Con datos como a continuación

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'', '', '', '', '', '', '', '', ' 13', 'PCH', '  9.0', '  0', '  1', '  0', ''], ['05:17:55.893', '', '', '', '', '', '', '', '', '', '', '', '', '', '  0', 'PSH', '-14.0', ' 16', '  4', '  0', ''], ['05:17:55.893', '', '', '', '', '', '', '', '', '', '', '', '', '', ' 13', 'PCH', ' 15.0', ' 48', '  5', '  0', ''], ['05:17:55.893', '', '', '', '', '', '', '', '', '', '', '', '', '', '  0', 'PSH', '-14.0', '  0', '  4', '  0', ''], ['05:17:55.893', '', '', '', '', '', '', '', '', '', '', '', '', '', ' 13', 'PCH', '  8.0', '  0', '  1', '  0', ''], ['05:17:55.893', '', '', '', '', '', '', '', '', '', '', '', '', '', '  0', 'PSH', '-11.0', '  0', '  8', '  0', ''], ['05:17:55.893', '', '', '', '', '', '', '', '', '', '', '', '', '', ' 13', 'PCH', ' 10.0', ' 48', '  1', '  0', ''], ['05:17:55.893', '', '', '', '', '', '', '', '', '', '', '', '', '', '  0', 'PSH', '-14.0', '  0', '  4', '  0', ''], ['05:17:55.893', '', '', '', '', '', '', '', '', '', '', '', '', '', ' 13', 'PCH', '  8.0', '  0', '  1', '  0', ''], ['05:17:55.893', '', '', '', '', '', '', '', '', '', '', '', '', '', '  0', 'PSH', '-11.0', '  0', '  8', '  0', ''], ['05:17:55.893', '', '', '', '', '', '', '', '', '', '', '', '', '', ' 13', 'PCH', ' 11.0', ' 48', '  1', '  0', ''], ['05:17:55.893', '', '', '', '', '', '', '', '', '', '', '', '', '', '  0', 'PSH', '-14.0', ' 16', '  4', '  0', ''], ['05:17:55.893', '', '', '', '', '', '', '', '', '', '', '', '', '', ' 13', 'PCH', '  9.0', ' 48', '  1', '  0', ''], ['05:17:55.893', '', '', '', '', '', '', '', '', '', '', '', '', '', '  0', 'PSH', ' -8.0', '  8', ' 16', '  0', ''], ['05:17:55.893', '', '', '', '', '', '', '', '', '', '', '', '', '', ' 13', 'PCH', '  9.0', ' 48', '  1', '  0', ''], ['05:17:55.893', '', '', '', '', '', '', '', '', '', '', '', '', '', '  0', 'PSH', '-15.0', ' 16', '  4', '  0', ''], ['05:17:55.893', '', '', '', '', '', '', '', '', '', '', '', '', '', ' 13', 'PCH', ' 10.0', ' 48', '  1', '  0', ''], ['05:17:55.893', '', '', '', '', '', '', '', '', '', '', '', '', '', ' 13', 'SS_0', ' -1.0', '  0', '216', '  0', ''], ['05:17:55.893', '', '', '', '', '', '', '', '', '', '', '', '', '', '  0', 'PSH', ' -9.0', '  0', ' 16', '  0', ''], ['05:17:55.893', '', '', '', '', '', '', '', '', '', '', '', '', '', ' 13', 'PCH', ' 14.0', ' 48', '  5', '  0', ''], ['05:17:55.893', '', '', '', '', '', '', '', '', '', '', '', '', '', '  0', 'PSH', '-15.0', ' 12', '  4', '  0', ''], ['05:17:55.893', '', '', '', '', '', '', '', '', '', '', '', '', '', ' 13', 'PCH', '  9.0', ' 48', '  1', '  0', ''], ['05:17:55.893', '', '', '', '', '', '', '', '', '', '', '', '', '', '  0', 'PSH', ' -9.0', '  8', ' 24', '  0', ''], ['05:17:55.893', '', '', '', '', '', '', '', '', '', '', '', '', '', ' 13', 'PCH', '  6.0', '  0', '  1', '  0', ''], ['05:17:55.893', '', '', '', '', '', '', '', '', '', '', '', '', '', '  0', 'PSH', '-16.0', ' 16', '  4', '  0', ''], ['05:17:55.893', '', '', '', '', '', '', '', '', '', '', '', '', '', ' 13', 'PCH', '  8.0', ' 48', '  1', '  0', ''], ['05:17:55.893', '', '', '', '', '', '', '', '', '', '', '', '', '', '  0', 'PSH', ' -6.0', '  8', ' 44', '  0', ''], ['05:17:55.893', '', '', '', '', '', '', '', '', '', '', '', '', '', ' 13', 'PCH', '  9.0', ' 48', '  1', '  0', ''], ['05:17:55.893', '', '', '', '', '', '', '', '', '', '', '', '', '', '  0', 'PSH', ' -6.0', '  0', ' 44', '  0', ''], ['05:17:55.893', '', '', '', '', '', '', '', '', '', '', '', '', '', ' 13', 'PCH', '  7.0', ' 48', '  1', '  0', ''], ['05:17:55.893', '', '', '', '', '', '', '', '', '', '', '', '', '', '  0', 'PSH', '-16.0', ' 16', '  4', '  0', ''], ['05:17:55.893', '', '', '', '', '', '', '', '', '', '', '', '', '', ' 13', 'PCH', '  8.0', ' 48', '  1', '  0', ''], ['05:17:55.893', '', '', '', '', '', '', '', '', '', '', '', '', '', '  0', 'PSH', '-17.0', ' 20', '  4', '  0', ''], ['05:17:55.893', '', '', '', '', '', '', '', '', '', '', '', '', '', ' 13', 'PCH', '  8.0', ' 48', '  1', '  0', ''], ['05:17:55.893', '', '', '', '', '', '', '', '', '', '', '', '', '', '  0', 'PSH', '-14.0', '  4', '  8', '  0', ''], ['05:17:55.893', '', '', '', '', '', '', '', '', '', '', '', '', '', ' 13', 'PCH', ' 14.0', ' 48', '  5', '  0', ''], ['05:17:55.893', '', '', '', '', '', '', '', '', '', '', '', '', '', '  0', 'PSH', '-16.0', '  0', '  4', '  0', ''], ['05:17:55.893', '', '', '', '', '', '', '', '', '', '', '', '', '', ' 13', 'PCH', '  9.0', ' 48', '  1', '  0', ''], ['05:17:55.893', '', '', '', '', '', '', '', '', '', '', '', '', '', '  0', 'PSH', '-13.0', '  0', '  8', '  0', ''], ['05:17:55.893', '', '', '', '', '', '', '', '', '', '', '', '', '', ' 13', 'PCH', ' 10.0', ' 48', '  1', '  0', ''], ['05:17:55.900', '', '', '', '', '', '21', '', '3', '   217', '911', '3', '', '', '', '', '', '', '', '', ''], ['05:17:55.900', '', '', '', '', '', '17', '', '3', '   217', '911', '5', '', '', '', '', '', '', '', '', ''], ['05:17:55.900', '', '', '', '', '', '17', '', '3', '   217', '911', '6', '', '', '', '', '', '', '', '', ''], ['05:17:55.900', '', '', '', '', '', '21', '', '3', '   105', '911', '7', '', '', '', '', '', '', '', '', ''], ['05:17:55.900', '', '', '', '', '', '21', '', '3', '    25', '911', '8', '', '', '', '', '', '', '', '', ''], ['05:17:55.900', '', '', '', '', '', '18', '', '3', '   217', '911', '10', '', '', '', '', '', '', '', '', ''], ['05:17:55.903', '', '', '', '', '', '21', '', '3', '   217', '911', '11', '', '', '', '', '', '', '', '', ''], ['05:17:55.903', '', '', '', '', '', '21', '', '3', '    57', '911', '12', '', '', '', '', '', '', '', '', ''], ['05:17:55.903', '', '', '', '', '', '21', '', '3', '   153', '911', '13', '', '', '', '', '', '', '', '', ''], ['05:17:55.903', '', '', '', '', '', '21', '', '3', '   217', '911', '15', '', '', '', '', '', '', '', '', ''], ['05:17:55.903', '', '', '', '', '', '21', '', '3', '   217', '911', '16', '', '', '', '', '', '', '', '', ''], ['05:17:55.903', '', '', '', '', '', '21', '', '3', '   137', '911', '17', '', '', '', '', '', '', '', '', ''], ['05:17:55.906', '268', '4', '', '-67.79', '', '', '', '', '', '', '', '', '14.234', '', '', '', '', '', '', ''], ['05:17:55.908', '', '', '28', '', '13', '18', '2', '3', '    57', '912', '0', '2', '', '', '', '', '', '', '', ''], ['05:17:55.908', '', '', '', '', '', '21', '', '3', '   185', '912', '1', '', '', '', '', '', '', '', '', ''], ['05:17:55.908', '', '', '', '', '', '21', '', '3', '    57', '912', '2', '', '', '', '', '', '', '', '', ''], ['05:17:55.908', '', '', '', '', '', '17', '', '3', '   137', '912', '5', '', '', '', '', '', '', '', '', ''], ['05:17:55.908', '', '', '', '', '', '17', '', '3', '   169', '912', '6', '', '', '', '', '', '', '', '', ''], ['05:17:55.923', '', '', '', '', '', '21', '', '3', '   217', '913', '11', '', '', '', '', '', '', '', '', ''], ['05:17:55.923', '', '', '', '', '', '21', '', '3', '    89', '913', '12', '', '', '', '', '', '', '', '', '']]
colNames = ['Time', 'Col01', 'Col02', 'Col03', 'Col04', 'Col05', 'Col06', 'Col07', 'Col08', 'Col09', 'Col10', 'Col11', 'Col12', 'Col13', 'Col14', 'Col15', 'Col16', 'Col17', 'Col18', 'Col19', 'Col20']

Estoy creando un marco de datos

df = pd.DataFrame(colVals, columns=colNames)

Esto da todas las columnas como objeto (cadenas)

df.dtypes
Out[28]: 
Time     object
Col01    object
Col02    object
Col03    object
Col04    object
Col05    object
Col06    object
Col07    object
Col08    object
Col09    object
Col10    object
Col11    object
Col12    object
Col13    object
Col14    object
Col15    object
Col15    object
Col16    object
Col17    object
Col18    object
Col19    object
dtype: object

Quiero convertir las columnas como tipos de datos apropiados como flotantes, int y cadenas. Por ejemplo, Col01 debe ser int, Col04 debe ser flotante y Col15 debe ser una cadena.

Usando la respuesta aquí -

df = df.set_index('Time')
def func(x):
    a = pd.to_numeric(x, errors='coerce')
    try:
        return a.astype('Int64')
    except:
        return a

df1 = df.apply(func)

Esto funciona para flotantes y columnas de tipo int, pero también convierte todas las cadenas a NaN. El resultado deseado es que solo las cadenas nulas '' se convertirán a NaN. ¿Como va esto?

0
user3206440 3 oct. 2019 a las 09:09

4 respuestas

La mejor respuesta

Creo que primero necesitas convertir a flotantes y luego a Int64:

df = df.set_index('Time')
df1 = df.mask(df.eq(''))

def func(x):
    try:
        return x.astype('float').astype('Int64') #pd 0.24+ 
    except:
        try:
            return x.astype('float')
        except:
            return x
df2 = df1.apply(func)

print (df2.dtypes)
Col01      Int64
Col02      Int64
Col03      Int64
Col04    float64
Col05      Int64
Col06      Int64
Col07      Int64
Col08      Int64
Col09      Int64
Col10      Int64
Col11      Int64
Col12      Int64
Col13    float64
Col14      Int64
Col15     object
Col16      Int64
Col17      Int64
Col18      Int64
Col19      Int64
Col20      Int64
dtype: object

Puede investigar valores únicos: float s se devuelven solo porque al menos 1 no NaN s flota (Col04, Col13) y se opone solo si al menos 1 no {{ X4}} valor de cadena (Col15):

print (df1.apply(lambda x: pd.Series(x.unique())))
   Col01 Col02 Col03   Col04 Col05 Col06 Col07 Col08   Col09 Col10 Col11  \
0    NaN   NaN   NaN     NaN   NaN    21   NaN     3     217   891    12   
1    268     4    28  -67.60    13    18     2   NaN     185   892    13   
2    NaN   NaN   NaN  -67.80    12    17   NaN   NaN      73   NaN    15   
3    NaN   NaN   NaN  -67.82   NaN   NaN   NaN   NaN     NaN   893    16   
4    NaN   NaN   NaN  -67.13   NaN    31   NaN   NaN      57   894    17   
5    NaN   NaN   NaN  -67.66   NaN    20   NaN   NaN     201   895    18   
6    NaN   NaN   NaN  -67.50   NaN    30   NaN   NaN      25   896     0   
7    NaN   NaN   NaN  -67.51   NaN    16   NaN   NaN      41   897     1   
8    NaN   NaN   NaN  -67.01   NaN    19   NaN   NaN     105   898     5   
9    NaN   NaN   NaN  -66.79   NaN   NaN   NaN   NaN      89   899     6   
10   NaN   NaN   NaN  -67.48   NaN   NaN   NaN   NaN     121   900     7   
11   NaN   NaN   NaN  -67.79   NaN   NaN   NaN   NaN       9   902   NaN   
12   NaN   NaN   NaN     NaN   NaN   NaN   NaN   NaN     169   903    10   
13   NaN   NaN   NaN     NaN   NaN   NaN   NaN   NaN     153   904    11   
14   NaN   NaN   NaN     NaN   NaN   NaN   NaN   NaN     137   906     2   
15   NaN   NaN   NaN     NaN   NaN   NaN   NaN   NaN     NaN   907     3   
16   NaN   NaN   NaN     NaN   NaN   NaN   NaN   NaN     NaN   908     8   
17   NaN   NaN   NaN     NaN   NaN   NaN   NaN   NaN     NaN   909   NaN   
18   NaN   NaN   NaN     NaN   NaN   NaN   NaN   NaN     NaN   910   NaN   
19   NaN   NaN   NaN     NaN   NaN   NaN   NaN   NaN     NaN   911   NaN   
20   NaN   NaN   NaN     NaN   NaN   NaN   NaN   NaN     NaN   912   NaN   
21   NaN   NaN   NaN     NaN   NaN   NaN   NaN   NaN     NaN   913   NaN   
22   NaN   NaN   NaN     NaN   NaN   NaN   NaN   NaN     NaN   NaN   NaN   
23   NaN   NaN   NaN     NaN   NaN   NaN   NaN   NaN     NaN   NaN   NaN   
24   NaN   NaN   NaN     NaN   NaN   NaN   NaN   NaN     NaN   NaN   NaN   
25   NaN   NaN   NaN     NaN   NaN   NaN   NaN   NaN     NaN   NaN   NaN   
26   NaN   NaN   NaN     NaN   NaN   NaN   NaN   NaN     NaN   NaN   NaN   

   Col12   Col13 Col14 Col15  Col16 Col17 Col18 Col19 Col20  
0    NaN     NaN   NaN   NaN    NaN   NaN   NaN   NaN   NaN  
1      2  13.128     0   PSH  -10.0     8     8     0   NaN  
2      1  12.902    13   PCH   13.0    48     1   NaN   NaN  
3    NaN  13.243   NaN  SS_0  -13.0    20     4   NaN   NaN  
4    NaN  13.685   NaN   NaN   17.0     0    12   NaN   NaN  
5    NaN  13.683   NaN   NaN   -9.0    24    24   NaN   NaN  
6    NaN  14.023   NaN   NaN   12.0     4    32   NaN   NaN  
7    NaN  14.370   NaN   NaN   -6.0    12   216   NaN   NaN  
8    NaN  14.227   NaN   NaN   -4.0    16     5   NaN   NaN  
9    NaN  13.969   NaN   NaN   11.0   NaN    44   NaN   NaN  
10   NaN  13.857   NaN   NaN    1.0   NaN    16   NaN   NaN  
11   NaN  14.234   NaN   NaN   16.0   NaN   NaN   NaN   NaN  
12   NaN     NaN   NaN   NaN   -3.0   NaN   NaN   NaN   NaN  
13   NaN     NaN   NaN   NaN    9.0   NaN   NaN   NaN   NaN  
14   NaN     NaN   NaN   NaN   10.0   NaN   NaN   NaN   NaN  
15   NaN     NaN   NaN   NaN  -14.0   NaN   NaN   NaN   NaN  
16   NaN     NaN   NaN   NaN   15.0   NaN   NaN   NaN   NaN  
17   NaN     NaN   NaN   NaN    8.0   NaN   NaN   NaN   NaN  
18   NaN     NaN   NaN   NaN  -11.0   NaN   NaN   NaN   NaN  
19   NaN     NaN   NaN   NaN   -8.0   NaN   NaN   NaN   NaN  
20   NaN     NaN   NaN   NaN  -15.0   NaN   NaN   NaN   NaN  
21   NaN     NaN   NaN   NaN   -1.0   NaN   NaN   NaN   NaN  
22   NaN     NaN   NaN   NaN   14.0   NaN   NaN   NaN   NaN  
23   NaN     NaN   NaN   NaN    6.0   NaN   NaN   NaN   NaN  
24   NaN     NaN   NaN   NaN  -16.0   NaN   NaN   NaN   NaN  
25   NaN     NaN   NaN   NaN    7.0   NaN   NaN   NaN   NaN  
26   NaN     NaN   NaN   NaN  -17.0   NaN   NaN   NaN   NaN  
3
jezrael 8 oct. 2019 a las 07:17

Puede enmascarar los valores ​​'' del marco de datos original usando máscara. Y use este marco de datos para reemplazar los valores NaN después de aplicar la función (sobre el marco de datos original df ) usando fillna

df = df.set_index('Time')
def func(x):
    a = pd.to_numeric(x, errors='coerce')
    try:
        return a.astype('Int64')
    except:
        return a
df1 = df.apply(func,axis=1).fillna(df.mask(df.eq('')))

Salida Col15 df:

df['Col15'].values

array(['', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '',
       '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '',
       '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '',
       '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '',
       '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '',
       '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '',
       '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '',
       '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '',
       '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '',
       '', '', 'PSH', 'PCH', 'PSH', 'PCH', 'PSH', 'PCH', 'PSH', 'PCH',
       'PSH', 'PCH', 'PCH', 'PSH', 'PCH', 'PCH', 'PSH', 'PCH', 'SS_0',
       'PCH', 'PSH', 'PCH', 'PCH', 'PSH', 'PCH', 'PSH', 'PCH', 'PSH',
       'PCH', 'PSH', 'PCH', 'PSH', 'PCH', 'PSH', 'PCH', 'PSH', 'PCH',
       'PSH', 'PCH', 'PSH', 'PCH', 'PSH', 'PCH', 'PSH', 'PCH', 'PSH',
       'PCH', 'PSH', 'PCH', 'SS_0', 'PSH', 'PCH', 'PSH', 'PCH', 'PSH',
       'PCH', 'PSH', 'PCH', 'PSH', 'PCH', 'PSH', 'PCH', 'PSH', 'PCH',
       'PSH', 'PCH', 'PSH', 'PCH', 'PSH', 'PCH', 'PSH', 'PCH', '', '', '',
       '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', ''],
      dtype=object)

Salida Col15 df1:

array([nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 'PSH',
       'PCH', 'PSH', 'PCH', 'PSH', 'PCH', 'PSH', 'PCH', 'PSH', 'PCH',
       'PCH', 'PSH', 'PCH', 'PCH', 'PSH', 'PCH', 'SS_0', 'PCH', 'PSH',
       'PCH', 'PCH', 'PSH', 'PCH', 'PSH', 'PCH', 'PSH', 'PCH', 'PSH',
       'PCH', 'PSH', 'PCH', 'PSH', 'PCH', 'PSH', 'PCH', 'PSH', 'PCH',
       'PSH', 'PCH', 'PSH', 'PCH', 'PSH', 'PCH', 'PSH', 'PCH', 'PSH',
       'PCH', 'SS_0', 'PSH', 'PCH', 'PSH', 'PCH', 'PSH', 'PCH', 'PSH',
       'PCH', 'PSH', 'PCH', 'PSH', 'PCH', 'PSH', 'PCH', 'PSH', 'PCH',
       'PSH', 'PCH', 'PSH', 'PCH', 'PSH', 'PCH', nan, nan, nan, nan, nan,
       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
       nan, nan], dtype=object)
0
ansev 5 oct. 2019 a las 10:43

Intente cambiar la función a:

def func(x):
    try:
        return x.astype('Int64')
    except:
        try:
            return x.astype('float')
        except:
            return x
0
Aryerez 3 oct. 2019 a las 07:48
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colNames = ['Time', 'Col01', 'Col02', 'Col03', 'Col04', 'Col05', 'Col06', 'Col07', 'Col08', 'Col09', 'Col10', 'Col11', 'Col12', 'Col13', 'Col14', 'Col15', 'Col16', 'Col17', 'Col18', 'Col19', 'Col20']
df = pd.DataFrame(colVals, columns=colNames)

Mantenga el tiempo col como índice

df = df.set_index('Time')

Complete '' con NaN

df1 = df.mask(df.eq(''))

Función para convertir a int else float else string

def func(x):
    try:
        return x.astype('Int64') #pd 0.24+ 
    except:
        try:
            return x.astype('float')
        except:
            return x
df2 = df1.apply(func)

El problema con esto es que los valores que buscan int también son

df2.dtypes
Col01    float64
Col02    float64
Col03    float64
Col04    float64
Col05    float64
Col06    float64
Col07    float64
Col08    float64
Col09    float64
Col10    float64
Col11    float64
Col12    float64
Col13    float64
Col14    float64
Col15     object
Col16    float64
Col17    float64
Col18    float64
Col19    float64
Col20      Int64
dtype: object

Parece que la conversión Int64 está fallando en todas las columnas, excepto Col20 donde todos los valores son NaN

Para verificar esto en Col01

df1["Col01"].unique()
Out[37]: array([nan, '268'], dtype=object)

Aplicando la conversión Int64 solo en Col01

dtype_dict = {'Col01': 'Int64'}
df1.astype(dtype_dict)

Esto arroja el error TypeError: object cannot be converted to an IntegerDtype

Mirando la versión de pandas

pd.__version__
Out[38]: '0.25.1'

¿Por qué la conversión Int64 falla aquí? De acuerdo con Tipo de datos enteros anulables, esto debería haber funcionado

0
user3206440 8 oct. 2019 a las 06:58
58213206