# Numpy n dimensional Arrays ( ndarray ) Numpy is used scientific computing with Python.
We can create powerful multidimensional arrays in numpy. Numpy requires all elements are to be of same data type. ( This is one of the main difference with Pandas which can accommodate different data types. ) After installation you can add this line to include Numpy in your program.
``import numpy as np``
Getting the version
``````import numpy as np
print("Numpy Version : ",np.__version__)``````

## Numpy

 NumPy Array Scientific computing with Python Array Methods Methods to get result ( output ) using NumPy array_split() break the array to get sub-arrays bincount frequency of occurrence of elements where Return elements depending on condition check shape Return shape of the array reshape Change the array shape and dimension

## Creating array

 create Creating array using Numpy eye array with ones and zeros empty array without initializing entries empty_like array of same shape and type full Aray filled with input value ones array filled with ones arange creating array of fixed steps linspace creating array of fixed number zeros Aray filled with zeros
 Exercise Creating different types of array

 random.randit Random integers with lower and upper limits and size random.rand Uniform distribution [0,1] of random numbers of given shape & population random.randn Normal distribution of random numbers of given shape & population random_sample Continuous uniform distribution over an interval

 sum() Sum of elements, or along the axis mean() Mean value of elements, or along the axis max() Maximum value among elements, or along the axis min() Minimum value among elements, or along the axis std() Standard deviation along the axis
 Exercise a Basics of Creating Arrays
 math functions Numpy math functions
Get the list of all functions of Pandas. ( Use dir() )
``````import numpy as np
print(len(dir(np))) # 622``````
List of functions
``````for i in dir(np):
print(i)``````

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