Splitting Csv File Of Multiple Objects Over Time By Time-point

Here I have an example file of multiple objects each measured at the same time-points (also ND.T represents each unique time point). I would like to split this file into separate f

Solution 1:

You can use pandas to achieve this:

import pandas as pd
df = pd.read_csv(your_file)
df.groupby('Time [s]').apply(lambda x: x.to_csv(str(x.name) + '.csv'))

The above will load your csv using read_csv and then group on the Time [s] column and use this to name the file

You can see that the df is grouped on the Time [s]:

In [108]:
df.groupby('Time [s]').apply(lambda x: print(x))
   ID  ND.T  Time [s]  Position X [%s]  Position Y [%s]  Speed [%s]  \
0   1     1      3.87           417.57            11.46        0.06   
3   2     1      3.87           439.01             6.59        0.02   

   Area [%s]  Width [%s]  MeanIntensity  
0     339.48       14.10         245.65  
3     342.61       11.66         204.47  
   ID  ND.T  Time [s]  Position X [%s]  Position Y [%s]  Speed [%s]  \
0   1     1      3.87           417.57            11.46        0.06   
3   2     1      3.87           439.01             6.59        0.02   

   Area [%s]  Width [%s]  MeanIntensity  
0     339.48       14.10         245.65  
3     342.61       11.66         204.47  
   ID  ND.T  Time [s]  Position X [%s]  Position Y [%s]  Speed [%s]  \
1   1     2      8.72           417.37            11.68       0.040   
4   2     2      8.72           438.97             6.65       0.007   

   Area [%s]  Width [%s]  MeanIntensity  
1     342.61       14.15         239.34  
4     342.61       10.70         197.96  
   ID  ND.T  Time [s]  Position X [%s]  Position Y [%s]  Speed [%s]  \
2   1     3     13.39           417.57            11.66        0.04   
5   2     3     13.39           438.94             6.66        0.03   

   Area [%s]  Width [%s]  MeanIntensity  
2     344.17       14.30         239.48  
5     345.74       11.03         214.74  

Out[108]:
Empty DataFrame
Columns: []
Index: []

Here groupby will group on 'Time [s]' column, we then call apply to apply a lambda where we call the method to_csv on each grouping, we can access the group name using name attribute which is of dtype int so we cast to str and construct our csv name:

In [109]:
df.groupby('Time [s]').apply(lambda x: print(str(x.name) + '.csv'))

3.87.csv
8.72.csv
13.39.csv
Out[109]:
Empty DataFrame
Columns: []
Index: []