Próbuję scalić dwie kolumny dataframes na datę (próbowałem zarówno jako typ object lub datetime.date, ale nie daje pożądanego wyniku scalania:
import pandas as pd
df1 = pd.DataFrame({"amt": {0: 1549367.9496070854,
1: 2175801.78219801,
2: 1915613.1629125737,
3: 1703063.8323954903,
4: 1770040.7987461537},
"month": {0: "2015-02-01",
1: "2015-03-01",
2: "2015-04-01",
3: "2015-05-01",
4: "2015-06-01"}})
print(df1)
amt month
0 1.549368e+06 2015-02-01
1 2.175802e+06 2015-03-01
2 1.915613e+06 2015-04-01
3 1.703064e+06 2015-05-01
4 1.770041e+06 2015-06-01
df2 = {"factor": {datetime.date(2015, 2, 1): 1.0,
datetime.date(2015, 3, 1): 1.0,
datetime.date(2015, 4, 1): 1.0,
datetime.date(2015, 5, 1): 1.0,
datetime.date(2015, 6, 1): 0.99889679025914435},
"month": {datetime.date(2015, 2, 1): datetime.date(2015, 2, 1),
datetime.date(2015, 3, 1): datetime.date(2015, 3, 1),
datetime.date(2015, 4, 1): datetime.date(2015, 4, 1),
datetime.date(2015, 5, 1): datetime.date(2015, 5, 1),
datetime.date(2015, 6, 1): datetime.date(2015, 6, 1)}}
df2 = pd.DataFrame(df2)
print(df2)
factor month
2015-02-01 1.000000 2015-02-01
2015-03-01 1.000000 2015-03-01
2015-04-01 1.000000 2015-04-01
2015-05-01 1.000000 2015-05-01
2015-06-01 0.998897 2015-06-01
pd.merge(df2, df1, how="outer", on="month")
factor month amt
0 1.000000 2015-02-01 NaN
1 1.000000 2015-03-01 NaN
2 1.000000 2015-04-01 NaN
3 1.000000 2015-05-01 NaN
4 0.998897 2015-06-01 NaN
5 NaN 2015-02-01 1.549368e+06
6 NaN 2015-03-01 2.175802e+06
7 NaN 2015-04-01 1.915613e+06
8 NaN 2015-05-01 1.703064e+06
9 NaN 2015-06-01 1.770041e+06
Odpowiedzi:
5 dla odpowiedzi № 1Myślę, że najpierw musisz przekonwertować obie kolumny to_datetime ponieważ potrzeba tego samego dtypes:
df1.month = pd.to_datetime(df1.month)
df2.month = pd.to_datetime(df2.month)
print (pd.merge(df2, df1, how="outer", on="month"))
factor month amt
0 1.000000 2015-02-01 1.549368e+06
1 1.000000 2015-03-01 2.175802e+06
2 1.000000 2015-04-01 1.915613e+06
3 1.000000 2015-05-01 1.703064e+06
4 0.998897 2015-06-01 1.770041e+06
#convert to str date column
df2.month = df2.month.astype(str)
print (pd.merge(df2, df1, how="outer", on="month"))
factor month amt
0 1.000000 2015-02-01 1.549368e+06
1 1.000000 2015-03-01 2.175802e+06
2 1.000000 2015-04-01 1.915613e+06
3 1.000000 2015-05-01 1.703064e+06
4 0.998897 2015-06-01 1.770041e+06