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.
CodePudding user response：
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()
