pythonpandasseabornbar-chartheatmap
How to align yticklabels when combining a barplot with heatmap
I have similar problems as this question; I am trying to combine three plots in Seaborn, but the labels on my y-axis are not aligned with the bars.
My code (now a working copy-paste example):
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from matplotlib.colors import LogNorm
### Generate example data
np.random.seed(123)
year = [2018, 2019, 2020, 2021]
task = [x + 2 for x in range(18)]
student = [x for x in range(200)]
amount = [x + 10 for x in range(90)]
violation = [letter for letter in "thisisjustsampletextforlabels"] # one letter labels
df_example = pd.DataFrame({
# some ways to create random data
'year':np.random.choice(year,500),
'task':np.random.choice(task,500),
'violation':np.random.choice(violation, 500),
'amount':np.random.choice(amount, 500),
'student':np.random.choice(student, 500)
})
### My code
temp = df_example.groupby(["violation"])["amount"].sum().sort_values(ascending = False).reset_index()
total_violations = temp["amount"].sum()
sns.set(font_scale = 1.2)
f, axs = plt.subplots(1,3,
figsize=(5,5),
sharey="row",
gridspec_kw=dict(width_ratios=[3,1.5,5]))
# Plot frequency
df1 = df_example.groupby(["year","violation"])["amount"].sum().sort_values(ascending = False).reset_index()
frequency = sns.barplot(data = df1, y = "violation", x = "amount", log = True, ax=axs[0])
# Plot percent
df2 = df_example.groupby(["violation"])["amount"].sum().sort_values(ascending = False).reset_index()
total_violations = df2["amount"].sum()
percent = sns.barplot(x='amount', y='violation', estimator=lambda x: sum(x) / total_violations * 100, data=df2, ax=axs[1])
# Pivot table and plot heatmap
df_heatmap = df_example.groupby(["violation", "task"])["amount"].sum().sort_values(ascending = False).reset_index()
df_heatmap_pivot = df_heatmap.pivot("violation", "task", "amount")
df_heatmap_pivot = df_heatmap_pivot.reindex(index=df_heatmap["violation"].unique())
heatmap = sns.heatmap(df_heatmap_pivot, fmt = "d", cmap="Greys", norm=LogNorm(), ax=axs[2])
plt.subplots_adjust(top=1)
axs[2].set_facecolor('xkcd:white')
axs[2].set(ylabel="",xlabel="Task")
axs[0].set_xlabel('Total amount of violations per year')
axs[1].set_xlabel('Percent (%)')
axs[1].set_ylabel('')
axs[0].set_ylabel('Violation')
The result can be seen here:
The y-labels are aligned according to my last plot, the heatmap. However, the bars in the bar plots are clipping at the top, and are not aligned to the labels. I just have to nudge the bars in the barplot -- but how? I've been looking through the documentation, but I feel quite clueless as of now.
Solution
- See here that none of the y-axis ticklabels are aligned because multiple dataframes are used for plotting. It will be better to create a single dataframe,
violations, with the aggregated data to be plotted. Start with the sum of amounts by violation, and then add a new percent column. This will insure the two bar plots have the same y-axis. - Instead of using
.groupbyand then.pivot, to createdf_heatmap_pivot, use.pivot_table, and then reindex usingviolations.violation. - Tested in
python 3.10,pandas 1.4.3,matplotlib 3.5.1,seaborn 0.11.2
DataFrames and Imports
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import LogNorm
# Generate example data
year = [2018, 2019, 2020, 2021]
task = [x + 2 for x in range(18)]
student = [x for x in range(200)]
amount = [x + 10 for x in range(90)]
violation = list("thisisjustsampletextforlabels") # one letter labels
np.random.seed(123)
df_example = pd.DataFrame({name: np.random.choice(group, 500) for name, group in
zip(['year', 'task', 'violation', 'amount', 'student'],
[year, task, violation, amount, student])})
# organize all of the data
# violations frequency
violations = df_example.groupby(["violation"])["amount"].sum().sort_values(ascending=False).reset_index()
total_violations = violations["amount"].sum()
# add percent
violations['percent'] = violations.amount.div(total_violations).mul(100).round(2)
# Use .pivot_table to create the pivot table
df_heatmap_pivot = df_example.pivot_table(index='violation', columns='task', values='amount', aggfunc='sum')
# Set the index to match the plot order of the 'violation' column
df_heatmap_pivot = df_heatmap_pivot.reindex(index=violations.violation)
Plotting
- Using
sharey='row'is causing the alignment problem. Usesharey=False, and remove theyticklabelsfromaxs[1]andaxs[2], withaxs[1 or 2].set_yticks([]).- This is the case because
ylimfor theheatmapis not the same as forbarplot. As such, theheatmapis shifted. .get_ylim()foraxs[0]andaxs[1]is(15.5, -0.5), which foraxs[2]is(16.0, 0.0).
- This is the case because
- See How to add value labels on a bar chart for additional details and examples using
.bar_label.
# set seaborn plot format
sns.set(font_scale=1.2)
# create the figure and set sharey=False
f, axs = plt.subplots(1, 3, figsize=(12, 12), sharey=False, gridspec_kw=dict(width_ratios=[3,1.5,5]))
# Plot frequency
sns.barplot(data=violations, x="amount", y="violation", log=True, ax=axs[0])
# Plot percent
sns.barplot(data=violations, x='percent', y='violation', ax=axs[1])
# add the bar labels
axs[1].bar_label(axs[1].containers[0], fmt='%.2f%%', label_type='edge', padding=3)
# add extra space for the annotation
axs[1].margins(x=1.3)
# plot the heatmap
heatmap = sns.heatmap(df_heatmap_pivot, fmt = "d", cmap="Greys", norm=LogNorm(), ax=axs[2])
# additional formatting
axs[2].set_facecolor('xkcd:white')
axs[2].set(ylabel="", xlabel="Task")
axs[0].set_xlabel('Total amount of violations per year')
axs[1].set_xlabel('Percent (%)')
axs[1].set_ylabel('')
axs[0].set_ylabel('Violation')
# remove yticks / labels
axs[1].set_yticks([])
_ = axs[2].set_yticks([])
- Comment out the last two lines to verify the yticklabels are aligned for each
axs.
DataFrame Views
df_example.head()
year task violation amount student
0 2020 2 i 84 59
1 2019 2 u 12 182
2 2020 5 s 20 9
3 2020 11 u 56 163
4 2018 17 t 59 125
violations
violation amount percent
0 s 4869 17.86
1 l 3103 11.38
2 t 3044 11.17
3 e 2634 9.66
4 a 2177 7.99
5 i 2099 7.70
6 h 1275 4.68
7 f 1232 4.52
8 b 1191 4.37
9 m 1155 4.24
10 o 1075 3.94
11 p 763 2.80
12 r 762 2.80
13 j 707 2.59
14 u 595 2.18
15 x 578 2.12
df_heatmap_pivot
task 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19
violation
s 62.0 36.0 263.0 273.0 191.0 250.0 556.0 239.0 230.0 188.0 185.0 516.0 249.0 331.0 212.0 219.0 458.0 411.0
l 83.0 245.0 264.0 451.0 155.0 314.0 98.0 125.0 310.0 117.0 21.0 99.0 98.0 50.0 40.0 268.0 192.0 173.0
t 212.0 255.0 45.0 141.0 74.0 135.0 52.0 202.0 107.0 128.0 158.0 NaN 261.0 137.0 339.0 207.0 362.0 229.0
e 215.0 315.0 NaN 116.0 213.0 165.0 130.0 194.0 56.0 355.0 75.0 NaN 118.0 189.0 160.0 177.0 79.0 77.0
a 135.0 NaN 165.0 156.0 204.0 115.0 77.0 65.0 80.0 143.0 83.0 146.0 21.0 29.0 285.0 72.0 116.0 285.0
i 209.0 NaN 20.0 187.0 83.0 136.0 24.0 132.0 257.0 56.0 201.0 52.0 136.0 226.0 104.0 145.0 91.0 40.0
h 27.0 NaN 255.0 NaN 99.0 NaN 71.0 53.0 100.0 89.0 NaN 106.0 NaN 170.0 86.0 79.0 140.0 NaN
f 75.0 23.0 99.0 NaN 26.0 103.0 NaN 185.0 99.0 145.0 NaN 63.0 64.0 29.0 114.0 141.0 38.0 28.0
b 44.0 70.0 56.0 12.0 55.0 14.0 158.0 130.0 NaN 11.0 21.0 NaN 52.0 137.0 162.0 NaN 231.0 38.0
m 86.0 NaN NaN 147.0 74.0 131.0 49.0 180.0 94.0 16.0 NaN 88.0 NaN NaN NaN 51.0 161.0 78.0
o 109.0 NaN 51.0 NaN NaN NaN 20.0 139.0 149.0 NaN 101.0 60.0 NaN 143.0 39.0 73.0 10.0 181.0
p 16.0 NaN 197.0 50.0 87.0 NaN 88.0 NaN 11.0 162.0 NaN 14.0 NaN 78.0 45.0 NaN NaN 15.0
r NaN 85.0 73.0 40.0 NaN NaN 68.0 77.0 NaN 26.0 122.0 105.0 NaN 98.0 NaN NaN NaN 68.0
j NaN 70.0 NaN NaN 73.0 76.0 NaN 150.0 NaN NaN NaN 81.0 NaN 97.0 97.0 63.0 NaN NaN
u 174.0 45.0 NaN NaN 32.0 NaN NaN 86.0 30.0 56.0 13.0 NaN 24.0 NaN NaN 69.0 54.0 12.0
x 69.0 29.0 NaN 106.0 NaN 43.0 NaN NaN NaN 97.0 56.0 29.0 149.0 NaN NaN NaN NaN NaN