CODEWITHSUNDEEP
pythonpandasdatetimetime
soderhans
soderhans

Reputation: 33

Data Manipulation with Python pandas

I am facing a problem with manipulation this table:

date            time         V_1    I_1      V_2    I_2     Temp
07.07.2017  12:36:27.801    0.000   0.532   0.001   1.289   25.655
07.07.2017  12:36:27.802    0.000   0.486   0.001   1.273   25.655
07.07.2017  12:36:27.803    0.000   0.482   0.001   1.322   25.655
07.07.2017  12:36:27.804    0.000   0.435   0.001   1.311   25.655

I need the time differences (802-801 = 1ms) between each row but I cannot find any solution to handling this without a loop. Is there a more pythonic way?

Upvotes: 1

Views: 71

Answers (1)

jezrael
jezrael

Reputation: 862641

Use to_datetime with diff and then get total_seconds and multiple by 1000 for ms:

df['diff'] = pd.to_datetime(df['date'] + ' ' + df['time']).diff().dt.total_seconds() * 1000
print (df)
         date          time  V_1    I_1    V_2    I_2    Temp  diff
0  07.07.2017  12:36:27.801  0.0  0.532  0.001  1.289  25.655   NaN
1  07.07.2017  12:36:27.802  0.0  0.486  0.001  1.273  25.655   1.0
2  07.07.2017  12:36:27.803  0.0  0.482  0.001  1.322  25.655   1.0
3  07.07.2017  12:36:27.804  0.0  0.435  0.001  1.311  25.655   1.0

For comparing first value with column use sub with iat for select first value of column:

df['diff'] = pd.to_datetime(df['date'] + ' ' + df['time'])
df['diff'] =  df['diff'].sub(df['diff'].iat[0]).dt.total_seconds() * 1000
print (df)
         date          time  V_1    I_1    V_2    I_2    Temp  diff
0  07.07.2017  12:36:27.801  0.0  0.532  0.001  1.289  25.655   0.0
1  07.07.2017  12:36:27.802  0.0  0.486  0.001  1.273  25.655   1.0
2  07.07.2017  12:36:27.803  0.0  0.482  0.001  1.322  25.655   2.0
3  07.07.2017  12:36:27.804  0.0  0.435  0.001  1.311  25.655   3.0

Upvotes: 1

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