将每年订购的数据框更改为季节性订购的数据框
Change yearly ordered dataframe to seasonly orderd dataframe
在Pandas中，我想创建一些列，这些列将代表从 11 月开始到明年 10 月结束的季节（例如旅游季节）。
这是我的代码片段：
from numpy import random
import pandas as pd
np.random.seed(0)
df = pd.DataFrame({
'date': pd.date_range('1990-01-01', freq='M', periods=12),
'travel_2016': random.randint(10, size=(12)),
'travel_2017': random.randint(10, size=(12)),
'travel_2018': random.randint(10, size=(12)),
'travel_2019': random.randint(10, size=(12)),
'travel_2020': random.randint(10, size=(12))})
df['month_date'] = df['date'].dt.strftime('%m')
df = df.drop(columns = ['date'])
我正在尝试这种方法
我在 'unpivoting' 和 table 两种解决方案之后都失败了。对我来说，保持支点 table 以备将来操作会更容易。
我想要的输出是这样的：
season_2016/2017 season_2017/2018 season_2018/2019 season_2019/2020 month_date
0 8 7 7 4 11
1 0 1 4 8 12
2 1 4 5 9 01
3 8 3 5 7 02
4 4 7 8 3 03
5 6 8 4 4 04
6 5 8 3 1 05
7 7 0 1 1 06
8 1 2 1 3 07
9 8 9 7 5 08
10 7 7 7 8 09
11 9 1 4 0 10
非常感谢！
您的 table 已经按照您的意愿进行了格式化，大致是：您基本上是将所有行向下移动 2，并将最下面的 2 行移至开头 - 但移至下一年。
>>> year_ends = df.shift(-10)
>>> year_ends = year_ends.drop(columns=['month_date']).shift(axis='columns').join(year_ends['month_date'])
>>> year_ends
travel_2016 travel_2017 travel_2018 travel_2019 travel_2020 month_date
0 NaN 7.0 8.0 3.0 2.0 11
1 NaN 6.0 9.0 3.0 7.0 12
2 NaN NaN NaN NaN NaN NaN
3 NaN NaN NaN NaN NaN NaN
4 NaN NaN NaN NaN NaN NaN
5 NaN NaN NaN NaN NaN NaN
6 NaN NaN NaN NaN NaN NaN
7 NaN NaN NaN NaN NaN NaN
8 NaN NaN NaN NaN NaN NaN
9 NaN NaN NaN NaN NaN NaN
10 NaN NaN NaN NaN NaN NaN
11 NaN NaN NaN NaN NaN NaN
剩下的很简单：
>>> seasons = df.shift(2).fillna(year_ends)
>>> seasons
travel_2016 travel_2017 travel_2018 travel_2019 travel_2020 month_date
0 NaN 7.0 8.0 3.0 2.0 11
1 NaN 6.0 9.0 3.0 7.0 12
2 5.0 8.0 4.0 3.0 2.0 01
3 0.0 8.0 3.0 7.0 0.0 02
4 3.0 1.0 0.0 0.0 0.0 03
5 3.0 6.0 3.0 1.0 4.0 04
6 7.0 7.0 5.0 9.0 5.0 05
7 9.0 7.0 0.0 9.0 5.0 06
8 3.0 8.0 2.0 0.0 6.0 07
9 5.0 1.0 3.0 4.0 8.0 08
10 2.0 5.0 8.0 7.0 4.0 09
11 4.0 9.0 1.0 3.0 1.0 10
当然，您现在应该适当地重命名列：
>>> seasons.rename(columns=lambda c: c if not c.startswith('travel_') else f"season_{int(c[7:]) - 1}/{c[7:]}")
season_2015/2016 season_2016/2017 season_2017/2018 season_2018/2019 season_2019/2020 month_date
0 NaN 7.0 8.0 3.0 2.0 11
1 NaN 6.0 9.0 3.0 7.0 12
2 5.0 8.0 4.0 3.0 2.0 01
3 0.0 8.0 3.0 7.0 0.0 02
4 3.0 1.0 0.0 0.0 0.0 03
5 3.0 6.0 3.0 1.0 4.0 04
6 7.0 7.0 5.0 9.0 5.0 05
7 9.0 7.0 0.0 9.0 5.0 06
8 3.0 8.0 2.0 0.0 6.0 07
9 5.0 1.0 3.0 4.0 8.0 08
10 2.0 5.0 8.0 7.0 4.0 09
11 4.0 9.0 1.0 3.0 1.0 10
请注意，2015 年的前两个值为 NaN，这是有道理的，因为它们不在初始数据框中。
另一种方法是使用日期时间工具。这可能更通用：
>>> data = df.set_index('month_date').rename_axis('year', axis='columns').stack().reset_index(name='data')
>>> data.head()
month_date year data
0 01 travel_2016 5
1 01 travel_2017 8
2 01 travel_2018 4
3 01 travel_2019 3
4 01 travel_2020 2
>>> dates = data['year'].str[7:].str.cat(data['month_date']).transform(pd.to_datetime, format='%Y%m')
>>> dates.head()
0 2016-01-01
1 2017-01-01
2 2018-01-01
3 2019-01-01
4 2020-01-01
Name: year, dtype: datetime64[ns]
然后按照链接问题获取从 11 月开始的财政年度：
>>> season = dates.dt.to_period('Q-OCT').dt.qyear.rename('season')
>>> seasonal_data = data.join(season).pivot('month_date', 'season', 'data')
>>> seasonal_data.rename(columns=lambda c: f"season_{c - 1}/{c}", inplace=True)
>>> seasonal_data.reindex([*df['month_date'][-2:], *df['month_date'][:-2]]).reset_index()
season month_date season_2015/2016 season_2016/2017 season_2017/2018 season_2018/2019 season_2019/2020 season_2020/2021
0 11 NaN 7.0 8.0 3.0 2.0 4.0
1 12 NaN 6.0 9.0 3.0 7.0 9.0
2 01 5.0 8.0 4.0 3.0 2.0 NaN
3 02 0.0 8.0 3.0 7.0 0.0 NaN
4 03 3.0 1.0 0.0 0.0 0.0 NaN
5 04 3.0 6.0 3.0 1.0 4.0 NaN
6 05 7.0 7.0 5.0 9.0 5.0 NaN
7 06 9.0 7.0 0.0 9.0 5.0 NaN
8 07 3.0 8.0 2.0 0.0 6.0 NaN
9 08 5.0 1.0 3.0 4.0 8.0 NaN
10 09 2.0 5.0 8.0 7.0 4.0 NaN
11 10 4.0 9.0 1.0 3.0 1.0 NaN
在Pandas中，我想创建一些列，这些列将代表从 11 月开始到明年 10 月结束的季节（例如旅游季节）。
这是我的代码片段：
from numpy import random
import pandas as pd
np.random.seed(0)
df = pd.DataFrame({
'date': pd.date_range('1990-01-01', freq='M', periods=12),
'travel_2016': random.randint(10, size=(12)),
'travel_2017': random.randint(10, size=(12)),
'travel_2018': random.randint(10, size=(12)),
'travel_2019': random.randint(10, size=(12)),
'travel_2020': random.randint(10, size=(12))})
df['month_date'] = df['date'].dt.strftime('%m')
df = df.drop(columns = ['date'])
我正在尝试这种方法
我想要的输出是这样的：
season_2016/2017 season_2017/2018 season_2018/2019 season_2019/2020 month_date
0 8 7 7 4 11
1 0 1 4 8 12
2 1 4 5 9 01
3 8 3 5 7 02
4 4 7 8 3 03
5 6 8 4 4 04
6 5 8 3 1 05
7 7 0 1 1 06
8 1 2 1 3 07
9 8 9 7 5 08
10 7 7 7 8 09
11 9 1 4 0 10
非常感谢！
您的 table 已经按照您的意愿进行了格式化，大致是：您基本上是将所有行向下移动 2，并将最下面的 2 行移至开头 - 但移至下一年。
>>> year_ends = df.shift(-10)
>>> year_ends = year_ends.drop(columns=['month_date']).shift(axis='columns').join(year_ends['month_date'])
>>> year_ends
travel_2016 travel_2017 travel_2018 travel_2019 travel_2020 month_date
0 NaN 7.0 8.0 3.0 2.0 11
1 NaN 6.0 9.0 3.0 7.0 12
2 NaN NaN NaN NaN NaN NaN
3 NaN NaN NaN NaN NaN NaN
4 NaN NaN NaN NaN NaN NaN
5 NaN NaN NaN NaN NaN NaN
6 NaN NaN NaN NaN NaN NaN
7 NaN NaN NaN NaN NaN NaN
8 NaN NaN NaN NaN NaN NaN
9 NaN NaN NaN NaN NaN NaN
10 NaN NaN NaN NaN NaN NaN
11 NaN NaN NaN NaN NaN NaN
剩下的很简单：
>>> seasons = df.shift(2).fillna(year_ends)
>>> seasons
travel_2016 travel_2017 travel_2018 travel_2019 travel_2020 month_date
0 NaN 7.0 8.0 3.0 2.0 11
1 NaN 6.0 9.0 3.0 7.0 12
2 5.0 8.0 4.0 3.0 2.0 01
3 0.0 8.0 3.0 7.0 0.0 02
4 3.0 1.0 0.0 0.0 0.0 03
5 3.0 6.0 3.0 1.0 4.0 04
6 7.0 7.0 5.0 9.0 5.0 05
7 9.0 7.0 0.0 9.0 5.0 06
8 3.0 8.0 2.0 0.0 6.0 07
9 5.0 1.0 3.0 4.0 8.0 08
10 2.0 5.0 8.0 7.0 4.0 09
11 4.0 9.0 1.0 3.0 1.0 10
当然，您现在应该适当地重命名列：
>>> seasons.rename(columns=lambda c: c if not c.startswith('travel_') else f"season_{int(c[7:]) - 1}/{c[7:]}")
season_2015/2016 season_2016/2017 season_2017/2018 season_2018/2019 season_2019/2020 month_date
0 NaN 7.0 8.0 3.0 2.0 11
1 NaN 6.0 9.0 3.0 7.0 12
2 5.0 8.0 4.0 3.0 2.0 01
3 0.0 8.0 3.0 7.0 0.0 02
4 3.0 1.0 0.0 0.0 0.0 03
5 3.0 6.0 3.0 1.0 4.0 04
6 7.0 7.0 5.0 9.0 5.0 05
7 9.0 7.0 0.0 9.0 5.0 06
8 3.0 8.0 2.0 0.0 6.0 07
9 5.0 1.0 3.0 4.0 8.0 08
10 2.0 5.0 8.0 7.0 4.0 09
11 4.0 9.0 1.0 3.0 1.0 10
请注意，2015 年的前两个值为 NaN，这是有道理的，因为它们不在初始数据框中。
另一种方法是使用日期时间工具。这可能更通用：
>>> data = df.set_index('month_date').rename_axis('year', axis='columns').stack().reset_index(name='data')
>>> data.head()
month_date year data
0 01 travel_2016 5
1 01 travel_2017 8
2 01 travel_2018 4
3 01 travel_2019 3
4 01 travel_2020 2
>>> dates = data['year'].str[7:].str.cat(data['month_date']).transform(pd.to_datetime, format='%Y%m')
>>> dates.head()
0 2016-01-01
1 2017-01-01
2 2018-01-01
3 2019-01-01
4 2020-01-01
Name: year, dtype: datetime64[ns]
然后按照链接问题获取从 11 月开始的财政年度：
>>> season = dates.dt.to_period('Q-OCT').dt.qyear.rename('season')
>>> seasonal_data = data.join(season).pivot('month_date', 'season', 'data')
>>> seasonal_data.rename(columns=lambda c: f"season_{c - 1}/{c}", inplace=True)
>>> seasonal_data.reindex([*df['month_date'][-2:], *df['month_date'][:-2]]).reset_index()
season month_date season_2015/2016 season_2016/2017 season_2017/2018 season_2018/2019 season_2019/2020 season_2020/2021
0 11 NaN 7.0 8.0 3.0 2.0 4.0
1 12 NaN 6.0 9.0 3.0 7.0 9.0
2 01 5.0 8.0 4.0 3.0 2.0 NaN
3 02 0.0 8.0 3.0 7.0 0.0 NaN
4 03 3.0 1.0 0.0 0.0 0.0 NaN
5 04 3.0 6.0 3.0 1.0 4.0 NaN
6 05 7.0 7.0 5.0 9.0 5.0 NaN
7 06 9.0 7.0 0.0 9.0 5.0 NaN
8 07 3.0 8.0 2.0 0.0 6.0 NaN
9 08 5.0 1.0 3.0 4.0 8.0 NaN
10 09 2.0 5.0 8.0 7.0 4.0 NaN
11 10 4.0 9.0 1.0 3.0 1.0 NaN