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Pandas - 如何僅將 dataframe 列中的空字符串值強制為 NaN

[英]Pandas - how to coerce only empty string values in dataframe column to NaN

 原文  2019-10-03 06:09:06  1   4    python/ pandas/ dataframe

問題描述

使用如下數據

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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']

我正在創建一個數據框

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

這將所有列作為 object （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

我想將列轉換為適當的數據類型，如浮點數、整數和字符串。 例如Col01應該是 int， Col04應該是 float， Col15應該是 string。

在這里使用答案-

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)

這適用於 float 和 int 類型的列，但它也將所有字符串轉換為NaN 。 期望的結果是只有 null 字符串''應轉換為NaN 。 如何 go 關於這個？

4 個解決方案

解決方案1
 3 已采納  2019-10-08 07:12:32

我認為您需要先轉換為浮點數，然后再轉換為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

您可以調查唯一值 - 只有在至少 1 個非NaN字符串值（ Col15 ）時至少有 1 個非NaN浮點數（ Col04 、 Col13 ）和 object 才返回float ：

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

解決方案2
 0  2019-10-03 06:18:53

嘗試將 function 更改為：

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

解決方案3
 0  2019-10-05 09:05:24

您可以使用mask 屏蔽原始數據框的值'' 。 並在應用 function （在原始數據幀df上）使用fillna后，使用此數據幀替換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 df output：

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)

Col15 df1 output：

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)

解決方案4
 0  2019-10-08 06:58:53

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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)

保持時間列作為索引

df = df.set_index('Time')

用NaN填充''

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

Function 轉換為 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)

問題在於 int 的值也是

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

似乎Int64轉換在除Col20之外的所有列上都失敗，其中所有值都是NaN

在Col01上檢查這個

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

僅在Col01上應用Int64轉換

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

這會引發錯誤TypeError: object cannot be converted to an IntegerDtype

看pandas版本

pd.__version__
Out[38]: '0.25.1'

為什么Int64轉換在這里失敗。 根據Nullable integer 數據類型，這應該有效

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