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https://colab.research.google.com/drive/19EuG9iFue70wNctnWxZFERZAfJxx40_4?authuser=0#scrollTo=hitfm1FBLqjO

Colab link:

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Dataset:

In Python, a dataset is a collection of data that can be used for machine learning, data analysis, and other tasks.
Datasets can be stored in a variety of formats, such as CSV file,XLS.

Csv file:

A CSV file (Comma Separated Values file) is a type of plain text file that uses specific structuring to arrange tabular data.

The text inside a csv file is laid out in row, and each of those has column ,all separated by commas which is used to define and separate cells.

Pandas:

Pandas is an open-source Python library that provides high performance, easy-to-use data structure, and data analysis tools for the Python programming language.

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#Importing pandas library

import pandas as pd

#Creating DataFrame

data= {'name': ['akshat', 'shivam', 'rohit'], 'roll_no': [10, 12, 15], 'marks':[7, 8, 9]}
df= pd.DataFrame(data)
#turning a dataframe in csv file
df.to_csv('student.csv')
cv = pd.read_csv('student.csv')
Cv
#Removing index
df.to_csv('student.csv', index=False)
cv = pd.read_csv('student.csv')
Cv
#customizing header
new_column=['fathers_name','marks','roll_no']
df.to_csv('student.csv', index=False, header=new_column)
cv=pd.read_csv('student.csv')
Cv

#Handling missing value
data1={'name':['faisal_iqbal', 'sadia_zaib', 'afia'], 'hometown':['wb', 'mp', pd.NA]}
df=pd.DataFrame(data1)
df.to_csv('student.csv', index=False, na_rep='unknown')
cv=pd.read_csv('student.csv')
cv

Reading and writing in csv file

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#Skipping Rows While Reading CSV
cv=pd.read_csv('student.csv', names=new_col, skiprows=[0])

cv=pd.read_csv('student.csv', names=new_col, skiprows=[0,2])
#Reading top element
cv=pd.read_csv('student.csv')
cv.head(2)

#Seeing from bottom

cv.tail(2)

Original Top 2 Element

Skipping 0 and 1

Bottom 2 element

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#delete (drop) if any of the columns is null
data1={'name':['faisal_iqbal', 'sadia_zaib', 'afia'], 'hometown':['wb', 'mp', pd.NA]}
df=pd.DataFrame(data1)
df.to_csv('student.csv')
cv= pd.read_csv('student.csv')
cv.dropna(inplace=True)
Cv
#filtering the data
data= {'name': ['akshat', 'shivam', 'rohit'], 'roll_no': [10, 12, 15], 'marks':[7, 8, 9]}
df= pd.DataFrame(data)
df.to_csv('student.csv')
cv=pd.read_csv('student.csv')
cv=cv.query('marks>7')
cv
#filtering the string
df = pd.read_csv('student.csv')
df = df[['name', 'marks']]
df = df[df['name'].isin(['shivam', 'rahul'])]
df.to_csv('filtered_data.csv')
cv1=pd.read_csv('filtered_data.csv')
cv1