How to set a cell to NaN in a pandas dataframe

Question

I d like to replace bad values in a column of a dataframe by NaN s   mydata     x     10  50  18  32  47  20    y      12    11    N A    13    15    N A    df   pd DataFrame mydata   df df y     N A    y     np nan   Though  the last line fails and throws a warning because it s working on a copy of df  So  what s the correct way to handle this  I ve seen many solutions with iloc or ix but here  I need to use a boolean condition

User · Answer

df loc df y     N A    y      np nan   This solve your problem  With the double     you are working on a copy of the DataFrame  You have to specify exact location in one call to be able to modify it

User · Answer

You can try these snippets    In  16  mydata     x     10  50  18  32  47  20    y      12    11    N A    13    15    N A    In  17  df pd DataFrame mydata   In  18  df y df y   N A   np nan  Out 19  df      x    y 0  10   12 1  50   11 2  18  NaN 3  32   13 4  47   15 5  20  NaN

User · Answer

You can use replace   df  y     df  y   replace   N A   np nan     Also be aware of the inplace parameter for replace  You can do something like   df replace   N A   np nan   inplace True    This will replace all instances in the df without creating a copy   Similarly  if you run into other types of unknown values such as empty string or None value   df  y     df  y   replace      np nan    df  y     df  y   replace  None  np nan     Reference  Pandas Latest - Replace

User · Answer

As of pandas 1 0 0  you no longer need to use numpy to create null values in your dataframe  Instead you can just use pandas NA  which is of type pandas  libs missing NAType   so it will be treated as null within the dataframe but will not be null outside dataframe context

User · Answer

While using replace seems to solve the problem  I would like to propose an alternative  Problem with mix of numeric and some string values in the column not to have strings replaced with np nan  but to make whole column proper  I would bet that original column most likely is of an object type  Name  y  dtype  object   What you really need is to make it a numeric column  it will have proper type and would be quite faster   with all non-numeric values replaced by NaN   Thus  good conversion code would be  pd to numeric df  y    errors  coerce     Specify errors  coerce  to force strings that can t be parsed to a numeric value to become NaN  Column type would be  Name  y  dtype  float64

User · Answer

df replace  columnvalue  np NaN inplace True

User · Answer

Most replies here need to import numpy as np There is a built-in solution into pandas itself  pd NA  to use like this  df replace  N A   pd NA

User · Answer

just use replace   In  106   df replace  N A  np NaN   Out 106       x    y 0  10   12 1  50   11 2  18  NaN 3  32   13 4  47   15 5  20  NaN   What you re trying is called chain indexing  http   pandas pydata org pandas-docs stable indexing html indexing-view-versus-copy  You can use loc to ensure you operate on the original dF   In  108   df loc df  y       N A   y     np nan df  Out 108       x    y 0  10   12 1  50   11 2  18  NaN 3  32   13 4  47   15 5  20  NaN

[python] How to set a cell to NaN in a pandas dataframe

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