3. Matplotlib Library

What is Matplotlib?

Matplotlib is a popular Python library used for creating graphical representations of data. It helps in visualizing information using charts and graphs, which makes it easier to understand patterns, relationships and trends within the data.

Using Matplotlib, different types of graphs can be created such as:

• Line Charts
• Bar Graphs
• Pie Charts
• Histograms
• Scatter Plots

Importing Matplotlib

import matplotlib.pyplot as plt

The module pyplot contains functions used for creating charts and visualizing datasets.

4. Data Structures in Pandas

Pandas provides powerful data structures that help organize and manipulate structured data efficiently.

The two primary data structures are:

• Series – A one-dimensional labeled array.
• DataFrame – A two-dimensional table consisting of rows and columns.

These structures are designed to handle large datasets and perform various operations such as filtering, sorting and statistical analysis.

5. Series in Pandas

A Series is a one-dimensional data structure capable of storing data values along with their corresponding labels called indexes. Each element of a Series consists of an index and its associated value.

Series can store different types of data including integers, strings and floating point numbers.

6. Creating a Series

Series from ndarray

import pandas as pd
import numpy as np

data = np.array([10,20,30,40])
s = pd.Series(data)

print(s)

Series from Dictionary

import pandas as pd

data = {'Math':90,'Science':85,'English':88}

s = pd.Series(data)

print(s)

Series from Scalar Value

import pandas as pd

s = pd.Series(5,index=['A','B','C','D'])

print(s)

7. Mathematical Operations on Series

Pandas allows mathematical operations to be applied directly to all elements of a Series.

import pandas as pd

s = pd.Series([10,20,30,40])

print(s + 5)
print(s * 2)

Other operations such as subtraction and division can also be performed.

8. Head() Function

The head() function is used to display the first few rows of a dataset. By default, it shows the first five records.

s.head()

You can also specify the number of rows.

s.head(3)

9. Tail() Function

The tail() function displays the last few rows of a dataset. By default it shows the last five records.

s.tail()

10. Selection, Indexing and Slicing

Indexing

Indexing allows accessing individual elements in a Series.

s = pd.Series([10,20,30,40])

print(s[1])

Slicing

Slicing retrieves a range of values.

print(s[1:3])

11. DataFrame in Pandas

A DataFrame is a two-dimensional data structure used to store data in tabular format with rows and columns similar to a spreadsheet.

12. Creating DataFrames

From Dictionary

import pandas as pd

data = {
'Name':['Rahul','Priya','Aman'],
'Marks':[85,92,78]
}

df = pd.DataFrame(data)

print(df)

From List of Dictionaries

data = [
{'Name':'Rahul','Marks':85},
{'Name':'Priya','Marks':92},
{'Name':'Aman','Marks':78}
]

df = pd.DataFrame(data)

From CSV File

df = pd.read_csv("students.csv")

13. Displaying DataFrame

print(df)

This displays all rows and columns of the DataFrame.

14. Iteration in DataFrame

Iteration allows accessing rows one by one.

for index,row in df.iterrows():
    print(index,row)

15. Operations on Rows and Columns

Adding a Column

df['Grade'] = ['A','A','B']

Selecting Columns

df['Marks']

Deleting Columns

df.drop('Grade',axis=1,inplace=True)

Renaming Columns

df.rename(columns={'Marks':'Score'},inplace=True)

16. Head() and Tail() in DataFrame

df.head()

Displays the first five rows.

df.tail()

Displays the last five rows.

17. Indexing Using Labels

Label based indexing can be done using the loc[] method.

df.loc[0]

df.loc[0,'Name']

18. Boolean Indexing

Boolean indexing is used to filter data based on conditions.

df[df['Marks'] > 80]

19. Importing Data between CSV Files and DataFrames

Importing Data

df = pd.read_csv("data.csv")

This command reads the CSV file and converts it into a DataFrame.

Exporting Data

df.to_csv("output.csv")

This saves the DataFrame data into a CSV file.