Create a readable graph for my code. I'm trying to show a graph that compares elder financial exploitation (listed under the suspicious activity column) to age. I believe I need to somehow group the ages into bins before displaying them in a graph, but I do not know how to do that.  I have over 3,000 ages in my population. Below is just a small sample of it. Here is the code I am running, which works, but lists every individual age. Also, the legend appears right over the graph, so I cannot read it clearly. (Not that is it clear anyway) Can anyone assist me with a code that will group the ages into ranges, such has 1-20, 21-30, etc.. I have ages that go up to 100. Any help is much appreciated! 

#Import Modules

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

#Round the age column
activity.round()

 

#Convert Age column to float
activity['Age'] = activity['Age'].astype(float)

 

Elder_Financial_Exploitation_df = activity[activity['Suspicious Activity'] == 'Elder Financial Exploitation']

age_group = Elder_Financial_Exploitation_df.groupby('Age').size().reset_index(name='Count')

sns.countplot(data=Elder_Financial_Exploitation_df, x= 'Suspicious Activity', hue='Age')

 

 

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