Pandas：将 timedelta 列添加到 datetime 列（矢量化）
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Pandas: add timedelta column to datetime column (vectorized)
提问by flyingmeatball
I have a pandas dataframe with two columns, a date column and an int column, and I'd simply like to add the int column (in days) to the date column. I found a solution using df.apply(), but that was too slow on my full dataset. I don't see a ton of documentation on doing this in a vectorized manner (the closest I could find was this), so I wanted to make sure the solution I found was the best way to go forward.
我有一个包含两列的 Pandas 数据框，一个日期列和一个 int 列，我只想将 int 列（以天为单位）添加到日期列中。我找到了一个使用 df.apply() 的解决方案，但这在我的完整数据集上太慢了。我没有看到大量关于以矢量化方式执行此操作的文档（我能找到的最接近的是this），因此我想确保我找到的解决方案是前进的最佳方式。
My raw data is just a column of strings as a column of ints (days).
我的原始数据只是一列字符串作为一列整数（天）。
import pandas as pd
from datetime import timedelta
df = pd.DataFrame([['2016-01-10',28],['2016-05-11',28],['2016-02-23',15],['2015-12-08',30]],
columns = ['ship_string','days_supply'])
print df
ship_string days_supply
0 2016-01-10 28
1 2016-05-11 28
2 2016-02-23 15
3 2015-12-08 30
My first thought (which worked) was to use .apply as follows:
我的第一个想法（有效）是使用 .apply 如下：
def f(x):
return x['ship_date'] + timedelta(days=x['days_supply'] )
df['ship_date'] = pd.to_datetime(df['ship_string'])
df['supply_ended'] = df.apply(f,axis = 1)
That worked, but is exceedingly slow. I've posted my alternate solution below as an answer to the question, but I'd like to get confirmation that it is "best practice". I couldn't find many good threads on adding timedelta columns to dates in pandas (especially in a vectorized manner), so thought I'd add one that is a little bit more user friendly and hopefully it will help the next poor soul trying to do this.
那行得通，但速度非常慢。我已经在下面发布了我的替代解决方案作为问题的答案，但我想确认这是“最佳实践”。我找不到很多关于将 timedelta 列添加到 Pandas 中的日期的好线程（尤其是以矢量化方式），所以我想我会添加一个对用户更友好的，希望它会帮助下一个尝试做这个。
回答by flyingmeatball
Full code solution:
完整代码解决方案：
import pandas as pd
from datetime import timedelta
df = pd.DataFrame([['2016-01-10',28],['2016-05-11',28],['2016-02-23',15],['2015-12-08',30]],
columns = ['ship_string','days_supply'])
df['ship_date'] = pd.to_datetime(df['ship_string'])
df['time_added'] = pd.to_timedelta(df['days_supply'],'d')
df['supply_ended'] = df['ship_date'] + df['time_added']
print df
ship_string days_supply ship_date time_added supply_ended
0 2016-01-10 28 2016-01-10 28 days 2016-02-07
1 2016-05-11 28 2016-05-11 28 days 2016-06-08
2 2016-02-23 15 2016-02-23 15 days 2016-03-09
3 2015-12-08 30 2015-12-08 30 days 2016-01-07
Please let me know in the comments below if this isn't a good vectorized solution and i'll edit.
如果这不是一个好的矢量化解决方案，请在下面的评论中告诉我，我会编辑。
