Pandas - 將列轉換為 int 並強制 NaN

Question

給定一個 dataframe 如下所示

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colNames = ["Time","Col2","Col3","Col4","Col5","Col6","Col7","Col8","Col9","Col10","Col11","Col12","Col13"]
df = pd.DataFrame(colVals, columns=colNames)
df = df.set_index('Time')
df = df.apply(pd.to_numeric, errors='coerce')


    print (df.dtypes)
    Col2     float64
    Col3     float64
    Col4     float64
    Col5     float64
    Col6     float64
    Col7     float64
    Col8     float64
    Col9     float64
    Col10    float64
    Col11    float64
    Col12    float64
    Col13    float64
    dtype: object

您可能會注意到這里所有的列都已轉換為float類型。 有沒有辦法將某些列轉換為int ？

嘗試了以下

type_dict = {'Col2': int}
df.astype(dtype_dict)

這會引發錯誤ValueError: Cannot convert non-finite values (NA or inf) to integer

閱讀Nullable integer 數據類型並嘗試

dtype_dict = {'Col2': 'Int64'}
df.astype(dtype_dict)

這會引發錯誤ValueError: Cannot convert non-finite values (NA or inf) to integer

Answer 1

我認為問題出在您的 pandas 版本上。 如果您使用pandas 0.24+和 integer 列，它工作正常：

df = df.apply(pd.to_numeric, errors='coerce')
dtype_dict = {'Col2': 'Int64'}
print (df.astype(dtype_dict).dtypes)
Col2       Int64
Col3     float64
Col4     float64
Col5     float64
Col6     float64
Col7     float64
Col8     float64
Col9     float64
Col10    float64
Col11    float64
Col12    float64
Col13    float64
dtype: object

但是在嘗試投射一個充滿float的列時它失敗了，這里Col5 ：

df = df.apply(pd.to_numeric, errors='coerce')

print (df['Col5'].tolist())
[nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, -67.6, 
 nan, nan, nan, nan, nan, nan, -67.8, nan, nan, nan, nan, nan, nan, 
 nan, nan, nan, -67.82, nan, nan, nan, nan, nan, nan, nan, nan, nan, 
 nan, nan, -67.13, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 
 nan, nan, -67.66, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 
 -67.5, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
 nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 
 -67.51, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, -67.01, 
 nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
 nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 
 -66.79, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 
 nan, nan, -67.48, nan, nan, nan, nan, nan, nan, nan, nan, nan,
 nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
 -67.79, nan, nan, nan, nan, nan, nan, nan, -68.13, nan, nan, nan, nan,
 nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, -67.45,
 nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, -67.22,
 nan, nan, nan, nan, nan, nan, nan, nan, -67.8, nan, nan, nan, nan, nan, 
 nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, -67.31, nan, nan, nan,
 nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 
 nan, nan, nan, nan, nan, nan, nan, -67.68, nan, nan, nan, nan, nan, nan]

dtype_dict = {'Col5': 'Int64'}
print (df.astype(dtype_dict).dtypes)

TypeError：不能安全地將非等效 float64 轉換為 int64

如果要將所有列轉換為整數（如果它們沒有浮點數）：

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)

print (df1.dtypes)
Col2       Int64
Col3       Int64
Col4       Int64
Col5     float64
Col6       Int64
Col7       Int64
Col8       Int64
Col9       Int64
Col10      Int64
Col11      Int64
Col12      Int64
Col13      Int64
dtype: object

Answer 2

在將行轉換為 int 之前，您必須使用 0 填充

你可以這樣做

df[["Col2","Col3","Col4","Col5","Col6"]] = df[["Col2","Col3","Col4","Col5","Col6"]].fillna(0).astype(np.int32, copy=False)

display(df)