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Pandas, DataFrame in Data Science

Pandas is a fast, powerful and easy to use open source data analysis and manipulation tool which is built on top of the Python programming language. This is built on top of Numpy and also called as Numpy with labels.

Pandas DataFrame is mutable, heterogeneous tabular data structure with labeled axes (rows and columns). Pandas DataFrame consists of three principal components, the data,rows, and columns.

In the real world, a Pandas DataFrame will be created by loading the datasets from existing storage, storage can be SQL Database, CSV file or Excel file. Pandas DataFrame can be created from the lists, dictionary, and from a list of dictionary etc. Dataframe can be created in different ways here are some ways by which we create a dataframe.

DataFrame using list: DataFrame can be created using a single list or a list of lists.

import pandas as pd
# list of strings
lt = ['This', 'is', 'Pandas']
# Calling DataFrame constructor on list
df = pd.DataFrame(lt)

 0    This 
 1     is 
 2    Pandas

Steps to create DataFrame from dict:

To create DataFrame from dict of narray/list, all the narray must be of same length. If index is passed then the length index should be equal to the length of arrays. If no index is passed, then by default, index will be range(n) where n is the array length.

import pandas as pd

data = {'Name':['Tom', 'James', 'Ayden'],

'Age':[20, 22, 10]}

# Create DataFrame

df = pd.DataFrame(data)

# Print the output.


    Name  Age 
0   Tom   20 
1   James 22 
2   Ayden 10

Steps to read csv file

import numpy as np

import pandas as pd

data= pd.read_csv('nobels.csv')

# display top 5 rows


# display last 5 rows


# if you want to see top 20 rows


# showcase name of all columns


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