How to find missing index values in a pandas dataframe?

Question

My dataframe is like this. I know what I lost some rows in data cleaning because len(df) was previously 500 and now it is 489. I can see, for example, that the row 496 is missing.


    all       month day year
0   03/25/93    03  25  93
...
480     2013    1   1   2013
481     1974    1   1   1974
482     1990    1   1   1990
483     1995    1   1   1995
484     2004    1   1   2004
485     1987    1   1   1987
486     1973    1   1   1973
487     1992    1   1   1992
488     1977    1   1   1977
489     1985    1   1   1985
490     2007    1   1   2007
491     2009    1   1   2009
492     1986    1   1   1986
493     1978    1   1   1978
494     2002    1   1   2002
495     1979    1   1   1979
497     2008    1   1   2008
498     2005    1   1   2005
499     1980    1   1   1980

how can I find out which rows are missing? If my question is a duplicate, please point me to the solution. thanks!

itmatters · Accepted Answer · 2020-08-13 12:37:42Z

6

The easiest, if you have unique index values, is probably to use the difference on the index, i.e. you could simply do:

df_original.index.difference(df_cleaned.index)

answered Aug 13, 2020 at 12:37

itmatters

6956 silver badges8 bronze badges

Sign up to request clarification or add additional context in comments.

Collectives™ on Stack Overflow

How to find missing index values in a pandas dataframe?

1 Answer 1

Comments

Your Answer

Hot Network Questions

Collectives™ on Stack Overflow

1 Answer 1

Comments

Your Answer

Sign up or log in

Post as a guest

Related