如何对熊猫的多索引进行分组？

Question

下面是我的数据框。 我进行了一些转换以创建类别列并删除它源自的原始列。 现在我需要进行分组以删除重复项，例如可以通过groupby总和汇总Love和Fashion 。

df.colunms = array([category, clicks, revenue, date, impressions, size], dtype=object)
df.values=
[[Love 0 0.36823 2013-11-04 380 300x250]
 [Love 183 474.81522 2013-11-04 374242 300x250]
 [Fashion 0 0.19434 2013-11-04 197 300x250]
 [Fashion 9 18.26422 2013-11-04 13363 300x250]]

这是我创建数据框时创建的索引

print df.index
array([ 0,  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])

我想我要删除索引，并创建日期，类别为multiindex ，然后做一个groupby指标的总和。 我如何在 Pandas 数据框中执行此操作？

df.head(15).to_dict()= {'category': {0: 'Love', 1: 'Love', 2: 'Fashion', 3: 'Fashion', 4: 'Hair', 5: 'Movies', 6: 'Movies', 7: 'Health', 8: 'Health', 9: 'Celebs', 10: 'Celebs', 11: 'Travel', 12: 'Weightloss', 13: 'Diet', 14: 'Bags'}, 'impressions': {0: 380, 1: 374242, 2: 197, 3: 13363, 4: 4, 5: 189, 6: 60632, 7: 269, 8: 40189, 9: 138, 10: 66590, 11: 2227, 12: 22668, 13: 21707, 14: 229}, 'date': {0: '2013-11-04', 1: '2013-11-04', 2: '2013-11-04', 3: '2013-11-04', 4: '2013-11-04', 5: '2013-11-04', 6: '2013-11-04', 7: '2013-11-04', 8: '2013-11-04', 9: '2013-11-04', 10: '2013-11-04', 11: '2013-11-04', 12: '2013-11-04', 13: '2013-11-04', 14: '2013-11-04'}, 'cpc_cpm_revenue': {0: 0.36823, 1: 474.81522000000001, 2: 0.19434000000000001, 3: 18.264220000000002, 4: 0.00080000000000000004, 5: 0.23613000000000001, 6: 81.391139999999993, 7: 0.27171000000000001, 8: 51.258200000000002, 9: 0.11536, 10: 83.966859999999997, 11: 3.43248, 12: 31.695889999999999, 13: 28.459320000000002, 14: 0.43524000000000002}, 'clicks': {0: 0, 1: 183, 2: 0, 3: 9, 4: 0, 5: 1, 6: 20, 7: 0, 8: 21, 9: 0, 10: 32, 11: 1, 12: 12, 13: 9, 14: 2}, 'size': {0: '300x250', 1: '300x250', 2: '300x250', 3: '300x250', 4: '300x250', 5: '300x250', 6: '300x250', 7: '300x250', 8: '300x250', 9: '300x250', 10: '300x250', 11: '300x250', 12: '300x250', 13: '300x250', 14: '300x250'}}

Python 是 2.7，pandas 在 ubuntu 12.04 上是 0.7.0。 下面是我运行下面的错误

import pandas
print pandas.__version__
df = pandas.DataFrame.from_dict(
    {
     'category': {0: 'Love', 1: 'Love', 2: 'Fashion', 3: 'Fashion', 4: 'Hair', 5: 'Movies', 6: 'Movies', 7: 'Health', 8: 'Health', 9: 'Celebs', 10: 'Celebs', 11: 'Travel', 12: 'Weightloss', 13: 'Diet', 14: 'Bags'}, 
     'impressions': {0: 380, 1: 374242, 2: 197, 3: 13363, 4: 4, 5: 189, 6: 60632, 7: 269, 8: 40189, 9: 138, 10: 66590, 11: 2227, 12: 22668, 13: 21707, 14: 229}, 
     'date': {0: '2013-11-04', 1: '2013-11-04', 2: '2013-11-04', 3: '2013-11-04', 4: '2013-11-04', 5: '2013-11-04', 6: '2013-11-04', 7: '2013-11-04', 8: '2013-11-04', 9: '2013-11-04', 10: '2013-11-04', 11: '2013-11-04', 12: '2013-11-04', 13: '2013-11-04', 14: '2013-11-04'}, 'cpc_cpm_revenue': {0: 0.36823, 1: 474.81522000000001, 2: 0.19434000000000001, 3: 18.264220000000002, 4: 0.00080000000000000004, 5: 0.23613000000000001, 6: 81.391139999999993, 7: 0.27171000000000001, 8: 51.258200000000002, 9: 0.11536, 10: 83.966859999999997, 11: 3.43248, 12: 31.695889999999999, 13: 28.459320000000002, 14: 0.43524000000000002}, 'clicks': {0: 0, 1: 183, 2: 0, 3: 9, 4: 0, 5: 1, 6: 20, 7: 0, 8: 21, 9: 0, 10: 32, 11: 1, 12: 12, 13: 9, 14: 2}, 'size': {0: '300x250', 1: '300x250', 2: '300x250', 3: '300x250', 4: '300x250', 5: '300x250', 6: '300x250', 7: '300x250', 8: '300x250', 9: '300x250', 10: '300x250', 11: '300x250', 12: '300x250', 13: '300x250', 14: '300x250'}
    }
)
df.set_index(['date', 'category'], inplace=True)
df.groupby(level=[0,1]).sum()


Traceback (most recent call last):
  File "/home/ubuntu/workspace/devops/reports/groupby_sub.py", line 9, in <module>
    df.set_index(['date', 'category'], inplace=True)
  File "/usr/lib/pymodules/python2.7/pandas/core/frame.py", line 1927, in set_index
    raise Exception('Index has duplicate keys: %s' % duplicates)
Exception: Index has duplicate keys: [('2013-11-04', 'Celebs'), ('2013-11-04', 'Fashion'), ('2013-11-04', 'Health'), ('2013-11-04', 'Love'), ('2013-11-04', 'Movies')]

Answer 1

您可以在现有数据帧上创建索引。 使用提供的数据子集，这对我有用：

import pandas
df = pandas.DataFrame.from_dict(
    {
     'category': {0: 'Love', 1: 'Love', 2: 'Fashion', 3: 'Fashion', 4: 'Hair', 5: 'Movies', 6: 'Movies', 7: 'Health', 8: 'Health', 9: 'Celebs', 10: 'Celebs', 11: 'Travel', 12: 'Weightloss', 13: 'Diet', 14: 'Bags'}, 
     'impressions': {0: 380, 1: 374242, 2: 197, 3: 13363, 4: 4, 5: 189, 6: 60632, 7: 269, 8: 40189, 9: 138, 10: 66590, 11: 2227, 12: 22668, 13: 21707, 14: 229}, 
     'date': {0: '2013-11-04', 1: '2013-11-04', 2: '2013-11-04', 3: '2013-11-04', 4: '2013-11-04', 5: '2013-11-04', 6: '2013-11-04', 7: '2013-11-04', 8: '2013-11-04', 9: '2013-11-04', 10: '2013-11-04', 11: '2013-11-04', 12: '2013-11-04', 13: '2013-11-04', 14: '2013-11-04'}, 'cpc_cpm_revenue': {0: 0.36823, 1: 474.81522000000001, 2: 0.19434000000000001, 3: 18.264220000000002, 4: 0.00080000000000000004, 5: 0.23613000000000001, 6: 81.391139999999993, 7: 0.27171000000000001, 8: 51.258200000000002, 9: 0.11536, 10: 83.966859999999997, 11: 3.43248, 12: 31.695889999999999, 13: 28.459320000000002, 14: 0.43524000000000002}, 'clicks': {0: 0, 1: 183, 2: 0, 3: 9, 4: 0, 5: 1, 6: 20, 7: 0, 8: 21, 9: 0, 10: 32, 11: 1, 12: 12, 13: 9, 14: 2}, 'size': {0: '300x250', 1: '300x250', 2: '300x250', 3: '300x250', 4: '300x250', 5: '300x250', 6: '300x250', 7: '300x250', 8: '300x250', 9: '300x250', 10: '300x250', 11: '300x250', 12: '300x250', 13: '300x250', 14: '300x250'}
    }
)
df.set_index(['date', 'category'], inplace=True)
df.groupby(level=[0,1]).sum()

如果完整数据集存在重复索引问题，则需要稍微清理数据。 如果可行，请删除重复的行。 如果重复的行是有效的，那么是什么使它们彼此区别开来？ 如果您可以将其添加到数据帧并将其包含在索引中，那就太理想了。 如果没有，只需创建一个默认为 1 的虚拟列，但在N重复的情况下可以是 2 或 3 或 ... N - 然后将该字段也包含在索引中。

或者，我很确定您可以跳过索引创建并直接对列进行groupby ：

df.groupby(by=['date', 'category']).sum()

同样，这适用于您发布的数据子集。

Answer 2

我通常在尝试取消堆叠多索引时尝试这样做，但由于存在重复值而失败。

这是我运行查找有问题项目的简单命令：

df.groupby(level=df.index.names).count()