How to find which columns contain any NaN value in Pandas dataframe

Question

Given a pandas dataframe containing possible NaN values scattered here and there   Question  How do I determine which columns contain NaN values  In particular  can I get a list of the column names containing NaNs

User · Answer

i use these three lines of code to print out the column names which contain at least one null value   for column in dataframe      if dataframe column  isnull   any           print   0  has  1  null values  format column  dataframe column  isnull   sum

User · Answer

You can use df isnull   sum    It shows all columns and the total NaNs of each feature

User · Answer

In datasets having large number of columns its even better to see how many columns contain null values and how many don t     print  No  of columns containing null values   print len df columns df isna   any       print  No  of columns not containing null values   print len df columns df notna   all       print  Total no  of columns in the dataframe   print len df columns     For example in my dataframe it contained 82 columns  of which 19 contained at least one null value     Further you can also automatically remove cols and rows depending on which has more null values Here is the code which does this intelligently     df   df drop df columns df isna   sum   gt len df columns   axis   1  df   df dropna axis   0  reset index drop True    Note  Above code removes all of your null values  If you want null values  process them before

User · Answer

df columns df isnull   any    tolist    it will return name of columns that contains null rows

User · Answer

I had a problem where I had to many columns to visually inspect on the screen so a short list comp that filters and returns the offending columns is   nan cols    i for i in df columns if df i  isnull   any      if that s helpful to anyone

User · Answer

This worked for me   1  For getting Columns having at least 1 null value   column names   data columns data isnull   any      2  For getting Columns with count  with having at least 1 null value   data data columns data isnull   any     isnull   sum      Optional  3  For getting percentage of the null count   data data columns data isnull   any     isnull   sum     100   data shape 0

User · Answer

UPDATE  using Pandas 0 22 0  Newer Pandas versions have new methods  DataFrame isna    and  DataFrame notna     In  71   df Out 71        a    b  c 0  NaN  7 0  0 1  0 0  NaN  4 2  2 0  NaN  4 3  1 0  7 0  0 4  1 0  3 0  9 5  7 0  4 0  9 6  2 0  6 0  9 7  9 0  6 0  4 8  3 0  0 0  9 9  9 0  0 0  1  In  72   df isna   any   Out 72   a     True b     True c    False dtype  bool   as list of columns   In  74   df columns df isna   any    tolist   Out 74     a    b     to select those columns  containing at least one NaN value    In  73   df loc    df isna   any    Out 73        a    b 0  NaN  7 0 1  0 0  NaN 2  2 0  NaN 3  1 0  7 0 4  1 0  3 0 5  7 0  4 0 6  2 0  6 0 7  9 0  6 0 8  3 0  0 0 9  9 0  0 0     OLD answer   Try to use isnull     In  97   df Out 97        a    b  c 0  NaN  7 0  0 1  0 0  NaN  4 2  2 0  NaN  4 3  1 0  7 0  0 4  1 0  3 0  9 5  7 0  4 0  9 6  2 0  6 0  9 7  9 0  6 0  4 8  3 0  0 0  9 9  9 0  0 0  1  In  98   pd isnull df  sum    gt  0 Out 98   a     True b     True c    False dtype  bool   or as  root proposed clearer version   In  5   df isnull   any   Out 5   a     True b     True c    False dtype  bool  In  7   df columns df isnull   any    tolist   Out 7     a    b     to select a subset - all columns containing at least one NaN value   In  31   df loc    df isnull   any    Out 31        a    b 0  NaN  7 0 1  0 0  NaN 2  2 0  NaN 3  1 0  7 0 4  1 0  3 0 5  7 0  4 0 6  2 0  6 0 7  9 0  6 0 8  3 0  0 0 9  9 0  0 0

User · Answer

df isna   return True values for NaN  False for the rest  So  doing  df isna   any   will return True for any column having a NaN  False for the rest

User · Answer

Both of these should work   df isnull   sum   df isna   sum     DataFrame methods isna   or isnull   are completely identical   Note  Empty strings    is considered as False  not considered NA

[python] How to find which columns contain any NaN value in Pandas dataframe

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