print out is producing same graph

[mbrown009]

I have the following code. It is grahing the aggregate case rate but I also want to do only the past 60 days. It produces 2 graphs. They are the same. I am confused on what I a doing wrong. Any suggestions would be helpful

#!/usr/bin/env python
__author__ = "Michael Brown"
 
import pandas as pd
import datetime as dt
from matplotlib import pyplot as plt
import seaborn as sns

statePopulation=6045680
 
CVD = pd.read_csv('https://opendata.arcgis.com/datasets/18582de727934249b92c52542395a3bf_0.csv')

#Adding additional columns
CVD['ndate'] = CVD['DATE']+'00'
CVD['DailyCases'] = CVD['Count_'].diff()
CVD['7Day'] = CVD['DailyCases'].rolling(window=7).mean()
CVD['Daily100k'] = (CVD['DailyCases'] / statePopulation) * 100000
CVD['100k7D'] = CVD['Daily100k'].rolling(window=7).mean()

#print(CVD.head())
#print(CVD.dtypes)

#Convert string value of date to datetime format
CVD['ndate'] = [dt.datetime.strptime(x,'%Y/%m/%d %H:%M:%S%z') 
               for x in CVD['ndate']] 
#print (CVD.dtypes)

#checking for missing data
print(CVD.isnull().sum())

#Change Column titles to something more appropriate 
CVD.columns = ['ObjectID', 'OldDate', 'Count', 'Date', 'DailyCases', '7Day', 'Daily100k', '100k7D']

#Creating new data frame for past 60 days 
CVDpast60days = CVD.iloc[-60:]

#Group them by date
CVD = pd.DataFrame(CVD.groupby(['Date'])['DailyCases', '7Day', 'Daily100k','100k7D'].sum()).reset_index()
#Group them by date
CVDpast60days = pd.DataFrame(CVD.groupby(['Date'])['DailyCases', '7Day', 'Daily100k','100k7D'].sum()).reset_index()

#print(CVD)
#=================================================================================
# Aggregate Case Rate
#=================================================================================
def plot_state_case_rate (df, title='Maryland State Case Rate', size = 1):
    f, ax = plt.subplots(1,1, figsize=(4*size,2*size))
    g = sns.lineplot(x="Date", y="100k7D", data=df, color='blue', label="State Case Rate")
    plt.xlabel('Date')
    plt.ylabel(' Cases per 100k ')
    plt.xticks(rotation=90)
    plt.title(f' {title} ')
    ax.grid(color='black', linestyle='dotted', linewidth=0.75)
    plt.show()

cvd_case_rate_aggregate = CVD.groupby(['Date']).sum().reset_index()
#print (cvd_case_rate_aggregate)
plot_state_case_rate(cvd_case_rate_aggregate, 'State Case Rate', size=4)

#=================================================================================
# Case Rate past 60 days
#=================================================================================
def plot_state_case_rate60days (df, title='Maryland State Case Rate', size = 1):
    f, az = plt.subplots(1,1, figsize=(4*size,2*size))
    g = sns.lineplot(x="Date", y="100k7D", data=df, color='blue', label="State Case Rate")
    plt.xlabel('Date')
    plt.ylabel(' Cases per 100k ')
    plt.xticks(rotation=90)
    plt.title(f' {title} ')
    az.grid(color='black', linestyle='dotted', linewidth=0.75)
    plt.show()

cvd_case_rate_aggregate60days = CVDpast60days.groupby(['Date']).sum().reset_index()
#print (cvd_case_rate_aggregate60days)
plot_state_case_rate60days(cvd_case_rate_aggregate60days, 'State Case Rate', size=4)

[mbrown009]

I did add the following code for debugging

index = CVD.index
numberofRows = len(index)

index1 = CVDpast60days.index
numRows = len(index1)

print (numberofRows)
print (numRows)

It produces the following

Output:
453
60

[mbrown009]

Resolved my own issue

#!/usr/bin/env python
__author__ = "Michael Brown"
 
import pandas as pd
import datetime as dt
from matplotlib import pyplot as plt
import seaborn as sns

statePopulation=6045680
 
CVD = pd.read_csv('https://opendata.arcgis.com/datasets/18582de727934249b92c52542395a3bf_0.csv')

#Adding additional columns
CVD['ndate'] = CVD['DATE']+'00'
CVD['DailyCases'] = CVD['Count_'].diff()
CVD['7Day'] = CVD['DailyCases'].rolling(window=7).mean()
CVD['Daily100k'] = (CVD['DailyCases'] / statePopulation) * 100000
CVD['100k7D'] = CVD['Daily100k'].rolling(window=7).mean()

#print(CVD.head())
#print(CVD.dtypes)

#Convert string value of date to datetime format
CVD['ndate'] = [dt.datetime.strptime(x,'%Y/%m/%d %H:%M:%S%z') 
               for x in CVD['ndate']] 
#print (CVD.dtypes)

#checking for missing data
print(CVD.isnull().sum())

#Change Column titles to something more appropriate 
CVD.columns = ['ObjectID', 'OldDate', 'Count', 'Date', 'DailyCases', '7Day', 'Daily100k', '100k7D']

#Creating new data frame for past 60 days 
CVDpast60days = CVD.iloc[-60:]


index = CVD.index
numberofRows = len(index)

index1 = CVDpast60days.index
numRows = len(index1)

print (numberofRows)
print (numRows)

#Group them by date
CVD = pd.DataFrame(CVD.groupby(['Date'])['DailyCases', '7Day', 'Daily100k','100k7D'].sum()).reset_index()
#Group them by date
CVDpast60days = pd.DataFrame(CVD.groupby(['Date'])['DailyCases', '7Day', 'Daily100k','100k7D'].sum()).reset_index()



#print(CVD)
#=================================================================================
# Aggregate Case Rate
#=================================================================================
def plot_state_case_rate (df, title='Maryland State Case Rate', size = 1):
    f, ax = plt.subplots(1,1, figsize=(4*size,2*size))
    g = sns.lineplot(x="Date", y="100k7D", data=df, color='blue', label="State Case Rate")
    plt.xlabel('Date')
    plt.ylabel(' Cases per 100k ')
    plt.xticks(rotation=90)
    plt.title(f' {title} ')
    ax.grid(color='black', linestyle='dotted', linewidth=0.75)
    plt.show()

cvd_case_rate_aggregate = CVD.groupby(['Date']).sum().reset_index()
cvd60d = cvd_case_rate_aggregate.iloc[-60:]
#print (cvd_case_rate_aggregate)
plot_state_case_rate(cvd_case_rate_aggregate, 'State Case Rate', size=4)
plot_state_case_rate(cvd60d, 'State Case Rate', size=4)