Pandas - Data Analysis and Manipulation

DataFrames

The core data structure in Pandas.

import pandas as pd

# Creating a DataFrame
data = {
    'Name': ['Alice', 'Bob', 'Charlie'],
    'Age': [25, 30, 28],
    'City': ['NYC', 'LA', 'Chicago']
}
df = pd.DataFrame(data)

# From CSV
df = pd.read_csv('data.csv')

# Basic info
print(df.head())        # First 5 rows
print(df.info())        # Data types and missing values
print(df.describe())    # Statistical summary

Accessing Data

# Column access
ages = df['Age']
names = df[['Name', 'City']]

# Row access
row = df.iloc[0]        # By position
row = df.loc[0]         # By label

# Filtering
young_people = df[df['Age'] < 30]

Data Manipulation

# Adding columns
df['Salary'] = [50000, 60000, 55000]

# Renaming
df.rename(columns={'Name': 'FullName'})

# Handling missing values
df.fillna(0)
df.dropna()

# Sorting
df.sort_values('Age')

Grouping and Aggregation

# Group by
grouped = df.groupby('City')['Age'].mean()

# Multiple operations
summary = df.groupby('City').agg({
    'Age': 'mean',
    'Salary': 'sum'
})

Common Methods

• merge() - Combine DataFrames
• concat() - Stack DataFrames
• pivot_table() - Reshape data
• apply() - Apply functions

Exporting Data

# Save to CSV
df.to_csv('output.csv', index=False)

# Save to Excel
df.to_excel('output.xlsx')

# Save to JSON
df.to_json('output.json')