问题描述
每个 ID 有一个记录，包括开始日期和结束日期
I have one record per ID with start date and end date
id age state start_date end_date
123 18 CA 2/17/2019 5/4/2019
223 24 AZ 1/17/2019 3/4/2019
我想为开始日和结束日之间的每一天创建一条记录，以便将每日活动数据加入其中.目标输出看起来像这样
I want to create a record for each day between the start and end day, so I can join daily activity data to it. The target output would look something like this
id age state start_date
123 18 CA 2/17/2019
123 18 CA 2/18/2019
123 18 CA 2/19/2019
123 18 CA 2/20/2019
123 18 CA 2/21/2019
…
123 18 CA 5/2/2019
123 18 CA 5/3/2019
123 18 CA 5/4/2019
当然，对数据集中的所有 id 及其各自的开始日期执行此操作.非常感谢任何帮助 - 谢谢！
And of course do this for all ids and their respective start dates in the dataset. Any help is much appreciated - thanks!
推荐答案
melt, GroupBy, resample &填充
首先我们melt (unpivot) 你的两个日期列合二为一.然后我们resample 按天计算:
melt, GroupBy, resample & ffill
First we melt (unpivot) your two date columns to one. Then we resample on day basis:
melt = df.melt(id_vars=['id', 'age', 'state'], value_name='date').drop('variable', axis=1)
melt['date'] = pd.to_datetime(melt['date'])
melt = melt.groupby('id').apply(lambda x: x.set_index('date').resample('d').first())
.ffill()
.reset_index(level=1)
.reset_index(drop=True)
输出
date id age state
0 2019-02-17 123.0 18.0 CA
1 2019-02-18 123.0 18.0 CA
2 2019-02-19 123.0 18.0 CA
3 2019-02-20 123.0 18.0 CA
4 2019-02-21 123.0 18.0 CA
.. ... ... ... ...
119 2019-02-28 223.0 24.0 AZ
120 2019-03-01 223.0 24.0 AZ
121 2019-03-02 223.0 24.0 AZ
122 2019-03-03 223.0 24.0 AZ
123 2019-03-04 223.0 24.0 AZ
[124 rows x 4 columns]
编辑:
我不得不在一个项目中重新审视这个问题，看起来像使用 DataFrame.apply 和 pd.date_range 和 DataFrame.explode 是快了近 3 倍:
I had to revisit this problem in a project, and looks like using DataFrame.apply with pd.date_range and DataFrame.explode is almost 3x faster:
df["date"] = df.apply(
lambda x: pd.date_range(x["start_date"], x["end_date"]), axis=1
)
df = (
df.explode("date", ignore_index=True)
.drop(columns=["start_date", "end_date"])
)
输出
id age state date
0 123 18 CA 2019-02-17
1 123 18 CA 2019-02-18
2 123 18 CA 2019-02-19
3 123 18 CA 2019-02-20
4 123 18 CA 2019-02-21
.. ... ... ... ...
119 223 24 AZ 2019-02-28
120 223 24 AZ 2019-03-01
121 223 24 AZ 2019-03-02
122 223 24 AZ 2019-03-03
123 223 24 AZ 2019-03-04
[124 rows x 4 columns]
这篇关于Pandas - 将具有开始和结束日期的数据框转换为每日数据的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持跟版网！