Pandas Cheat Sheet

Pandas data manipulation, filtering, grouping, and analysis techniques.

2 PagesIntermediateMay 10, 2026

Reading Data

Load data into a DataFrame.

python

import pandas as pddf = pd.read_csv("data.csv")df.head()          # First 5 rowsdf.info()          # Column types & nullsdf.describe()      # Summary statistics

Selecting Data

Access rows and columns.

python

df["age"]                 # Single column (Series)df[["name", "age"]]       # Multiple columnsdf.loc[0, "name"]         # By labeldf.iloc[0, 1]              # By positiondf[df["age"] > 18]        # Boolean filter

Grouping & Aggregation

Summarize data by group.

python

df.groupby("category")["price"].mean()df.groupby("category").agg({"price": "sum", "id": "count"})df.sort_values("price", ascending=False)

Cleaning Data

Handle missing or duplicate values.

python

df.dropna()                       # Drop rows with NaNdf.fillna(0)                      # Fill NaN with a valuedf.drop_duplicates()              # Remove duplicate rowsdf["price"] = df["price"].astype(float)

Pro Tip

Chain operations with method chaining (df.dropna().sort_values(...)) for cleaner, more readable pipelines.

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