Python 带有 base 10 错误的 long() 的熊猫无效文字
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pandas invalid literal for long() with base 10 error
提问by Night Walker
I am trying to do: df['Num_Detections'] = df['Num_Detections'].astype(int)
我正在尝试做： df['Num_Detections'] = df['Num_Detections'].astype(int)
And i get following error:
我收到以下错误：
ValueError: invalid literal for long() with base 10: '12.0'
ValueError：long() 的无效文字，基数为 10：'12.0'
My data looks looks following:
我的数据看起来如下：
>>> df['Num_Detections'].head()
Out[6]:
sku_name
DOBRIY MORS GRAPE-CRANBERRY-RASBERRY 1L 12.0
AQUAMINERALE 5.0L 9.0
DOBRIY PINEAPPLE 1.5L 2.0
FRUKT.SAD APPLE 0.95L 154.0
DOBRIY PEACH-APPLE 0.33L 71.0
Name: Num_Detections, dtype: object
Any idea how to do the conversion correctly ?
知道如何正确进行转换吗？
Thanks for help.
感谢帮助。
回答by jezrael
There is some value, which cannot be converted to int.
有一些值，无法转换为int.
You can use to_numericand get NaNwhere is problematic value:
您可以使用to_numeric并获取NaN有问题的值：
df['Num_Detections'] = pd.to_numeric(df['Num_Detections'], errors='coerce')
If need check rows with problematic values, use boolean indexingwith mask with isnull:
如果需要检查具有问题值的行，请使用boolean indexing带掩码的isnull：
print (df[ pd.to_numeric(df['Num_Detections'], errors='coerce').isnull()])
Sample:
样本：
df = pd.DataFrame({'Num_Detections':[1,2,'a1']})
print (df)
Num_Detections
0 1
1 2
2 a1
print (df[ pd.to_numeric(df['Num_Detections'], errors='coerce').isnull()])
Num_Detections
2 a1
df['Num_Detections'] = pd.to_numeric(df['Num_Detections'], errors='coerce')
print (df)
Num_Detections
0 1.0
1 2.0
2 NaN
