Question

我有一个pandas数据框，它是旋转的结果。它有多个指数。我想得到一个正常的＆＃34;数据框从这个旋转的df ...中，以便我可以对新的df进行一些正常的操作。

以下是一个示例：我的透视数据框如下所示：

                      feature_value
feature_type          f1  f2  f3  f4  f5
time         name
2016-05-10   Clay     0   1   30   4  40
2016-05-10   John     0   4   10   4  66
2016-05-10   Mary     0   1   40   4  46
2016-05-10   Boby     2   0   30   4  59
2016-05-10   Lucy     5   8   20   4  41

以下是我想要的新df：

time         name     f1  f2  f3  f4  f5
2016-05-10   Clay     0   1   30   4  40
2016-05-10   John     0   4   10   4  66
2016-05-10   Mary     0   1   40   4  46
2016-05-10   Boby     2   0   30   4  59
2016-05-10   Lucy     5   8   20   4  41

我该怎么做？

pivoted_df.to_dict（）如下所示：

{('feature_value', 'f1'): {(Timestamp('2016-05-10'), 'Clay'): 0, (Timestamp('2016-05-10'), 'John'): 0, (Timestamp('2016-05-10'), 'Mary'): 0, (Timestamp('2016-05-10'), 'Boby'): 2, (Timestamp('2016-05-10'), 'Lucy'): 5}, ('feature_value', 'f2'): {(Timestamp('2016-05-10'), 'Clay'): 1, (Timestamp('2016-05-10'), 'John'): 4, (Timestamp('2016-05-10'), 'Mary'): 1, (Timestamp('2016-05-10'), 'Boby'): 0, (Timestamp('2016-05-10'), 'Lucy'): 8}, ('feature_value', 'f3'): {(Timestamp('2016-05-10'), 'Clay'): 30, (Timestamp('2016-05-10'), 'John'): 10, (Timestamp('2016-05-10'), 'Mary'): 40, (Timestamp('2016-05-10'), 'Boby'): 30, (Timestamp('2016-05-10'), 'Lucy'): 20}, ('feature_value', 'f4'): {(Timestamp('2016-05-10'), 'Clay'): 4, (Timestamp('2016-05-10'), 'John'): 4, (Timestamp('2016-05-10'), 'Mary'): 4, (Timestamp('2016-05-10'), 'Boby'): 4, (Timestamp('2016-05-10'), 'Lucy'): 4}, ('feature_value', 'f5'): {(Timestamp('2016-05-10'), 'Clay'): 40, (Timestamp('2016-05-10'), 'John'): 66, (Timestamp('2016-05-10'), 'Mary'): 46, (Timestamp('2016-05-10'), 'Boby'): 59, (Timestamp('2016-05-10'), 'Lucy'): 41}}

Answer 1

致电pivot_table时，请务必指定values参数：

df.pivot_table(index=['time', 'name'], columns=['feature_type'], 
               values='feature_value')

没有values='feature_value'，您将获得一个MultiIndex列索引（可能）有一个外部级别，例如'feature_value'。

df.pivot_table(index=['time', 'name'], ...)还会返回一个DataFrame，其中包含time和name级别的MultiIndex行索引。要使这些索引级别成为常规列，请调用reset_index()：

result = df.pivot_table(index=['time', 'name'], 
                        columns=['feature_type'],
                        values='feature_value').reset_index()

例如，

import numpy as np
import pandas as pd
np.random.seed(2016)

N = 10
df = pd.DataFrame(
    {'time': np.random.choice(pd.date_range('2016-05-10', '2016-05-12'), size=N),
     'name': np.random.choice(['Clay', 'John', 'Mary', 'Boby', 'Lucy'], size=N),
     'feature_type': np.random.choice(['f{}'.format(i) for i in range(1,6)], size=N),
     'feature_value': np.random.randint(100, size=N)})

orig = df.pivot_table(index=['time', 'name'], columns=['feature_type'])
print(orig)

alt = df.pivot_table(index=['time', 'name'], 
                     columns=['feature_type'],
                     values='feature_value').reset_index()
alt.columns.name = None
print(alt)

orig看起来像这样：

                feature_value                        
feature_type               f1    f2    f3    f4    f5
time       name                                      
2016-05-10 John           NaN  50.0   NaN   NaN  91.0
           Lucy           NaN   NaN   NaN  28.0   NaN
           Mary           NaN   NaN  19.0   NaN  27.0
2016-05-11 Clay           2.0   NaN   NaN   NaN   NaN
           Lucy          24.0   NaN   NaN   NaN   NaN
2016-05-12 Boby           NaN  16.0   NaN   NaN   NaN
           John           NaN   NaN   NaN   NaN  62.0
           Mary           NaN   NaN   NaN  84.0   NaN

虽然alt看起来像

        time  name    f1    f2    f3    f4    f5
0 2016-05-10  John   NaN  50.0   NaN   NaN  91.0
1 2016-05-10  Lucy   NaN   NaN   NaN  28.0   NaN
2 2016-05-10  Mary   NaN   NaN  19.0   NaN  27.0
3 2016-05-11  Clay   2.0   NaN   NaN   NaN   NaN
4 2016-05-11  Lucy  24.0   NaN   NaN   NaN   NaN
5 2016-05-12  Boby   NaN  16.0   NaN   NaN   NaN
6 2016-05-12  John   NaN   NaN   NaN   NaN  62.0
7 2016-05-12  Mary   NaN   NaN   NaN  84.0   NaN

Answer 2

蛮力：

df.columns = df.columns.droplevel()
df = df.reset_index()

在旋转数据框之后检索正常数据帧

2 个答案: