-
Notifications
You must be signed in to change notification settings - Fork 2
/
1-Data_Prepare.py
321 lines (288 loc) · 17.1 KB
/
1-Data_Prepare.py
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
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
import pandas as pd
import os
import datetime
import matplotlib.pyplot as plt
from pandas.tseries.holiday import USFederalHolidayCalendar
import numpy as np
import matplotlib.dates as mdates
os.chdir(r'D:\COVID-19\PNAS_SECOND')
# Read Cases
url = 'https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_US.csv'
Case_num = pd.read_csv(url, error_bad_lines=False, dtype={'FIPS': str}).rename(
columns={'Confirmed': 'Confirmed_Cases', 'FIPS': 'CTFIPS'})
Case_num = Case_num.dropna(subset=['CTFIPS']).reset_index(drop=True)
Case_num = Case_num[Case_num['Country_Region'] == 'US']
Case_num = Case_num.dropna(subset=['CTFIPS']).reset_index(drop=True)
Case_num['CTFIPS'] = Case_num['CTFIPS'].astype(float).astype(int).astype(str).apply(lambda x: x.zfill(5))
Case_num = Case_num.drop(['UID', 'iso2', 'iso3', 'code3', 'Admin2', 'Province_State',
'Country_Region', 'Lat', 'Long_', 'Combined_Key'], axis=1)
Case_num = Case_num.melt(id_vars=['CTFIPS'], var_name='date')
Case_num['value'] = Case_num['value'].astype(int)
Case_num['date'] = pd.to_datetime(Case_num['date'])
Case_num = Case_num.sort_values(by=['CTFIPS', 'date']).reset_index(drop=True)
Case_num['New_cases'] = Case_num.groupby('CTFIPS')['value'].diff()
Case_num.loc[Case_num['New_cases'].isna(), 'New_cases'] = Case_num.loc[Case_num['New_cases'].isna(), 'value']
Case_num.rename({'value': 'Agg_cases', 'date': 'Date'}, axis=1, inplace=True)
# Some cases is negative, fill with 0
num = Case_num._get_numeric_data()
num[num < 0] = 0
del num
# Read OD
'''
data_range = list(pd.date_range('2020-01-01', '2020-06-13', freq='d').strftime("%m%d"))
All_OD = pd.DataFrame()
for date in data_range:
print('___________' + date + '___________')
tem = pd.read_pickle(r'D:\COVID-19\OD_RAW_1\OD_Count_' + date)
All_OD = All_OD.append(tem)
All_OD.to_pickle('All_OD_0613')
'''
All_OD = pd.read_pickle(r'D:\COVID-19\All_OD_0613')
# Calculate Inflow trips
All_OD_Ex = All_OD[All_OD['CTFIPS_1'] != All_OD['CTFIPS_2']]
Agg_In = All_OD_Ex.groupby(['CTFIPS_2', 'Date']).sum().reset_index()
Agg_In.columns = ['CTFIPS', 'Date', 'In_Flow', 'InFlow_Weight']
# Internal trips
All_OD_In = All_OD[All_OD['CTFIPS_1'] == All_OD['CTFIPS_2']]
Agg_Inter = All_OD_In.groupby(['CTFIPS_1', 'Date']).sum().reset_index()
Agg_Inter.columns = ['CTFIPS', 'Date', 'Enter_Flow', 'Enter_Weight']
# Outer trips
Agg_Out = All_OD_Ex.groupby(['CTFIPS_1', 'Date']).sum().reset_index()
Agg_Out.columns = ['CTFIPS', 'Date', 'Out_Flow', 'OutFlow_Weight']
# Merge
Agg_In = Agg_In.merge(Agg_Inter, on=['CTFIPS', 'Date'], how='outer')
Agg_In = Agg_In.merge(Agg_Out, on=['CTFIPS', 'Date'], how='outer')
Agg_In = Agg_In.fillna(0)
# Merge with cases
Agg_Trips_1 = Agg_In.copy()
Agg_Trips_1['Date'] = pd.to_datetime(Agg_Trips_1['Date'])
Agg_Trips_1['CTFIPS'] = Agg_Trips_1['CTFIPS'].astype(str).apply(lambda x: x.zfill(5))
# Merge with cases
Agg_Trips_1 = Agg_Trips_1.merge(Case_num, on=['CTFIPS', 'Date'], how='outer')
Agg_Trips_1 = Agg_Trips_1[Agg_Trips_1['Date'] < datetime.datetime(2020, 6, 14)].reset_index(drop=True)
Agg_Trips_1 = Agg_Trips_1.sort_values(by=['CTFIPS', 'Date']).reset_index(drop=True)
Agg_Trips_1['STFIPS'] = Agg_Trips_1['CTFIPS'].str[0:2]
# Drop the outliers
# Agg_Trips_1['STFIPS'].value_counts()
def changeNY(ColName, Agg_Trips_1):
Agg_Trips_1.loc[Agg_Trips_1['CTFIPS'] == '36061', ColName] = \
list(Agg_Trips_1.loc[Agg_Trips_1['CTFIPS'] == '36061', ColName].reset_index(drop=True) + \
Agg_Trips_1.loc[Agg_Trips_1['CTFIPS'] == '36005', ColName].reset_index(drop=True) + \
Agg_Trips_1.loc[Agg_Trips_1['CTFIPS'] == '36047', ColName].reset_index(drop=True) + \
Agg_Trips_1.loc[Agg_Trips_1['CTFIPS'] == '36081', ColName].reset_index(drop=True))
return Agg_Trips_1
for columns in ['In_Flow', 'InFlow_Weight', 'Enter_Flow', 'Enter_Weight', 'Out_Flow', 'OutFlow_Weight']:
Agg_Trips_1 = changeNY(columns, Agg_Trips_1)
Agg_Trips_1 = Agg_Trips_1[~Agg_Trips_1['CTFIPS'].isin(['36005', '36047', '36081'])].reset_index(drop=True)
Agg_Trips_1 = Agg_Trips_1[~Agg_Trips_1['STFIPS'].isin(['02', '15', 'Ou', '99', '88', '78', '00', '90', '80', '72'])]
# Agg_Trips_1.info()
Agg_Trips_1 = Agg_Trips_1.fillna(0)
Agg_Trips_1 = Agg_Trips_1.sort_values(by=['CTFIPS', 'Date']).reset_index(drop=True)
# Agg_Trips_1.to_pickle('Agg_Trips_Cases_0613')
Agg_Trips_1.groupby('Date').sum()['In_Flow'].plot()
plt.title('Total_Inflow')
Agg_Trips_1.groupby('Date').sum()['InFlow_Weight'].plot()
plt.title('Weight_Inflow')
# Read other data
Others = pd.read_csv(r'All_XY_Features_To_R_County_Level_0708_NoRef.csv', index_col=0)
Others['CTFIPS'] = Others['CTFIPS'].astype(str).apply(lambda x: x.zfill(5)) # zfill with 0
Others['Date'] = pd.to_datetime(Others['Date'])
Others = Others[['CTFIPS', 'CTNAME', 'social_distance', 'Pct_staying_home',
'Trips_person', 'Pct_out-of-county', 'Miles_person',
'Work_trips_person', 'Non-work_trips_person', 'Date', 'Month', 'Week',
'Adj_New_cases', 'Adj_Agg_Cases', 'Enforcement', 'Is_ReOpen', 'PRCP', 'TMAX', 'TMIN', 'Pct_Male',
'Pct_Age_0_24', 'Pct_Age_25_40', 'Pct_Age_40_65', 'Pct_White',
'Pct_Black', 'Pct_Indian', 'Pct_Asian', 'Pct_Unemploy', 'Total_Population', 'Med_House_Income', 'LAT',
'LNG', 'LAND', 'Is_Weekend', 'Time_Index', 'National_Cases', 'Population_density',
'Employment_density']]
Others1 = Others.merge(Agg_Trips_1, on=['CTFIPS', 'Date'], how='outer')
# RE-UNIT
for colu in ['National_Cases', 'Employment_density', 'Adj_New_cases', 'Adj_Agg_Cases', 'Total_Population',
'Med_House_Income', 'Population_density', ]:
Others1[colu] = Others1[colu] * 1e3
Others1['TMAX'] = Others1['TMAX'] * 0.1
Others1['TMIN'] = Others1['TMIN'] * 0.1
Others1['PRCP'] = Others1['PRCP'] * 0.1
Others1.describe().T
# Calculate based on some transfer
# Drop the holidays
cal = USFederalHolidayCalendar()
holidays = cal.holidays(start='2020-01-01', end='2020-01-31').to_pydatetime()
Others1 = Others1[~Others1['Date'].isin(holidays)].reset_index(drop=True)
All_Y_Ref = Others1[Others1['Month'] == 1].groupby(['CTFIPS', 'Week']).mean().reset_index()
All_Y_Ref.columns = [var + '_ref' for var in list(All_Y_Ref.columns)]
All_Y_Ref.rename({'CTFIPS_ref': 'CTFIPS', 'Week_ref': 'Week'}, axis=1, inplace=True)
# Calculate the flow reference
Others1 = Others1[Others1['Month'] != 1]
Others1 = Others1.merge(All_Y_Ref, on=['CTFIPS', 'Week'])
for each in ['In_Flow', 'InFlow_Weight', 'Enter_Flow', 'Enter_Weight', 'Out_Flow', 'OutFlow_Weight']:
# All_Y[each] = (All_Y[each] - All_Y[each + '_ref']) / All_Y[each + '_ref']
Others1.loc[Others1[each] == 0, each] = 1
Others1[each + '_rate'] = Others1[each] / Others1[each + '_ref']
Others1.groupby(['Date']).mean()['In_Flow_rate'].plot()
# Calculate the case rate
# avg(3)/avg(7) and log
Others1 = Others1.sort_values(by=['CTFIPS', 'Date']).reset_index(drop=True)
Others1['Shift_sum_case_3'] = Others1.groupby(['CTFIPS'])['New_cases'].rolling(3).sum().reset_index()['New_cases']
Others1['Shift_sum_case_7'] = Others1.groupby(['CTFIPS'])['New_cases'].rolling(7).sum().reset_index()['New_cases']
Others1['New_cases_rate'] = np.log(Others1['Shift_sum_case_3'] / 3) / np.log(Others1['Shift_sum_case_7'] / 7)
# tem = Others1[['CTFIPS', 'New_cases', 'Shift_sum_case_7', 'Shift_sum_case_3', 'New_cases_rate']]
# Others1 = Others1.fillna(0)
Others1 = Others1[
['CTFIPS', 'CTNAME', 'social_distance', 'Pct_staying_home', 'Trips_person', 'Pct_out-of-county', 'Miles_person',
'Work_trips_person', 'Non-work_trips_person', 'Date', 'Month', 'Week', 'Adj_New_cases', 'Adj_Agg_Cases',
'Enforcement', 'Is_ReOpen', 'PRCP', 'TMAX', 'TMIN', 'Pct_Male', 'Pct_Age_0_24', 'Pct_Age_25_40', 'Pct_Age_40_65',
'Pct_White', 'Pct_Black', 'Pct_Indian', 'Pct_Asian', 'Pct_Unemploy', 'Total_Population', 'Med_House_Income', 'LAT',
'LNG', 'LAND', 'Is_Weekend', 'Time_Index', 'National_Cases', 'Population_density', 'Employment_density', 'In_Flow',
'InFlow_Weight', 'Enter_Flow', 'Enter_Weight', 'Out_Flow', 'OutFlow_Weight', 'Agg_cases', 'New_cases', 'STFIPS',
'In_Flow_rate', 'InFlow_Weight_rate', 'Enter_Flow_rate', 'Enter_Weight_rate', 'Out_Flow_rate',
'OutFlow_Weight_rate', 'New_cases_rate']]
# Weighted trips by cases
All_OD_Ex['Date'] = pd.to_datetime(All_OD_Ex['Date'], format='%Y%m%d')
Columns_list = list(Case_num.columns)
Case_num.columns = [var + '_1' for var in Columns_list]
Case_num.rename({'Date_1': "Date"}, axis=1, inplace=True)
All_OD_Ex_cases = All_OD_Ex.merge(Case_num, on=['CTFIPS_1', 'Date'], how='left')
Case_num.columns = [var + '_2' for var in Columns_list]
Case_num.rename({'Date_2': "Date"}, axis=1, inplace=True)
All_OD_Ex_cases = All_OD_Ex_cases.merge(Case_num, on=['CTFIPS_2', 'Date'], how='left')
All_OD_Ex_cases.fillna(0, inplace=True)
All_OD_Ex_cases = All_OD_Ex_cases.sort_values(by=['CTFIPS_1', 'Date']).reset_index(drop=True)
CT_POP = Others1[['CTFIPS', 'Total_Population']]
CT_POP = CT_POP.drop_duplicates()
CT_POP.columns = ['CTFIPS_1', 'Population_1']
All_OD_Ex_cases = All_OD_Ex_cases.merge(CT_POP, on=['CTFIPS_1'])
CT_POP.columns = ['CTFIPS_2', 'Population_2']
All_OD_Ex_cases = All_OD_Ex_cases.merge(CT_POP, on=['CTFIPS_2'])
All_OD_Ex_cases['Risked_WInput'] \
= (All_OD_Ex_cases['Agg_cases_1'] / (All_OD_Ex_cases['Population_1'])) * All_OD_Ex_cases['Weight']
Agg_Weight = All_OD_Ex_cases.groupby(['CTFIPS_2', 'Date']).sum()['Risked_WInput'].reset_index()
Agg_Weight.rename({'CTFIPS_2': 'CTFIPS'}, axis=1, inplace=True)
plt.plot(Agg_Weight['Risked_WInput'])
Agg_Weight = changeNY('Risked_WInput', Agg_Weight)
Agg_Weight = Agg_Weight[~Agg_Weight['CTFIPS'].isin(['36005', '36047', '36081'])].reset_index(drop=True)
Agg_Weight = Agg_Weight[
~Agg_Weight['CTFIPS'].str[0:2].isin(['02', '15', 'Ou', '99', '88', '78', '00', '90', '80', '72'])]
# All_OD_Ex_cases = All_OD_Ex_cases.sort_values(by=['CTFIPS_1', 'CTFIPS_2', 'Date']).reset_index(drop=True)
# len(Others1)/len(set(Others1['Date']))
# len(set(Agg_Weight['Date']))
Others1 = Others1.merge(Agg_Weight, on=['CTFIPS', 'Date'], how='left')
Others1['Risked_WInput'] = Others1['Risked_WInput'].fillna(0)
# Read State info
State_info = pd.read_csv(r'D:\COVID-19\Second_Paper_County_Stay_at_Home\state-geocodes-v2017.csv')
State_info['STFIPS'] = State_info['STFIPS'].astype(str).apply(lambda x: x.zfill(2))
State_info = State_info.drop_duplicates(subset=['STFIPS']).reset_index(drop=True)
Others1 = Others1.merge(State_info, on='STFIPS', how='left')
# PRCP should be relative
PRCP_RELT = Others1.groupby('CTFIPS').mean()['PRCP'].reset_index()
PRCP_RELT.columns = ['CTFIPS', 'REF_PRCP']
PRCP_RELT.loc[PRCP_RELT['REF_PRCP'] == 0, 'REF_PRCP'] = 0.1
Others1 = Others1.merge(PRCP_RELT, on='CTFIPS')
Others1['PRCP_NEW'] = Others1['PRCP'] / Agg_Trips_1['REF_PRCP']
Others1.to_csv('All_XY_Features_To_R_County_Level_0731.csv')
# Add lags/plot variables
os.chdir(r'D:\COVID-19\PNAS_SECOND')
# Read input data
Agg_Trips_1 = pd.read_csv('All_XY_Features_To_R_County_Level_0731.csv', index_col=0)
Agg_Trips_1['Date'] = pd.to_datetime(Agg_Trips_1['Date'])
Agg_Trips_1['CTFIPS'] = Agg_Trips_1['CTFIPS'].astype(str).apply(lambda x: x.zfill(5))
# Split by reopen
# 1, 4, 8, 13, 16, 17, 18, 19, 23, 27, 28, 35, 38, 40,45, 46, 47, 48, 49
myFmt = mdates.DateFormatter('%b-%d')
Agg_Trips_1.columns
temp = Agg_Trips_1[['STFIPS', 'New_cases', 'Date']]
temp_reopen = temp[temp['STFIPS'].isin([1, 4, 8, 13, 16, 17, 18, 19, 23, 27, 28, 35, 38, 40, 45, 46, 47, 48, 49])]
temp_close = temp[~temp['STFIPS'].isin([1, 4, 8, 13, 16, 17, 18, 19, 23, 27, 28, 35, 38, 40, 45, 46, 47, 48, 49])]
sum_temp_reopen = temp_reopen.groupby(['Date']).sum()['New_cases'].reset_index()
sum_temp_close = temp_close.groupby(['Date']).sum()['New_cases'].reset_index()
sum_temp_reopen['Date'] = pd.to_datetime(sum_temp_reopen['Date'])
sum_temp_close['Date'] = pd.to_datetime(sum_temp_close['Date'])
sum_temp_reopen.columns = ['Date', 'National_Cases_Reopen']
sum_temp_close.columns = ['Date', 'National_Cases_Close']
Agg_Trips_1 = Agg_Trips_1.merge(sum_temp_reopen, on='Date', how='left')
Agg_Trips_1 = Agg_Trips_1.merge(sum_temp_close, on='Date', how='left')
# Agg_Trips_1.groupby(['Date']).sum()[['National_Cases_Reopen', 'National_Cases_Close']].plot()
# Lag and Log
time_window = 7
time_lag = 7
# Log
for colu in ['National_Cases', 'National_Cases_Reopen', 'National_Cases_Close', 'New_cases', 'InFlow_Weight',
'Risked_WInput', 'Enter_Weight', 'OutFlow_Weight']:
Agg_Trips_1['Log_' + colu] = np.log(Agg_Trips_1[colu])
# Lag1
for colu in ['National_Cases', 'National_Cases_Reopen', 'National_Cases_Close', 'New_cases', 'InFlow_Weight',
'Risked_WInput', 'Enter_Weight', 'OutFlow_Weight']:
Agg_Trips_1['Lag1_Log_' + colu] = Agg_Trips_1.groupby(['CTFIPS']).shift(1)['Log_' + colu]
# Lag7
for colu in ['National_Cases', 'National_Cases_Reopen', 'National_Cases_Close', 'New_cases', 'InFlow_Weight',
'Risked_WInput', 'Enter_Weight', 'OutFlow_Weight']:
Agg_Trips_1['Lag7_Log_' + colu] = Agg_Trips_1.groupby(['CTFIPS']).shift(7)['Log_' + colu]
Agg_Trips_1 = Agg_Trips_1.replace([np.inf, -np.inf], np.nan)
Agg_Trips_1.columns
Agg_Trips_1.to_csv('All_XY_Features_To_R_County_Level_0731_toR.csv')
# Plot cases and flow
# How many counties
Agg_Trips_1 = pd.read_csv('All_XY_Features_To_R_County_Level_0731_toR.csv')
temp_county = Agg_Trips_1[['STFIPS', 'CTFIPS', 'New_cases', 'Date']]
print(len(set(
temp_county[temp_county['STFIPS'].isin([1, 4, 8, 13, 16, 17, 18, 19, 23, 27, 28, 35, 38, 40, 45, 46, 47, 48, 49])][
'CTFIPS'])))
print(len(set(
temp_county[~temp_county['STFIPS'].isin([1, 4, 8, 13, 16, 17, 18, 19, 23, 27, 28, 35, 38, 40, 45, 46, 47, 48, 49])][
'CTFIPS'])))
temp = Agg_Trips_1[['STFIPS', 'New_cases', 'InFlow_Weight', 'Date']]
temp_reopen = temp[temp['STFIPS'].isin([1, 4, 8, 13, 16, 17, 18, 19, 23, 27, 28, 35, 38, 40, 45, 46, 47, 48, 49])]
temp_close = temp[~temp['STFIPS'].isin([1, 4, 8, 13, 16, 17, 18, 19, 23, 27, 28, 35, 38, 40, 45, 46, 47, 48, 49])]
sum_temp_reopen = temp_reopen.groupby(['Date']).sum()[['New_cases', 'InFlow_Weight']].reset_index()
sum_temp_close = temp_close.groupby(['Date']).sum()[['New_cases', 'InFlow_Weight']].reset_index()
sum_temp_reopen['Date'] = pd.to_datetime(sum_temp_reopen['Date'])
sum_temp_close['Date'] = pd.to_datetime(sum_temp_close['Date'])
myFmt = mdates.DateFormatter('%b-%d')
fig, ax = plt.subplots(ncols=1, nrows=1, figsize=(8, 5))
ln1 = ax.plot(sum_temp_reopen['Date'], sum_temp_reopen['InFlow_Weight'] / 1480, '-o', color='#10375c', markersize=3,
alpha=1)
ln2 = ax.plot(sum_temp_close['Date'], sum_temp_close['InFlow_Weight'] / 1625, '->', color='#b83b5e', markersize=3,
alpha=1)
plt.legend(['Inflow ("Reopened" counties as of 05/01)', 'Inflow ("Locked-down" counties)'], frameon=False,
loc=(.45, .86))
ax.xaxis.set_major_formatter(myFmt)
ax.ticklabel_format(axis="y", style="sci", scilimits=(0, 0), useMathText=True)
ax2 = ax.twinx()
ln3 = ax2.bar(sum_temp_reopen['Date'], sum_temp_reopen['New_cases'] / 1480, color='#10375c', alpha=0.2)
ln4 = ax2.bar(sum_temp_close['Date'], sum_temp_close['New_cases'] / 1625, bottom=sum_temp_reopen['New_cases'] / 1480,
color='#b83b5e', alpha=0.2)
ax.set_xlim([datetime.date(2020, 2, 1), datetime.date(2020, 6, 10)])
ax.xaxis.set_major_locator(mdates.WeekdayLocator(interval=3))
# fig.autofmt_xdate()
plt.legend(['Cases ("Reopened" counties as of 05/01)', 'Cases ("Locked-down" counties)'], frameon=False, loc=(.45, .75))
ax2.set_ylabel('Number of new cases per county (Daily)')
ax.set_ylabel('Number of inflow per county (Daily)')
ax.set_xlabel('Date')
ax.set_ylim([0.2 * 1e5, 1.1 * 1e5])
ax2.set_ylim([0, 30])
plt.tight_layout()
plt.savefig('Figure 2a.pdf', dpi=1000)
plt.savefig('Figure 2a.png', dpi=1000)
# Output
Agg_Trips_1 = pd.read_csv('All_XY_Features_To_R_County_Level_0731_toR.csv', index_col=0)
Agg_Trips_1['National_Cases'] = Agg_Trips_1['National_Cases'] / 1e3
Agg_Trips_1.columns
Agg_Trips_1[['CTFIPS', 'CTNAME', 'Date', 'Month', 'Week',
'Adj_New_cases', 'Adj_Agg_Cases', 'Enforcement', 'Is_ReOpen', 'PRCP',
'TMAX', 'TMIN', 'Pct_Male', 'Pct_Age_0_24', 'Pct_Age_25_40',
'Pct_Age_40_65', 'Pct_White', 'Pct_Black', 'Pct_Indian', 'Pct_Asian',
'Total_Population', 'Med_House_Income', 'LAT', 'LNG',
'LAND', 'Is_Weekend', 'Time_Index', 'National_Cases',
'Population_density', 'Employment_density', 'In_Flow', 'InFlow_Weight',
'Agg_cases', 'New_cases', 'STFIPS', 'Risked_WInput', 'STNAME',
'PRCP_NEW', 'National_Cases_Reopen', 'National_Cases_Close',
'Log_National_Cases', 'Log_National_Cases_Reopen',
'Log_National_Cases_Close', 'Log_New_cases', 'Log_InFlow_Weight',
'Log_Risked_WInput',
'Lag1_Log_National_Cases', 'Lag1_Log_National_Cases_Reopen',
'Lag1_Log_National_Cases_Close', 'Lag1_Log_New_cases',
'Lag1_Log_InFlow_Weight', 'Lag1_Log_Risked_WInput',
'Lag7_Log_National_Cases', 'Lag7_Log_National_Cases_Reopen',
'Lag7_Log_National_Cases_Close', 'Lag7_Log_New_cases',
'Lag7_Log_InFlow_Weight', 'Lag7_Log_Risked_WInput']].to_csv(
'All_XY_Features_To_R_County_Level_0731_toR.csv.gz', compression='gzip', index=False)