I have the following code which converts index to week number as well as another column is added with week number:
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import yfinance as yf2
import pandas as pd3
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df = yf.download('f', interval = '1wk')5
df['week'] = df.index.isocalendar().week6
df.index = pd.to_datetime(df.index)7
df.index = df.index.strftime('%U')8
df = df[df['week'] ==12]9
print(df) 10
Open High Low Close Adj Close Volume week11
Date 12
11 2.092394 2.092394 1.950468 2.011294 0.262594 7956172.0 1213
11 1.682837 1.690947 1.646341 1.654451 0.229500 8985757.0 1214
11 1.192178 1.248949 1.184068 1.224618 0.184168 10983278.0 1215
11 1.820708 1.849093 1.784212 1.800432 0.286859 16121960.0 1216
12 1.901808 1.909918 1.804487 1.808542 0.303502 11485741.0 1217
12 1.809556 1.829831 1.794350 1.809556 0.326586 11250230.0 1218
11 1.758868 1.784212 1.758868 1.769006 0.346003 13469697.0 1219
11 1.196233 1.211440 1.135408 1.145545 0.248064 21795165.0 1220
11 0.907313 1.054307 0.902244 1.013757 0.237924 33698903.0 1221
12 0.846487 0.876900 0.821143 0.841418 0.206226 25282194.0 1222
12 1.642286 1.692974 1.596667 1.616942 0.396302 32589171.0 1223
12 2.356985 2.364588 2.197318 2.258144 0.563657 50150085.0 1224
11 2.623096 2.661112 2.569874 2.592684 0.680899 70440945.0 1225
11 4.409843 4.964875 4.394637 4.820415 1.337498 77535017.0 1226
11 7.276241 7.470122 7.093765 7.424503 2.144691 40895183.0 1227
12 8.097384 8.120194 7.664003 7.664003 2.311799 25503044.0 1228
12 9.055384 9.101003 8.781670 8.850099 2.806630 22920790.0 1229
11 8.827289 8.918527 8.667622 8.804480 2.974335 23601426.0 1230
11 6.295431 6.432288 5.862050 5.976097 2.199914 26059828.0 1231
11 6.637574 7.413098 6.637574 7.344669 2.845779 78490758.0 1232
12 9.420337 9.762480 9.420337 9.557194 3.842265 39688451.0 1233
12 11.245099 11.701290 10.948576 10.994195 4.545142 56170594.0 1234
12 9.351908 9.397527 9.032575 9.306289 3.979715 57608865.0 1235
11 11.997814 12.454005 11.997814 12.271528 5.494052 41265643.0 1236
11 11.587242 11.860957 11.496004 11.587242 5.431107 25078605.0 1237
11 21.897152 23.288532 21.805914 23.220104 11.327546 32670825.0 1238
12 32.182701 32.972672 30.362335 31.289692 15.777828 22828360.0 1239
12 24.214306 25.244701 23.493029 24.523424 12.810479 44824758.0 1240
11 28.400000 30.090000 27.020000 27.900000 15.203670 28355400.0 1241
11 17.000000 17.270000 16.299999 16.670000 9.438626 36153400.0 1242
11 6.700000 8.030000 6.600000 8.020000 4.709421 89985100.0 1243
11 13.270000 13.440000 13.000000 13.060000 7.929460 39367600.0 1244
12 11.310000 11.630000 10.940000 11.290000 7.051045 58470600.0 1245
12 7.850000 8.250000 7.810000 8.090000 5.279247 93573100.0 1246
11 7.580000 8.230000 7.570000 7.890000 5.259418 201311700.0 1247
11 5.020000 5.730000 4.950000 5.620000 3.746252 393914200.0 1248
11 2.280000 2.780000 2.090000 2.750000 1.833130 298598800.0 1249
12 12.980000 14.300000 12.810000 13.860000 9.238977 578653600.0 1250
12 14.700000 15.200000 14.020000 15.010000 10.005558 342805300.0 1251
12 12.520000 12.680000 12.180000 12.320000 8.244656 215053600.0 1252
11 13.160000 13.420000 12.800000 13.260000 9.070373 158372200.0 1253
11 15.180000 15.740000 15.160000 15.470000 10.870311 132509200.0 1254
11 16.260000 16.540001 16.110001 16.480000 11.979823 152515700.0 1255
12 13.640000 13.760000 12.780000 13.060000 10.109502 114791900.0 1256
12 12.480000 12.490000 11.500000 11.620000 9.458299 332979200.0 1257
11 11.150000 11.190000 10.510000 10.560000 9.152753 238518100.0 1258
11 8.450000 8.870000 8.420000 8.540000 7.878255 237838700.0 1259
11 5.040000 5.220000 4.100000 4.330000 4.257940 596481500.0 1260
12 12.850000 12.930000 11.720000 12.300000 12.095303 312358300.0 1261
12 16.870001 17.309999 16.330000 16.469999 16.361143 331864800.0 1262
As you can see there are instances where week 11 is being shown in the index column using df.index.strftime('%U') when the week number is showing as 12 using df.index.isocalendar().week Why are there week number different in index column as compare to column week ?
Edit
With this:
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df = yf.download('f', interval = '1wk')2
print(len(df.index.year))3
df['week'] = df.index.isocalendar().week4
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df.index = pd.to_datetime(df.index)6
df['dates_with_strf'] = df.index.strftime('%U')7
df = (df[df['week'] ==12]) 8
print(df) 9
I have dates
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Open High Low Volume week dates_with_strf2
Date 3
1973-03-19 2.092394 2.092394 1.950468 7956172.0 12 114
1974-03-18 1.682837 1.690947 1.646341 8985757.0 12 115
1975-03-17 1.192178 1.248949 1.184068 10983278.0 12 116
1976-03-15 1.820708 1.849093 1.784212 16121960.0 12 117
1977-03-21 1.901808 1.909918 1.804487 11485741.0 12 128
1978-03-20 1.809556 1.829831 1.794350 11250230.0 12 129
1979-03-19 1.758868 1.784212 1.758868 13469697.0 12 1110
1980-03-17 1.196233 1.211440 1.135408 21795165.0 12 1111
1981-03-16 0.907313 1.054307 0.902244 33698903.0 12 1112
1982-03-22 0.846487 0.876900 0.821143 25282194.0 12 1213
1983-03-21 1.642286 1.692974 1.596667 32589171.0 12 1214
1984-03-19 2.356985 2.364588 2.197318 50150085.0 12 1215
1985-03-18 2.623096 2.661112 2.569874 70440945.0 12 1116
1986-03-17 4.409843 4.964875 4.394637 77535017.0 12 1117
1987-03-16 7.276241 7.470122 7.093765 40895183.0 12 1118
1988-03-21 8.097384 8.120194 7.664003 25503044.0 12 1219
1989-03-20 9.055384 9.101003 8.781670 22920790.0 12 1220
1990-03-19 8.827289 8.918527 8.667622 23601426.0 12 1121
1991-03-18 6.295431 6.432288 5.862050 26059828.0 12 1122
1992-03-16 6.637574 7.413098 6.637574 78490758.0 12 1123
1993-03-22 9.420337 9.762480 9.420337 39688451.0 12 1224
1994-03-21 11.245099 11.701290 10.948576 56170594.0 12 1225
1995-03-20 9.351908 9.397527 9.032575 57608865.0 12 1226
1996-03-18 11.997814 12.454005 11.997814 41265643.0 12 1127
1997-03-17 11.587242 11.860957 11.496004 25078605.0 12 1128
1998-03-16 21.897152 23.288532 21.805914 32670825.0 12 1129
1999-03-22 32.182701 32.972672 30.362335 22828360.0 12 1230
2000-03-20 24.214306 25.244701 23.493029 44824758.0 12 1231
2001-03-19 28.400000 30.090000 27.020000 28355400.0 12 1132
2002-03-18 17.000000 17.270000 16.299999 36153400.0 12 1133
2003-03-17 6.700000 8.030000 6.600000 89985100.0 12 1134
2004-03-15 13.270000 13.440000 13.000000 39367600.0 12 1135
2005-03-21 11.310000 11.630000 10.940000 58470600.0 12 1236
2006-03-20 7.850000 8.250000 7.810000 93573100.0 12 1237
2007-03-19 7.580000 8.230000 7.570000 201311700.0 12 1138
2008-03-17 5.020000 5.730000 4.950000 393914200.0 12 1139
2009-03-16 2.280000 2.780000 2.090000 298598800.0 12 1140
2010-03-22 12.980000 14.300000 12.810000 578653600.0 12 1241
2011-03-21 14.700000 15.200000 14.020000 342805300.0 12 1242
2012-03-19 12.520000 12.680000 12.180000 215053600.0 12 1243
2013-03-18 13.160000 13.420000 12.800000 158372200.0 12 1144
2014-03-17 15.180000 15.740000 15.160000 132509200.0 12 1145
2015-03-16 16.260000 16.540001 16.110001 152515700.0 12 1146
2016-03-21 13.640000 13.760000 12.780000 114791900.0 12 1247
2017-03-20 12.480000 12.490000 11.500000 332979200.0 12 1248
2018-03-19 11.150000 11.190000 10.510000 238518100.0 12 1149
2019-03-18 8.450000 8.870000 8.420000 237838700.0 12 1150
2020-03-16 5.040000 5.220000 4.100000 596481500.0 12 1151
2021-03-22 12.850000 12.930000 11.720000 312358300.0 12 1252
2022-03-21 16.870001 17.309999 16.330000 331864800.0 12 1253
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Answer
data
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import pandas.util.testing2
df= pd.util.testing.makeMixedDataFrame().set_index('D').rename_axis(None)3
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df['week_strf'] =df.index.strftime('%U')5
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df['week_iso'] = df.index.isocalendar().week7
Check documentation
stftime, Sunday as the first day of the week
ISO weeks start on a Monday and end on a Sunday
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A B C week_strf week_iso2
2009-01-01 0.0 0.0 foo1 00 13
2009-01-02 1.0 1.0 foo2 00 14
2009-01-05 2.0 0.0 foo3 01 25
2009-01-06 3.0 1.0 foo4 01 26
2009-01-07 4.0 0.0 foo5 01 27