Data Analysis

NumPy, pandas and Matplotlib run directly in your browser here. The first run downloads the packages once; after that they are cached.

1NumPy BasicsNumPy is a library that stores numbers in an array — a grid of values that all share one type — and lets you …
2NumPy Indexing and SlicingNumPy indexing is how you pick values out of an array: by position and slice (arr[row, col]), by a True/False…
3NumPy Math and BroadcastingNumPy maths applies functions and summaries to whole arrays, and broadcasting lets arrays of different shapes…
4Pandas Basicspandas is a library for working with tables: a DataFrame holds rows and named columns like a spreadsheet, and…
5pandas Selecting DataSelecting in pandas means choosing rows and columns from a DataFrame by name with df["col"], by label with .l…
6pandas Data CleaningData cleaning in pandas means finding and fixing missing values, duplicates, wrong types, inconsistent text a…
7pandas groupby and Pivot TablesA pandas groupby splits a table into groups that share a key, applies a summary to each group and combines th…
8pandas Merge, Join and ConcatCombining DataFrames means either stacking tables that share columns with pd.concat, or matching rows from tw…
9Matplotlib BasicsMatplotlib is Python's main charting library: you give it lists or arrays of numbers, choose a chart type, ad…
10Matplotlib Chart TypesEach Matplotlib chart type answers a different question, so you choose the chart from the question you are as…

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About Data Analysis

Data analysis in Python rests on three libraries. NumPy gives fast arrays and vectorised maths, pandas adds labelled tables for selecting, cleaning, grouping and merging data, and Matplotlib draws the charts. All three run in your browser here, so you can experiment without installing anything.

Related reference: NumPy cheat sheet · pandas cheat sheet