Summary Numpy Library in Data Science using python

numpy - Jupyter Notebook http://localhost:8888/notebooks/numpy/numpy.ip




import
In [2]: import numpy as np




1D array
In [45]: a1 = np.array([1,2,3]) # 1 2 3 1D array 1 row and 3 columns shape=(1,3)
type(a1)


Out[45]: numpy.ndarray


In [7]: a1.shape

Out[7]: (3,)



2D array
In [46]: a2= np.array([[1,1,1],[2,2,2]]) # 1 1 1
# 2 2 2 2rows and 3 columns shape = (2,3
a2.shape

Out[46]: (2, 3)



3D array
In [29]: a3 = np.array([[[1,1,1], # This is how it looks
[2,2,2], # 4 4 4 # back 2D
[3,3,3]], # 5 5 5
# 6 6 6



[[4,4,4], # 1 1 1
[5,5,5], # 2 2 2
[6,6,6] # 3 3 3 # front 2D

]])
a3.shape

Out[29]: (2, 3, 3)




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,numpy - Jupyter Notebook http://localhost:8888/notebooks/numpy/numpy.ip




In [10]: a3.dtype

Out[10]: dtype('int32')



to check the number of dimensions
In [11]: a1.ndim,a2.ndim,a3.ndim

Out[11]: (1, 2, 3)



to get the size of the array (number of
elements)
In [12]: a1.size,a2.size,a3.size

Out[12]: (3, 6, 18)



converting an arrray to a DF
In [13]: import pandas as pd
df= pd.DataFrame(a2)

In [14]: df


Out[14]: 0 1 2

0 1 1 1

1 2 2 2




creating an arrays

zeros/ones




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,numpy - Jupyter Notebook http://localhost:8888/notebooks/numpy/numpy.ip



In [15]: zeros =np.zeros((2,4))
zeros

Out[15]: array([[0., 0., 0., 0.],
[0., 0., 0., 0.]])

In [16]: zeros.shape

Out[16]: (2, 4)


In [17]: type(zeros
)

Out[17]: numpy.ndarray


In [18]: zeros.dtype

Out[18]: dtype('float64')


In [19]: ones=np.ones((2,4))
ones

Out[19]: array([[1., 1., 1., 1.],
[1., 1., 1., 1.]])

In [20]: ones.size


Out[20]: 8



Creating an array with a cerain range
In [21]: array_range = np.arange(0,10,2) # creates an array from 0 to 10(not included )
array_range

Out[21]: array([0, 2, 4, 6, 8])



Creating a random array of a given shape
In [3]: random_array= np.random.randint(0,10,size=(2,4)) # creates a 2D array of shpe
random_array

Out[3]: array([[3, 5, 3, 2],
[4, 7, 2, 0]])




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