Python Docs

Pandas for Data Science

Pandas provides high-performance tools for loading, cleaning, transforming, aggregating, and joining tabular datasets using DataFrames. It is the foundation of most data science workflows in Python.

Why Pandas?

Pandas is essential because it provides:

  • Easy loading of CSV, Excel, SQL, JSON
  • Powerful filtering and indexing
  • Groupby operations for summaries
  • Merging & joining like SQL
  • Handling missing values
  • Reshaping (pivot, melt)
  • Time-series and rolling operations

Load & Inspect Data

Use Pandas to load files and quickly inspect their structure.

Example

import pandas as pd

df = pd.read_csv('sales.csv')
print(df.head())   # preview first 5 rows
print(df.info())   # column types + non-null counts

What this shows:

  • head() quickly previews your dataset.
  • info() reveals dtypes and missing values.
  • Useful before any cleaning or modeling.

Select, Group & Join

These three operations form the core of data analysis: filtering rows, summarizing data, and combining datasets.

Examples

# filtering high-value transactions
high = df[df['amount'] > 1000]

# groupby: total sales by region
agg = df.groupby('region')['amount'].sum().reset_index()

# join with metadata
meta = pd.read_csv('regions.csv')
merged = df.merge(meta, on='region', how='left')
print(merged.head())

Explanation:

  • Filtering: Select rows where amount > 1000.
  • Groupby: Summarize sales by region — common in dashboards.
  • Merging: Join sales.csv with regions.csv(SQL-style join: left/inner/right/outer).

Handling Missing Values

Missing values must be handled before training ML models or running analysis.

Example

df['amount'] = df['amount'].fillna(df['amount'].median())

Alternatives:

  • df.dropna() — remove missing rows entirely
  • df.fillna(0) — replace missing values with default
  • df.fillna(method='ffill') — forward fill for time-series

Real-World Uses of Pandas in Data Science

  • Sales forecasting (cleaning, aggregating, merging datasets)
  • Customer segmentation (groupby, statistics)
  • Analysis of product performance (join with metadata)
  • Preprocessing for machine learning models
  • ETL pipelines (Load → Clean → Transform → Export)

Summary

Pandas is essential for all data science workflows — from loading and cleaning to grouping, joining, and preparing data for ML. Its expressive syntax makes complex analytics easy to perform.