ValueError: Could not interpret value

mbrown009 · (This post was last modified: Aug-11-2022, 02:27 AM by mbrown009.)

I am running into an issue. The ReportDate field is something that comes with the csv pull but that is not what I want used on the X-axis. I want DataDate = ReportDate - 1

I am getting the following error:

Error:Traceback (most recent call last):
  at block 1, line 107
  at block 1, line 91, in plot_county_7DAvg(df, title, size)
  at /opt/python/envs/default/lib/python3.8/site-packages/seaborn/_decorators.pyline 46, in inner_f(*args, **kwargs)
  at /opt/python/envs/default/lib/python3.8/site-packages/seaborn/relational.pyline 692, in lineplot(x, y, hue, size, style, data, palette, hue_order, hue_norm, sizes, size_order, size_norm, dashes, markers, style_order, units, estimator, ci, n_boot, seed, sort, err_style, err_kws, legend, ax, **kwargs)
  at /opt/python/envs/default/lib/python3.8/site-packages/seaborn/relational.pyline 367, in __init__(self, data, variables, estimator, ci, n_boot, seed, sort, err_style, err_kws, legend)
  at /opt/python/envs/default/lib/python3.8/site-packages/seaborn/_core.pyline 605, in __init__(self, data, variables)
  at /opt/python/envs/default/lib/python3.8/site-packages/seaborn/_core.pyline 668, in assign_variables(self, data, variables)
  at /opt/python/envs/default/lib/python3.8/site-packages/seaborn/_core.pyline 903, in _assign_variables_longform(self, data, **kwargs)
ValueError: Could not interpret value `DataDate` for parameter `x`

The full code is here:

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

41

42

43

44

45

46

47

48

49

50

51

52

53

54

55

56

57

58

59

60

61

62

63

64

65

66

67

68

69

70

71

72

73

74

75

76

77

78

79

80

81

82

83

84

85

86

87

88

89

90

91

92

93

94

95

96

97

98

99

100

101

102

103

104

105

106

107

108

109

110

111

112

113

114

115

116

117

118

119

120

121

122

123

124

125

126

127

128

129

130

131

132

133

134

135

136

137

138

139

140

141

142

143

144

# !/usr/bin/env python
author = "Michael Brown"
license = "Based off of sript by Sreenivas Bhattiprolu of Python for Microscopists"
 
import pandas as pd
import datetime as dt
from matplotlib import pyplot as plt
from datetime import timedelta
 
from warnings import simplefilter
simplefilter(action="ignore", category=pd.errors.PerformanceWarning)
 
# ===================================================================
# Example of Calcuation for Case Rate
# ===================================================================
# CVD['Daily100k'] = (CVD['DailyCases'] / statePopulation) * 100000
# CVD['100k7D'] = CVD['Daily100k'].rolling(window=7).mean()
 
 
 
CVD = pd.read_csv('https://opendata.maryland.gov/api/views/mgd3-qk8t/rows.csv?accessType=DOWNLOAD')
 
#Creating new data frame 
AACounty = pd.DataFrame()
AACounty['ReportDate'] = CVD['ReportDate']
AACounty['DataDate'] = CVD['ReportDate']
 
#converting Report Date to string
AACounty['ReportDate'] = AACounty['ReportDate'].astype('str')
AACounty['DataDate'] = AACounty['DataDate'].astype('str')
 
 
#getting the date portion of the string
AACounty['ReportDate'] = AACounty.ReportDate.str.slice(0,10)
AACounty['DataDate'] = AACounty.DataDate.str.slice(0,10)
 
 
#converting string to date format
AACounty['ReportDate'] = [dt.datetime.strptime(x, '%m/%d/%Y')
                for x in AACounty['ReportDate']]
 
AACounty['DataDate'] = [dt.datetime.strptime(x, '%m/%d/%Y')
                for x in AACounty['DataDate']]
 
#adding new column for data date vs reported date - data date is 1 day prior to report date
AACounty['DataDate'] = AACounty['DataDate'] - timedelta(days=1)
 
 
AnneArundelPopulationDecennial2020 =588261
AnneArundelPOpulationACS2020 = 575421
AnneArundelPopulationACS2019 = 571275
AnneArundelPopulationPEP2019 = 579234
AnneArundelPopulationCounty = 579630
 
    
 
#Anne Arundel
AACounty['AnneArundel'] = CVD['ANNE']
AACounty['AADailyCases'] = AACounty['AnneArundel'].diff()
AACounty['AA7Day'] = AACounty['AADailyCases'].rolling(window=7).mean()
 
AACounty['AADaily100k2020Census'] = (AACounty['AADailyCases'] / AnneArundelPopulationDecennial2020) * 100000
AACounty['AA100k7D-2020Census'] = AACounty['AADaily100k2020Census'].rolling(window=7).mean()
 
AACounty['AACounty100k2020ACS'] = (AACounty['AADailyCases'] / AnneArundelPOpulationACS2020) * 100000
AACounty['AA100k7D-2020ACS'] = AACounty['AACounty100k2020ACS'].rolling(window=7).mean()
 
 
AACounty['AADaily100k2019ACS'] = (AACounty['AADailyCases'] / AnneArundelPopulationACS2019) * 100000
AACounty['AA100k7D-2019ACS'] = AACounty['AADaily100k2019ACS'].rolling(window=7).mean()
 
AACounty['AADaily100k2019PEP'] = (AACounty['AADailyCases'] / AnneArundelPopulationPEP2019) * 100000
AACounty['AA100k7D-2019PEP'] = AACounty['AADaily100k2019PEP'].rolling(window=7).mean()
 
AACounty['AADaily100kCounty'] = (AACounty['AADailyCases'] / AnneArundelPopulationCounty) * 100000
AACounty['AA100k7D-County'] = AACounty['AADaily100kCounty'].rolling(window=7).mean()
 
# Saving Data to CSV
print(AACounty.dtypes)
AACounty.to_csv('AnneArundel.csv')
 
#=================================================================================
# 7-Day Running Average - Daily Cases
#=================================================================================
 
def plot_county_7DAvg(df, title='7-Day Case Count', size=1):
    #=================================================================================
    # 7-Day Running Average - Daily Cases - All Counties
    #=================================================================================
    f, ax = plt.subplots(1, 1, figsize=(4 * size, 2 * size))
    g = sns.lineplot(x="DataDate", y="AA7Day", data=df, color='red', label="Anne Arundel")
    plt.legend(loc='upper left')
    plt.xlabel('Date')
    plt.ylabel(' 7-Day Average ')
    plt.xticks(rotation=90)
    plt.title(f' {title} ')
    ax.grid(color='black', linestyle='dotted', linewidth=0.75)
    plt.savefig(f'{title}.png')
    plt.show()
 
 
 
cvd_case_rate_aggregate = AACounty.groupby(['ReportDate']).sum().reset_index()
sevenDayAverage60day = cvd_case_rate_aggregate.iloc[-60:]
sevenDayAverage30day = cvd_case_rate_aggregate.iloc[-30:]
sevenDayAverage7day = cvd_case_rate_aggregate.iloc[-7:]
plot_county_7DAvg(cvd_case_rate_aggregate, 'County Aggregate 7-Day Running Average', size=4)
plot_county_7DAvg(sevenDayAverage60day, 'County Past 60 Days 7-Day Running Average', size=4)
plot_county_7DAvg(sevenDayAverage30day, 'County Past 30 Days 7-Day Running Average', size=4)
plot_county_7DAvg(sevenDayAverage7day, 'County Past 7 Days 7-Day Running Average', size=4)
 
 
def plot_aacounty_7D100kAvg(df, title='Aggregate Anne Arundel County Case Rate', size=1):
 
    #=================================================================================
    # 7-Day Running Average - Case Rate - Anne Arundel County
    #=================================================================================
    f, ax = plt.subplots(1, 1, figsize=(5 * size, 3 * size))
    g = sns.lineplot(x="ReportDate", y="AA100k7D-2020Census", data=df, color='black', linewidth=3.0, label="Anne Arundel Decennial 2020")
    g = sns.lineplot(x="ReportDate", y="AA100k7D-2020ACS", data=df, color = 'darkgreen', linewidth=2.0, label="Anne Arundel ACS 2020")
    g = sns.lineplot(x="ReportDate", y="AA100k7D-2019PEP", data=df, color='darkblue', linewidth=3.0, label="Anne Arundel PEP 2019 Estimate")
    g = sns.lineplot(x="ReportDate", y="AA100k7D-2019ACS", data=df, color='darkmagenta', linewidth=2.0, label="Anne Arundel ACS 2019 Estimate")
    g = sns.lineplot(x="ReportDate", y="AA100k7D-County", data=df, color='red', linewidth=2.0, label="Anne Arundel Estimate")
 
 
 
    #g = sns.lineplot(x="ReportDate", y="AA100k7D", data=df, color='green', label="Anne Arundel")
    plt.rcParams["figure.figsize"] = (200,100)    
    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.savefig(f'{title}.png')
    plt.show()
 
aasevenday100kcvd_case_rate_aggregate = AACounty.groupby(['ReportDate']).sum().reset_index()
aasevenday100ksevenDayAverage60day = aasevenday100kcvd_case_rate_aggregate.iloc[-60:]
aasevenday100ksevenDayAverage30day = aasevenday100kcvd_case_rate_aggregate.iloc[-30:]
aasevenday100ksevenDayAverage7day = aasevenday100kcvd_case_rate_aggregate.iloc[-7:]
plot_aacounty_7D100kAvg(aasevenday100kcvd_case_rate_aggregate, 'County Case Rate Aggregate 7-Day Running Average', size=4)
plot_aacounty_7D100kAvg(aasevenday100ksevenDayAverage60day, 'County Case Rate  Past 60 Days 7-Day Running Average', size=4)
plot_aacounty_7D100kAvg(aasevenday100ksevenDayAverage30day, 'County Case Rate  Past 30 Days 7-Day Running Average', size=4)
plot_aacounty_7D100kAvg(aasevenday100ksevenDayAverage7day, 'County Case Rate  Past 7 Days 7-Day Running Average', size=4)

mbrown009 · Aug-11-2022, 02:34 AM

I found the issue. I had the reset index with the wrong field.

ValueError: Could not interpret value

User Panel Messages

Announcements