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python - 使用 xs 对多索引 pandas 数据框进行切片

问题描述

我有一个df

df_test = pd.DataFrame.from_dict({('group', ''): {0: 'A',
  1: 'A',
  2: 'A',
  3: 'A',
  4: 'A',
  5: 'A',
  6: 'A',
  7: 'A',
  8: 'A',
  9: 'B',
  10: 'B',
  11: 'B',
  12: 'B',
  13: 'B',
  14: 'B',
  15: 'B',
  16: 'B',
  17: 'B',
  18: 'all',
  19: 'all'},
 ('category', ''): {0: 'Amazon',
  1: 'Apple',
  2: 'Facebook',
  3: 'Google',
  4: 'Netflix',
  5: 'Tesla',
  6: 'Total',
  7: 'Uber',
  8: 'total',
  9: 'Amazon',
  10: 'Apple',
  11: 'Facebook',
  12: 'Google',
  13: 'Netflix',
  14: 'Tesla',
  15: 'Total',
  16: 'Uber',
  17: 'total',
  18: 'Total',
  19: 'total'},
 (pd.Timestamp('2020-06-29'), 'last_sales'): {0: 195.0,
  1: 61.0,
  2: 106.0,
  3: 61.0,
  4: 37.0,
  5: 13.0,
  6: 954.0,
  7: 4.0,
  8: 477.0,
  9: 50.0,
  10: 50.0,
  11: 75.0,
  12: 43.0,
  13: 17.0,
  14: 14.0,
  15: 504.0,
  16: 3.0,
  17: 252.0,
  18: 2916.0,
  19: 2916.0},
 (pd.Timestamp('2020-06-29'), 'sales'): {0: 1268.85,
  1: 18274.385000000002,
  2: 19722.65,
  3: 55547.255,
  4: 15323.800000000001,
  5: 1688.6749999999997,
  6: 227463.23,
  7: 1906.0,
  8: 113731.615,
  9: 3219.6499999999996,
  10: 15852.060000000001,
  11: 17743.7,
  12: 37795.15,
  13: 5918.5,
  14: 1708.75,
  15: 166349.64,
  16: 937.01,
  17: 83174.82,
  18: 787625.7400000001,
  19: 787625.7400000001},
 (pd.Timestamp('2020-06-29'), 'difference'): {0: 0.0,
  1: 0.0,
  2: 0.0,
  3: 0.0,
  4: 0.0,
  5: 0.0,
  6: 0.0,
  7: 0.0,
  8: 0.0,
  9: 0.0,
  10: 0.0,
  11: 0.0,
  12: 0.0,
  13: 0.0,
  14: 0.0,
  15: 0.0,
  16: 0.0,
  17: 0.0,
  18: 0.0,
  19: 0.0},
 (pd.Timestamp('2020-07-06'), 'last_sales'): {0: 26.0,
  1: 39.0,
  2: 79.0,
  3: 49.0,
  4: 10.0,
  5: 10.0,
  6: 436.0,
  7: 5.0,
  8: 218.0,
  9: 89.0,
  10: 34.0,
  11: 133.0,
  12: 66.0,
  13: 21.0,
  14: 20.0,
  15: 732.0,
  16: 3.0,
  17: 366.0,
  18: 2336.0,
  19: 2336.0},
 (pd.Timestamp('2020-07-06'), 'sales'): {0: 3978.15,
  1: 12138.96,
  2: 19084.175,
  3: 40033.46000000001,
  4: 4280.15,
  5: 1495.1,
  6: 165548.29,
  7: 1764.15,
  8: 82774.145,
  9: 8314.92,
  10: 12776.649999999996,
  11: 28048.075,
  12: 55104.21000000002,
  13: 6962.844999999999,
  14: 3053.2000000000003,
  15: 231049.11000000002,
  16: 1264.655,
  17: 115524.55500000001,
  18: 793194.8000000002,
  19: 793194.8000000002},
 (pd.Timestamp('2020-07-06'), 'difference'): {0: 0.0,
  1: 0.0,
  2: 0.0,
  3: 0.0,
  4: 0.0,
  5: 0.0,
  6: 0.0,
  7: 0.0,
  8: 0.0,
  9: 0.0,
  10: 0.0,
  11: 0.0,
  12: 0.0,
  13: 0.0,
  14: 0.0,
  15: 0.0,
  16: 0.0,
  17: 0.0,
  18: 0.0,
  19: 0.0},
 (pd.Timestamp('2021-06-28'), 'last_sales'): {0: 96.0,
  1: 56.0,
  2: 106.0,
  3: 44.0,
  4: 34.0,
  5: 13.0,
  6: 716.0,
  7: 9.0,
  8: 358.0,
  9: 101.0,
  10: 22.0,
  11: 120.0,
  12: 40.0,
  13: 13.0,
  14: 8.0,
  15: 610.0,
  16: 1.0,
  17: 305.0,
  18: 2652.0,
  19: 2652.0},
 (pd.Timestamp('2021-06-28'), 'sales'): {0: 5194.95,
  1: 19102.219999999994,
  2: 22796.420000000002,
  3: 30853.115,
  4: 11461.25,
  5: 992.6,
  6: 188143.41,
  7: 3671.15,
  8: 94071.705,
  9: 6022.299999999998,
  10: 7373.6,
  11: 33514.0,
  12: 35943.45,
  13: 4749.000000000001,
  14: 902.01,
  15: 177707.32,
  16: 349.3,
  17: 88853.66,
  18: 731701.46,
  19: 731701.46},
 (pd.Timestamp('2021-06-28'), 'difference'): {0: 0.0,
  1: 0.0,
  2: 0.0,
  3: 0.0,
  4: 0.0,
  5: 0.0,
  6: 0.0,
  7: 0.0,
  8: 0.0,
  9: 0.0,
  10: 0.0,
  11: 0.0,
  12: 0.0,
  13: 0.0,
  14: 0.0,
  15: 0.0,
  16: 0.0,
  17: 0.0,
  18: 0.0,
  19: 0.0},
 (pd.Timestamp('2021-07-07'), 'last_sales'): {0: 45.0,
  1: 47.0,
  2: 87.0,
  3: 45.0,
  4: 13.0,
  5: 8.0,
  6: 494.0,
  7: 2.0,
  8: 247.0,
  9: 81.0,
  10: 36.0,
  11: 143.0,
  12: 56.0,
  13: 9.0,
  14: 9.0,
  15: 670.0,
  16: 1.0,
  17: 335.0,
  18: 2328.0,
  19: 2328.0},
 (pd.Timestamp('2021-07-07'), 'sales'): {0: 7556.414999999998,
  1: 14985.05,
  2: 16790.899999999998,
  3: 36202.729999999996,
  4: 4024.97,
  5: 1034.45,
  6: 163960.32999999996,
  7: 1385.65,
  8: 81980.16499999998,
  9: 5600.544999999999,
  10: 11209.92,
  11: 32832.61,
  12: 42137.44500000001,
  13: 3885.1499999999996,
  14: 1191.5,
  15: 194912.34000000003,
  16: 599.0,
  17: 97456.17000000001,
  18: 717745.3400000001,
  19: 717745.3400000001},
 (pd.Timestamp('2021-07-07'), 'difference'): {0: 0.0,
  1: 0.0,
  2: 0.0,
  3: 0.0,
  4: 0.0,
  5: 0.0,
  6: 0.0,
  7: 0.0,
  8: 0.0,
  9: 0.0,
  10: 0.0,
  11: 0.0,
  12: 0.0,
  13: 0.0,
  14: 0.0,
  15: 0.0,
  16: 0.0,
  17: 0.0,
  18: 0.0,
  19: 0.0}}).set_index(['group','category'])

我正在尝试创建一个year_to_date列sumaxis=1

我有一个问题，我尝试选择我想要的所有日期，因为我想要这样'2020-06-29':'2021-06-28'。

我试过：

df_test.xs((pd.to_datetime('2020-07-06') : pd.to_datetime('2021-06-28')), 
           axis='columns', level=0).reset_index()[['sales']].sum(axis = 1)

但我得到一个错误:，我尝试使用(pd.to_datetime('2020-07-06'), pd.to_datetime('2021-06-28'))但抛出KeyError: (Timestamp('2020-07-06 00:00:00'), Timestamp('2021-06-28 00:00:00'。

我只是想选择索引中的相关日期并求和sales，axis=1以便我想要一个sum可以选择从 - 到日期的所需列的总数。

标签： pythonpandasmulti-indexnumpy-slicing

解决方案

我认为你可以DataFrame.loc使用axis=1：

s = pd.to_datetime('2020-07-06')
e = pd.to_datetime('2021-06-28')

df_test = df_test.loc(axis=1)[s : e,'sales'].sum(axis = 1)
print(df_test)
    group  category
A      Amazon         9173.100
       Apple         31241.180
       Facebook      41880.595
       Google        70886.575
       Netflix       15741.400
       Tesla          2487.700
       Total        353691.700
       Uber           5435.300
       total        176845.850
B      Amazon        14337.220
       Apple         20150.250
       Facebook      61562.075
       Google        91047.660
       Netflix       11711.845
       Tesla          3955.210
       Total        408756.430
       Uber           1613.955
       total        204378.215
all    Total       1524896.260
       total       1524896.260
dtype: float64

如果需要列表中指定的多个值：

s = pd.to_datetime('2020-07-06')
e = pd.to_datetime('2021-06-28')
df_test = df_test.loc(axis=1)[s : e,['sales','last_sales']].groupby(level=1, axis=1).sum()
print(df_test)
                last_sales        sales
group category                         
A     Amazon         122.0     9173.100
      Apple           95.0    31241.180
      Facebook       185.0    41880.595
      Google          93.0    70886.575
      Netflix         44.0    15741.400
      Tesla           23.0     2487.700
      Total         1152.0   353691.700
      Uber            14.0     5435.300
      total          576.0   176845.850
B     Amazon         190.0    14337.220
      Apple           56.0    20150.250
      Facebook       253.0    61562.075
      Google         106.0    91047.660
      Netflix         34.0    11711.845
      Tesla           28.0     3955.210
      Total         1342.0   408756.430
      Uber             4.0     1613.955
      total          671.0   204378.215
all   Total         4988.0  1524896.260
      total         4988.0  1524896.260

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