pandas-dev/pandas

Analysis updated 2026-06-20

★ 48,678PythonAudience · dataComplexity · 2/5Setup · easy

Mindmap

mindmap
  root((pandas))
    Core structures
      DataFrame table
      Series column
    Key operations
      Filter and sort
      Group and aggregate
      Merge and join
      Handle missing data
    File formats
      CSV and Excel
      JSON and SQL
      HDF5
    Time series
      Date ranges
      Resampling
      Rolling windows
    Audience
      Data scientists
      Analysts and engineers

mindmap root((pandas)) Core structures DataFrame table Series column Key operations Filter and sort Group and aggregate Merge and join Handle missing data File formats CSV and Excel JSON and SQL HDF5 Time series Date ranges Resampling Rolling windows Audience Data scientists Analysts and engineers

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What do people build with it?

USE CASE 1

Load a CSV of sales records, group by month, and compute total revenue with a few lines of Python.

USE CASE 2

Join two datasets from different database exports on a shared customer ID column, like a SQL JOIN but in Python.

USE CASE 3

Clean up a messy exported spreadsheet by dropping empty rows, standardizing date formats, and filling missing values.

USE CASE 4

Resample a dataset of daily stock prices into weekly or monthly averages for trend analysis.

What is it built with?

PythonNumPyCythonC

How does it compare?

	pandas-dev/pandas	nanmicoder/mediacrawler	lllyasviel/fooocus
Stars	48,678	48,940	48,399
Language	Python	Python	Python
Setup difficulty	easy	moderate	moderate
Complexity	2/5	3/5	2/5
Audience	data	developer	vibe coder

Figures from each repo's GitHub metadata at analysis time.

How do you get it running?

Difficulty · easy Time to first run · 5min

In plain English

Pandas is the most widely used Python library for working with structured data, tables, spreadsheets, time series, and similar formats. It solves the core problem of loading, cleaning, transforming, and analyzing data that comes in rows and columns, without needing a database or specialized software. The library introduces two main data structures. A Series is a one-dimensional labeled array, similar to a single column in a spreadsheet. A DataFrame is a two-dimensional table with labeled rows and columns, similar to an Excel sheet or a SQL database table, but in memory and scriptable with Python. These structures support a wide range of operations: filtering rows by condition, grouping and aggregating data, merging multiple datasets together like SQL joins, pivoting data into summary tables, handling missing values (data gaps) gracefully, and reading or writing to formats like CSV, Excel, JSON, SQL databases, and HDF5 files. Time series analysis is a particular strength, pandas has built-in support for date ranges, frequency resampling (converting daily data to monthly, for example), moving window calculations (like rolling averages), and timezone handling. Data scientists, analysts, and engineers use pandas every day for tasks like loading a CSV of sales data and computing monthly totals, joining customer records from two different databases, cleaning up messy exported spreadsheets, or feeding processed data into machine learning models. It is typically one of the first imports in any data analysis Python script. The tech stack is Python with a core that uses NumPy (a numerical array library) for fast computation. Performance-critical internal code is written in Cython (a compiled language that extends Python) and C. Pandas is installed via pip or conda and runs on any platform where Python runs.

Copy-paste prompts

Prompt 1

Using pandas, write code to load a CSV called 'sales.csv', group by the 'month' column, and output total revenue per month sorted highest to lowest.

Prompt 2

I have two pandas DataFrames: one with customer IDs and names, another with orders. Write the code to join them on customer_id and find customers who placed more than 3 orders.

Prompt 3

Write a pandas pipeline that reads an Excel file, drops rows where any column is empty, converts a 'date' column to datetime format, and saves the result as a new CSV.

Prompt 4

Using pandas, load a time series CSV with a date index and compute a 7-day rolling average for a column called 'value', then print both the original and smoothed values side by side.

Frequently asked questions

What is pandas?

The go-to Python library for working with tables of data, load, clean, reshape, and analyze spreadsheets, CSVs, and databases in a few lines of code.

What language is pandas written in?

Mainly Python. The stack also includes Python, NumPy, Cython.

How hard is pandas to set up?

Setup difficulty is rated easy, with roughly 5min to a first successful run.

Who is pandas for?

Mainly data.

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