heatmap¶

This section describes various options available for plotting heatmaps in fivecentplots

Setup¶

Import packages:

import fivecentplots as fcp
import pandas as pd
from pathlib import Path

Read some fake heatmap data filled with absolutely made-up basketball stats:

df = pd.read_csv(Path(fcp.__file__).parent / 'test_data/fake_data_heatmap.csv')
df.head()

	Player	Category	Average
0	Lebron James	Points	27.5
1	Lebron James	Assists	9.1
2	Lebron James	Rebounds	8.6
3	Lebron James	Blocks	0.9
4	James Harden	Points	30.4

Optionally set the design theme (skipping here and using default):

#fcp.set_theme('gray')
#fcp.set_theme('white')

Categorical heatmap¶

First consider a case where both the x and y DataFrame columns contain categorical data values:

No data labels¶

fcp.heatmap(df, x='Category', y='Player', z='Average')

..note:: 1. Colorbars are enabled by default for heatmaps and must be intentionally disabled with cbar=False to remove 2. Heatmaps the x tick labels are rotated 90° by default. This can be overridden via the keyword tick_labels_major_x_rotation

With data labels¶

Using keyword data_labels=True we can overlay the numerical value of the z column on top of the heatmap cells:

fcp.heatmap(df, x='Category', y='Player', z='Average', data_labels=True,
            heatmap_font_color='#aaaaaa', tick_labels_major_y_edge_width=0, ws_ticks_ax=5)

Cell size¶

The size of the heatmap cell will default to a width of 60 pixels unless: (1) the keyword heatmap_cell_size (or cell_size when directly supplying the value to the function call) is specified; or (2) ax_size is explicitly defined:

fcp.heatmap(df, x='Category', y='Player', z='Average', data_labels=True,
            heatmap_font_color='#aaaaaa', tick_labels_major_y_edge_width=0, ws_ticks_ax=5, cell_size=100)

..note:: Heatmap cells are always square with width = height

Non-uniform data¶

A major difference between heatmaps and contour plots is that contour plots assume that the x and y DataFrame column values are numerical and continuous. With a heatmap, we can cast numerical data into categorical form

# Read the contour DataFrame
df2 = pd.read_csv(Path(fcp.__file__).parent / 'test_data/fake_data_contour.csv')

fcp.heatmap(df2, x='X', y='Y', z='Value', row='Batch', col='Experiment',
            cbar=True, share_z=True, ax_size=[400, 400], data_labels=False,
            label_rc_font_size=12, filter='Batch==103', cmap='viridis')

..note:: 1. any missing values get mapped as nan values are not not plotted 2. the x-axis width is not 400px as specified by the keyword ax_scale. This occurs because the data set does not have as many values on the x-axis as on the y-axis. fivecentplots applies the axis size to the axis with the most items and scales the other axis accordingly

imshow alternative¶

As of v0.6, fcp.heatmap no longer supports image display. Please use imshow instead (link to docs)