Load data from CSV and plot using Seaborn barplot method

For my PDS class!

%matplotlib inline

import mysql.connector
from datetime import date, datetime
import sys
import pandas as pd

import seaborn as sns

user,pw, host,db = 'root','it8701','127.0.0.1','anotherdatabase2'

cnx = mysql.connector.connect(user=user, password=pw, host=host, database=db)
cursor = cnx.cursor()

select_stmt = ("SELECT * FROM cea_salespersons")

try:
  cursor.execute(select_stmt)
  df = pd.DataFrame(cursor.fetchall(), columns = ['cea_salesperson_id','salesperson_name', 'registration_no',
                                                  'registration_start_date',  'registration_end_date', 
                                                  'estate_agent_name','estate_agent_license_no'])
    
  df["registration_start_date"] = pd.to_datetime(df["registration_start_date"], errors="coerce")

  # Create a new DataFrameGroupBy object that groups the data by registration year
  group_by_years = df.groupby(df["registration_start_date"].dt.year)
  # Let's count how many real estate agents registered in each  year  
  group_by_years_count = group_by_years.count()

  # We have too many columns to display, let's reduce the number of columns to just one
  group_by_years_count = group_by_years_count[["cea_salesperson_id"]]
  # rename the column so it looks more proper on the barplot later
  group_by_years_count.rename(columns={'cea_salesperson_id':'count'},inplace=True)
  # currently the index is the year, but we want to pass it as x value later, so reset it 
  group_by_years_count.reset_index(inplace=True)

  # https://seaborn.pydata.org/generated/seaborn.barplot.html#seaborn.barplot
  sns.set(style="whitegrid")
  ax = sns.barplot(x="registration_start_date", y="count", data=group_by_years_count)


except:
   print("Unexpected error:", sys.exc_info()[0])
   print("Unexpected error:", sys.exc_info()[1])
   exit()

finally:
  cursor.close()
  cnx.close() 
    

This is the graph produced