# Introduction to NumPy

In the following examples, the `np.size()` function from NumPy shows the number of data items of an array, and the `np.std()` function is used to calculate standard deviation:

```>>>import numpy as np
>>>x= np.array([[1,2,3],[3,4,6]])     # 2 by 3 matrix
>>>np.size(x)                         # number of data items
6
>>>np.size(x,1)                       # show number of columns
3
>>>np.std(x)
1.5723301886761005
>>>np.std(x,1)
Array([ 0.81649658, 1.24721913]
>>>total=x.sum()                      # attention to the format
>>>z=np.random.rand(50)               #50 random obs from [0.0, 1)
>>>y=np.random.normal(size=100)       # from standard normal
>>>r=np.array(range(0,100),float)/100 # from 0, .01,to .99```

Compared with a Python array, a NumPy array is a contiguous piece of memory that is passed directly to LAPACK, which is ...

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