heatmap#

This section describes various options available for plotting heatmaps in fivecentplots

See the full API

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')
../_images/plot_types_heatmap_13_0.png

Note

Colorbars are enabled by default for heatmaps and must be intentionally disabled with cbar=False to remove

Note

heatmaps with x-axis 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)
../_images/plot_types_heatmap_17_0.png

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)
../_images/plot_types_heatmap_20_0.png

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')
../_images/plot_types_heatmap_25_0.png

  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)