CODEWITHSUNDEEP
pythonmatplotlibplotseabornvisualization
rod41
rod41

Reputation: 83

How to create a FacetGrid stacked barplot using Seaborn?

I am trying to plot a facet_grid with stacked bar charts inside.

I would like to use Seaborn. Its barplot function does not include a stacked argument.

I tried to use FacetGrid.map with a custom callable function.

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

def custom_stacked_barplot(col_day, col_time, col_total_bill, **kwargs):
    dict_df={}
    dict_df['day']=col_day
    dict_df['time']=col_time
    dict_df['total_bill']=col_total_bill
    df_data_graph=pd.DataFrame(dict_df)
    df = pd.crosstab(index=df_data_graph['time'], columns=tips['day'], values=tips['total_bill'], aggfunc=sum)
    df.plot.bar(stacked=True)

tips=sns.load_dataset("tips")
g = sns.FacetGrid(tips, col='size',  row='smoker')
g = g.map(custom_stacked_barplot, "day", 'time', 'total_bill')

However I get an empty canvas and stacked bar charts separately.

Empty canvas:

enter image description here

Graph1 apart:

enter image description here

Graph2:.

enter image description here

How can I fix this issue? Thanks for the help!

Upvotes: 8

Views: 8192

Answers (2)

Zephyr
Zephyr

Reputation: 12524

The simplest code to achive that result is this:

import seaborn as sns
import matplotlib.pyplot as plt
sns.set()

tips=sns.load_dataset("tips")
g = sns.FacetGrid(tips, col = 'size',  row = 'smoker', hue = 'day')
g = (g.map(sns.barplot, 'time', 'total_bill', ci = None).add_legend())

plt.show()

which gives this result:

enter image description here

Upvotes: 8

Parfait
Parfait

Reputation: 107767

Your different mixes of APIs (pandas.DataFrame.plot) appears not to integrate with (seaborn.FacetGrid). Since stacked bar plots are not supported in seaborn plotting, consider developing your own version with matplotlib subplots by iterating across groupby levels: 

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

def custom_stacked_barplot(t, sub_df, ax):
    plot_df = pd.crosstab(index=sub_df["time"], columns=sub_df['day'], 
                           values=sub_df['total_bill'], aggfunc=sum)

    p = plot_df.plot(kind="bar", stacked=True, ax = ax, 
                     title = " | ".join([str(i) for i in t]))   
    return p

tips = sns.load_dataset("tips")
g_dfs = tips.groupby(["smoker", "size"])

# INITIALIZE PLOT
# sns.set()
fig, axes = plt.subplots(nrows=2, ncols=int(len(g_dfs)/2)+1, figsize=(15,6))

# BUILD PLOTS ACROSS LEVELS
for ax, (i,g) in zip(axes.ravel(), sorted(g_dfs)):
    custom_stacked_barplot(i, g, ax)

plt.tight_layout()
plt.show()
plt.clf()
plt.close()

Plot Output 1

And use seaborn.set to adjust theme and pallette:

Plot Output 2

Upvotes: 3

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