Categorical plot of with data of multiple columns and their mean - pandas

Question

I'd like to create a categorical plot of two pandas DataFrame columns a and b in the same figure with shared x and different y axis:
import pandas as pd
import seaborn as sns
example = [
('exp1','f0', 0.25, 2),
('exp1','f1', 0.5, 3),
('exp1','f2', 0.75, 4),
('exp2','f1', -0.25, 1),
('exp2','f2', 1, 2),
('exp2','f3', 0, 3)
]
df = pd.DataFrame(example, columns=['exp', 'split', 'a', 'b'])
mean_df = df.groupby('exp')['a'].mean()
g = sns.catplot(x='exp', y='a', data=df, jitter=False)
ax2 = plt.twinx()
sns.catplot(x='exp', y='b', data=df, jitter=False, ax=ax2)
In this implementation I have the problem that the colors are different for categories (x-values), not for the columns. Can I sole this or do I have to change the data structure?
I would also like to connect the means of the categorical values like in the image like this:

Answer 1

You may want to melt your data first:
data = df.melt(id_vars='exp', value_vars=['a','b'])
fig, ax = plt.subplots()
sns.scatterplot(data=data,
x='exp',
hue='variable',
y='value',
ax=ax)
(data.groupby(['exp','variable'])['value']
.mean()
.unstack('variable')
.plot(ax=ax, legend=False)
)
ax.set_xlim(-0.5, 1.5);
Output:

Answer 2

df = pd.DataFrame(example, columns=['exp', 'split', 'a', 'b'])
mean_df = df.groupby('exp').mean().reset_index()
fig, ax1 = plt.subplots()
ax2 = ax1.twinx()
sns.scatterplot(x='exp', y='a', data=df, color='C0', ax=ax1)
sns.scatterplot(x='exp', y='b', data=df, color='C1', ax=ax2)
sns.lineplot(x='exp',y='a', data=mean_df, color='C0', ax=ax1)
sns.lineplot(x='exp',y='b', data=mean_df, color='C1', ax=ax2)