Pandas - Data Manipulation and Analysis

Pandas is a powerful data analysis and manipulation library for Python. It provides two core data structures: Series (1D) and DataFrame (2D), which are built on top of NumPy and allow for easy and efficient data handling.

Key Features

• Easy handling of missing data
• Label-based indexing for rows and columns
• Built-in methods for data aggregation, filtering, and transformation
• Tools for reading/writing data from CSV, Excel, SQL, and more
• Time series functionality

10 Examples

import pandas as pd

# Example 1: Create a DataFrame
data = {'Name': ['Alice', 'Bob'], 'Age': [25, 30]}
df = pd.DataFrame(data)
print(df)

# Example 2: Read CSV
df = pd.read_csv('data.csv')
print(df.head())

# Example 3: Select columns and rows
print(df['Name'])
print(df.loc[0])

# Example 4: Filtering data
print(df[df['Age'] > 26])

# Example 5: Adding new column
df['Age+10'] = df['Age'] + 10
print(df)

# Example 6: Group by and aggregate
print(df.groupby('Name').mean())

# Example 7: Handling missing values
df['City'] = [None, 'NY']
print(df.fillna('Unknown'))

# Example 8: Sorting
print(df.sort_values(by='Age'))

# Example 9: Merging DataFrames
df2 = pd.DataFrame({'Name': ['Alice'], 'Score': [90]})
print(pd.merge(df, df2, on='Name'))

# Example 10: Export to CSV
df.to_csv('output.csv', index=False)