Keep column when resampling hourly to daily data in pandas - python-3.x

Question

I have a dataset of hourly weather observations in this format:
df = pd.DataFrame({ 'date': ['2019-01-01 09:30:00', '2019-01-01 10:00', '2019-01-02 04:30:00','2019-01-02 05:00:00','2019-07-04 02:00:00'],
'windSpeedHigh': [155,90,35,45,15],
'windSpeedHigh_Dir':['NE','NNW','SW','W','S']})
My goal is to find the highest wind speed each day and the wind direction associated with that maximum daily wind speed.
Using resample, I have sucessfully found the maximum wind speed for each day, but not its associated direction:
df['date'] = pd.to_datetime(df['date'])
df['windSpeedHigh'] = pd.to_numeric(df['windSpeedHigh'])
df_daily = df.resample('D', on='date')[['windSpeedHigh_Dir','windSpeedHigh']].max()
df_daily
Results in:
windSpeedHigh_Dir windSpeedHigh
date
2019-01-01 NNW 155.0
2019-01-02 W 45.0
2019-01-03 NaN NaN
2019-01-04 NaN NaN
2019-01-05 NaN NaN
... ... ...
2019-06-30 NaN NaN
2019-07-01 NaN NaN
2019-07-02 NaN NaN
2019-07-03 NaN NaN
2019-07-04 S 15.0
This is incorrect as this resample is also grabbing the max() for 'windSpeedHigh_Dir'. For 2019-01-01 the direction for the associated windspeed should be 'NE' not 'NNW', because the wind direction df['windSpeedHigh_Dir'] == 'NE' when the maximum wind speed occurred.
So my question is, is it possible for me to resample this dataset from half-hourly to daily maximum wind speed while keeping the wind direction associated with that speed?

Answer 1

Use DataFrameGroupBy.idxmax for indices by dates first:
df_daily = df.loc[df.groupby(df['date'].dt.date)['windSpeedHigh'].idxmax()]
print (df_daily)
date windSpeedHigh windSpeedHigh_Dir
0 2019-01-01 09:30:00 155 NE
3 2019-01-02 05:00:00 45 W
4 2019-07-04 02:00:00 15 S
And then for add DatetimeIndex use DataFrame.set_index with Series.dt.normalize and DataFrame.asfreq:
df_daily = df_daily.set_index(df_daily['date'].dt.normalize().rename('day')).asfreq('d')
print (df_daily)
date windSpeedHigh windSpeedHigh_Dir
day
2019-01-01 2019-01-01 09:30:00 155.0 NE
2019-01-02 2019-01-02 05:00:00 45.0 W
2019-01-03 NaT NaN NaN
2019-01-04 NaT NaN NaN
2019-01-05 NaT NaN NaN
... ... ...
2019-06-30 NaT NaN NaN
2019-07-01 NaT NaN NaN
2019-07-02 NaT NaN NaN
2019-07-03 NaT NaN NaN
2019-07-04 2019-07-04 02:00:00 15.0 S
[185 rows x 3 columns]
Your solution shoudl working with custom function, because idxmax failed for missing values with DataFrame.join:
f = lambda x: x.idxmax() if len(x) > 0 else np.nan
df_daily = df.resample('D', on='date')['windSpeedHigh'].agg(f).to_frame('idx').join(df, on='idx')
print (df_daily)
idx date windSpeedHigh windSpeedHigh_Dir
date
2019-01-01 0.0 2019-01-01 09:30:00 155.0 NE
2019-01-02 3.0 2019-01-02 05:00:00 45.0 W
2019-01-03 NaN NaT NaN NaN
2019-01-04 NaN NaT NaN NaN
2019-01-05 NaN NaT NaN NaN
... ... ... ...
2019-06-30 NaN NaT NaN NaN
2019-07-01 NaN NaT NaN NaN
2019-07-02 NaN NaT NaN NaN
2019-07-03 NaN NaT NaN NaN
2019-07-04 4.0 2019-07-04 02:00:00 15.0 S
[185 rows x 4 columns]