LabelEncoder TypeError not supported between instances of float and str

Question

I m facing this error for multiple variables even treating missing values  For example   le   preprocessing LabelEncoder   categorical   list df select dtypes include   object    columns values  for cat in categorical      print cat      df cat  fillna  UNK   inplace True      df cat    le fit transform df cat         print le classes         print le transform le classes      --------------------------------------------------------------------------- TypeError                                 Traceback  most recent call last   lt ipython-input-24-424a0952f9d0 gt  in  lt module gt          4     print cat        5     df cat  fillna  UNK   inplace True  ---- gt  6     df cat    le fit transform df cat  fillna  UNK          7       print le classes         8       print le transform le classes     C  Users paula ceccon ribeiro AppData Local Continuum Anaconda3 lib site-packages sklearn preprocessing label py in fit transform self  y      129         y   column or 1d y  warn True      130          check numpy unicode bug y  -- gt  131         self classes   y   np unique y  return inverse True      132         return y     133   C  Users paula ceccon ribeiro AppData Local Continuum Anaconda3 lib site-packages numpy lib arraysetops py in unique ar  return index  return inverse  return counts      209      210     if optional indices  -- gt  211         perm   ar argsort kind  mergesort  if return index else  quicksort       212         aux   ar perm      213     else   TypeError    gt   not supported between instances of  float  and  str    Checking the variable that lead to the error results ins   df  CRM do M  dico   isnull   sum   0   Besides nan values  what could be causing this error

User · Accepted Answer

This is due to the series df cat  containing elements that have varying data types e g  strings and or floats    This could be due to the way the data is read  i e  numbers are read as float and text as strings or the datatype was float and changed after the fillna operation   In other words      pandas data type  Object  indicates mixed types rather than str type   so using the following line   df cat    le fit transform df cat  astype str     should help

User · Answer

Or use a cast with split to uniform type of str  unique  counts   numpy unique str a  split    return counts True

User · Answer

As string data types have variable length  it is by default stored as object type  I faced this problem after treating missing values too  Converting all those columns to type  category  before label encoding worked in my case   df cat  df cat  astype  category     And then check df dtypes and perform label encoding

[python] LabelEncoder: TypeError: '>' not supported between instances of 'float' and 'str'

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