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

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) <ipython-input-24-424a0952f9d0> in <module>() 4 print(cat) 5 df[cat].fillna('UNK', inplace=True) ----> 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) --> 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: --> 211 perm = ar.argsort(kind='mergesort' if return_index else 'quicksort') 212 aux = ar[perm] 213 else: TypeError: '>' not supported between instances of 'float' and 'str' 

Checking the variable that lead to the error results ins:

df['CRM do Médico'].isnull().sum() 0 

Besides nan values, what could be causing this error?

3 Answers

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

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.

Or use a cast with split to uniform type of str

unique, counts = numpy.unique(str(a).split(), return_counts=True)