CC0
Palmer penguins
344 penguins from Palmer Station, with bill and flipper measurements and a few missing values.
penguins.csv
Practice
A notebook for NumPy, Pandas, and Matplotlib. Each page is a short document: read a note, run a cell, and see the text or chart under that cell. Later cells keep the names you created.
NumPy course
Arrays, masks, and broadcasting
Pandas course
Tables, cleaning, and groupby
Matplotlib course
Figures, charts, and subplots
CC0
344 penguins from Palmer Station, with bill and flipper measurements and a few missing values.
penguins.csv
Public domain
150 iris flowers, four measurements, and a species label. A small numeric table for arrays and histograms.
iris.csv
Public domain
Four tiny datasets with nearly the same summary statistics and very different shapes.
anscombe.csv
50 problems
Arrays, indexing, masks, broadcasting, reductions, shape, sorting, linear algebra, and the Iris measurements. Each problem has a starter and a folded solution.
Build an array, double it, and keep the values that pass a test. The second cell uses the name from the first.
Broadcast a length-3 bonus across a 2 by 3 grade table.
Load penguins.csv, then count species from the DataFrame the first cell created.
Keep one species and one new column, using the table from the cell above.
Create a figure and an axes, then plot five temperatures.
Drop missing measurements, then plot bill length against bill depth.
Turn the Iris sepal column into an array and compute its mean with NumPy.
Use a boolean mask on the Iris petal-length column.
Filter Palmer penguins to one species and count the rows.
Group the penguins table and average body mass.
Bin the Iris sepal lengths and label the axes.
Plot each Anscombe dataset in its own panel, with shared axes.