pandas style can't set background gradient on multiindex after dataframe division-CodePudding

I can do a pandas styles heatmap over a multi-index no problem:

df = sns.load_dataset('geyser').reset_index()

df['3m_duration'] = df.duration > 3

group_cols = ['kind', '3m_duration']

count_gpby = df[
    group_cols   ['index']
].groupby(
    group_cols
)

count_gpby.count().style.background_gradient(cmap ='Blues')

I can also divide a subset groupby by the total groupby to get a comparative rate/ratio per group:

df['binary'] = 'A'
df.loc[100:, 'binary'] = 'B'

subset_gpby = df[
    group_cols   ['index']
].loc[df.binary=='B'].groupby(
    group_cols
).count()

(subset_gpby / gpby).style.background_gradient(cmap ='Blues')

But then I try to combine these two "views" as two columns in the same multi-index dataframe so that I can see a raw count and a comparative ratio at the same time. This has no issue printing:

But it cant be displayed with the Pandas style heatmap background gradient because of "non-unique indices":

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-275-f82cbb6545e2> in <module>
----> 1 pd.concat([(subset_gpby / gpby), gpby], axis=1).style.background_gradient(cmap ='Blues')

C:\ProgramData\Anaconda3\envs\venv\lib\site-packages\pandas\core\frame.py in style(self)
    959         from pandas.io.formats.style import Styler
    960 
--> 961         return Styler(self)
    962 
    963     _shared_docs[

C:\ProgramData\Anaconda3\envs\venv\lib\site-packages\pandas\io\formats\style.py in __init__(self, data, precision, table_styles, uuid, caption, table_attributes, cell_ids, na_rep, uuid_len)
    161             data = data.to_frame()
    162         if not data.index.is_unique or not data.columns.is_unique:
--> 163             raise ValueError("style is not supported for non-unique indices.")
    164 
    165         self.data = data

ValueError: style is not supported for non-unique indices.

However,

pd.concat([(subset_gpby / gpby), gpby], axis=1).index.value_counts()

> (short, False)    1
> (short, True)     1
> (long, True)      1
> (long, False)     1
> dtype: int64

shows that there's only one instance of each index, and the index is equal to the previous indices that render with no problem:

pd.concat([(subset_gpby / gpby), gpby], axis=1).index == (subset_gpby / gpby).index

> array([ True,  True,  True,  True])

Why does this error appear?

CodePudding user response：

In pandas, both the "Index", and "Columns" are of type

Setup used:

import pandas as pd

import seaborn as sns

# Setup Data
df = sns.load_dataset('geyser').reset_index()
group_cols = ['kind', '3m_duration']
df['3m_duration'] = df['duration'].gt(3)
subset_df = df[[*group_cols, 'index']].copy()
# Build Count DataFrames
gpby = subset_df.groupby(group_cols).count()
subset_gpby = subset_df.loc[100:, :].groupby(group_cols).count()