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

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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