Categorical data visualization - scatter plot with multiple X using Pandas and Seaborn-CodePudding

I spent many hours looking for tips how to create categorical plot using Seaborn and Pandas having several Xs to be added on x-axis, but I have not found the solution.

For specified columns from excel (for example: S1_1, S1_2, S1_3) I would like to create one scatterplot with readings - it means for each column header 9 measurements are expected. Please refer to the image to see the data structure in excel. I was unable to find the right function.

I tried with the following code, but this is not what I wanted to achieve.

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

df = pd.read_excel("panda.xlsx")

dfx = pd.DataFrame({"CHAR": ["S1_1","S1_2","S1_3"]})

sns.stripplot(x=dfx['CHAR'],y=df['S1_1'],color='black')
sns.stripplot(x=dfx['CHAR'],y=df['S1_2'],color='black')
sns.stripplot(x=dfx['CHAR'],y=df['S1_3'],color='black')

plt.xlabel('X Axis')
plt.ylabel('Y Axis')
plt.show()

Expected vs obtained plot:

CodePudding user response：

You're overthinking things. You don't need to call stripplot separately for each column. I generated new random data since you didn't share yours in a copy-and-pastable form, but stripplot will basically do what I think you want with a very short invocation.

> print(df)


         S1        S2        S3        S4
0  0.314097  0.678525  0.228356  0.770293
1  0.207790  0.739484  0.965662  0.604426
2  0.975562  0.959384  0.088162  0.265529
3  0.616823  0.902795  0.015561  0.662020
4  0.210507  0.287713  0.660347  0.763312
5  0.763505  0.381314  0.759422  0.257578
6  0.707832  0.912063  0.774681  0.534284
7  0.996891  0.258103  0.313047  0.729142
8  0.121308  0.797310  0.286265  0.757299    

> sns.stripplot(data=df[["S1", "S2", "S3"]], color='black')
> plt.xlabel("X Axis")