如何從整個數據框中刪除所有非數字數字：調試

Question

我正在嘗試使用一行代碼從我的 dataframe 中刪除所有非數字字符（即 ]$^M# 等字符）。 數據框是 Google Play 商店應用程序數據集。

df = pd.read_csv("googleplaystore.csv")

df['Rating'].fillna(value = '0.0', inplace = True)

#sample data#

   Rating    Reviews                Size     Installs  Type Price  \
0        4.1     159                 19M      10,000+  Free     0   
1        3.9     967                 14M     500,000+  Free     0   
2        4.7   87510                8.7M   5,000,000+  Free     0   
3        4.5  215644                 25M  50,000,000+  Free     0   
4        4.3     967                2.8M     100,000+  Free     0   
...      ...     ...                 ...          ...   ...   ...   
10836    4.5      38                 53M       5,000+  Free     0   
10837      5       4                3.6M         100+  Free     0   
10838    0.0       3                9.5M       1,000+  Free     0   
10839    4.5     114  Varies with device       1,000+  Free     0   
10840    4.5  398307                 19M  10,000,000+  Free     0   


Content Rating                     Genres      Last Updated  \
0           Everyone               Art & Design   January 7, 2018   
1           Everyone  Art & Design;Pretend Play  January 15, 2018   
2           Everyone               Art & Design    August 1, 2018   
3               Teen               Art & Design      June 8, 2018   
4           Everyone    Art & Design;Creativity     June 20, 2018   
...              ...                        ...               ...   
10836       Everyone                  Education     July 25, 2017   
10837       Everyone                  Education      July 6, 2018   
10838       Everyone                    Medical  January 20, 2017   
10839     Mature 17+          Books & Reference  January 19, 2015   
10840       Everyone                  Lifestyle     July 25, 2018

clean_data = df.replace('[^\d.]', '', regex = True).astype(float)

本質上，我試圖從數字后面的大小列中刪除“M”以及“安裝”列中的“+”號。

但是我收到了這條錯誤消息；

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-325-887d47a9889e> in <module>
----> 1 data_ = df.replace('[^\d.]', '', regex = True).astype(float)

~\anaconda3\lib\site-packages\pandas\core\generic.py in astype(self, dtype, copy, errors)
   5696         else:
   5697             # else, only a single dtype is given
-> 5698             new_data = self._data.astype(dtype=dtype, copy=copy, errors=errors)
   5699             return self._constructor(new_data).__finalize__(self)
   5700 

~\anaconda3\lib\site-packages\pandas\core\internals\managers.py in astype(self, dtype, copy, errors)
    580 
    581     def astype(self, dtype, copy: bool = False, errors: str = "raise"):
--> 582         return self.apply("astype", dtype=dtype, copy=copy, errors=errors)
    583 
    584     def convert(self, **kwargs):

~\anaconda3\lib\site-packages\pandas\core\internals\managers.py in apply(self, f, filter, **kwargs)
    440                 applied = b.apply(f, **kwargs)
    441             else:
--> 442                 applied = getattr(b, f)(**kwargs)
    443             result_blocks = _extend_blocks(applied, result_blocks)
    444 

~\anaconda3\lib\site-packages\pandas\core\internals\blocks.py in astype(self, dtype, copy, errors)
    623             vals1d = values.ravel()
    624             try:
--> 625                 values = astype_nansafe(vals1d, dtype, copy=True)
    626             except (ValueError, TypeError):
    627                 # e.g. astype_nansafe can fail on object-dtype of strings

~\anaconda3\lib\site-packages\pandas\core\dtypes\cast.py in astype_nansafe(arr, dtype, copy, skipna)
    895     if copy or is_object_dtype(arr) or is_object_dtype(dtype):
    896         # Explicit copy, or required since NumPy can't view from / to object.
--> 897         return arr.astype(dtype, copy=True)
    898 
    899     return arr.view(dtype)

ValueError: could not convert string to float: 

如果可能，請協助調試。 我真的很想將它保留在整個數據框的一行代碼中。 先感謝您。

Answer 1

我認為問題是需要指定用於替換的列並將空值替換為NaN或0如果不是數字，例如倒數第二個Size值：

cols = ['Size','Installs']
df[cols] = df[cols].replace('[^\d.]', '', regex = True).replace('',np.nan).astype(float)

print (df)
       Rating  Reviews  Size    Installs  Type  Price
0         4.1      159  19.0     10000.0  Free      0
1         3.9      967  14.0    500000.0  Free      0
2         4.7    87510   8.7   5000000.0  Free      0
3         4.5   215644  25.0  50000000.0  Free      0
4         4.3      967   2.8    100000.0  Free      0
10836     4.5       38  53.0      5000.0  Free      0
10837     5.0        4   3.6       100.0  Free      0
10838     0.0        3   9.5      1000.0  Free      0
10839     4.5      114   NaN      1000.0  Free      0
10840     4.5   398307  19.0  10000000.0  Free      0

Answer 2

問題是您將 dataframe 中的所有非數字字符替換為“”。

這意味着非數字字符串以“”結尾 - 長度為零的字符串。 這不能解釋為浮點數，因此您會收到錯誤消息。

如果您僅在評級列上運行替換

df["Rating"].replace('[^\d.]', '', regex = True).astype(float)

那么它就起作用了，因為從該列中刪除非數字字符會導致一列僅填充可以轉換為數字的字符。

但是，在整個 dataframe 上運行它是行不通的，因為您的許多值都是純非數字的。 例如，流派列最終將只是一列空字符串，從而引發錯誤。