如何在数据框中强制空字符串

Question

数据如下

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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']

我正在创建一个数据帧

^{pr2}$

这会将所有列作为对象（字符串）

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

我想将列转换为适当的数据类型，如float、int和string。例如，Col01应该是int，Col04应该是float，Col15应该是字符串。在

使用答案here-

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)

这适用于float和int类型的列，但它也将所有字符串转换为NaN。期望的结果是，只有空字符串''将被转换为NaN。怎么办？在

Answer 1

我认为您需要先转换为float，然后再转换为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)

---

^{pr2}$

您可以研究唯一值-float仅因为至少有1个非NaNs浮动（Col04，Col13）而返回对象，只有在至少有1个非NaN字符串值（Col15）时才返回object：

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  

Answer 2

尝试将函数更改为：

def func(x):
    try:
        return x.astype('Int64')
    except:
        try:
            return x.astype('float')
        except:
            return x

Answer 3

可以使用mask屏蔽原始数据帧的值​​''。使用fillna在应用函数（在原始数据框df上）之后，使用此数据帧替换NaN值

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('')))

Col15测向输出：

^{pr2}$

Col15 df1输出：

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)