Intoduction to numpy

Numpy Tutorial
NumPy 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.
What is an array
An array is a data structure that stores values of same data type. In Python, this is the main
difference between arrays and lists. While python lists can contain values corresponding to
different data types, arrays in python can only contain values corresponding to same data type.
Why we use array
Array operations are very very fast.
Most of the time we use two dimentional array in Exploratory Data Analysis.
In [6]: import numpy as np
In [ ]: my_list = [1,2,3,4,5]
arr = np.array(my_list)
In [ ]: print(arr)
In [36]: my_list1 = (1,2,3,4,5)
my_list2 = (6,7,8,9,10)
my_list3 = (2,4,6,8,10)
arr = np.array([my_list1,my_list2,my_list3])
In [37]: arr
In [38]: arr.shape
In [41]: arr.reshape(5,3)
In [9]: arr
In [7]: arr = np.array(range(11))
In [ ]:
In [ ]: arr = np.arrary
Indexing
In [49]: arr[1:3,2:4]
In [50]: arr[1:2,1:-1]
In [26]: arr = np.array(range(11))
In [27]: arr
In [12]: arr_1 = arr
arr_1
In [11]: # Defining the problem
arr_1[5:] = 100 # making change in arr_1
print("arr_1 =",arr_1) # arr_1 change
print("arr_1 =",arr) # arr also change
In [13]: # Defining solution with copy() method.
arr = np.array(range(11))
print("arr =",arr)
arr_1 = arr.copy() # Making a copy of arr and assigning to arr_1
arr_1[5:] = 100 # Making change in arr_1 but this time not effect on
arr
print("arr_1 =",arr_1)
print("arr =",arr)
Some Important Conditions used in Exploratory Data
Analysis
In [16]: val = 5
arr < 5
In [24]: val = 2
print(arr, 'arr')
print(arr + val, 'arr + val')
print(arr - val, 'arr - val')
print(arr * val, 'arr * val')
print(arr / val, 'arr / val')
print(arr % val, 'arr % val')
print(arr > val, 'arr > val')
print(arr < val, 'arr < val')
# print(arr)
In [35]: print(f"{arr[arr%2 == 0]}tt= arr%2 == 0")
print(f"{arr[arr > 2]}t= arr > 2")
print(f"{arr[arr != 5]}t= arr != 5")
print(f"{arr[arr == 5]}tttt= arr == 5")
print(arr)
Applying arithmatic operation between two arrays.
In [39]: array_1 = np.arange(10).reshape(2,5)
print(array_1)
array_2 = np.arange(10).reshape(2,5)
print(array_1)
In [40]: array_1 * array_2
In [ ]:
In [56]: arange = np.arange(2,21,2)
In [33]: arr
In [31]: arr1 = arr[:]
print(arr1)
In [32]: arr1[3:] = 50
print(arr1)
In [29]: #copy function and broadcasting.
arr[3:]=100
In [1]: arr
Some inbuilt method
arange()
it is like range() function ### linspace()
it generate special kind series in which the differnce between all elements is equal. ### copy()
is used to copy an arry while assigning one arry form one variable to another, this provide
solution of ie:.reference type vs value type, by creating a new memory for copied variable.
In [60]: n p.linspace(1,10,50)
Out[37]: array([[ 1, 2, 3, 4, 5],
[ 6, 7, 8, 9, 10],
[ 2, 4, 6, 8, 10]])
Out[38]: (3, 5)
Out[41]: array([[ 1, 2, 3],
[ 4, 5, 6],
[ 7, 8, 9],
[10, 2, 4],
[ 6, 8, 10]])
Out[9]: array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
Out[49]: array([[8, 9],
[6, 8]])
Out[50]: array([[7, 8, 9]])
Out[27]: array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
Out[12]: array([ 0, 1, 2, 3, 4, 100, 100, 100, 100, 100, 100])
arr_1 = [ 0 1 2 3 4 100 100 100 100 100 100]
arr_1 = [ 0 1 2 3 4 100 100 100 100 100 100]
arr = [ 0 1 2 3 4 5 6 7 8 9 10]
arr_1 = [ 0 1 2 3 4 100 100 100 100 100 100]
arr = [ 0 1 2 3 4 5 6 7 8 9 10]
Out[16]: array([ True, True, True, True, True, False, False, False, False,
False, False])
[ 0 1 2 3 4 5 6 7 8 9 10] arr
[ 2 3 4 5 6 7 8 9 10 11 12] arr + val
[-2 -1 0 1 2 3 4 5 6 7 8] arr - val
[ 0 2 4 6 8 10 12 14 16 18 20] arr * val
[0. 0.5 1. 1.5 2. 2.5 3. 3.5 4. 4.5 5. ] arr / val
[0 1 0 1 0 1 0 1 0 1 0] arr % val
[False False False True True True True True True True True] arr
> val
[ True True False False False False False False False False False] arr
< val
[ 0 2 4 6 8 10] = arr%2 == 0
[ 3 4 5 6 7 8 9 10] = arr > 2
[ 0 1 2 3 4 6 7 8 9 10] = arr != 5
[5] = arr == 5
[ 0 1 2 3 4 5 6 7 8 9 10]
[[0 1 2 3 4]
[5 6 7 8 9]]
[[0 1 2 3 4]
[5 6 7 8 9]]
Out[40]: array([[ 0, 1, 4, 9, 16],
[25, 36, 49, 64, 81]])
Out[33]: array([ 0, 1, 2, 50, 50, 50, 50, 50, 50, 50, 50])
[ 0 1 2 100 100 100 100 100 100 100 100]
[ 0 1 2 50 50 50 50 50 50 50 50]
------------------------------------------------------------------------
---
NameError Traceback (most recent call la
st)
<ipython-input-1-24a6d41c5b66> in <module>
----> 1 arr
NameError: name 'arr' is not defined
Out[60]: array([ 1. , 1.18367347, 1.36734694, 1.55102041, 1.73469388,
1.91836735, 2.10204082, 2.28571429, 2.46938776, 2.65306122,
2.83673469, 3.02040816, 3.20408163, 3.3877551 , 3.57142857,
3.75510204, 3.93877551, 4.12244898, 4.30612245, 4.48979592,
4.67346939, 4.85714286, 5.04081633, 5.2244898 , 5.40816327,
5.59183673, 5.7755102 , 5.95918367, 6.14285714, 6.32653061,
6.51020408, 6.69387755, 6.87755102, 7.06122449, 7.24489796,
7.42857143, 7.6122449 , 7.79591837, 7.97959184, 8.16326531,

Intoduction to numpy

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Intoduction to numpy