Two sided grouped barplots with Python seaborn-CodePudding

I'm trying to draw a two-sided graph similar to the population pyramid explained

CodePudding user response：

Try something like this:

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

# data
data = {'species': ['X', 'X', 'Y', 'Y', 'Z', 'Z', 'X', 'X', 'Y', 'Y', 'Z', 'Z'],
        'sex': ['male', 'female', 'male', 'female', 'male', 'female', 'male', 'female', 'male', 'female', 'male', 'female'], 
        'mass (g)': [4000, 3500, 3800, 3200, 5500, 4900, 2500, 2100, 2400, 2000, 4200, 3800],
        'age': ['adult', 'adult', 'adult', 'adult', 'adult', 'adult', 'juvenile', 'juvenile', 'juvenile', 'juvenile', 'juvenile', 'juvenile']}
df = pd.DataFrame(data)

# convert juvenile mass to negative
df.loc[df.age.eq('juvenile'), 'mass (g)'] = df['mass (g)'].mul(-1)

# plot
sns.set_theme(style="whitegrid")
fig, ax = plt.subplots(figsize=(10,5))
df_reshape = df.set_index(['species','sex','age']).unstack(['age','sex'])['mass (g)']
df_reshape.loc[:, 'adult'].plot.barh(ax=ax)
df_reshape.loc[:, 'juvenile'].plot.barh(legend=False, ax=ax)
plt.show()

Output:

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

# data
data = {'species': ['X', 'X', 'Y', 'Y', 'Z', 'Z', 'X', 'X', 'Y', 'Y', 'Z', 'Z'],
        'sex': ['male', 'female', 'male', 'female', 'male', 'female', 'male', 'female', 'male', 'female', 'male', 'female'], 
        'mass (g)': [4000, 3500, 3800, 3200, 5500, 4900, 2500, 2100, 2400, 2000, 4200, 3800],
        'age': ['adult', 'adult', 'adult', 'adult', 'adult', 'adult', 'juvenile', 'juvenile', 'juvenile', 'juvenile', 'juvenile', 'juvenile']}
df = pd.DataFrame(data)

# convert juvenile mass to negative
df.loc[df.age.eq('juvenile'), 'mass (g)'] = df['mass (g)'].mul(-1)

# plot
sns.set_theme(style="whitegrid")
fig, ax = plt.subplots(figsize=(10,5))
df_reshape = df.set_index(['species','sex','age']).unstack(['age','sex'])['mass (g)']
df_reshape.loc[:, ['adult']].plot.barh(ax=ax, edgecolor='k')
df_reshape.loc[:, ['juvenile']].plot.barh(ax=ax, label='Juvenile', color=['navy','red'], alpha=.6, edgecolor='k', hatch='/')
plt.show()

Output:

CodePudding user response：

If you mix positive and negative values, by default seaborn's barplot will average them out.

You could draw two barplots back to back and reverse the left one:

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

data = {'species': ['X', 'X', 'Y', 'Y', 'Z', 'Z', 'X', 'X', 'Y', 'Y', 'Z', 'Z'],
        'sex': ['male', 'female', 'male', 'female', 'male', 'female', 'male', 'female', 'male', 'female', 'male', 'female'],
        'mass (g)': [4000, 3500, 3800, 3200, 5500, 4900, 2500, 2100, 2400, 2000, 4200, 3800],
        'age': ['adult', 'adult', 'adult', 'adult', 'adult', 'adult', 'juvenile', 'juvenile', 'juvenile', 'juvenile', 'juvenile', 'juvenile']}
df = pd.DataFrame(data)
df['sex'] = pd.Categorical(df['sex'])  # make hue column categorical, forcing a fixed order

sns.set_theme(style='whitegrid')
fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(10, 5), sharey=True, gridspec_kw={'wspace': 0})


# draw adult subplot at the right
sns.barplot(data=df[df['age'] == 'adult'], x='mass (g)', y='species', hue='sex',
            ci=False, orient='horizontal', dodge=True, ax=ax2)
ax2.yaxis.set_label_position('right')
ax2.tick_params(axis='y', labelright=True, right=True)
ax2.set_title('  ' 'adult', loc='left')
ax2.legend_.remove()  # remove the legend; the legend will be in ax1

# draw juvenile subplot at the left
sns.barplot(data=df[df['age'] == 'juvenile'], x='mass (g)', y='species', hue='sex',
            ci=False, orient='horizontal', dodge=True, ax=ax1)

# optionally use the same scale left and right
xmax = max(ax1.get_xlim()[1], ax2.get_xlim()[1])
ax1.set_xlim(xmax=xmax)
ax2.set_xlim(xmax=xmax)

ax1.invert_xaxis()  # reverse the direction
ax1.tick_params(labelleft=False, left=False)
ax1.set_ylabel('')
ax1.set_title('juvenile' '  ', loc='right')

plt.tight_layout()
plt.show()

An interesting feature of seaborn's barplots is that it will also do the work of averaging out the values given a dataframe with a row for each individual (and calculate a confidence interval).

CodePudding user response：

I just used `pivot to shape the data correctly

import pandas as pd
import seaborn as sns

# data
data = {'species': ['X', 'X', 'Y', 'Y', 'Z', 'Z', 'X', 'X', 'Y', 'Y', 'Z', 'Z'],
        'sex': ['male', 'female', 'male', 'female', 'male', 'female', 'male', 'female', 'male', 'female', 'male', 'female'], 
        'mass (g)': [4000, 3500, 3800, 3200, 5500, 4900, 2500, 2100, 2400, 2000, 4200, 3800],
        'age': ['adult', 'adult', 'adult', 'adult', 'adult', 'adult', 'juvenile', 'juvenile', 'juvenile', 'juvenile', 'juvenile', 'juvenile']}
df = pd.DataFrame(data)

# convert juvenile mass to negative
df.loc[df.age.eq('juvenile'), 'mass (g)'] = df['mass (g)'].mul(-1)

# pivot data
df=df.pivot(columns=['age'], index=['species', 'sex'], values=['mass (g)']).reset_index()
df = df.set_index(['species', 'sex'])['mass (g)'].reset_index()

# plot
sns.set_theme(style="whitegrid")
fig, ax = plt.subplots(figsize=(10,5))
sns.barplot(data=df, x='adult', y='species', hue='sex', ci=False, orient='horizontal', dodge=True)
sns.barplot(data=df, x='juvenile', y='species', hue='sex', ci=False, orient='horizontal', dodge=True)