @@ odf["lb"] = odf["mean"] - 1.96 * odf["se"] | |
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| Since plotnine plots are created by adding layers of information, let's get | ||
| started by creating the variable `p` to hold our plot as we build it up piece by | ||
| piece. Below is the code we used from [Week 03](../week-03.md). | ||
| piece. Below is the code we used from [Week 03](aggregating). | ||
|
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| ```{code-cell} | ||
|
|
@@ -155,7 +158,7 @@ information in the data are contained. | |
| By specifying a categorical variable for the keyword argument `color` within | ||
| `aes`, the jittered points will show up in unique colors for each category of | ||
| the variable specified, `vore` in this case. Further, a legend will | ||
| automatically be displayed. Change the category names of the variable `vore` is | ||
| automatically be displayed. Changing the category names of the variable `vore` is | ||
| best done by renaming the categories, as in [Week 02: Categorical | ||
| variables](categorical-variables), but we can easily change the | ||
| legend title from plotnine. | ||
|
|
@@ -244,11 +247,6 @@ The main reason behind this is that file paths can be confusing at | |
| first. And so we've delayed this topic until near the end. | ||
| Invariably, though, you will need to load your own dataset. | ||
|
|
||
| To help you learn about file paths, we have set up a Colab notebook | ||
| specific for practicing file paths. Please follow along in the Colab | ||
| notebook | ||
| [file-paths](https://colab.research.google.com/drive/12q0ostmi7b7LUFOv6srC2xBuN7y7Gz9g?usp=sharing). | ||
|
|
||
| ### directories | ||
|
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| All computers organize their files in **directories**, which are sometimes | ||
|
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@@ -348,7 +346,7 @@ In a .csv file, the entire table of data above would look as follows. | |
|
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| ```{code-cell} | ||
| --- | ||
| tags: [raises-exception] | ||
| tags: [raises-exception, remove-output] | ||
| --- | ||
| type,size,color | ||
|
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@@ -508,8 +506,50 @@ file into a DataFrame, just as we worked with beginning in Week 01. | |
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| ```{code-cell} | ||
| --- | ||
| tags: [raises-exception] | ||
| tags: [raises-exception, remove-output] | ||
| --- | ||
| df = pd.read_csv("./path/to/your/data.csv") | ||
| ``` | ||
|
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| #### your data in a Google Colab notebook | ||
|
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| When dealing with your own data while working in a Google Colab | ||
| notebook, you have to remember that the computer on which your | ||
| notebook runs is not the computer in front of you. This means, to | ||
| read data into Python, you need to *upload* any dataset onto the Colab | ||
| notebook computer. | ||
|
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||
| To upload a dataset to the notebook computer, click on the folder | ||
| looking icon, <span class="material-symbols-outlined">folder</span>, | ||
| near the top left of the Colab notebook within your browser. | ||
| Confusingly, the tooltip you get when hovering over the folder icon | ||
| reads Files. Then click the upload file icon, <span | ||
| class="material-symbols-outlined">upload_file</span>, to upload the | ||
| dataset you want. | ||
|
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||
| Let's say the file you uploaded is named `research_data.csv`. If | ||
| upload your dataset into the current working directory on the notebook | ||
| computer, then the file path will be `./research_data.csv`, and the | ||
| code to read this file into Python, using Pandas, is | ||
|
|
||
| ```{code-cell} | ||
| --- | ||
| tags: [raises-exception, remove-output] | ||
| --- | ||
| df = pd.read_csv("./research_data.csv") | ||
| ``` | ||
|
|
||
| If, before uploading your dataset, you created a new directory named | ||
| `data`, and then you uploaded `research_data.csv` into the directory | ||
| `data`, the file path will be `./data/research_data.csv`, and the code | ||
| to read this file into Python, using Pandas, is | ||
|
|
||
| ```{code-cell} | ||
| --- | ||
| tags: [raises-exception, remove-output] | ||
| --- | ||
| df = pd.read_csv("/path/to/your/data.csv") | ||
| df = pd.read_csv("./data/research_data.csv") | ||
| ``` | ||
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