All About Pandas in Data Science | python crash course_02

All About Pandas in Data Science:

Hello friend, Today I am going to explain What is pandas in Data Science? so Let start:

This tutorial has been prepared for those who seek to learn the basic and various functions of Pandas. It will specifically useful for people working with data cleansing and analysis. After completing this tutorial, you will find yourself at moderate level of expertise from where you can take yourself to higher level of expertise.
Pandas is an open-source, BSD-licen Python library providing high-performance, easy-to-use data structures and data analysis tool for the Python programming language. Python with Pandas is used in wide range of field including academic and commercial domain including finance, economic, Statistic, analytics, etc. In this tutorial, we will learn the various feature of Python Pandas and how to use them in a practice.

Pandas deals with the following three data structure 

Series

DataFrame

Panel

These data structure are built on top of Numpy array, which mean they are fast.

Standard Python distribution doesn not come bundled with Pandas module. A lightweight alternative is install Numpy using popular Python package installer, pip.

pip install pandas 

import pandas as pd

SOME EXAMPLE OF PANDAS LIBRARY:

There are two kind of sorting available in Pandas. They are 

• 1.By label
• 2.By Actual Value

import pandas as pd
import numpy as np

unsorted_df=pd.DataFrame(np.random.randn(10,2),index=[1,4,6,2,3,5,9,8,0,7],colu
mns=['col2','col1'])
print unsorted_df

OUTPUT:

        col2       col1
1  -2.063177   0.537527
4   0.142932  -0.684884
6   0.012667  -0.389340
2  -0.548797   1.848743
3  -1.044160   0.837381
5   0.385605   1.300185
9   1.031425  -1.002967
8  -0.407374  -0.435142
0   2.237453  -1.067139
7  -1.445831  -1.701035

From 3D ndarray (FOR PANEL CREATION)

import pandas as pd
import numpy as np

data = np.random.rand(2,4,5)
p = pd.Panel(data)
print p

output:

<class 'pandas.core.panel.Panel'>
Dimensions: 2 (item) x 4 (major_axis) x 5 (minor_axis)
Items axis: 0 to 1
Major_axis axis: 0 to 3
Minor_axis axis: 0 to 4

FOR DETAIL PLEASE DRIVE LINK:
https://drive.google.com/open?id=1RKv0v0V9kfvMceWb2QHdWfavDffsGAgV

FOR MORE DETAILED ABOUT PANDAS PLEASE OPEN BELOW LINK:

CLICK HERE