如何按天拆分 Pandas 数据帧或系列（可能使用迭代器）
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How to split a pandas dataframe or series by day (possibly using an iterator)
提问by Mannaggia
I have a long time series, eg.
我有一个很长的时间序列，例如。
import pandas as pd
index=pd.date_range(start='2012-11-05', end='2012-11-10', freq='1S').tz_localize('Europe/Berlin')
df=pd.DataFrame(range(len(index)), index=index, columns=['Number'])
Now I want to extract all sub-DataFrames for each day, to get the following output:
现在我想每天提取所有子数据帧，以获得以下输出：
df_2012-11-05: data frame with all data referring to day 2012-11-05
df_2012-11-06: etc.
df_2012-11-07
df_2012-11-08
df_2012-11-09
df_2012-11-10
What is the most effective way to do this avoiding to check if the index.date==give_date which is very slow. Also, the user does not know a priory the range of days in the frame.
避免检查是否非常慢的 index.date==give_date 的最有效方法是什么。此外，用户事先不知道框架中的天数范围。
Any hint do do this with an iterator?
有什么提示可以用迭代器做到这一点吗？
My current solution is this, but it is not so elegant and has two issues defined below:
我目前的解决方案是这样的，但它不是那么优雅，并且有两个问题定义如下：
time_zone='Europe/Berlin'
# find all days
a=np.unique(df.index.date) # this can take a lot of time
a.sort()
results=[]
for i in range(len(a)-1):
day_now=pd.Timestamp(a[i]).tz_localize(time_zone)
day_next=pd.Timestamp(a[i+1]).tz_localize(time_zone)
results.append(df[day_now:day_next]) # how to select if I do not want day_next included?
# last day
results.append(df[day_next:])
This approach has the following problems:
这种方法存在以下问题：
- a=np.unique(df.index.date) can take a lot of time
- df[day_now:day_next] includes the day_next, but I need to exclude it in the range
- a=np.unique(df.index.date) 可能需要很多时间
- df[day_now:day_next] 包括 day_next，但我需要将其排除在范围内
回答by Woody Pride
Perhaps groupby?
也许是groupby？
DFList = []
for group in df.groupby(df.index.day):
DFList.append(group[1])
Should give you a list of data frames where each data frame is one day of data.
应该给你一个数据框列表，其中每个数据框都是一天的数据。
Or in one line:
或者在一行中：
DFList = [group[1] for group in df.groupby(df.index.day)]
Gotta love python!
一定要爱蟒蛇！
回答by Peque
If you want to group by date (AKA: year+month+day), then use df.index.date:
如果要按日期分组（又名：年+月+日），请使用df.index.date：
result = [group[1] for group in df.groupby(df.index.date)]
As df.index.daywill use the day of the month (i.e.: from 1 to 31) for grouping, which could result in undesirable behavior if the input dataframe dates extend to multiple months.
由于df.index.day将使用月份中的日期（即：从 1 到 31）进行分组，如果输入数据框日期延长到多个月，这可能会导致不良行为。
