pandas - Auto join a python dataframe to update it -


i perform auto-join on python dataframe update it. here situation, have first df 3 columns: in, out & date. means @ specific date item "out" replaced "in".

import pandas pd import numpy np datetime import datetime data = [[1,10,"2017-01-01"],[2,10,"2017-01-01"],[10,11,"2017-06-01"],[4,14,"2017-04-01"],[5,14,"2017-12-01"]] label = ["out","in","date"] df = pd.dataframe(data,columns=label) df['date'] = pd.to_datetime(df['date']) print(df)     out  in       date 0    1  10 2017-01-01 1    2  10 2017-01-01 2   10  11 2017-06-01 3    4  14 2017-04-01 4    5  14 2017-12-01

for example means here of first of jan 2017, item #1 replaced item #10. trick of june 2017, item #10 replaced item #11. #1 becomes #10 becomes #11.

now populate final table gives final relationships date.

if date = 2017-08-01, table

date = pd.to_datetime("2017-08-01") data = [[1,11],[2,11],[10,11],[4,14]] df_final = pd.dataframe(data,columns=["out","in"]) print(df_final)     out  in 0   1   11 1   2   11 2  10   11 3   4   14

would know how perform such auto join?

thanks,

you can use list comprehension methods , .loc locate values.

import pandas pd import numpy np datetime import datetime data = [[1,10,"2017-01-01"],[2,10,"2017-01-01"],[10,11,"2017-06-01"],[4,14,"2017-04-01"],[5,14,"2017-12-01"],[11,18,"2017-12-01"]] label = ["out","in","date"] df = pd.dataframe(data,columns=label) df['date'] = pd.to_datetime(df['date']) print(df)     out  in       date 0    1  10    2017-01-01 1    2  10     2017-01-01 2   10  11     2017-06-01 3    4  14     2017-04-01 4    5  14     2017-12-01 5   11  18     2017-12-01  l=[] row in df.iterrows():     x = row[1]['out']     y = row[1]['in']     while y in df.out.values.tolist():         y = df.loc[df['out'] == y,'in'].iloc[0]     l.append((x,y))  df2 = pd.dataframe(l, columns=['out', 'in']) print(df2)  out  in 1    18 2    18 10   18 4    14 5    14 11   18

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