问题：
熊猫-将列转换为int并强制NaN
景元忠
给定如下所示的数据帧
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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
您可能会注意到，这里所有的列都已转换为浮动类型。是否有办法将某些列转换为int？
尝试了以下方法
type_dict = {'Col2': int}
df.astype(dtype_dict)
这会引发错误ValueError:无法将非有限值（NA或inf）转换为整数
阅读有关可为空的整数数据类型的信息，然后重试
dtype_dict = {'Col2': 'Int64'}
df.astype(dtype_dict)
这会引发错误ValueError:无法将非有限值（NA或inf）转换为整数
共有2个答案
陶沛
在将行转换为int之前，必须用0填充na
你可以这样做
df[["Col2","Col3","Col4","Col5","Col6"]] = df[["Col2","Col3","Col4","Col5","Col6"]].fillna(0).astype(np.int32, copy=False)
display(df)
陶飞鸿
我认为问题在于你的熊猫版本。如果使用0.24和整数列，则其工作正常：
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，当尝试强制转换一个填充了float数字的列时失败，这里是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:无法将非等效float64安全地强制转换为int64
如果要将没有浮点数的所有列转换为整数：
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
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