[英]Pandas - DateTime groupby to structured dict
我有一个包含DateTime字段的数据集。 我需要按hours
分组并将每个组分派到具有以下结构的字典:
{year_1:
{month_1:
{week_1:
{day_1:
{hour_1: df_1, hour_2: df_2}
}
},
{week_2:
{day_1:
{hour_1: df_1}
}
}
},
{month_3:
{week_1:
{day_1:
{hour_1: df_1, hour_2: df_2}
}
}
},
year_2:
{month_5:
{week_1:
{day_1:
{hour_2: df_2}
}
}
}
}
为此,我使用以下代码:
import pandas as pd
df = df = pd.DataFrame({'date': [pd.datetime(2015,3,17,2), pd.datetime(2014,3,24,3), pd.datetime(2014,3,17,4)], 'hdg_id': [4041,4041,4041],'stock': [1.0,1.0,1.0]})
df.loc[:,'year'] = [x.year for x in df['date']]
df.loc[:,'month'] = [x.month for x in df['date']]
df.loc[:,'week'] = [x.week for x in df['date']]
df.loc[:,'day'] = [x.day for x in df['date']]
df.loc[:,'hour'] = [x.hour for x in df['date']]
result = {}
for to_unpack, df_hour in df.groupby(['year','month','day','week','hour']):
year, month, week, day, hour = to_unpack
try:
result[year]
except KeyError:
result[year] = {}
try:
result[year][month]
except KeyError:
result[year][month] = {}
try:
result[year][month][week]
except KeyError:
result[year][month][week] = {}
try:
result[year][month][week][day]
except KeyError:
result[year][month][week][day] = {}
result[year][month][week][day][hour] = df_hour
正如你所看到的,这几乎是一个蛮力的解决方案,我正在寻找看起来更干净,更容易理解的东西。 此外,它也非常慢。 我尝试了不同的分组方法( 使用日期时间数据按日期分类的Python Pandas Group ),我还尝试了datetime的每个组件的多索引(带有MultiIndex的Pandas DataFrame:按日期时间级别值分组 )。 但是,问题始终是如何创建字典。 理想情况下,我想写一些类似于:
result[year][month][week][day][hour] = df_hour
但据我所知,我首先需要初始化每个字典。
result = {}
for to_unpack, df_hour in df.groupby(['year','month','day','week','hour']):
year, month, week, day, hour = to_unpack
result.setdefault(year, {}) \
.setdefault(month, {}) \
.setdefault(week, {}) \
.setdefault(day, {}) \
.setdefault(hour, df_hour)
您也可以继承dict
来执行此操作
class Fict(dict):
def __getitem__(self, item):
return super().setdefault(item, type(self)())
result = Fict()
for to_unpack, df_hour in df.groupby(['year','month','day','week','hour']):
year, month, week, day, hour = to_unpack
result[year][month][week][day][hour] = df_hour
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