TypeError when plotting heatmap with seaborn

Question

I have the following dataset in a pandas DataFrame, which I've tidied and saved into "filename1.csv" :

import pandas as pd
df = pd.read_csv("filename1.csv")
print(df)

    samples a       b       c       percent_a   percent_c   ratio_a:b   ratio_c:b
0   sample1 185852  6509042 253303  0.028553    0.038916    35.022717   25.696664
1   sample2 218178  6456571 273448  0.033792    0.042352    29.593135   23.611696
2   sample3 251492  6353453 343252  0.039584    0.054026    25.263042   18.509588
3   sample4 232299  6431376 284522  0.036120    0.044240    27.685767   22.604143
..............................

I would like to plot this DataFrame as a heatmap using seaborn. At first, it would be interesting to see the samples (one sample per row) against two columns, percent_a and percent_c :

import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
# drop unnecessary columns
df = df.drop(["a", "b", "c", "ratio_a:b", "ratio_c:b"], axis = 1)
sns.heatmap(df)
plt.show()

However, this throws an error:

TypeError: ufunc 'isnan' not supported for the input types, and the inputs 
could not be safely coerced to any supported types according to the casting rule ''safe''

I originally thought this meant that there were NaN values in this DataFrame. However, it appears wrong, as

df.isnull().values.any()

outputs False . So, I suspect this is because samples is a column of non-numerical values.

How do I plot a seaborn heatmap such that these categorical values are shown?

Answer 1

If you just drop the "samples" column as well, isn't that what you are looking for?! You can then put the sample names in later using matplotlib's ax.set_yticklabels function. Note that you need to reverse sample names list, since matplotlib starts the labeling from the bottom.

import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd

df = pd.read_csv("SO_pandassnsheatmap.txt", delim_whitespace=True)
df2 = df.drop(["samples", "a", "b", "c", "ratio_a:b", "ratio_c:b"], axis = 1)
ax = sns.heatmap(df2)
ax.set_yticklabels(df.samples.values[::-1])

plt.show()