Pandas 并排堆积条形图

Question

I want to create a stacked bar plot of the titanic dataset . The plot needs to group by "Pclass", "Sex" and "Survived". I have managed to do this with a lot of tedious numpy manipulation to produce the normalized plot below (where "M" is male and "F" is female)

Is there a way to do this using pandas inbuilt plotting functionality?

I have tried this:

import pandas as pd
import matplotlib.pyplot as plt
df = pd.read_csv('train.csv')
df_grouped = df.groupby(['Survived','Sex','Pclass'])['Survived'].count()
df_grouped.unstack().plot(kind='bar',stacked=True,  colormap='Blues', grid=True, figsize=(13,5));

Which is not what I want. Is there anyway to produce the first plot using pandas plotting? Thanks in advance

Answer 1

The resulting bars will not neighbour each other as in your first figure, but outside of that, pandas lets you do what you want as follows:

df_g = df.groupby(['Pclass', 'Sex'])['Survived'].agg([np.mean, lambda x: 1-np.mean(x)])
df_g.columns = ['Survived', 'Died']
df_g.plot.bar(stacked=True)

Here, the horizontal grouping of patches is complicated by the requirement of stacking. If, for instance, we only cared about the value of "Survived", pandas could take care of it out-of-the-box.

df.groupby(['Pclass', 'Sex'])['Survived'].mean().unstack().plot.bar()

If an ad hoc solution suffices for post-processing the plot, doing so is also not terribly complicated:

import numpy as np
from matplotlib import ticker

df_g = df.groupby(['Pclass', 'Sex'])['Survived'].agg([np.mean, lambda x: 1-np.mean(x)])
df_g.columns = ['Survived', 'Died']
ax = df_g.plot.bar(stacked=True)

# Move back every second patch
for i in range(6):
    new_x = ax.patches[i].get_x() - (i%2)/2
    ax.patches[i].set_x(new_x)
    ax.patches[i+6].set_x(new_x)

# Update tick locations correspondingly
minor_tick_locs = [x.get_x()+1/4 for x in ax.patches[:6]]
major_tick_locs = np.array([x.get_x()+1/4 for x in ax.patches[:6]]).reshape(3, 2).mean(axis=1)
ax.set_xticks(minor_tick_locs, minor=True)
ax.set_xticks(major_tick_locs)

# Use indices from dataframe as tick labels
minor_tick_labels = df_g.index.levels[1][df_g.index.labels[1]].values
major_tick_labels = df_g.index.levels[0].values
ax.xaxis.set_ticklabels(minor_tick_labels, minor=True)
ax.xaxis.set_ticklabels(major_tick_labels)

# Remove ticks and organize tick labels to avoid overlap
ax.tick_params(axis='x', which='both', bottom='off')
ax.tick_params(axis='x', which='minor', rotation=45)
ax.tick_params(axis='x', which='major', pad=35, rotation=0)