python - Pandas: Transmitir columna a cadena no funciona

Question

Tengo un dataframe resultstatsDF

resultstatsDF = DataFrame({''a'': [1,2,3,4,5]})
resultstatsDF[''file''] = ''asdf''
resultstatsDF.dtypes
a        int64
file    object
dtype: object

con el file columna de object que me gustaría convertir en cadena:

Lo intenté

resultstatsDF = resultstatsDF.astype({''file'': str})
resultstatsDF[''file''] = resultstatsDF[''file''].astype(str)
resultstatsDF[''file''] = resultstatsDF[''file''].to_string
resultstatsDF[''file''] = resultstatsDF.file.apply(str)
resultstatsDF[''file''] = resultstatsDF[''file''].apply(str)

pero haga lo que haga, cuando verifique con

resultstatsDF.dtypes

el file columna permanece para ser del object tpye.

Answer 1

dtype de string , dict , list siempre es object , para el type prueba necesita seleccionar algún valor de columna, por ejemplo, por iat :

type(resultstatsDF[''file''].iat[0])

Muestra:

resultstatsDF = pd.DataFrame({''file'':[''a'',''d'',''f'']})
print (resultstatsDF)
  file
0    a
1    d
2    f

print (type(resultstatsDF[''file''].iloc[0]))
<class ''str''>

print (resultstatsDF[''file''].apply(type))
0    <class ''str''>
1    <class ''str''>
2    <class ''str''>
Name: file, dtype: object

Muestra:

df = pd.DataFrame({''strings'':[''a'',''d'',''f''],
                   ''dicts'':[{''a'':4}, {''c'':8}, {''e'':9}],
                   ''lists'':[[4,8],[7,8],[3]],
                   ''tuples'':[(4,8),(7,8),(3,)],
                   ''sets'':[set([1,8]), set([7,3]), set([0,1])] })

print (df)
      dicts   lists    sets strings  tuples
0  {''a'': 4}  [4, 8]  {8, 1}       a  (4, 8)
1  {''c'': 8}  [7, 8]  {3, 7}       d  (7, 8)
2  {''e'': 9}     [3]  {0, 1}       f    (3,)

Todos los valores tienen los mismos dtypes :

print (df.dtypes)
dicts      object
lists      object
sets       object
strings    object
tuples     object
dtype: object

Pero el type es diferente, si es necesario, verifíquelo por bucle:

for col in df:
    print (df[col].apply(type))

0    <class ''dict''>
1    <class ''dict''>
2    <class ''dict''>
Name: dicts, dtype: object
0    <class ''list''>
1    <class ''list''>
2    <class ''list''>
Name: lists, dtype: object
0    <class ''set''>
1    <class ''set''>
2    <class ''set''>
Name: sets, dtype: object
0    <class ''str''>
1    <class ''str''>
2    <class ''str''>
Name: strings, dtype: object
0    <class ''tuple''>
1    <class ''tuple''>
2    <class ''tuple''>
Name: tuples, dtype: object

O primer valor de columnas:

print (type(df[''strings''].iat[0]))
<class ''str''>

print (type(df[''dicts''].iat[0]))
<class ''dict''>

print (type(df[''lists''].iat[0]))
<class ''list''>

print (type(df[''tuples''].iat[0]))
<class ''tuple''>

print (type(df[''sets''].iat[0]))
<class ''set''>

Con la boolean indexing si es posible una columna mixta (entonces se pueden romper algunas funciones de pandas), es posible filtrar por type :

df = pd.DataFrame({''mixed'':[''3'', 5, 9,''2'']})
print (df)
  mixed
0     3
1     5
2     9
3     2

print (df.dtypes)
mixed    object
dtype: object

for col in df:
    print (df[col].apply(type))
0    <class ''str''>
1    <class ''int''>
2    <class ''int''>
3    <class ''str''>
Name: mixed, dtype: object

#python 3 - string
#python 2 - basestring
mask = df[''mixed''].apply(lambda x: isinstance(x,str))
print (mask)
0     True
1    False
2    False
3     True
Name: mixed, dtype: bool

df = df[mask]
print (df)
  mixed
0     3
3     2