Library cheatsheet

pandas and NumPy 3 Cheatsheet

Load, clean, reshape, group and analyze tabular data with pandas, and work with arrays in NumPy.

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Import the libraries — The standard import names.
Read a CSV file — Load a CSV into a DataFrame, parsing a column as dates.
Read Excel, JSON or Parquet — pandas reads many formats with similar functions.
Look at the first rows — Show the first rows. Use tail() for the last ones.
Check columns and types — Show column names, types, non-null counts and memory use.
Summarize numeric columns — Show count, mean, spread and quartiles.
Get the dimensions — Return the number of rows and columns as a tuple.
Count each distinct value — Count how often each value occurs in a column.
Create a DataFrame by hand — Build a small DataFrame from a dictionary.

Select columns — Pick one column as a Series, or several as a DataFrame.
Filter rows — Keep rows that meet conditions. Combine them with & and |, each in parentheses.
Filter with a string expression — Write conditions as readable text.
Select by label — Select rows and columns by label with loc.
Select by position — Select the first five rows and first two columns by position.
Match a list of values — Keep rows whose value is in a list.
Sort rows — Sort by one or more columns.
Get the top rows — Return the five rows with the largest values.

Count missing values — Count the missing values in each column.
Drop rows with missing values — Remove rows that have a missing value in the given columns.
Fill missing values — Replace missing values in a column with a default.
Remove duplicate rows — Keep the first row for each email.
Rename columns — Rename some columns with a mapping.
Drop columns — Remove columns you don't need.
Change a column's type — Convert a column to another type.
Add a column — Create a column from others. Vectorized operations are much faster than apply.
Add a column based on a condition — Choose one of two values per row.
Clean text columns — Apply string methods to a whole column.
Parse dates — Convert text to datetimes, then pull out parts.

Group and aggregate — Compute several statistics per group with named results.
Create a pivot table — Summarize values across two dimensions.
Join two DataFrames — Join on a key, like SQL. how can be inner, left, right or outer.
Stack DataFrames — Append DataFrames with the same columns.
Compute a moving average — Average over a sliding window of seven rows.
Aggregate by time period — Sum values by month. The index must be a datetime.
Save to a CSV file — Write the DataFrame to a file without the row index.

Create an array — Create an array from a list. Arrays hold one type and operate on all items at once.
Create a range — Create evenly spaced values, either by step or by count.
Create filled arrays — Create arrays of zeros, ones or a given value.
Reshape an array — Change the shape without changing the data. -1 lets NumPy work out the size.
Generate random numbers — Use the modern generator with a seed so results can be reproduced.
Compute statistics along an axis — Average each column (axis 0) or each row (axis 1).
Filter with a boolean mask — Select the items that satisfy a condition.
Multiply matrices — Use the @ operator for matrix multiplication.
Sort and find positions — Sort values, or get the indices that would sort them.
Find distinct values — Get the sorted unique values and how often each occurs.

Hugging Face cheatsheet — The hf command line, and Transformers and Datasets code for downloading, running, training and sharing models.
Matplotlib and seaborn 3 cheatsheet — Draw line, scatter, bar and histogram charts with Matplotlib, and statistical charts with seaborn.
Polars 2 cheatsheet — Fast DataFrames in Python: expressions, filtering, grouping, joins, lazy queries and file formats.
Prisma ORM cheatsheet — Model your database in a schema, run migrations, and query with the type-safe Prisma Client. Examples use the prisma-client-js generator; check the docs for newer generators.
PyTorch 2 cheatsheet — Tensors, autograd, models, training loops, saving and speed-ups in PyTorch 2.
React 19 cheatsheet — Hooks, components and patterns for React 19 in TypeScript or JavaScript.