matplotlib + Ibis

If you don’t have data to visualize, you can load an example table:

Code

import ibis import ibis.selectors as s  ibis.options.interactive = True  t = ibis.examples.penguins.fetch() t.head(3)

┏━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━┓ ┃ species ┃ island ┃ bill_length_mm ┃ bill_depth_mm ┃ flipper_length_mm ┃ body_mass_g ┃ sex ┃ year ┃ ┡━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━╇━━━━━━━┩ │ string │ string │ float64 │ float64 │ int64 │ int64 │ string │ int64 │ ├─────────┼───────────┼────────────────┼───────────────┼───────────────────┼─────────────┼────────┼───────┤ │ Adelie  │ Torgersen │ 39.1 │ 18.7 │ 181 │ 3750 │ male  │ 2007 │ │ Adelie  │ Torgersen │ 39.5 │ 17.4 │ 186 │ 3800 │ female │ 2007 │ │ Adelie  │ Torgersen │ 40.3 │ 18.0 │ 195 │ 3250 │ female │ 2007 │ └─────────┴───────────┴────────────────┴───────────────┴───────────────────┴─────────────┴────────┴───────┘

Using matplotlib with Ibis

Refer to the matplotlib documentation. matplotlib has not implemented the dataframe interchange protocol so it is recommended to call to_pandas() on the Ibis table before plotting.

import matplotlib.pyplot as plt  grouped = t.group_by("species").aggregate(count=ibis._.count()) grouped = grouped.mutate(row_number=ibis.row_number().over()).select(  "row_number",  (  ~s.cols("row_number") & s.all()  ), # see https://github.com/ibis-project/ibis/issues/6803 ) grouped

┏━━━━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━┓ ┃ row_number ┃ species ┃ count ┃ ┡━━━━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━┩ │ int64 │ string │ int64 │ ├────────────┼───────────┼───────┤ │ 0 │ Adelie  │ 152 │ │ 1 │ Chinstrap │ 68 │ │ 2 │ Gentoo  │ 124 │ └────────────┴───────────┴───────┘

# https://stackoverflow.com/questions/9101497/matplotlib-bar-graph-x-axis-wont-plot-string-values plt.figure(figsize=(6, 4)) plt.bar(grouped["row_number"].to_pandas(), grouped["count"].to_pandas()) plt.title("Penguin species counts") plt.xlabel("Species") plt.xticks(grouped["row_number"].to_pandas(), grouped["species"].to_pandas()) plt.ylabel("Count") plt.show()