Pandas Cheatsheet: The Ultimate Guide for Data Analysis in Python

Working with data often means juggling messy files, cleaning inconsistencies, and reshaping tables until they’re analysis-ready. That’s where Pandas shines it’s the backbone of data manipulation in Python. But with hundreds of methods and options, it can be overwhelming to remember them all.

This cheatsheet brings together the most essential Pandas methods, organized by category, with short explanations you can scan quickly. Whether you’re importing data, cleaning missing values, reshaping tables, or analyzing trends, this guide will help you find the right function fast without digging through endless documentation.

1. Data Creation & I/O

pd.read_csv() – Load CSV file
pd.read_excel() – Load Excel file
pd.read_json() – Load JSON file
pd.read_sql(query, con) – Load data from SQL
pd.read_html() – Parse tables from HTML
pd.read_clipboard() – Load data copied to clipboard
df.to_csv() – Save DataFrame to CSV
df.to_excel() – Save DataFrame to Excel
df.to_json() – Save DataFrame to JSON
df.to_sql() – Write DataFrame to SQL
df.to_clipboard() – Copy DataFrame to clipboard
df.to_markdown() – Export as Markdown table
df.to_latex() – Export as LaTeX
df.to_html() – Export as HTML

2. Inspection & Metadata

df.head() – First rows
df.tail() – Last rows
df.info() – DataFrame summary
df.describe() – Statistical summary
df.dtypes – Column data types
df.columns – Column labels
df.index – Row index
df.axes – Row and column labels
df.shape – Dimensions of DataFrame
df.memory_usage() – Memory usage per column
df.size – Number of elements
df.empty – Check if empty

3. Selection & Indexing

df[“col”] – Select column
df[[“col1″,”col2”]] – Select multiple columns
df.loc[] – Select by label
df.iloc[] – Select by position
df.at[] – Fast scalar access (label)
df.iat[] – Fast scalar access (position)
df.where() – Conditional selection
df.mask() – Replace based on condition
df.query() – SQL-like queries
df.take() – Select rows by index

4. Modifying Data

df.assign() – Add or modify columns
df.insert() – Insert new column
df.update() – Update with another DataFrame
df.drop() – Drop rows or columns
df.rename() – Rename labels
df.replace() – Replace values
df.eval() – Evaluate string expressions

5. Handling Missing Data

df.isna() – Detect missing values
df.notna() – Opposite of isna
df.fillna() – Fill missing values
df.dropna() – Remove missing values
df.interpolate() – Interpolate values

6. Sorting & Ranking

df.sort_values() – Sort by column values
df.sort_index() – Sort by index
df.rank() – Rank elements
df.nlargest() – Largest N values
df.nsmallest() – Smallest N values

7. Aggregation & Statistics

df.min(), df.max() – Minimum / maximum
df.sum(), df.mean() – Sum / mean
df.median() – Median
df.mode() – Mode
df.std(), df.var() – Standard deviation / variance
df.count() – Count non-missing values
df.cumsum(), df.cumprod() – Cumulative sum/product
df.cummin(), df.cummax() – Cumulative min/max
df.any(), df.all() – Boolean checks

8. Grouping & Window Functions

df.groupby() – Group by column(s)
df.agg() – Aggregate with functions
df.transform() – Transform group values
df.ngroup() – Number groups
df.size() – Size of groups
df.rolling() – Rolling window
df.expanding() – Expanding window

9. String Methods (Series.str)

str.upper(), str.lower() – Case conversion
str.len() – String length
str.strip() – Trim spaces
str.split() – Split string
str.get() – Extract element by index
str.contains() – Check substring
str.replace() – Replace text
str.startswith(), str.endswith() – Start/end check
str.extract() – Extract regex groups

10. Categorical Data

astype(“category”) – Convert to category
cat.categories – List categories
cat.codes – Category codes
cat.add_categories() – Add new category
cat.remove_unused_categories() – Remove unused categories

11. Index & Reindexing

df.set_index() – Set column as index
df.reset_index() – Reset index
df.reindex() – Conform to new index
df.set_axis() – Rename axis labels
df.swaplevel() – Swap MultiIndex levels
df.sort_index() – Sort by index
df.reorder_levels() – Reorder MultiIndex

12. MultiIndex & Hierarchical Data

pd.MultiIndex.from_tuples() – Create MultiIndex
df.xs() – Cross-section selection
df.stack() – Pivot columns to rows
df.unstack() – Pivot rows to columns

13. Time Series Methods

pd.to_datetime() – Convert to datetime
df[“Date”].dt.year – Extract year
df[“Date”].dt.month – Extract month
df[“Date”].dt.day – Extract day
df[“Date”].dt.weekday – Day of week
df[“Date”].dt.is_month_end – Month-end check
df[“Date”].dt.is_leap_year – Leap year check
df.resample() – Resample time series
df.asfreq() – Change frequency
df.shift() – Shift values
df.diff() – Row differences
df.pct_change() – Percentage change

14. Reshaping & Combining

df.melt() – Unpivot columns to rows
df.pivot() – Reshape table
df.pivot_table() – Pivot with aggregation
df.concat() – Concatenate DataFrames
df.merge() – SQL-style merge
df.join() – Join on index or key
df.add(), df.sub(), df.mul(), df.div() – Arithmetic operations
df.combine_first() – Fill NA from another DataFrame

15. Apply & Map

df.apply() – Apply function along axis
df.applymap() – Apply function element-wise
df.map() – Map values in Series

16. Visualization

df.plot() – Line plot
df.plot.bar() – Bar plot
df.plot.hist() – Histogram
df.plot.box() – Boxplot
df.plot.area() – Area plot
df.plot.scatter() – Scatter plot

Conclusion

Pandas is vast, but you don’t need to memorize everything. Having a structured cheatsheet at your fingertips saves time, reduces friction, and lets you focus on the real task: extracting insights from your data.

This reference covered the most useful methods across I/O, selection, cleaning, grouping, reshaping, and visualization giving you a solid toolkit for nearly any workflow. Keep it open while you work, and over time you’ll find the commands becoming second nature.

With this guide, you’re better equipped to work faster, cleaner, and smarter with data in Python.

External Link

Pandas Official Docs

Pandas API Reference

W3Schools Pandas Guide

DataCamp Pandas Cheat Sheet

Python for Data Analysis by Wes McKinney (Author of Pandas)