How to make a multi-level chart column label by hue

Question

This is a continuation of this question. But now I have a bar-chart with hue.

Here’s what I have:

df = pd.DataFrame({'age': ['20-30', '20-30', '20-30', '30-40', '30-40', '30-40', '40-50', '40-50', '40-50', '50-60', '50-60', '50-60'],
               'expenses':['50$', '100$', '200$', '50$', '100$', '200$', '50$', '100$', '200$', '50$', '100$', '200$'],
               'users': [59, 42, 57, 68, 47, 98, 75, 73, 54, 81, 52, 43],
               'buyers': [22, 35, 18, 27, 12, 57, 19, 29, 31, 47, 10, 5],
               'percentage': [37.2881, 83.3333, 31.5789, 39.7058, 25.5319, 58.1632, 25.3333, 39.7260, 57.4074, 58.0246, 19.2307, 11.6279]})

index	age	expenses	users	buyers	percentage
0	20-30	50$	59	22	37.2881
1	20-30	100$	42	35	83.3333
2	20-30	200$	57	18	31.5789
3	30-40	50$	68	27	39.7058
4	30-40	100$	47	12	25.5319
5	30-40	200$	98	57	58.1632
6	40-50	50$	75	19	25.3333
7	40-50	100$	73	29	39.726
8	40-50	200$	54	31	57.4074
9	50-60	50$	81	47	58.0246
10	50-60	100$	52	10	19.2307
11	50-60	200$	43	5	11.6279

fig, ax = plt.subplots(figsize=(20, 10))

# Plot the all users
sns.barplot(x='age', y='users', data=df, hue='expenses', palette='Blues', edgecolor='grey', alpha=0.7, ax=ax)
# Plot the buyers
sns.barplot(x='age', y='buyers', data=df, hue='expenses', palette='Blues', edgecolor='darkgrey', hatch='//', ax=ax)

plt.show()

I need to get the same chart. In the case of hue, the code:

# extract the separate containers
c1, c2 = ax.containers

# annotate with the users values
ax.bar_label(c1, fontsize=13)

# annotate with the buyer and percentage values
l2 = [f"{v.get_height()}: {df.loc[i, 'percentage']}%" for i, v in enumerate(c2)]
ax.bar_label(c2, labels=l2, fontsize=8, label_type='center', fontweight='bold')

no longer works.
I would be glad for any hints.

Asked By: Kate

||

Source

Answer 1

Each object in ax.containers represents the bars for a single hue group.
- When using bar_label, the annotations for each bar in '50$', then '100$', and then '200$' are added.
I think it’s easier to select the correct data by annotating the 'buyers' group separately.
- The answer to your previous question selects the data from the entire dataframe, but here Boolean indexing is used to select only a segment of the dataframe. Using print(data) in each loop will help with understanding.

fig, ax = plt.subplots(figsize=(20, 10))

# plot the all users
sns.barplot(x='age', y='users', data=df, hue='expenses', palette='Blues', edgecolor='grey', alpha=0.7, ax=ax)

# annotate the bars in the 3 containers (1 container per hue group)
for c in ax.containers:
    ax.bar_label(c)
    
# plot the 'buyers', which adds 3 more containers to ax
sns.barplot(x='age', y='buyers', data=df, hue='expenses', palette='Blues', edgecolor='darkgrey', hatch='//', ax=ax)

# iterate through the last 3 new containers containing the hatched groups 
for c in ax.containers[3:]:
    
    # get the hue label, which will be used to select the data group
    hue_label = c.get_label()
    # select the data based on hue_label
    data = df.loc[df.expenses.eq(hue_label), ['buyers', 'percentage']]
    # customize the labels
    labels = [f"{v.get_height()}: {data.iloc[i, 1]:0.2f}%" for i, v in enumerate(c)]
    # add the labels
    ax.bar_label(c, labels=labels)

plt.show()

Answered By: Trenton McKinney

How to make a multi-level chart column label by hue

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