A basic comparison graph in Python between one data point within one column to another column and I am not sure where to start. Specifically, I would like to compare elder financial exploitation within the suspicious activity column to gender, then repeat with that same activity and compare it to the state then age (So 4 graphs all together). I am sure I can figure out how to repeat the coding, but need help getting it started.

Below is a snapshot of some of the data I am playing around with, as well as some of my code. Any help is greatly appreciated!

#Import Modules

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from matplotlib import gridspec

Data columns (total 9 columns):

 #   Column               Non-Null Count         Dtype  

0   Suspicious Activity  3328 non-null      object 

1   Gender               3328 non-null               object 

2   Age                  3328 non-null               object 

3   State                3328 non-null                object  

4   Age               3328 non-null                    float64

Suspicious Activity	Gender	State	Age
Unusual/unexplained Activity	Female	Georgia	56
Unusual/unexplained Activity	Male	Georgia	59
Fraudulent Patterns	Female	Michigan	85
Unknown Source of Funds	Male	New York	60
Unknown Source of Funds	Female	New York	45
ACH Fraud	Male	Nevada	64
Fraudulent Patterns	Male	Michigan	69
Suspicious Call	Male	California	66
Suspicious Call	Female	Pennsylvania	75
ACH Fraud	Female	Ohio	68
ACH Fraud	Male	Washington	48
Fraudulent Patterns	Male	Washington	48
Elder Financial Exploitation	Female	Mississippi	82
ACH Fraud	Male	California	81
Fraudulent Patterns	Male	Ohio	66
Elder Financial Exploitation	Female	Illinois	84
Unusual/unexplained Activity	Male	New York	93
Fraudulent Documents	Female	California	44
Fraudulent Documents	Male	California	73
Fraudulent Patterns	Female	Illinois	91
Suspicious Call	Female	California	62

#Group the data by "Suspicious Activity" and "Gender" and count the number of occurrences

gender_group = df.groupby(["Suspicious Activity", "Gender"]).size().reset_index(name="Count")

sns.barplot(x="Elder Financial Exploitation", y="Count", hue="Gender", data=gender_group) plt.show()