Pandas - Drop duplicates but change keep:first/last according to a value in a column

Question

I am trying to keep the first row for each unique date for Mondays, but keep the last row for each unique date for Fridays.

I tried adding an indicator column where Monday = 1 and Friday = 2.

I can't seem to figure out what would be the best way to go about this? I tried using drop_duplicates but I only know how to use it to select columns for which duplicates will be checked against but I can't figure out how to toggle the keep between first and last depending on the value of "Day" in each row.

         year  month  day  Time   Close       Date     Day    %Delta  MFIndicator
2926     2001      1    5     0  0.9525 2001-01-05  Friday -0.000734            2
2985     2001      1    5     1  0.9534 2001-01-05  Friday  0.000945            2
3045     2001      1    5     2  0.9534 2001-01-05  Friday  0.000000            2
3105     2001      1    5     3  0.9541 2001-01-05  Friday  0.000734            2
3165     2001      1    5     4  0.9574 2001-01-05  Friday  0.003459            2
...       ...    ...  ...   ...     ...        ...     ...       ...          ...
6700166  2019     12   30    19  1.1200 2019-12-30  Monday -0.000179            1
6700226  2019     12   30    20  1.1195 2019-12-30  Monday -0.000446            1
6700286  2019     12   30    21  1.1198 2019-12-30  Monday  0.000268            1
6700346  2019     12   30    22  1.1197 2019-12-30  Monday -0.000089            1
6700406  2019     12   30    23  1.1200 2019-12-30  Monday  0.000268            1

This is the expected result: (XXXX,YYYY are some index but I think the picture is clear)

         year  month  day  Time   Close       Date     Day    %Delta  MFIndicator
XXXX     2001      1    5    23  0.9574 2001-01-05  Friday  0.003459            2
...       ...    ...  ...   ...     ...        ...     ...       ...          ...
YYYY     2019     12   30     0  0.1200 2019-12-30  Monday -0.000179            1

Answer 1

df = pd.DataFrame({'A': ['m', 'f', 'm', 'm', 'f', 'f'], 
                   'B': np.arange(6), 'C': np.arange(6)})

---

    A   B   C
0   m   0   0
1   f   1   1
2   m   2   2
3   m   3   3
4   f   4   4
5   f   5   5

---

(df.groupby('A', as_index=False).apply(lambda x: x.iloc[[0]] if x['A'].values[0]=='m' else x.iloc[[-1]])
   .reset_index(level=0, drop=True))

---

    A   B   C
4   f   4   4
0   m   0   0

Logic will be same. In place of A you will have Day column and you will do groupby Date column.

Answer 2

Keep all, then postprocess within a groupby, kind of like this:

dup_mask = df.duplicated(subset=['Date'], keep=False)
df_dup = df[dup_mask].groupby('Date', as_index=False).apply(lambda gr: gr.iloc[0] if gr['Day'].iloc[0] == 'Monday' else gr.iloc[-1])
df = pd.concat(df[~dup_mask], df_dup)