Python: Create matplotlib subplots using a for loop

Question

I would like to know how can I make my code more efficient with a for loop. I'm interested in making multiple subplots. In this example I'm using 4 but in reality I have 14.

So far, I've been copying/pasting the same block of code

df_A = df.loc[df['category'] == 'A'].copy()
df_B = df.loc[df['category'] == 'B'].copy()
df_C = df.loc[df['category'] == 'C'].copy()
df_D = df.loc[df['category'] == 'D'].copy()
dataframes = [df_A, df_B, df_C, df_D]
fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(12, 12), dpi=100)
fig.suptitle('Distributions per category in minutes', fontweight="bold", fontsize=15)
# category A
ax1.hist(df_A['time_spent'], color="darkcyan", edgecolor='black', bins=20)
ax1.set_title('Category A', fontsize=10)
# category B
ax2.hist(df_B['time_spent'], color="darkcyan", edgecolor='black', bins=20)
ax2.set_title('Category B', fontsize=10)
# category C
ax3.hist(df_C['time_spent'], color="darkcyan", edgecolor='black', bins=20)
ax3.set_title('Category C', fontsize=10)
# category D
ax4.hist(df_D['time_spent'], color="darkcyan", edgecolor='black', bins=20)
ax4.set_title('Category D', fontsize=10)
fig.tight_layout()
plt.show()

Answer 1

You could store the axes and conditions within lists and iterate over them to create your plots. Something like this would do the job:

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from itertools import chain
df = pd.DataFrame(
 {
 "category": ["A", "B", "C", "D"] * 1000,
 "time_spent": np.random.rand(4000)
 }
)
# categories = ["A", "B", "C", "D"]
categories = sorted(list(df["category"].unique()))
fig, axes = plt.subplots(2, 2, figsize=(12, 12), dpi=100)
axes = list(chain.from_iterable(axes))
fig.suptitle('Distributions per category in minutes', fontweight="bold", fontsize=15)
for i in range(len(axes)):
 axes[i].hist(df.loc[df['category'] == categories[i], 'time_spent'], color="darkcyan", edgecolor='black', bins=20)
 axes[i].set_title(f'Category {categories[i]}', fontsize=10)
 
fig.tight_layout()
plt.show()