Run pandas and NumPy Online

Libraries ready to import

• pandasDataFrames, CSV parsing, filtering, groupby, merging, pivot tables and dates.
• NumPyFast arrays, matrix math, statistics, random numbers and reshaping.
• SciPyScientific functions for statistics, optimization and linear algebra.
• MatplotlibCharts and plots, with a dedicated Matplotlib editor that shows them in the page.
• scikit-learnMachine learning models, preprocessing and evaluation.
• openpyxlRead and write Excel workbooks from Python.

Example: summarize sales with pandas

This script reads CSV data, groups it by region to get the total and average, and filters the months with high sales. Open it in the editor, select Run, then change the data or the grouping and run it again.

import pandas as pd
from io import StringIO

csv_text = """region,month,sales
North,Jan,120
South,Jan,95
North,Feb,140
South,Feb,110
North,Mar,135
South,Mar,125"""

sales = pd.read_csv(StringIO(csv_text))

# Total and average sales per region, highest total first.
summary = sales.groupby("region")["sales"].agg(["sum", "mean"]).sort_values("sum", ascending=False)
print(summary)

# Months where sales were above 120.
print(sales[sales["sales"] > 120])

Example: statistics with NumPy

A 2D array holds three students' scores in three tests. NumPy computes the mean per student, the best score per test, the standard deviation and a filtered view in a few lines.

import numpy as np

scores = np.array([[72, 85, 90], [64, 78, 88], [95, 91, 84]])

print("Mean per student:", scores.mean(axis=1).round(1))
print("Best score per test:", scores.max(axis=0))
print("Standard deviation:", round(float(scores.std()), 2))
print("Scores above 85:", scores[scores > 85])

pandas and NumPy lessons

Short lessons with code you can run, each focused on one task.

• NumPy Array: Create and Use np.array in Python
• NumPy Reshape: Change an Array's Shape with reshape()
• NumPy Matrix Multiplication with the @ Operator
• NumPy Mean, Median and Standard Deviation (np.mean, np.std)
• Filter a NumPy Array
• NumPy Random Numbers with np.random
• NumPy Concatenate: Join Arrays with np.concatenate
• NumPy Unique Values with np.unique
• NumPy Transpose a Matrix (np.transpose and .T)
• pandas DataFrame: Create and Use DataFrames
• pandas read_csv(): Read CSV Files in Python
• Filter pandas DataFrame Rows
• pandas groupby() with Examples
• Handle Missing Values in pandas with fillna()
• pandas sort_values(): Sort a DataFrame by Column
• pandas merge(): Merge Two DataFrames
• pandas Pivot Table with Examples
• pandas to_datetime(): Work with Dates in pandas

Frequently asked questions