Question

我有两个DataFrame。 df提供了大量数据。 test_df描述了某些测试是否已通过。我需要通过在df中查找此信息，从test_df中仅选择测试未失败的行。到目前为止，我可以将test_df缩减为passed_tests。那么，剩下的就是只选择df中行索引的相关部分位于passed_tests的行。我怎么能这样做？

更新

test_db没有唯一的行。如果存在重复行（并且可能有多于1个重复），则最正面的测试优先。即真＆gt;好的＆gt;假。

我的代码：

import pandas as pd
import numpy as np


index = [np.array(['foo', 'foo', 'foo', 'foo', 'qux', 'qux', 'qux']), np.array(['a', 'a', 'b', 'b', 'a', 'b', 'b'])]
data = np.array(['False', 'True', 'False', 'False', 'False', 'Ok', 'False'])
columns = ["Passed?"]
test_df = pd.DataFrame(data, index=index, columns=columns)
print test_df

index = [np.array(['foo', 'foo', 'foo', 'foo', 'qux', 'qux', 'qux', 'qux']),
         np.array(['a', 'a', 'b', 'b', 'a', 'a', 'b', 'b']),
         np.array(['1', '2', '1', '2', '1', '2', '1', '2'])]
data = np.random.randn(8, 2)
columns = ["X", "Y"]
df = pd.DataFrame(data, index=index, columns=columns)
print df

passed_tests = test_df.loc[test_df['Passed?'].isin(['True', 'Ok'])]
print passed_tests

DF

                X         Y
foo a 1  0.589776 -0.234717
      2  0.105161  1.937174
    b 1 -0.092252  0.143451
      2  0.939052 -0.239052
qux a 1  0.757239  2.836032
      2 -0.445335  1.352374
    b 1  2.175553 -0.700816
      2  1.082709 -0.923095

test_df

     Passed?
foo a   False
    a    True
    b   False
    b   False
qux a   False
    b      Ok
    b   False

passed_tests

      Passed?
foo a    True
qux b      Ok

必需的解决方案

                X         Y
foo a 1  0.589776 -0.234717
      2  0.105161  1.937174
qux b 1  2.175553 -0.700816
      2  1.082709 -0.923095

Answer 1

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Advice

编辑：

对于删除重复项，这里更容易使用：

从reset_index

method='ffill'

sort_values - print (test_df.reindex(df.index, method='ffill')) Passed? foo a 1 True 2 True b 1 False 2 False qux a 1 False 2 False b 1 Ok 2 Ok mask = test_df.reindex(df.index, method='ffill').isin(['True', 'Ok'])['Passed?'] print (mask) foo a 1 True 2 True b 1 False 2 False qux a 1 False 2 False b 1 True 2 True Name: Passed?, dtype: bool print (df[mask]) X Y foo a 1 -0.580448 -0.168951 2 -0.875165 1.304745 qux b 1 -0.147014 -0.787483 2 0.188989 -1.159533列降序，第一和第二升序
drop_duplicates - 仅保留第一个值
set_index for MultiIndex back
rename_axis用于删除索引名称

MultiIndex

另一种解决方案更简单 - 首先排序，然后Passed?排序test_df = test_df.reset_index() .sort_values(['level_0','level_1', 'Passed?'], ascending=[1,1,0]) .drop_duplicates(['level_0','level_1']) .set_index(['level_0','level_1']) .rename_axis([None, None]) print (test_df) Passed? foo a True b False qux a False b Ok：

groupby

EDIT1：

将值转换为ordered Categorical。

first

如何从一个DataFrame中选择行，其中行的索引的一部分位于另一个DataFrame的索引中并满足特定条件？

1 个答案: