Pandas Is it possible to add new time values with empty values in columns in a csv with a time seque-CodePudding

I have a csv file that looks something like this

Time	OI	V
10:00:23	5.4	27
10:00:24	-0.7	1
10:00:28	-0.5	4
10:00:29	0.2	12

Can I somehow add new time values using Pandas while filling the columns with zeros or Nan? For the entire csv file.

What would have turned out something like that ?

Time	OI	V
10:00:23	5.4	27
10:00:24	-0.7	1
10:00:25	0	Nan
10:00:26	0	Nan
10:00:27	0	Nan
10:00:28	-0.5	4
10:00:29	0.2	12

CodePudding user response：

Convert column to datetimes, create DatetimeIndex and add missing values by DataFrame.asfreq, last replace NaNs in OI column:

df['Time'] = pd.to_datetime(df['Time'])

df = df.set_index('Time').asfreq('S').fillna({'OI':0})
df.index = df.index.time
print (df)
           OI     V
10:00:23  5.4  27.0
10:00:24 -0.7   1.0
10:00:25  0.0   NaN
10:00:26  0.0   NaN
10:00:27  0.0   NaN
10:00:28 -0.5   4.0
10:00:29  0.2  12.0

df['Time'] = pd.to_datetime(df['Time'])

df = df.set_index('Time').asfreq('S').fillna({'OI':0}).reset_index()
df['Time'] = df['Time'].dt.time
print (df)
       Time   OI     V
0  10:00:23  5.4  27.0
1  10:00:24 -0.7   1.0
2  10:00:25  0.0   NaN
3  10:00:26  0.0   NaN
4  10:00:27  0.0   NaN
5  10:00:28 -0.5   4.0
6  10:00:29  0.2  12.0