Pandas adding Time column to Date index

Question

I have a dataframe, Date index type is Timestamp, Time column is datetime.Time:

            Time  Value
Date
2004-05-01  0:15  3.58507  
2004-05-02  0:30  3.84625
              ...

How do I convert it to:

                    Value
Date
2004-05-01 0:15     3.74618
2004-05-01 0:30     3.58507
2004-05-01 0:45     3.30998

I wrote a code which does work, but it's not very pythonic:

ind = frame.index.get_level_values(0).tolist()
tms = frame['Time']
new_ind = []
for i in range(0, len(ind)):
    tm = tms[i]
    val = ind[i] + timedelta(hours=tm.hour, minutes=tm.minute, seconds=tm.second)
    new_ind.append(val)
frame.index = new_ind
del frame['Time']

Solution

You can first convert column Time to_timedelta, then add to index, drop column Time and if necessary set index name:

df.Time = pd.to_timedelta(df.Time + ':00', unit='h')
df.index = df.index + df.Time
df = df.drop('Time', axis=1)
df.index.name = 'Date'
print (df)
                       Value
Date                        
2004-05-01 00:15:00  3.58507
2004-05-02 00:30:00  3.84625

If column Time is datetime.time for me works cast to string first (if necessary add :00):

df.Time = pd.to_timedelta(df.Time.astype(str), unit='h')
df.index = df.index + df.Time
df = df.drop('Time', axis=1)
df.index.name = 'Date'
print (df)
                       Value
Date                        
2004-05-01 00:15:00  3.58507
2004-05-02 00:30:00  3.84625

Comments

[alexbk66]

You are quick @jezrael! Problem is that Pandas converts Time strings to datetime.Time, so how do I convert it to timedelta? Or disable auto converting? BTW, it's an excel file, so I use pd.read_excel()

[alexbk66]

Normally I use parse_dates = {'Date': ['Date','Time']} in pd.read_excel() to combine two columns automatically, but in my datafile some dates are missing, which confuses Pandas index. So I have to do it manually, unfortunately. I might ask a separate question

[jezrael]

Some dates are missing? Then need NaT ? Or replace missing values in dates by some value like 2004-01-01? Or need remove rows with missing dates?

[jezrael]

You can first not set Date column as index and use df.Date = pd.to_datetime(df.Date, errors='coerce') for replace problematic values to NaT. Can you change sample your data with missing value in column Date and add desired output?

[alexbk66]

I remove the rows with missing Date: frame = frame.loc[pd.notnull(frame.index)]

[alexbk66]

Yeah, unfortunatelly that's what I have to do: # Remove records with empty index (date) frame = frame.loc[pd.notnull(frame.index)] frame.is_copy = False # Disable SettingWithCopyWarning # Now add values from 'Time' column to Index (Date) frame.Time = pd.to_timedelta(frame.Time.astype(str), unit='h') frame.index = frame.index + frame.Time frame = frame.drop('Time', axis=1)

[jezrael]

You can use frame= frame[pd.notnull(frame.index)] instead frame = frame.loc[pd.notnull(frame.index)], I think loc is not necessary.