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numpy.py
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# -*- coding: utf-8 -*-
"""Numpy.ipynb
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/154SW6ldF3r2LRSeV96GxRMIBNeeSLal_
# **Numpy**
##**NumPy** is a Python package. It stands for '**Numerical Python**'. It is a library consisting of multidimensional array.
##Some of python’s leading package rely on NumPy as a fundamental piece of their infrastructure (examples include scikit-learn, SciPy, pandas, and tensorflow).
## Why use Numpy?
* Numpy provided efficient storage
* Provides better ways of handling data for processing
* It is faster.
* Easy to learn
* Use less memory to store data
## Lets import numpy library.
"""
# Statement to import the numpy library
import numpy as np
""""""
# One-dimensional array of some prime numbers
prime_array = np.array([2,3,5,7,11])
print(prime_array)
type(prime_array)
prime_array.dtype
prime_array.shape
# This array has only one row there is no column as it is one dimensional array
# We can also change the datatype of an array
prime_array = np.array([2,3,5,7,11], np.int8)
prime_array.dtype
# Get the elements at the second position
prime_array[2]
# If it is a two dimensional array
myarr = np.array([[1, 3, 5], [2, 4, 6]])
myarr.shape
myarr.dtype
# TO get the position of an array
myarr[0, 2]
myarr[1,2]
# TO change the position of an array
myarr[1,2] = 7
myarr[1, 2]
myarr
# Create a two-dimensional array
another_array = np.array([[1,2],[3,4]])
print(another_array)
# We can also create 3 dimensional array
myarr = np.array([[[1]]])
myarr
myarr.shape
"""# ***Numpy Methods and Attributes***
## Conversion from other Python Structure example lists, tuples.
"""
# Creating array from the list
listarray = np.array([[1, 2, 3], [8, 4, 8], [4, 0, 9]])
listarray
listarray.dtype
listarray.shape
listarray.size
# We can also create array with the help of Dictionary
Dict = np.array({39, 41, 85})
Dict
# You can see here the type is object which not very efficient.
# Always remember that when we have to work with integers or a float values we use array.
"""## Instrinsic numpy array creation object example arange, ones, zeros
### By using Zeros function in numpy
"""
# Here we will use some function of numpy to built array
zeros = np.zeros((2, 5))
# This function will give you an array of zeros values
# It will have 2 rows and 5 columns.
zeros
zeros.shape
zeros.dtype
zeros.size
"""### By Using Arange method """
# Using arange function
# This function will give you numpy array.
# This function is similar to the range function in python.
Range = np.arange(15)
# This function will give you set of number from 0 to n-1
Range
"""### By using linspace method"""
# Using linspace function
lspace = np.linspace(1, 50, 10)
# This function will give 10 equally linearly spaced elements between 1 to 50.
lspace
lspace1 = np.linspace(1, 4, 4)
lspace1
"""### By using empty and empty_like method
This method will give you random values between the number of rows and columns
"""
emp = np.empty((4, 6))
# It will give you array of 4 rows and 6 columns
# This array will contains random values.
emp
# We can also create using empty_like method
emp_like = np.empty_like(lspace)
# Here it will give you new array of emp_like whose values is same as that of lspace array.
emp_like
"""### Identity method"""
# This method will give you an identity matrix
id = np.identity(45)
# Here it will give you 45 by 45 identity matrix
id
id.shape
id.size
id.dtype
"""### We can also break the array or in other words change the order of an array by using the reshape method.
### Note here that only those arrays can change their shape who having proper order.
"""
# Let's take an array of arr
arr = np.arange(99)
# It will give you array of 99 elements.
arr
# we can break it into 3*33 or 33*3 that will give you 99 elements
# Basically the product should be same as that of the original
# If this condition does not satisfy then it will give an error.
arr.reshape(3, 33)
# But our shape of our ;arr' does not change.
arr.shape
# For changing the shape of 'arr' we have to assign this
arr = arr.reshape(3, 33)
arr.shape
# TO make arr array as a 1 D array we can use ravel function
arr.ravel()
# But the arr is still b3 by 33 matrix
# To make it 1 D array we have to assign the arr values
arr = arr.ravel()
arr.shape
"""## **Numpy Axis**
NumPy axes are the directions along the rows and columns. Just like coordinate systems, NumPy arrays also have axes.
Axis in numpy array starts with 0(zero).
1 D array has only one axis that is axis 0.
2 D array has two axis that is axis 0 and axis 1.
Similarly for 3 D array. For more about axis click [here](https://www.sharpsightlabs.com/blog/numpy-axes-explained/)
"""
# Lets take an array
x = [[1, 2, 3], [4, 5, 6], [7, 1, 0]]
ar = np.array(x)
ar
ar.sum(axis=0) # axis 0 will add them in vertical direction
ar.sum(axis=1) # axis 1 will add them in horizontal direction
"""### If we want to create a transpose of an array """
ar
# Using T we can create the transpose of an array
ar.T
"""### If we want each and every element of the array then we can use flat function"""
ar.flat
for item in ar.flat:
print(item)
# It will give row-wise
"""### To see the number of dimensions of an array we can use ndim function."""
ar.ndim
ar.nbytes # It will give you the total bytes consume.
"""### To get the index of where your maximum and minimun elements lies we can use argmax and argmin function"""
# Let take a 1D array
one = np.array([1, 5 , 2, 493, 45, 17])
one.argmax()
# The maximum elemet lies in the 3rd index. In python counting begins from 0.
one.argmin()
"""### Similarly we have argsort method. It will give you are index values."""
one.argsort()
# This will give you the index values
# It shows that the lowest element is at index 0 then another lowest element is at index 2 and so on.
# At highest element is at index 3
"""### For 2 D array argmin, argmax and argsort method."""
ar
ar.argmax()
# At index 6 is our highest element.
# First it will make the array as 1 D array and then it will count
ar.argmin()
# Similarly at index 8 we have out lowest element.
# But if we provide the axis
ar.argmax(axis=0)
# It shows that in axis 0 that is in vertical direction
# We have our highest element at 2nd index that is 7
# THen in second column we have our highest value at index 1 that is 5 and so on.
# Similarly for axis 1
ar.argmax(axis=1)
# Similarly for argsort
ar.argsort(axis=0)
ar.argsort(axis=1)
"""#You can access the official documentation of numpy by clicking [here](https://docs.scipy.org/doc//numpy-1.17.0/reference/arrays.ndarray.html)"""