How to insert missing row into pandas dataframe?

Building off of my answer to your previous question, first expand your DataFrame to include NaN rows for missing minutes.

time = pd.to_timedelta(df['time'].astype(str)).astype('timedelta64[m]')
out = df.set_index(time).reindex(np.arange(time[0], time.iloc[len(df)-1]+1)).reset_index(drop=True)

then given your missing data DataFrame

missing_data = pd.DataFrame({'ticker': ['AACG'], 'date': ['2022-01-06'], 'time': ['09:51:00'], 
                             'vol': [0], 'vwap': [0.0], 'open': [0.0], 'high': [0.0], 'low': [0.0], 
                             'close': [0.0], 'lbh': [0.0], 'lah': [0.0], 'trades': [0]}, index=[21])

which looks like:

  ticker        date      time  vol  vwap  open  high  low  close  lbh  lah  trades
21   AACG  2022-01-06  09:51:00    0   0.0   0.0   0.0  0.0    0.0  0.0  0.0       0
        

you can update out:

out.update(missing_data)

Then out becomes:

  ticker        date        time    vol     vwap    open    high    low     close   lbh     lah     trades
0   AACG    2022-01-06  09:51:00    0.0     0.0000  0.0000  0.0000  0.0000  0.0000  0.0     0.0     0.0
1   AACG    2022-01-06  09:31:00    5306.0  1.9073  1.9100  1.9200  1.8801  1.9100  0.0     0.0     27.0
2   AACG    2022-01-06  09:32:00    3496.0  1.8964  1.9100  1.9193  1.8800  1.8900  0.0     0.0     17.0
3   AACG    2022-01-06  09:33:00    5897.0  1.9377  1.8900  1.9500  1.8900  1.9500  0.0     0.0     15.0
...
20  AACG    2022-01-06  09:50:00    9224.0  1.9603  1.9600  1.9613  1.9600  1.9613  0.0     0.0     4.0
21  AACG    2022-01-06  09:51:00    0.0     0.0000  0.0000  0.0000  0.0000  0.0000  0.0     0.0     0.0
22  AACG    2022-01-06  09:52:00    16914.0 1.9921  1.9800  2.0400  1.9750  2.0399  0.0     0.0     67.0
23  AACG    2022-01-06  09:53:00    4665.0  1.9866  1.9900  2.0395  1.9801  1.9900  0.0     0.0     37.0

I used the code that you provided and then iterated over the result in order to add the missing rows. The final result is then sorted again to get the order and indices correctly.

import datetime

# Reading your dataframe
df = pd.read_csv('missing_minute.csv', sep=';', index_col='index')

# Define a default row to add for missing rows
default_row = {'ticker':'AACG', 'date':'2022-01-06', 'time': '00:00:00', 'vol':0.0, 'vwap':0.0, 'open':0.0, 'high':0.0, 'low':0.0, 'close':0.0, 'lbh':0.0, 'lah':0.0, 'trades':0.0}

# Your logic to find the rows before the missing
time_in_minutes = pd.to_timedelta(df['time'].astype(str)).astype('timedelta64[m]')
indices_where_the_next_minute_is_missing = np.where(np.diff(time_in_minutes) != 1)[0]
out = df.loc[indices_where_the_next_minute_is_missing]


# Iterating over the rows
for i, e in out.iterrows():

    # Extract the time of the previous row and convert it to date
    time_of_previous_row = datetime.datetime.strptime(e['time'], '%H:%M:%S')
    
    # Add one minute for the new entry
    time_of_new_row = (time_of_previous_row + datetime.timedelta(minutes=1)).strftime("%H:%M:%S")
    
    # Set new time to the default row and append it to the dataframe
    default_row['time'] = time_of_new_row
    df = df.append(default_row, ignore_index=True)

# Sort the dataframe by the time column and reset the index
df = df.sort_values(by='time').reset_index(drop=True)
df

Output:

    ticker  date    time    vol vwap    open    high    low close   lbh lah trades
0   AACG    2022-01-06  09:30:00    33042.0 1.8807  1.89    1.92    1.87    1.9017  0.0 0.0 68.0
1   AACG    2022-01-06  09:31:00    5306.0  1.9073  1.91    1.92    1.8801  1.91    0.0 0.0 27.0
2   AACG    2022-01-06  09:32:00    3496.0  1.8964  1.91    1.9193  1.88    1.89    0.0 0.0 17.0
3   AACG    2022-01-06  09:33:00    5897.0  1.9377  1.89    1.95    1.89    1.95    0.0 0.0 15.0
4   AACG    2022-01-06  09:34:00    1983.0  1.9362  1.92    1.9499  1.92    1.92    0.0 0.0 9.0
5   AACG    2022-01-06  09:35:00    10725.0 1.9439  1.94    1.96    1.9201  1.9306  0.0 0.0 87.0
6   AACG    2022-01-06  09:36:00    5942.0  1.938   1.9307  1.94    1.93    1.94    0.0 0.0 48.0
7   AACG    2022-01-06  09:37:00    5759.0  1.9428  1.9659  1.9659  1.94    1.95    0.0 0.0 11.0
8   AACG    2022-01-06  09:38:00    4855.0  1.9424  1.95    1.95    1.9401  1.9495  0.0 0.0 10.0
9   AACG    2022-01-06  09:39:00    6275.0  1.9514  1.95    1.97    1.945   1.97    0.0 0.0 14.0
10  AACG    2022-01-06  09:40:00    13695.0 2.015   1.9799  2.05    1.9749  2.02    0.0 0.0 59.0
11  AACG    2022-01-06  09:41:00    3252.0  2.0209  2.0275  2.03    2.02    2.02    0.0 0.0 14.0
12  AACG    2022-01-06  09:42:00    12082.0 2.0117  2.03    2.04    1.98    1.99    0.0 0.0 41.0
13  AACG    2022-01-06  09:43:00    5148.0  1.9802  1.98    1.9999  1.975   1.9999  0.0 0.0 11.0
14  AACG    2022-01-06  09:44:00    2764.0  1.9927  1.9901  1.9943  1.9901  1.9943  0.0 0.0 5.0
15  AACG    2022-01-06  09:45:00    2379.0  1.9576  1.9601  1.9601  1.9201  1.9201  0.0 0.0 10.0
16  AACG    2022-01-06  09:46:00    8762.0  1.9852  1.955   1.99    1.955   1.99    0.0 0.0 35.0
17  AACG    2022-01-06  09:47:00    1343.0  1.9704  1.97    1.9738  1.97    1.9701  0.0 0.0 5.0
18  AACG    2022-01-06  09:48:00    17080.0 1.9696  1.97    1.98    1.96    1.96    0.0 0.0 9.0
19  AACG    2022-01-06  09:49:00    9004.0  1.96    1.96    1.96    1.96    1.96    0.0 0.0 9.0
20  AACG    2022-01-06  09:50:00    9224.0  1.9603  1.96    1.9613  1.96    1.9613  0.0 0.0 4.0
21  AACG    2022-01-06  09:51:00    0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
22  AACG    2022-01-06  09:52:00    16914.0 1.9921  1.98    2.04    1.975   2.0399  0.0 0.0 67.0
23  AACG    2022-01-06  09:53:00    4665.0  1.9866  1.99    2.0395  1.9801  1.99    0.0 0.0 37.0
24  AACG    2022-01-06  09:54:00    0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
25  AACG    2022-01-06  09:55:00    2107.0  2.0049  1.99    2.01    1.99    2.0099  0.0 0.0 10.0
26  AACG    2022-01-06  09:56:00    3003.0  2.0028  2.0 2.0099  2.0 2.0099  0.0 0.0 23.0
27  AACG    2022-01-06  09:57:00    8489.0  2.0272  2.01    2.04    2.01    2.03    0.0 0.0 34.0
28  AACG    2022-01-06  09:58:00    6050.0  2.0155  2.03    2.03    2.015   2.015   0.0 0.0 6.0
29  AACG    2022-01-06  09:59:00    61623.0 2.0449  2.03    2.07    2.03    2.0699  0.0 0.0 83.0
30  AACG    2022-01-06  10:00:00    19699.0 2.0856  2.0699  2.1199  2.06    2.11    0.0 0.0 54.0

P.S.: This only works if only one entry is missing at a time and not multiple entries in one sequence (e.g. 09:51 and 09:52 are missing). This could be added if you check how many rows in a sequence are missing.

Also, if you have data over multiple days, the code has to be adapted a little bit. First, set the date in the loop. Second, sort by date and time in the end.

Similar to enke's answer.

It sounds like you may want to create a new data frame with the proper date and time index that you desire (without missing rows). Then fill in the new data frame with the data that you have.

You can do this using pandas.DataFrame.update() https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.update.html

Here is an example:

start_datetime = '2022-01-06 09:30:00'
end_datetime = '2022-01-06 10:00:00'
cols = ['ticker','date','time','vol','vwap','open','high','low','close','lbh','lah','trades']
new_df = pd.DataFrame(columns=cols,
                      index=pd.date_range(start=start_datetime,
                                          end=end_datetime, freq='min'))

new_df['date'] = [d.date() for d in new_df.index]
new_df['time'] = [d.time() for d in new_df.index]
new_df.fillna(0.0)

start_datetime = '2022-01-06 09:35:00'
end_datetime = '2022-01-06 9:40:00'
cols = ['ticker','date','time','vol','vwap','open','high','low','close','lbh','lah','trades']
other_df = pd.DataFrame(columns=cols,
                      index=pd.date_range(start=start_datetime,
                                          end=end_datetime, freq='min'))

other_df['date'] = [d.date() for d in other_df.index]
other_df['time'] = [d.time() for d in other_df.index]
other_df.fillna(3)

final_df = new_df.copy()
final_df.update(other_df)