I have numerous sets of seasonal data that I am looking to show in a heatmap format. I am not worried about the magnitude of the values in the dataset but more the overall direction and any patterns that i can look at in more detail later. To do this I want to create a heatmap that only shows 2 colours (red for below zero and green for zero and above).
I can create a normal heatmap with seaborn but the normal colour maps do not have only 2 colours and I am not able to create one myself. Even if I could I am unable to set the parameters to reflect the criteria of below zero = red and zero+ = green.
I managed to create this simply by styling the dataframe but I was unable to export it as a .png because the table_criteria=’matplotlib’ option removes the formatting.
Below is an example of what I would like to create made from random data, could someone help or point me in the direction of a helpful Stackoverflow answer?
I have also included the code I used to style and export the dataframe.
Desired output – this is created with random data in an Excel spreadsheet
#Code to create a regular heatmap - can this be easily amended?
df_hm = pd.read_csv(filename+h)
pivot = df_hm.pivot_table(index='Year', columns='Month', values='delta', aggfunc='sum')
fig, ax = plt.subplots(figsize=(10,5))
ax.set_title('M1 '+h[:-7])
sns.heatmap(pivot, annot=True, fmt='.2f', cmap='RdYlGn')
plt.savefig(chartpath+h[:-7]+" M1.png", bbox_inches='tight')
plt.close()
#code used to export dataframe that loses format in the .png
import matplotlib.pyplot as plt
import dataframe_image as dfi
#pivot is the dateframe name
pivot = pd.DataFrame(np.random.randint(-100,100,size= (5, 12)),columns=list ('ABCDEFGHIJKL'))
styles = [dict(selector="caption", props=[("font-size", "120%"),("font-weight", "bold")])]
pivot = pivot.style.format(precision=2).highlight_between(left=-100000, right=-0.01, props='color:white;background-color:red').highlight_between(left=0, right= 100000, props='color:white;background-color:green').set_caption(title).set_table_styles(styles)
dfi.export(pivot, root+'testhm.png', table_conversion='matplotlib',chrome_path=None)
>Solution :
Welcome to SO!
To achieve what you need, you just need to pass delta through the sign function, here’s an example code:
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
arr = np.random.randn(25,25)
sns.heatmap(np.sign(arr))
Which results in a binary heatmap, albeit one with a quite ugly colormap, still, you can fiddle around with Seaborn’s colormaps in order to make it look like excel.