Mixed object type columns and managing duplicates

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I have merged 4 datasets and I can note duplicated rows in the data frame. However, when I command pandas to show me the duplicated rows, it says there is none and hence my codes to remove duplicated rows is not responding. Any help would be appreciated.



Dataframe sample:



end_time_x  start_time_x    duration    deviceuuid  time_offset_x   exercise_type   max_speed   calorie mean_speed  distance    ... time_offset create_time weekday month   startsleep  wakeup  sleep_duration  duration_mins   powernaps   weekend
0 2018-01-07 10:01:00-04:00 2018-01-07 07:21:00-04:00 831210 F/D7+hL5E5 UTC-0300 1001 1.750000 54.340 1.376099 905.360 ... UTC-0400 2018-01-07 10:15:59.770000-04:00 6 1 7 10 02:40:00 160.0 False True
1 2018-01-07 10:01:00-04:00 2018-01-07 07:21:00-04:00 831210 F/D7+hL5E5 UTC-0300 1001 1.750000 54.340 1.376099 905.360 ... UTC-0400 2018-01-07 05:12:34.278000-04:00 6 1 0 4 04:12:00 252.0 False True
2 2018-01-07 10:01:00-04:00 2018-01-07 07:21:00-04:00 831210 F/D7+hL5E5 UTC-0300 1001 1.750000 54.340 1.376099 905.360 ... UTC-0400 2018-01-08 07:45:13.936000-04:00 6 1 22 7 09:11:00 551.0 False True
3 2018-01-07 10:01:00-04:00 2018-01-07 07:21:00-04:00 831210 F/D7+hL5E5 UTC-0300 1001 1.750000 54.340 1.376099 905.360 ... UTC-0400 2018-01-07 10:15:59.770000-04:00 6 1 7 10 02:40:00 160.0 False True


I have tried the code below yet they yield the same result if I omit the drop_duplicates lines.



code for checking duplicates:



df_merged.duplicated().sum()
df_merged.loc[df_merged.duplicated(),:]


code for merging data frames by first dropping duplicates in 2 out of 4 data frames:



df_exercise_cleaned=df_exercise.drop_duplicates()
df_HR_cleaned=df_HR.drop_duplicates() df_merged=df_exercise_cleaned.merge(df_HR_cleaned,on='date',how='inner').merge(df_FC, on='date',how='inner').merge(df_sleep,on='date',how='inner')


adding the dtypes post checking for mixed object columns and converting date to dt:
df dtypes










share|improve this question

























  • check the datatypes, if there is a mismatch , this could happen : stackoverflow.com/questions/50686970/…

    – anky_91
    Dec 31 '18 at 12:17











  • Thank you - yes, my 'date' column has mixed object types. All comments and questions appear to ask for a way to check for the error; how can we get the resolution to address it?

    – SFSN
    Dec 31 '18 at 12:31











  • can you assign a datetime to all components by df[['time1','time2','time3']] = df[['time1','time2','time3']].apply(pd.to_datetime,errors='coerce') and then dedup? replace time1,2 and 3 with original column names

    – anky_91
    Dec 31 '18 at 12:44













  • I applied datetime to the date column with mixed object types (without errors 'coerce' ) but it still props up as a mixed object type column: df_merged['date']=pd.to_datetime(df_merged['date'])

    – SFSN
    Dec 31 '18 at 18:55













  • Can't seem to move this to chat but wouldn't that impact my int and float columns that are needed for analysis and visualization?

    – SFSN
    Dec 31 '18 at 20:41
















0















I have merged 4 datasets and I can note duplicated rows in the data frame. However, when I command pandas to show me the duplicated rows, it says there is none and hence my codes to remove duplicated rows is not responding. Any help would be appreciated.



Dataframe sample:



end_time_x  start_time_x    duration    deviceuuid  time_offset_x   exercise_type   max_speed   calorie mean_speed  distance    ... time_offset create_time weekday month   startsleep  wakeup  sleep_duration  duration_mins   powernaps   weekend
0 2018-01-07 10:01:00-04:00 2018-01-07 07:21:00-04:00 831210 F/D7+hL5E5 UTC-0300 1001 1.750000 54.340 1.376099 905.360 ... UTC-0400 2018-01-07 10:15:59.770000-04:00 6 1 7 10 02:40:00 160.0 False True
1 2018-01-07 10:01:00-04:00 2018-01-07 07:21:00-04:00 831210 F/D7+hL5E5 UTC-0300 1001 1.750000 54.340 1.376099 905.360 ... UTC-0400 2018-01-07 05:12:34.278000-04:00 6 1 0 4 04:12:00 252.0 False True
2 2018-01-07 10:01:00-04:00 2018-01-07 07:21:00-04:00 831210 F/D7+hL5E5 UTC-0300 1001 1.750000 54.340 1.376099 905.360 ... UTC-0400 2018-01-08 07:45:13.936000-04:00 6 1 22 7 09:11:00 551.0 False True
3 2018-01-07 10:01:00-04:00 2018-01-07 07:21:00-04:00 831210 F/D7+hL5E5 UTC-0300 1001 1.750000 54.340 1.376099 905.360 ... UTC-0400 2018-01-07 10:15:59.770000-04:00 6 1 7 10 02:40:00 160.0 False True


I have tried the code below yet they yield the same result if I omit the drop_duplicates lines.



code for checking duplicates:



df_merged.duplicated().sum()
df_merged.loc[df_merged.duplicated(),:]


code for merging data frames by first dropping duplicates in 2 out of 4 data frames:



df_exercise_cleaned=df_exercise.drop_duplicates()
df_HR_cleaned=df_HR.drop_duplicates() df_merged=df_exercise_cleaned.merge(df_HR_cleaned,on='date',how='inner').merge(df_FC, on='date',how='inner').merge(df_sleep,on='date',how='inner')


adding the dtypes post checking for mixed object columns and converting date to dt:
df dtypes










share|improve this question

























  • check the datatypes, if there is a mismatch , this could happen : stackoverflow.com/questions/50686970/…

    – anky_91
    Dec 31 '18 at 12:17











  • Thank you - yes, my 'date' column has mixed object types. All comments and questions appear to ask for a way to check for the error; how can we get the resolution to address it?

    – SFSN
    Dec 31 '18 at 12:31











  • can you assign a datetime to all components by df[['time1','time2','time3']] = df[['time1','time2','time3']].apply(pd.to_datetime,errors='coerce') and then dedup? replace time1,2 and 3 with original column names

    – anky_91
    Dec 31 '18 at 12:44













  • I applied datetime to the date column with mixed object types (without errors 'coerce' ) but it still props up as a mixed object type column: df_merged['date']=pd.to_datetime(df_merged['date'])

    – SFSN
    Dec 31 '18 at 18:55













  • Can't seem to move this to chat but wouldn't that impact my int and float columns that are needed for analysis and visualization?

    – SFSN
    Dec 31 '18 at 20:41














0












0








0








I have merged 4 datasets and I can note duplicated rows in the data frame. However, when I command pandas to show me the duplicated rows, it says there is none and hence my codes to remove duplicated rows is not responding. Any help would be appreciated.



Dataframe sample:



end_time_x  start_time_x    duration    deviceuuid  time_offset_x   exercise_type   max_speed   calorie mean_speed  distance    ... time_offset create_time weekday month   startsleep  wakeup  sleep_duration  duration_mins   powernaps   weekend
0 2018-01-07 10:01:00-04:00 2018-01-07 07:21:00-04:00 831210 F/D7+hL5E5 UTC-0300 1001 1.750000 54.340 1.376099 905.360 ... UTC-0400 2018-01-07 10:15:59.770000-04:00 6 1 7 10 02:40:00 160.0 False True
1 2018-01-07 10:01:00-04:00 2018-01-07 07:21:00-04:00 831210 F/D7+hL5E5 UTC-0300 1001 1.750000 54.340 1.376099 905.360 ... UTC-0400 2018-01-07 05:12:34.278000-04:00 6 1 0 4 04:12:00 252.0 False True
2 2018-01-07 10:01:00-04:00 2018-01-07 07:21:00-04:00 831210 F/D7+hL5E5 UTC-0300 1001 1.750000 54.340 1.376099 905.360 ... UTC-0400 2018-01-08 07:45:13.936000-04:00 6 1 22 7 09:11:00 551.0 False True
3 2018-01-07 10:01:00-04:00 2018-01-07 07:21:00-04:00 831210 F/D7+hL5E5 UTC-0300 1001 1.750000 54.340 1.376099 905.360 ... UTC-0400 2018-01-07 10:15:59.770000-04:00 6 1 7 10 02:40:00 160.0 False True


I have tried the code below yet they yield the same result if I omit the drop_duplicates lines.



code for checking duplicates:



df_merged.duplicated().sum()
df_merged.loc[df_merged.duplicated(),:]


code for merging data frames by first dropping duplicates in 2 out of 4 data frames:



df_exercise_cleaned=df_exercise.drop_duplicates()
df_HR_cleaned=df_HR.drop_duplicates() df_merged=df_exercise_cleaned.merge(df_HR_cleaned,on='date',how='inner').merge(df_FC, on='date',how='inner').merge(df_sleep,on='date',how='inner')


adding the dtypes post checking for mixed object columns and converting date to dt:
df dtypes










share|improve this question
















I have merged 4 datasets and I can note duplicated rows in the data frame. However, when I command pandas to show me the duplicated rows, it says there is none and hence my codes to remove duplicated rows is not responding. Any help would be appreciated.



Dataframe sample:



end_time_x  start_time_x    duration    deviceuuid  time_offset_x   exercise_type   max_speed   calorie mean_speed  distance    ... time_offset create_time weekday month   startsleep  wakeup  sleep_duration  duration_mins   powernaps   weekend
0 2018-01-07 10:01:00-04:00 2018-01-07 07:21:00-04:00 831210 F/D7+hL5E5 UTC-0300 1001 1.750000 54.340 1.376099 905.360 ... UTC-0400 2018-01-07 10:15:59.770000-04:00 6 1 7 10 02:40:00 160.0 False True
1 2018-01-07 10:01:00-04:00 2018-01-07 07:21:00-04:00 831210 F/D7+hL5E5 UTC-0300 1001 1.750000 54.340 1.376099 905.360 ... UTC-0400 2018-01-07 05:12:34.278000-04:00 6 1 0 4 04:12:00 252.0 False True
2 2018-01-07 10:01:00-04:00 2018-01-07 07:21:00-04:00 831210 F/D7+hL5E5 UTC-0300 1001 1.750000 54.340 1.376099 905.360 ... UTC-0400 2018-01-08 07:45:13.936000-04:00 6 1 22 7 09:11:00 551.0 False True
3 2018-01-07 10:01:00-04:00 2018-01-07 07:21:00-04:00 831210 F/D7+hL5E5 UTC-0300 1001 1.750000 54.340 1.376099 905.360 ... UTC-0400 2018-01-07 10:15:59.770000-04:00 6 1 7 10 02:40:00 160.0 False True


I have tried the code below yet they yield the same result if I omit the drop_duplicates lines.



code for checking duplicates:



df_merged.duplicated().sum()
df_merged.loc[df_merged.duplicated(),:]


code for merging data frames by first dropping duplicates in 2 out of 4 data frames:



df_exercise_cleaned=df_exercise.drop_duplicates()
df_HR_cleaned=df_HR.drop_duplicates() df_merged=df_exercise_cleaned.merge(df_HR_cleaned,on='date',how='inner').merge(df_FC, on='date',how='inner').merge(df_sleep,on='date',how='inner')


adding the dtypes post checking for mixed object columns and converting date to dt:
df dtypes







python pandas merge duplicates






share|improve this question















share|improve this question













share|improve this question




share|improve this question








edited Dec 31 '18 at 20:48







SFSN

















asked Dec 31 '18 at 12:12









SFSNSFSN

206




206













  • check the datatypes, if there is a mismatch , this could happen : stackoverflow.com/questions/50686970/…

    – anky_91
    Dec 31 '18 at 12:17











  • Thank you - yes, my 'date' column has mixed object types. All comments and questions appear to ask for a way to check for the error; how can we get the resolution to address it?

    – SFSN
    Dec 31 '18 at 12:31











  • can you assign a datetime to all components by df[['time1','time2','time3']] = df[['time1','time2','time3']].apply(pd.to_datetime,errors='coerce') and then dedup? replace time1,2 and 3 with original column names

    – anky_91
    Dec 31 '18 at 12:44













  • I applied datetime to the date column with mixed object types (without errors 'coerce' ) but it still props up as a mixed object type column: df_merged['date']=pd.to_datetime(df_merged['date'])

    – SFSN
    Dec 31 '18 at 18:55













  • Can't seem to move this to chat but wouldn't that impact my int and float columns that are needed for analysis and visualization?

    – SFSN
    Dec 31 '18 at 20:41



















  • check the datatypes, if there is a mismatch , this could happen : stackoverflow.com/questions/50686970/…

    – anky_91
    Dec 31 '18 at 12:17











  • Thank you - yes, my 'date' column has mixed object types. All comments and questions appear to ask for a way to check for the error; how can we get the resolution to address it?

    – SFSN
    Dec 31 '18 at 12:31











  • can you assign a datetime to all components by df[['time1','time2','time3']] = df[['time1','time2','time3']].apply(pd.to_datetime,errors='coerce') and then dedup? replace time1,2 and 3 with original column names

    – anky_91
    Dec 31 '18 at 12:44













  • I applied datetime to the date column with mixed object types (without errors 'coerce' ) but it still props up as a mixed object type column: df_merged['date']=pd.to_datetime(df_merged['date'])

    – SFSN
    Dec 31 '18 at 18:55













  • Can't seem to move this to chat but wouldn't that impact my int and float columns that are needed for analysis and visualization?

    – SFSN
    Dec 31 '18 at 20:41

















check the datatypes, if there is a mismatch , this could happen : stackoverflow.com/questions/50686970/…

– anky_91
Dec 31 '18 at 12:17





check the datatypes, if there is a mismatch , this could happen : stackoverflow.com/questions/50686970/…

– anky_91
Dec 31 '18 at 12:17













Thank you - yes, my 'date' column has mixed object types. All comments and questions appear to ask for a way to check for the error; how can we get the resolution to address it?

– SFSN
Dec 31 '18 at 12:31





Thank you - yes, my 'date' column has mixed object types. All comments and questions appear to ask for a way to check for the error; how can we get the resolution to address it?

– SFSN
Dec 31 '18 at 12:31













can you assign a datetime to all components by df[['time1','time2','time3']] = df[['time1','time2','time3']].apply(pd.to_datetime,errors='coerce') and then dedup? replace time1,2 and 3 with original column names

– anky_91
Dec 31 '18 at 12:44







can you assign a datetime to all components by df[['time1','time2','time3']] = df[['time1','time2','time3']].apply(pd.to_datetime,errors='coerce') and then dedup? replace time1,2 and 3 with original column names

– anky_91
Dec 31 '18 at 12:44















I applied datetime to the date column with mixed object types (without errors 'coerce' ) but it still props up as a mixed object type column: df_merged['date']=pd.to_datetime(df_merged['date'])

– SFSN
Dec 31 '18 at 18:55







I applied datetime to the date column with mixed object types (without errors 'coerce' ) but it still props up as a mixed object type column: df_merged['date']=pd.to_datetime(df_merged['date'])

– SFSN
Dec 31 '18 at 18:55















Can't seem to move this to chat but wouldn't that impact my int and float columns that are needed for analysis and visualization?

– SFSN
Dec 31 '18 at 20:41





Can't seem to move this to chat but wouldn't that impact my int and float columns that are needed for analysis and visualization?

– SFSN
Dec 31 '18 at 20:41












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