NumPy (Numerical Python) is an open source Python library that’s used for working with numerical data in Python. It is a general-purpose array-processing package. It provides a high-performance multidimensional array object, and tools for working with these arrays. It is the fundamental package for scientific computing with Python.
Numpy can also be used as an efficient multi-dimensional container of generic data.
- Basic working knowledge of Python.
In the Numpy definition, you read that they basically work with Arrays so what are Arrays???
- An array is a collection of items stored at continuous memory locations.
- All the stored items should be of same type.
Arrays are of two types:
- One-Dimensional Array
- Multi-Dimensional Array
A one-dimensional array (or single dimension array) is a type of linear array.Accessing its elements involves a single subscript which can either represent a row or column index. Example:
# A character array in C/C++/Java
char arr1[] = {'D','e','v','I','n','c','e','p','t'};
# An Integer array in C/C++/Java
int arr2[] = {10, 20, 30, 40, 50};
To access the elements of a single-dimensional Array you can use the indexes and most of the Arrays are zero-indexed.
arr1[0]; # gives us D
arr1[2]; # gives us v
arr2[1]; # gives us 20
arr2[4]; # gives us 50
Note: Array of characters is called a string. Yes!! These are the same strings which we use in Pyhton they are also Arrayss hidden in plain sight.
A multi-dimensional array is an array with more than one level or dimension. For example, a 2D array, or two-dimensional array. Meaning it is a matrix of rows and columns (think of a table).
Example:
int two_d[10][20];
int x[3][4] = {{0,1,2,3}, {4,5,6,7}, {8,9,10,11}};
char y[3][4] = {{'a','b','c','d'}, {'D','e','V','I'}, {'n','c','e','p'}};
To access the elements of a multi-dimensional Array you can use the indexes and most of the Arrays are zero-indexed.
x[0][1]; # gives us 1
y[1][2]; # gives us V
You can access elements in a multi-dimensional like in the example showed above..
Few simple Examples:
list=['DevIncept',10,'Sam',100,20]
print(list[0])
print(list[2])
print(list[3])A Python list may contain different types! Indeed, you can store a number, a string, and even another list within a single list. Result:
DevIncept
Sam
100You can tinker with the above example in the provided Jupyter Notebook..
Note:
All of the elements in a NumPy array should be homogeneous.
If you already have Python, you can install NumPy with:
pip install numpyOr
conda install numpyIf you don’t have Python yet, you might want to consider using Anaconda. It’s the easiest way to get started.
Any time you want to use a package or library in your code, you first need to make it accessible. In order to start using NumPy and all of the functions available in NumPy, you’ll need to import it. This can be easily done with this import statement:
import numpy as np(You can use any variable instead of np)
Those who have problems installing Pyhton on PCs or have problem Installing Jupyter Notebooks and using them.Instead of that you can use Google Colab its a jupyter notebook on cloud which anyone can use and its free.Its fast and you can do all the coding you want in there.:smile::smiley:
To create a basic NumPy Array you can use the function np.array() The array object in NumPy is called ndarray
import numpy as np
arr1=np.array([1,2,3])
arr2=np.array(['a','b','c','d','e'])
arr3= np.array((1, 2, 3, 4, 5)) # You can also pass a tuple to creat an Array
print(arr1)
print(arr2)
print(arr3)
print(type(arr1))Result:
[1 2 3]
['a' 'b' 'c' 'd' 'e']
[1 2 3 4 5]
<class 'numpy.ndarray'>
Besides the above examples you can create Arrays using builtin function
- To create an Array of Zeroes use np.zeros()
- To create an Array of Ones use np.ones()
- To create an Array with a range of elements use np.arange()
import numpy as np
a=np.zeros(3)
b=np.ones(5)
c=np.arange(10)
d=np.arange(2,9) # To specify start and stop
e=np.arange(1,10,2) # To specify start and stop with step size
print(a)
print(b)
print(c)
print(d)
print(e)Result:
[ 0. 0. 0.]
[ 1. 1. 1. 1. 1.]
[0 1 2 3 4 5 6 7 8 9]
[2 3 4 5 6 7 8]
[1 3 5 7 9]You can try the code yourself in the provided Jupyter Notebook
- An array class in Numpy is called as ndarray.
- Number of dimensions of the array is called rank of the array.
Lets Create a 2-D(Dimensional) Array
- They are like a matrix or you can say a table
import numpy as np
arr1=np.array([[1,2,3],[4,5,6]])
arr2=np.array([[1,2,3],[4,5,6],[7,8,9]])
arr3=np.array([['a','b','c'],['d','e','f']])
#creates a 3X3 array with all zeros
zeros=np.zeros((3,3))
#creates a 2X2 array with all ones
ones=np.ones((2,2),dtype='int64') #specify the type with (dtype) parameter
print(arr1)
print(arr2)
print(arr3)
print(zeros)
print(ones)Result:
[[1 2 3]
[4 5 6]]
[[1 2 3]
[4 5 6]
[7 8 9]]
[['a' 'b' 'c']
['d' 'e' 'f']]
# 3X3 Matrix
[[ 0. 0. 0.]
[ 0. 0. 0.]
[ 0. 0. 0.]]
# 2X2 Matrix
[[1 1]
[1 1]]NumPy provides some cool methods you can check the number of dimensions using ndim
import numpy as np
a = np.array(42)
b = np.array([1, 2, 3, 4, 5])
c = np.array([[1, 2, 3], [4, 5, 6]])
d = np.array([[[1, 2, 3], [4, 5, 6]], [[1, 2, 3], [4, 5, 6]]])
print(a.ndim)
print(b.ndim)
print(c.ndim)
print(d.ndim)Result:
0
1
2
3You can also do basic arthemetic operations with arrays.
import numpy as np
arr1=np.array([5,5,5,5])
arr2=np.array([3,3,3,3])
c=arr1+arr2
d=arr1-arr2
e=arr1/arr2
print(arr1)
print(arr2)
print(c) # addition
print(d) # subtraction
print(e) # division
print("Adding one to all elements:",c+1)
print("Subtracting one from all elements:",d-1)Result:
[5 5 5 5]
[3 3 3 3]
[8 8 8 8]
[2 2 2 2]
[1.66666667 1.66666667 1.66666667 1.66666667]
Adding one to all elements: [9 9 9 9]
Subtracting one from all elements: [1 1 1 1]You can also do other basic operations. Try them yourself in the Jupyter Notebook provided.
I know just for basics also this is too much.If you have reached till this point and still not sure, DONT WORRY!!!!
You can practice all the examples in the Jupyter Notebook provided, Don't just see them, change them put new values and see what happens..
- Array indexing is the same as accessing an array element.
- You can access an array element by referring to its index number.
import numpy as np
arr1= np.array([3,2,3,4])
arr2= np.array([2,1,1,3])
arr3= np.array(['D','e','V','I','n','c','e','p','t'])
arr4= np.array([[1,2,3,4,5], [6,7,8,9,10]])
print(arr1[0]) # prints first element
print(arr1[-1]) # prints last element
print(arr2[2]) # prints third element
print(arr3[0]) # prints first element
print(arr4[1][3]) #prints 9
print(arr4[1,3]) # Or you can use this type Result:
3
4
1
D
9
9
Array slicing is same as String Slicing so don't worry...
- Slicing in python means taking elements from one given index to another given index.
- We pass slice instead of index like this:
[start:end]. - You can also provide Step size as:
[start:end:step].
import numpy as np
arr1=np.array(['D','e','V','I','n','c','e','p','t'])
arr2=np.array([1,2,3,4,5,6,7,8,9])
#2-D Slicing
arr3=np.array([[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]])
print(arr1[:3]) # default is taken as 0
print(arr1[0:3])
print(arr1[3:]) # taken till the end
print(arr1[::2])
print(arr2[-4:-1])
print(arr2[::2]) # step size as 2
# 2-D Slicing
print(arr3[1,1:4])
print(arr3[0:2, 2])
print(arr3[0:2, 1:4])Result:
['D' 'e' 'V']
['D' 'e' 'V']
['I' 'n' 'c' 'e' 'p' 't']
['D' 'V' 'n' 'e' 't']
[6 7 8]
[1 3 5 7 9]
# Go over these results carefully they are bit tricky..
[7 8 9]
[3 8]
[[2 3 4]
[7 8 9]] import numpy as np
data = np.array([1, 2, 3])
print(data[1])
print(data[0:2])
print(data[1:])
print(data[-2:])Result:
2
[1 2]
[2 3]
[2 3]
ndarray.shape will display a tuple of integers that indicate the number of elements stored along each dimension of the array. If, for example, you have a 2-D array with 2 rows and 3 columns, the shape of your array is (2, 3).
import numpy as np
arr = np.array([[1, 2, 3, 4], [5, 6, 7, 8]])
arr1= np.array([1,2,3,4])
print(arr.shape)
print(arr1.shape)Result:
(2, 4)
(4,)All In One Example
array_example = np.array([[[0, 1, 2, 3],
[4, 5, 6, 7]],
[[0, 1, 2, 3],
[4, 5, 6, 7]],
[[0 ,1 ,2, 3],
[4, 5, 6, 7]]])
print('No of dimensions of the array:',array_example.ndim) # .ndim for dimensions
print('Total no of elements of the array:',array_example.size) # .size for size
print('Shape of the array:',array_example.shape) # .shape for shapeResult:
No of dimensions of the array: 3
Total no of elements of the array: 24
Shape of the array: (3, 2, 4)Using arr.reshape() will give a new shape to an array without changing the data.
Just remember that when you use the reshape method, the array you want to produce needs to have the same number of elements as the original array. Reshape from 1-D to 2-D
import numpy as np
arr1 = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12])
newarr1 = arr1.reshape(4, 3)
arr2 = np.array(['D','e','V','I','n','c','e','p','t'])
newarr2 = arr2.reshape(3,3)
print(newarr1)
print(newarr2)Result:
[[ 1 2 3]
[ 4 5 6]
[ 7 8 9]
[10 11 12]]
[['D' 'e' 'V']
['I' 'n' 'c']
['e' 'p' 't']]Reshape from 1-D to 3-D
import numpy as np
arr = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12])
newarr = arr.reshape(2, 3, 2)
print(newarr)Result:
[[[ 1 2]
[ 3 4]
[ 5 6]]
[[ 7 8]
[ 9 10]
[11 12]]]Yes, as long as the elements required for reshaping are equal in both shapes.
We can reshape an 8 elements 1D array into 4 elements in 2 rows 2D array but we cannot reshape it into a 3 elements 3 rows 2D array as that would require 3x3 = 9 elements.
- We can use
reshape(-1)to convert mult-dimensional array into 1-D array.
import numpy as np
arr = np.array([[1, 2, 3], [4, 5, 6]])
newarr = arr.reshape(-1)
print(newarr)Result:
[1 2 3 4 5 6]- Sorting means putting elements in an ordered sequence.
- The NumPy ndarray object has a function called
sort(), that will sort a specified array.
import numpy as np
arr1 = np.array([3, 2, 0, 1])
arr2 = np.array(['red','blue','green'])
# 2-D array
arr3 = np.array([[3, 2, 4], [5, 0, 1]])
print(np.sort(arr1))
print(np.sort(arr2))
print(np.sort(arr3))Result:
[0 1 2 3]
['blue' 'green' 'red']
[[2 3 4]
[0 1 5]]Searching is an operation or a technique that helps finds the place of a given element or value in the list. Any search is said to be successful or unsuccessful depending upon whether the element that is being searched is found or not.
Returns the indices of the searched value.
import numpy as np
arr1 = np.array([1, 2, 3, 4, 5, 4, 4])
x = np.where(arr1 == 4)
y = np.where(arr1%2 == 0)
z = np.where(arr1%2 == 1)
print(x)
print(y)
print(z)Result:
(array([3, 5, 6]),) # Which means that the value 4 is present at index 3, 5, and 6.
(array([1, 3, 5, 6]),)
(array([0, 2, 4]),)Performs a binary search in the array, and returns the index where the specified value would be inserted to maintain the search order.
The searchsorted() method is assumed to be used on sorted arrays.
import numpy as np
arr1 = np.array([6, 7, 8, 9])
x = np.searchsorted(arr1, 7)
print(x)
# Multiple values
arr2 = np.array([1, 3, 5, 7])
y = np.searchsorted(arr2, [2, 4, 6])
print(y)Result:
1
[1 2 3]For more advanced sorting and searching example you can follow the link here ---->> Advanced Sorting And Searching
Hooray!!! If you are reading this message you have covered a lot of ground and you are not a Novice anymore.. 😄
Yes these are the topics you must have studied in High school...You must be wondering what their use.??
To say they are very important and NumPy also have functions for them. You can use these function on large data sets and find the answers in just seconds..:smile:
- Mean - The average value
- To calculate the mean, find the sum of all values, and divide the sum by the number of values.
As you all know Python makes everything easy see it yourself.
import numpy as np
arr=np.array([19,13,18,21,44,17,12,31,37,8,12,27])
x=np.mean(arr)
print(x)Result:
21.5833333333- Median - The mid point value
- The median value is the value in the middle, after you have sorted all the values.
import numpy
# For one value in the middle
speed = [99,86,87,88,111,86,103,87,94,78,77,85,86]
x = numpy.median(speed)
print(x)
# For two values in the middle
speed1 = [99,86,87,88,86,103,87,94,78,77,85,86]
y = numpy.median(speed1)
print(y)Result:
87.0
86.5- Mode - The most common value
- The Mode value is the value that appears the most number of times.
This function comes under SciPy
from scipy import stats
speed = [99,86,87,88,111,86,103,87,94,78,77,85,86]
x = stats.mode(speed)
print(x)Result:
ModeResult(mode=array([86]), count=array([3]))
##The mode() method returns a ModeResult object that contains the mode number (86), and count (how many times the mode number appeared (3)).- Standard deviation is a number that describes how spread out the values are.
- A low standard deviation means that most of the numbers are close to the mean (average) value.
- A high standard deviation means that the values are spread out over a wider range.
Use the NumPy std() method to find the standard deviation:
import numpy
speed = [86,87,88,86,87,85,86]
x = numpy.std(speed)
print(x)
speed1 = [32,111,138,28,59,77,97]
y = numpy.std(speed1)
print(y)Result:
0.9035079029052513
37.84501153334721
If you really developed an interest in NumPy and want dive deep into it, check out the resources below.....
Contributor: MUTUKUNDU MAHENDRA REDDY
You can reach me on my mail at [email protected].