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 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 |

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 |

```
import numpy as np
print(len(dir(np))) # 622
```

List of functions
```
for i in dir(np):
print(i)
```

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