<jupyter_start><jupyter_text>--- - Column descriptions can be found at "https://covidtracking.com/data/api" under "Historic values for a single state" ---<jupyter_code>df.info()
#remove columns with no data recorded, columns not necessary for analysis, 
#columns displaying the same data as others, & depreciated data
columns = ['deathConfirmed', 'deathProbable', 'hospitalized', 
         'hospitalizedCumulative', 'inIcuCumulative', 'negativeTestsAntibody', 'pending',
         'negativeTestsPeopleAntibody', 'onVentilatorCumulative',
         'positiveTestsAntigen', 'positiveTestsPeopleAntibody', 'positiveTestsPeopleAntigen',
         'totalTestEncountersViral', 'totalTestsAntigen', 'totalTestsPeopleAntibody', 
         'totalTestsPeopleAntigen', 'hospitalizedIncrease', 'hash', 'commercialScore',
         'negativeRegularScore', 'negativeScore', 'positiveScore', 'score',
          'grade', 'totalTestResultsSource', 'state', 'lastUpdateEt', 'dateModified',
          'checkTimeEt', 'dateChecked', 'fips', 'total', 'posNeg', 'dataQualityGrade']
df_1 = df.drop(columns, axis=1)
df_1.head()
df_1.info()
#create column with daily positive result rate
df_1['positive_rate'] = round((df_1['positiveIncrease']/df_1['totalTestResultsIncrease']) * 100, 2)
df_1['positive_rate'].head()
#plot daily positive rate by date

# Create figure and plot space
fig, ax = plt.subplots(figsize=(25, 10))

# Add x-axis and y-axis
ax.plot(df_1['date'],
        df_1['positive_rate'],
        color='red')

# Set title and labels for axes
ax.set(xlabel="Date",
       ylabel="Positive Rate",
       title="Daily Positive Test Rate")

plt.show()
df_1['positive_rate'].unique()
df_1.loc[df_1['positive_rate']== 100.00]
#for positive_rate graph: need to remove columns with 100% positive rate, 
                         #minimal testing done and all positive results. 
                         #seems like incomplete data
df_1.loc[df_1['positive_rate']== 0.00]
df_1.loc[df_1['positive_rate']== -1.12]
df_1.loc[df_1['positive_rate'].isna()]
# Investigate data quality grade further
# FOR PLOTTING PURPOSES OF POSITIVE RATE, CREATE NEW DF WITH:

#remove row 153: incomplete data with negative positive rate
#create new df without sundays
#remove rows with 100% positive rate<jupyter_output><empty_output>
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