Auto Fill calendar with missing months

Question

I have a table like this with date and values but, as you can see, some months are not in the list. (5, 6, 8, 10 and 1 from the next year).

Date	Any other value
2020-01-01	value
2020-02-01	value
2020-02-04	value
2020-02-04	value
2020-03-11	value
2020-04-04	value
2020-07-04	value
2020-07-04	value
2020-09-01	value
2020-11-06	value
2020-12-02	value
2021-02-04	value
2021-03-11	value

Is any way that I can automatically fill this table with those months, to become?

Date	Any other value
2020-01-01	value
2020-02-01	value
2020-02-04	value
2020-02-04	value
2020-03-11	value
2020-04-04	value
2020-05-01	NaN
2020-06-01	NaN
2020-07-04	value
2020-07-04	value
2020-08-01	NaN
2020-09-01	value
2020-10-01	NaN
2020-11-06	value
2020-12-02	value
2021-01-01	NaN
2021-02-04	value
2021-03-11	value

Thank to you all!

Answer 1

Logically it's an outer join to all the months you are interested in.

import pandas as pd

df = pd.DataFrame({"Date":["2019-12-31","2020-01-31","2020-02-03","2020-02-03","2020-03-10","2020-04-03","2020-07-03","2020-07-03","2020-08-31","2020-11-05","2020-12-01","2021-02-03","2021-03-10"],"Any other value":["value","value","value","value","value","value","value","value","value","value","value","value","value"]})
df["Date"] = pd.to_datetime(df["Date"])

df["month"] = (df['Date'] - pd.offsets.MonthBegin(1)).dt.floor('d')


df = df.merge(
pd.DataFrame({"month":pd.date_range(df["month"].min(), df["month"].max(), freq="MS")}),
    on="month", how="outer")
df["Date"].fillna(df["month"], inplace=True)
df = df.drop(columns="month")

print(df.to_string(index=False))

output

      Date Any other value
2019-12-31           value
2020-01-31           value
2020-02-03           value
2020-02-03           value
2020-03-10           value
2020-04-03           value
2020-07-03           value
2020-07-03           value
2020-08-31           value
2020-11-05           value
2020-12-01           value
2021-02-03           value
2021-03-10           value
2020-05-01             NaN
2020-06-01             NaN
2020-09-01             NaN
2020-10-01             NaN
2020-12-01             NaN
2021-01-01             NaN