python - 重置从groupby或pivot创建的pandas DataFrame的索引？

Question

我的数据包含各种金融证券的价格、数量和其他数据。我的输入数据如下所示:

import numpy as np
import pandas

prices = np.random.rand(15) * 100
volumes = np.random.randint(15, size=15) * 10
idx = pandas.Series([2007, 2007, 2007, 2007, 2007, 2008,
                     2008, 2008, 2008, 2008, 2009, 2009,
                     2009, 2009, 2009], name='year')
df = pandas.DataFrame.from_items([('price', prices), ('volume', volumes)])
df.index = idx

# BELOW IS AN EXMPLE OF WHAT INPUT MIGHT LOOK LIKE
# IT WON'T BE EXACT BECAUSE OF THE USE OF RANDOM
#           price  volume
# year
# 2007   0.121002      30
# 2007  15.256424      70
# 2007  44.479590      50
# 2007  29.096013       0
# 2007  21.424690       0
# 2008  23.019548      40
# 2008  90.011295       0
# 2008  88.487664      30
# 2008  51.609119      70
# 2008   4.265726      80
# 2009  34.402065     140
# 2009  10.259064     100
# 2009  47.024574     110
# 2009  57.614977     140
# 2009  54.718016      50

我想生成一个如下所示的数据框:

year       2007       2008       2009
0      0.121002  23.019548  34.402065
1     15.256424  90.011295  10.259064
2     44.479590  88.487664  47.024574
3     29.096013  51.609119  57.614977
4     21.424690   4.265726  54.718016

我知道一种使用 groupby 生成上述输出的方法:

df = df.reset_index()
grouper = df.groupby('year')
df2 = None
for group, data in grouper:
    series = data['price'].copy()
    series.index = range(len(series))
    series.name = group
    df2 = pandas.DataFrame(series) if df2 is None else pandas.concat([df2, series], axis=1)

而且我还知道您可以执行透视来获取数据帧，该数据帧对于透视上缺少的索引具有 NaN:

# df = df.reset_index()
df.pivot(columns='year', values='price')

# Output
# year       2007       2008       2009
# 0      0.121002        NaN        NaN
# 1     15.256424        NaN        NaN
# 2     44.479590        NaN        NaN
# 3     29.096013        NaN        NaN
# 4     21.424690        NaN        NaN
# 5           NaN  23.019548        NaN
# 6           NaN  90.011295        NaN
# 7           NaN  88.487664        NaN
# 8           NaN  51.609119        NaN
# 9           NaN   4.265726        NaN
# 10          NaN        NaN  34.402065
# 11          NaN        NaN  10.259064
# 12          NaN        NaN  47.024574
# 13          NaN        NaN  57.614977
# 14          NaN        NaN  54.718016

我的问题如下:

有没有一种方法可以让我在 groupby 中创建输出 DataFrame 而无需创建系列，或者有没有一种方法可以重新索引我的输入 DataFrame 以便使用数据透视表获得所需的输出？

Answer 1

您需要将每年标记为 0-4。为此，请在分组后使用cumcount。然后，您可以使用该新列作为索引来正确旋转。

df['year_count'] = df.groupby(level='year').cumcount()
df.reset_index().pivot(index='year_count', columns='year', values='price')

year             2007       2008       2009
year_count                                 
0           61.682275  32.729113  54.859700
1           44.231296   4.453897  45.325802
2           65.850231  82.023960  28.325119
3           29.098607  86.046499  71.329594
4           67.864723  43.499762  19.255214