日期偏移量取决于 pandas df 中的其他列

Question

Hi I am new to python switching from R and I have a hard time with this pretty simple task of changing a date based on another column of a pandas data frame. I read several other questions on this and I was hoping that someone could just quickly solve my issue, since I have nobody else to ask but the internet.

I think I have all the ingredients (functions) but I really struggle using pandas df compared to what I am used to in R.

import datetime
from datetime import datetime
import pandas as pd
import numpy as np

today=pd.to_datetime(datetime.today().strftime('%Y-%m-%d'))

d={"Start_Date":[today,today]}
df=pd.DataFrame(data=d)
n=len(df)
df["Distance"]=np.round_(np.random.uniform(low=1, high=14, size=n)).astype(int)

df.loc[:,"FutureDate"]=""
for index, row in df.iterrows():
    print(row["Start_Date"]+pd.DateOffset(row["Distance"]))  
    row["FutureDate"]=row["Start_Date"]+pd.DateOffset(row["Distance"])

Why is my FutureDate column empty if the print statement works? Is there a more elegant solution than using a loop? I am used to data.table where I would use write the function in one line.

Answer 1

Try this code.. it worked for me:

import datetime
from datetime import datetime
import pandas as pd
import numpy as np

today=pd.to_datetime(datetime.today().strftime('%Y-%m-%d'))

d={"Start_Date":[today,today]}
df=pd.DataFrame(data=d)
n=len(df)
df["Distance"]=np.round_(np.random.uniform(low=1, high=14, size=n)).astype(int)

df.loc[:,"FutureDate"]=""
for index, row in df.iterrows():
    print(row["Start_Date"]+pd.DateOffset(row["Distance"]))  
    df.loc[index, "FutureDate"]=row["Start_Date"]+pd.DateOffset(row["Distance"])

In fact we need to assign the values on the df itself with the correct index and column.

EDIT:

A more elegant way:

import datetime
# from datetime import datetime
import pandas as pd
import numpy as np

today=pd.to_datetime(datetime.datetime.today().strftime('%Y-%m-%d'))

d={"Start_Date":[today,today]}
df=pd.DataFrame(data=d)
n=len(df)
df["Distance"]=np.round_(np.random.uniform(low=1, high=14, size=n)).astype(int)

df["FutureDate"]=df["Start_Date"] + pd.to_timedelta(df['Distance'],'d')

Hope this helps.