Run Pandas Online – Free Pandas Online Compiler

Run Pandas Series and DataFrames in your browser with our free online Pandas compiler. No installation or signup required - Try It Now.

Start Running Pandas →See the Code

Try This Pandas Example

import pandas as pd

# --- Series: a single labelled column of data ---
scores = pd.Series([92.5, 87.3, 95.1, 88.7],
                   index=['Alice', 'Bob', 'Charlie', 'Diana'],
                   name='Score')
print("Series:")
print(scores)
print(f"\nMean: {scores.mean():.2f}  |  Max: {scores.max()}  |  Min: {scores.min()}")

# --- DataFrame: multiple Series combined into a table ---
df = pd.DataFrame({
    'Name':  ['Alice', 'Bob', 'Charlie', 'Diana'],
    'Age':   [25, 30, 35, 28],
    'Score': scores.values,
})

print("\nDataFrame:")
print(df)
print("\nScore statistics:")
print(df['Score'].describe())
print("\nFilter — Age > 28:")
print(df[df['Age'] > 28])
print("\nSorted by Score (descending):")
print(df.sort_values('Score', ascending=False).reset_index(drop=True))
Open in full editor →Loads instantly in your browser. No install.

What You Can Do With Pandas Online

Series & DataFrames

Start with a pd.Series for a single labelled column, then combine into a pd.DataFrame for full tabular data. Both work exactly as they do locally.

Analyse Real Data

Run groupby, merge, pivot_table, and statistical functions like describe() and value_counts() — all without leaving your browser.

No Setup Needed

Pandas, NumPy, and Matplotlib are pre-installed. Open the editor and start coding immediately — zero configuration required.

How to Analyze Data with Pandas Online

Ready to manipulate data structures efficiently? Our Pandas online compiler gives you immediate access to robust data analysis tools right inside your browser. Here is a quick workflow:

  1. Import the Library: Start by typing import pandas as pd in the code editor.
  2. Create a DataFrame: You can easily initialize a pd.DataFrame() using Python dictionaries or lists. Alternatively, build a 1-dimensional pd.Series() for individual columns.
  3. Filter and Aggregate: Use powerful Pandas methods like .groupby(), .merge(), and .pivot_table() to transform your dataset.
  4. View the Output: Run functions like df.describe() or df.head() and print the results to see detailed statistical summaries instantly in the console.

If you want to dive deeper into time series analysis, missing data handling, or advanced merging techniques, head over to the official Pandas documentation.

Frequently Asked Questions

Can I run pandas online without installing Python?

Yes. PythonHere runs Python entirely in your browser using WebAssembly (Pyodide). Pandas, NumPy, and Matplotlib are pre-loaded — no installation required.

Does this support pandas Series and DataFrames?

Yes. Both pd.Series and pd.DataFrame work fully. Create a Series for single-column labelled data, then build a DataFrame by combining multiple Series — just like you would locally.

Is it free?

100% free, forever. No account, no credit card, no time limit.

Can I use NumPy with pandas here?

Yes. NumPy is available alongside pandas. Use import numpy as np directly in the editor.

Start Running Pandas in Your Browser

Free forever. No install. No signup.

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