Pandas Basics Cheat Sheet

Pandas is a powerful Python library for data manipulation, analysis, and visualization.

1. Installing and Importing Pandas

pip install pandas

import pandas as pd

2. Creating DataFrames and Series

Create a DataFrame from a Dictionary

data = {
    'Name': ['Alice', 'Bob', 'Charlie'],
    'Age': [25, 30, 35],
    'City': ['NY', 'LA', 'SF']
}
df = pd.DataFrame(data)

Create a Series

s = pd.Series([1, 2, 3, 4])

3. Reading and Writing Data

Read CSV File

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

Write to CSV

df.to_csv('output.csv', index=False)

Read Excel File

df = pd.read_excel('data.xlsx')

Read JSON

df = pd.read_json('data.json')

4. DataFrame Overview

df.head()        # First 5 rows
df.tail()        # Last 5 rows
df.info()        # Info about DataFrame
df.describe()    # Summary statistics
df.shape         # Shape (rows, columns)
df.columns       # Column names
df.index         # Row indices

5. Selecting Data

Select Columns

df['Name']              # Single column
df[['Name', 'Age']]     # Multiple columns

Select Rows by Index

df.loc[0]               # Select row by label
df.iloc[0]              # Select row by index
df.loc[0:2]             # Slice rows by label
df.iloc[0:2]            # Slice rows by position

6. Filtering Data

df[df['Age'] > 30]                      # Filter rows
df[(df['Age'] > 25) & (df['City'] == 'NY')]  # Multiple conditions
df.query('Age > 25')                    # Query method

7. Adding and Modifying Data

Add New Column

df['Salary'] = [70000, 80000, 90000]

Modify Values

df['Age'] = df['Age'] + 1

Apply Functions

df['Age'] = df['Age'].apply(lambda x: x + 5)

8. Dropping Data

df.drop('Salary', axis=1, inplace=True)  # Drop column
df.drop(1, axis=0, inplace=True)         # Drop row

9. Sorting Data

df.sort_values(by='Age', ascending=False)

10. Handling Missing Data

Check for Missing Values

df.isnull().sum()

Drop Missing Values

df.dropna()

Fill Missing Values

df['Age'].fillna(df['Age'].mean(), inplace=True)

11. Aggregation and Grouping

df.groupby('City')['Age'].mean()      # Group by and aggregate
df.groupby('City').agg({'Age': 'max', 'Salary': 'mean'})  # Multiple aggregations

12. Merging and Joining DataFrames

Concatenate DataFrames

pd.concat([df1, df2], axis=0)  # Vertical (rows)
pd.concat([df1, df2], axis=1)  # Horizontal (columns)

Merge DataFrames (SQL-like joins)

pd.merge(df1, df2, on='ID')  # Inner join by default
pd.merge(df1, df2, on='ID', how='left')  # Left join

13. Pivot Tables

df.pivot_table(index='City', values='Salary', aggfunc='mean')

14. Working with Dates

df['Date'] = pd.to_datetime(df['Date'])
df['Year'] = df['Date'].dt.year
df['Month'] = df['Date'].dt.month

15. Exporting Data

df.to_csv('output.csv')
df.to_excel('output.xlsx')
df.to_json('output.json')

16. Common DataFrame Operations

17. Visualization with Pandas

df['Age'].plot(kind='hist')     # Histogram
df.plot(kind='line')            # Line plot
df.plot(kind='bar')             # Bar plot

Tips for Learning Pandas

• Practice with Real Data – Use datasets from Kaggle or CSV files.
• Understand DataFrame Operations – Master filtering, grouping, and aggregation.
• Explore Pandas Documentation – It has extensive resources and examples.
• Combine with Matplotlib/Seaborn – Enhance data visualization.

Pandas is essential for data analysis