Merge multiple rows in pandas Dataframe based on multiple column values

Question

I have this table in pandas

      | id      |      date | freq| year |        c1|         c2|    c3|
0     |C35600010|   20080922|    A| 2004 | d20040331|        NaN|   NaN|
1     |C35600010|   20080922|    A| 2004 |       NaN|      s2003|   NaN|
2     |C35600010|   20080922|    A| 2004 |       NaN|        NaN|    s3|
3     |C35600010|   20080922|    Q| 2004 |       NaN|        NaN|    s3|
4     |C35600010|   20080923|    A| 2004 |       NaN|        NaN|    s3|

and I want to merge it into

      |       id|      date | freq| year |        c1|         c2|    c3|
0     |C35600010|   20080922|    A| 2004 | d20040331|      s2003|    s3|
1     |C35600010|   20080922|    Q| 2004 |       NaN|        NaN|    s3|
2     |C35600010|   20080923|    A| 2004 |       NaN|        NaN|    s3|   

Basically where id, date, freq & year are same, merge the rows. It is guaranteed that only one NaN value will exist. Anyway to do it?

I tried Merging same-indexed rows by taking non-NaNs from all of them in pandas dataframe didn't really work as it throws error

df = df.groupby(["id", "date", "freq", "year"]).max()
ValueError: Wrong number of items passed 1, placement implies 4

Edit 1: There can be multiple dates associated with each id, same with freq & year. I don't want to merge them into single row.

When id, date, freq, year .. all are same then merge the rows for columns c1, c2, c3.

Answer 1

Does this do what you want?

df.groupby(["id", "date", "freq", "year"]).first().reset_index()

Output:

          id      date freq  year         c1     c2  c3
0  C35600010  20080922    A  2004  d20040331  s2003  s3
1  C35600010  20080922    Q  2004       None   None  s3
2  C35600010  20080923    A  2004       None   None  s3