Pandas: DataFrame Operation

Understanding various DataFrame options provided by the pandas lib

This notebook covers essential operations on pandas DataFrames: reading, viewing, selecting, filtering, and indexing.

Reading Data

Load data from files like CSV into DataFrames using pd.read_csv().

import pandas as pd

df = pd.read_csv('example.csv')
df

	Name	Age	City
0	Adil	23	Matannur
1	Aman	19	Vellore
2	Ziya	15	Tly
3	Zahra	9	Knr

Viewing Data

Use head() and tail() to preview the first/last rows of your DataFrame.

df.head(2)

	Name	Age	City
0	Adil	23	Matannur
1	Aman	19	Vellore

df.tail(2)

	Name	Age	City
2	Ziya	15	Tly
3	Zahra	9	Knr

Selecting Columns

Select specific columns using bracket notation or .loc.

df[['Name', 'City']]

	Name	City
0	Adil	Matannur
1	Aman	Vellore
2	Ziya	Tly
3	Zahra	Knr

df[['Name', 'City']].values

array([['Adil', 'Matannur'],
       ['Aman', 'Vellore'],
       ['Ziya', 'Tly'],
       ['Zahra', 'Knr']], dtype=object)

Filtering Data

Filter rows based on conditions using boolean indexing.

agegt20 = df[df['Age']>=20]
agegt20

	Name	Age	City
0	Adil	23	Matannur

agelt20 = df[df['Age']<20]
agelt20

	Name	Age	City
1	Aman	19	Vellore
2	Ziya	15	Tly
3	Zahra	9	Knr

df_ziya = df[df['Name']=='Ziya']
df_ziya

	Name	Age	City
2	Ziya	15	Tly

df_mult_cond = df[(df['City']=='Matannur') | (df['Age']<=15 )]
df_mult_cond

	Name	Age	City
0	Adil	23	Matannur
2	Ziya	15	Tly
3	Zahra	9	Knr

selected_cities = ['Tly', 'Knr']
df_tlyorknr = df[df['City'].isin(selected_cities)]

df_tlyorknr

	Name	Age	City
2	Ziya	15	Tly
3	Zahra	9	Knr

df[df['Name'].str.startswith('Z')]

	Name	Age	City
2	Ziya	15	Tly
3	Zahra	9	Knr

Indexing and Slicing

Use .iloc for position-based and .loc for label-based indexing.

df.iloc[0:2]

	Name	Age	City
0	Adil	23	Matannur
1	Aman	19	Vellore

df.loc[df['Age']>20, ['Name', 'City']]

	Name	City
0	Adil	Matannur

Best Practices

• Use .copy() when assigning filtered DataFrames to avoid modifying originals.
• Combine conditions with & and | for complex filters.
• Prefer .loc for clarity in production code.

Summary

This notebook covered key DataFrame operations: reading, viewing, selecting, filtering, and indexing. These form the foundation for data manipulation!