Convert multiple SQL CASE statements to Pandas syntax

Question

CASE
  WHEN a.sch_end_locn_id          != a.ats_sta_id 
  AND a.PLC_ACTUAL_DEPART_TIME    IS NULL
  AND a.BEACON_ACTUAL_DEPART_TIME IS NULL
  THEN

    CASE
      WHEN a.ITRAC_ACTUAL_DEPART_TIME IS NOT NULL
      THEN a.ITRAC_ACTUAL_DEPART_TIME
      
      WHEN a.PLC_ACTUAL_DEPART_TIME_CLEAR IS NOT NULL
      THEN a.PLC_ACTUAL_DEPART_TIME_CLEAR - 15/(24*60*60)
      
      WHEN a.PLC_ACTUAL_ARRIVE_TIME_DWELL IS NOT NULL
      THEN a.PLC_ACTUAL_ARRIVE_TIME_DWELL + a.median_dwell
     
      WHEN a.PLC_ACTUAL_ARRIVE_TIME IS NOT NULL
      THEN a.PLC_ACTUAL_ARRIVE_TIME + a.median_track_occ
      
      WHEN a.ITRAC_ACTUAL_ARRIVE_TIME IS NOT NULL
      THEN a.ITRAC_ACTUAL_ARRIVE_TIME + 30/(24*60*60) 
      ELSE NULL

    END

  ELSE COALESCE(a.BEACON_ACTUAL_DEPART_TIME, a.PLC_ACTUAL_DEPART_TIME, a.ITRAC_ACTUAL_DEPART_TIME)

I want to convert this multiple case statement to python syntax using np.where. Lets just assume the dataframe name is df. I’m just confused specifically on the operators to use within the second set of case statements. This is how I got started but I’m stuck on adding the other cases.

np.where((df['SCH_END_LOCN_ID'] != df['ATS_STA_ID']) & ((df['PLC_ACTUAL_DEPART_TIME'] == np.datetime64('NaT')) & (df['BEACON_ACTUAL_DEPART_TIME'] == np.datetime64('NaT')) & df['ITRAC_ACTUAL_DEPART_TIME'] != np.datetime64('NaT')), 
        df['ITRAC_ACTUAL_DEPART_TIME'],  
        df[["BEACON_ACTUAL_ARRIVE_TIME", "PLC_ACTUAL_ARRIVE_TIME", "ITRAC_ACTUAL_ARRIVE_TIME",]].bfill(axis=1).iloc[:, 0])

Asked By: jjoa

||

Source

Answer 1

Here’s a way to do what your question asks:

    res = pd.Series(np.where(
        (df.SCH_END_LOCN_ID != df.ATS_STA_ID) & 
            df.PLC_ACTUAL_DEPART_TIME.isna() & 
            df.BEACON_ACTUAL_DEPART_TIME.isna(),
        np.where(
            df.ITRAC_ACTUAL_DEPART_TIME.notna(),
            df.ITRAC_ACTUAL_DEPART_TIME,
            np.where(
                df.PLC_ACTUAL_DEPART_TIME_CLEAR.notna(),
                df.PLC_ACTUAL_DEPART_TIME_CLEAR - 15/(24*60*60),
                np.where(
                    df.PLC_ACTUAL_ARRIVE_TIME_DWELL.notna(),
                    df.PLC_ACTUAL_ARRIVE_TIME_DWELL + df.MEDIAN_DWELL,
                    np.where(
                        df.PLC_ACTUAL_ARRIVE_TIME.notna(),
                        df.PLC_ACTUAL_ARRIVE_TIME + df.MEDIAN_TRACK_OCC,
                        np.where(
                            df.ITRAC_ACTUAL_ARRIVE_TIME.notna(),
                            df.ITRAC_ACTUAL_ARRIVE_TIME + 30/(24*60*60),
                            None
                        )
                    )
                )
            )
        ),
        np.where(
            df.BEACON_ACTUAL_DEPART_TIME.notna(),
            df.BEACON_ACTUAL_DEPART_TIME,
            np.where(
                df.PLC_ACTUAL_DEPART_TIME.notna(),
                df.PLC_ACTUAL_DEPART_TIME,
                df.ITRAC_ACTUAL_DEPART_TIME
            )
        )
    ), index=df.index, dtype=float)

Sample input:

   SCH_END_LOCN_ID  ATS_STA_ID  PLC_ACTUAL_DEPART_TIME  BEACON_ACTUAL_DEPART_TIME  ITRAC_ACTUAL_DEPART_TIME  PLC_ACTUAL_DEPART_TIME_CLEAR  PLC_ACTUAL_ARRIVE_TIME_DWELL  PLC_ACTUAL_ARRIVE_TIME  ITRAC_ACTUAL_ARRIVE_TIME  MEDIAN_DWELL  MEDIAN_TRACK_OCC
0                1          99                     NaN                        NaN                       1.0                           NaN                           NaN                     NaN                       NaN          0.25               0.5
1                2          99                     NaN                        NaN                       NaN                           2.0                           NaN                     NaN                       NaN          0.25               0.5
2                3          99                     NaN                        NaN                       NaN                           NaN                           3.0                     NaN                       NaN          0.25               0.5
3                4          99                     NaN                        NaN                       NaN                           NaN                           NaN                     4.0                       NaN          0.25               0.5
4                5          99                     NaN                        NaN                       NaN                           NaN                           NaN                     5.0                       NaN          0.25               0.5
5                6          99                     NaN                        NaN                       NaN                           NaN                           NaN                     NaN                       6.0          0.25               0.5
6                7           7                     NaN                        NaN                       7.0                           NaN                           NaN                     NaN                       7.0          0.25               0.5
7                8           8                    88.0                        NaN                       NaN                           NaN                           NaN                     NaN                       8.0          0.25               0.5
8                9           9                    88.0                        NaN                       NaN                           NaN                           NaN                     NaN                       NaN          0.25               0.5
9               10          10                    88.0                       99.0                       NaN                           NaN                           NaN                     NaN                       NaN          0.25               0.5

Output:

0     1.000000
1     1.999826
2     3.250000
3     4.500000
4     5.500000
5     6.000347
6     7.000000
7    88.000000
8    88.000000
9    99.000000
dtype: float64

Answered By: constantstranger

Convert multiple SQL CASE statements to Pandas syntax

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