[FIXED] How to create a 100% stacked bar plot from a categorical dataframe

Issue

I have a dataframe structured like this:

User	Food 1	Food 2	Food 3	Food 4
Steph	Onions	Tomatoes	Cabbages	Potatoes
Tom	Potatoes	Tomatoes	Potatoes	Potatoes
Fred	Carrots	Cabbages		Eggplant
Phil	Onions	Eggplant	Eggplant

I want to use the distinct values from across the food columns as categories. I then want to create a Seaborn plot so the % of each category for each column is plotted as a 100% horizontal stacked bar.

My attempt to do this:

data = {
    'User' : ['Steph', 'Tom', 'Fred', 'Phil'],
    'Food 1' : ["Onions", "Potatoes", "Carrots", "Onions"],
    'Food 2' : ['Tomatoes', 'Tomatoes', 'Cabbages', 'Eggplant'],
    'Food 3' : ["Cabbages", "Potatoes", "", "Eggplant"],
    'Food 4' : ['Potatoes', 'Potatoes', 'Eggplant', ''],    
}

df = pd.DataFrame(data)

x_ax = ["Onions", "Potatoes", "Carrots", "Onions", "", 'Eggplant', "Cabbages"]

df.plot(kind="barh", x=x_ax, y=["Food 1", "Food 2", "Food 3", "Food 4"], stacked=True, ax=axes[1])

plt.show()

Solution

1. Replace '' with np.nan because empty stings will be counted as values.
2. Use pandas.DataFrame.melt to convert the dataframe to a long form.
3. Use pandas.crosstab to get a frequency count table
4. Get the total for each row.
5. Calculate the category percent for each row.
6. Plot the dataframe with pandas.DataFrame.plot and kind='barh'.
   • Putting the food names on the x-axis is not the correct way to create a 100% stacked bar plot. One axis must be numeric. The bars will be colored by food type.
7. Annotate the bars based on this answer.
8. Move the legend outside the plot based on this answer.

• seaborn is a high-level API for matplotlib, and pandas uses matplotlib as the default backend, and it's easier to produce a stacked bar plot with pandas.
  • seaborn doesn't support stacked barplots, unless histplot is used in a hacked way, as shown in this answer, and would require an extra step of melting percent.
• Tested in python 3.10, pandas 1.4.2, matplotlib 3.5.1
  • Assignment expressions (:=) require python >= 3.8. Otherwise, use [f'{v.get_width():.2f}%' if v.get_width() > 0 else '' for v in c ].

import pandas as pd
import numpy as np

# using the dataframe in the OP

# 1.
df = df.replace('', np.nan)

# 2.
dfm = df.melt(id_vars='User', var_name='Food', value_name='Type')

# 3.
ct = pd.crosstab(dfm.Food, dfm.Type)

# 4.
total = ct.sum(axis=1)

# 5. 
percent = ct.div(total, axis=0).mul(100).round(2)

# 6.
ax = percent.plot(kind='barh', stacked=True, figsize=(8, 6))

# 7.
for c in ax.containers:
    
    # customize the label to account for cases when there might not be a bar section
    labels = [f'{w:.2f}%' if (w := v.get_width()) > 0 else '' for v in c ]
    
    # set the bar label
    ax.bar_label(c, labels=labels, label_type='center')

# 8.
ax.legend(bbox_to_anchor=(1, 1.02), loc='upper left')

DataFrame Views

dfm

     User    Food      Type
0   Steph  Food 1    Onions
1     Tom  Food 1  Potatoes
2    Fred  Food 1   Carrots
3    Phil  Food 1    Onions
4   Steph  Food 2  Tomatoes
5     Tom  Food 2  Tomatoes
6    Fred  Food 2  Cabbages
7    Phil  Food 2  Eggplant
8   Steph  Food 3  Cabbages
9     Tom  Food 3  Potatoes
10   Fred  Food 3       NaN
11   Phil  Food 3  Eggplant
12  Steph  Food 4  Potatoes
13    Tom  Food 4  Potatoes
14   Fred  Food 4  Eggplant
15   Phil  Food 4       NaN

ct

Type    Cabbages  Carrots  Eggplant  Onions  Potatoes  Tomatoes
Food                                                           
Food 1         0        1         0       2         1         0
Food 2         1        0         1       0         0         2
Food 3         1        0         1       0         1         0
Food 4         0        0         1       0         2         0

total

Food
Food 1    4
Food 2    4
Food 3    3
Food 4    3
dtype: int64

percent

Type    Cabbages  Carrots  Eggplant  Onions  Potatoes  Tomatoes
Food                                                           
Food 1      0.00     25.0      0.00    50.0     25.00       0.0
Food 2     25.00      0.0     25.00     0.0      0.00      50.0
Food 3     33.33      0.0     33.33     0.0     33.33       0.0
Food 4      0.00      0.0     33.33     0.0     66.67       0.0

Answered By - Trenton McKinney