Could not interpret value ` ` for parameter `hue`

I am trying to color a scatterplot by a categorical column. Here is a sample data, the column I want to color the scatterplot by is 'cat'.

data = {
'x': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], 
'y': [2, 3, 5, 7, 11, 13, 17, 19, 23, 29], 
'z': [1, 2, 2, 3, 3, 4, 4, 5, 6, 6],
'cat': ['A', 'A', 'B', 'B', 'A', 'A', 'B', 'B', 'A', 'A'] 
}

pandas_df = pd.DataFrame(data)
pyspark_df = spark.createDataFrame(pandas_df)

I created the following function to test the output. If I remove "hue" from the parameters, everything works fine, but i cannot seem to get it working correctly with 'hue'.

def facet_plot(df, x, y, color, facet_col, bins = None):
    pd_df = df.toPandas()

    if bins is not None:
        # check col type
        if pd_df[facet_col].dtype.name in ['float64', 'int64']:
            # bin the facet column
            pd_df['facet_col_binned']= pd.cut(pd_df[facet_col], bins = bins)     
            # convert intervals to midpoints
            pd_df['facet_col_binned'] = pd_df['facet_col_binned'].apply(lambda interval: round(interval.mid, 1) if pd.notna(interval) else None)
            pd_df['facet_col_binned'] = pd.Categorical(pd_df['facet_col_binned'])
        
            # assigning x as 'x_binned' for remaining code
            facet_col = 'facet_col_binned'

    pd_df[color] = pd_df[color].astype(str)

    g = sns.FacetGrid(pd_df, col=facet_col, col_wrap=4, height=5, aspect=2)
    g.map(sns.scatterplot, x, y, hue=color)

    # if row => then change to row_template = '{row_name}'
    g.set_titles(col_template = '{col_name}')
    g.set_axis_labels(x, y)

    plt.show()

facet_plot(pyspark_df, 'x', 'y', color = 'cat', facet_col='cat', bins = 2)

First off, creating a more minimal example helps to pinpoint the problem:

import seaborn as sns
import pandas as pd

data = {
    'x': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
    'y': [2, 3, 5, 7, 11, 13, 17, 19, 23, 29],
    'z': [1, 2, 2, 3, 3, 1, 1, 2, 3, 3],
    'cat': ['A', 'A', 'B', 'B', 'A', 'A', 'B', 'B', 'A', 'A']
}
pd_df = pd.DataFrame(data)

g = sns.FacetGrid(pd_df, col='z', col_wrap=3, height=3, aspect=2)
g.map(sns.scatterplot, 'x', 'y', hue='cat')

The main problem is that g.map doesn't provide the full dataframe in the call to sns.scatterplot. It only replaces 'x' and 'y' with the corresponding columns of the dataframe. As such, g.map() can't resolve ("interpret") the 'cat' column.

One possibility is to use g.map_dataframe instead. As no figure legend is created automatically, you'll also need to call g.add_legend().

A better solution is to add hue= to sns.FacetGrid(...., hue='cat') and leave it out in g.map(sns.scatterplot, 'x', 'y').

The recommended solution is to use the "figure level" version of your function. For sns.scatterplot this is sns.relplot. This also creates a FacetGrid, but is more fine-tuned for the scatter plot.

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

data = {
    'x': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
    'y': [2, 3, 5, 7, 11, 13, 17, 19, 23, 29],
    'z': [1, 2, 2, 3, 3, 1, 1, 2, 3, 3],
    'cat': ['A', 'A', 'B', 'B', 'A', 'A', 'B', 'B', 'A', 'A']
}
pd_df = pd.DataFrame(data)

g = sns.relplot(pd_df, x='x', y='y', hue='cat',  col='z', col_wrap=3, height=3, aspect=2)

plt.show()