Plot a 2D Colormap/Heatmap in matplotlib with x y z data from a pandas dataframe

Question

I have a pandas dataframe which looks like this

X	Y	Z
-1	-1	123
-1	0	456
-1	1	678
0	-1	124
0	0	890
0	1	121
1	-1	767
1	0	987
1	1	450

• The actual size is (121,3)

I would like to plot a 2D colourmap where the X and Y are on the x,y axes and the Y shows the intensity of the value as a colourbar on the right side of the plot.

Note that here the values of the column Z are not a function of X and Y.

I tried to convert this dataframe into a numpy array. I'm having trouble creating a Z mesh with a the size of (121,121) when I do not have a dependency of the Z on the X and Y. I've tried using imshow and pcolor and looked at other similar answers but I coudn't find anything.

Answer 1

The most straightforward approach would be Seaborn's heatmap:

import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
from io import StringIO

data_str = '''-1    -1  123
-1  0   456
-1  1   678
0   -1  124
0   0   890
0   1   121
1   -1  767
1   0   987
1   1   450'''
df = pd.read_csv(StringIO(data_str), names=['x', 'y', 'z'], delim_whitespace=True)
df_pivoted = df.pivot(columns='x', index='y', values='z')
ax = sns.heatmap(data=df_pivoted, annot=True, fmt='d', cmap='RdYlGn', cbar=True, cbar_kws={'label': 'z'}, square=True)
ax.tick_params(labelrotation=0)
plt.show()