[FIXED] How to align the x position of the dots in seaborn scatterplot to a nested bar plot

Issue

I am trying to plot a scatter plot on top of a bar plot using sns.scatterplot() and df.plot(kind='bar'); The figure turns out to be fine, but it would be even nicer if I can align each of the scatter points to its corresponding bar with an identical label.

I have read the document on Rectangle of matplotlib.pyplot that it has a get_x() method that can "Return the left coordinate of the rectangle";

I wonder if there is a way for me to assign these coordinates to the scatter points that'd be plotted by seaborn?

Code

fig, ax = plt.subplots(nrows=1, ncols=1)
fig.set_size_inches(9, 9)
fig.set_dpi(300)

bar_df.plot(kind='bar', ax=ax)

ax2 = ax.twinx()

sns.scatterplot(data=line_df, ax=ax2)

Dataframes

bar_df

year	apple	banana	citrus	...
2020	12	34	56	78
2025	12	34	56	78
2030	12	34	56	78
2035	12	34	56	78

line_df

year	apple	banana	citrus	...
2020	23	45	67	89
2025	23	45	67	89
2030	23	45	67	89
2035	23	45	67	89

It'd be really nice if I could make the points in the same vertical line as the bar with the same header;

Solution

sns.scatterplot interprets the x-axis as numeric. As such, it doesn't align well with a bar plot, nor does it have a dodge= parameter. You can use sns.stripplot instead.

Seaborn works easiest with its data in "long form", which can be achieved via pandas pd.melt.

Here is some example code:

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

bar_df, line_df = pd.read_html('https://stackoverflow.com/questions/73191315')
bar_df_long = bar_df.melt(id_vars='year', var_name='fruit', value_name='bar_value')
line_df_long = line_df.melt(id_vars='year', var_name='fruit', value_name='line_value')

fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(6,6), dpi=300)
sns.barplot(data=bar_df_long, x='year', y='bar_value', hue='fruit', dodge=True, ax=ax)

ax2 = ax.twinx()
sns.stripplot(data=line_df_long, x='year', y='line_value', hue='fruit', dodge=True, jitter=False,
              edgecolor='black', linewidth=1, ax=ax2)
ax2.legend_.remove() # remove the second legend

plt.tight_layout()
plt.show()

Answered By - JohanC