Python 3.x pandas.dataframe.astype未转换数据类型
我正在尝试将一些列从对象转换为分类列Python 3.x pandas.dataframe.astype未转换数据类型,python-3.x,pandas,dataframe,categorical-data,Python 3.x,Pandas,Dataframe,Categorical Data,我正在尝试将一些列从对象转换为分类列 # dtyp_cat = 'category' # mapper = {'Segment':dtyp_cat, # "Sub-Category":dtyp_cat, # "Postal Code":dtyp_cat, # "Region":dtyp_cat,
# dtyp_cat = 'category'
# mapper = {'Segment':dtyp_cat,
# "Sub-Category":dtyp_cat,
# "Postal Code":dtyp_cat,
# "Region":dtyp_cat,
# }
df.astype({'Segment':'category'})
df.dtypes
但输出仍然是对象类型数据集位于:
url = r"https://raw.githubusercontent.com/jaegarbomb/TSF_GRIP/main/Retail_EDA/Superstore.csv"
df = pd.read_csv(url)
这样做:
df['Segment'] = df.Segment.astype('category')
返回
RangeIndex: 9994 entries, 0 to 9993
Data columns (total 13 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 Ship Mode 9994 non-null object
1 Segment 9994 non-null category
2 Country 9994 non-null object
3 City 9994 non-null object
4 State 9994 non-null object
5 Postal Code 9994 non-null int64
6 Region 9994 non-null object
7 Category 9994 non-null object
8 Sub-Category 9994 non-null object
9 Sales 9994 non-null float64
10 Quantity 9994 non-null int64
11 Discount 9994 non-null float64
12 Profit 9994 non-null float64
dtypes: category(1), float64(3), int64(2), object(7)
memory usage: 946.9+ KB
编辑
如果您想转换多个列(在您的例子中,我假设所有列都是对象,那么您需要删除那些不是对象的列,转换剩下的列,然后重新连接其他列)
df2 = df.drop([ 'Postal Code', 'Sales', 'Quantity', 'Discount', 'Profit'], axis=1)
df3 = df2.apply(lambda x: x.astype('category'))
给
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 9994 entries, 0 to 9993
Data columns (total 8 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 Ship Mode 9994 non-null category
1 Segment 9994 non-null category
2 Country 9994 non-null category
3 City 9994 non-null category
4 State 9994 non-null category
5 Region 9994 non-null category
6 Category 9994 non-null category
7 Sub-Category 9994 non-null category
dtypes: category(8)
memory usage: 115.2 KB
谢谢,这很管用,但我希望有一个多列解决方案,这样我就可以一次替换多列。我的代码在你这方面也不管用吗-(
df4 = pd.concat([df3, df], axis=1, sort=False)
df_final = df4.loc[:,~df4.columns.duplicated()]