Python - 选择具有特定数据类型的列
要选择具有特定数据类型的列,请使用select_dtypes()方法和include参数。首先,创建一个包含2列的DataFrame-
dataFrame = pd.DataFrame( { "Student": ['Jack', 'Robin', 'Ted', 'Marc', 'Scarlett', 'Kat', 'John'],"Roll Number": [ 5, 10, 3, 8, 2, 9, 6] } )
现在,选择具有各自特定数据类型的2列-
column1 = dataFrame.select_dtypes(include=['object']).columns column2 = dataFrame.select_dtypes(include=['int64']).columns
示例
以下是代码-
import pandas as pd #CreateDataFrame dataFrame = pd.DataFrame( { "Student": ['Jack', 'Robin', 'Ted', 'Marc', 'Scarlett', 'Kat', 'John'],"Roll Number": [ 5, 10, 3, 8, 2, 9, 6] } ) print"DataFrame ...\n",dataFrame print"\nInfo of the entire dataframe:\n" #getthedescription print(dataFrame.info()) #selectcolumnswithspecificdatatype column1 = dataFrame.select_dtypes(include=['object']).columns column2 = dataFrame.select_dtypes(include=['int64']).columns print"Column 1 with object type = ",column1 print"Column 2 with int64 type = ",column2输出结果
这将产生以下输出-
DataFrame ... Roll Number Student 0 5 Jack 1 10 Robin 2 3 Ted 3 8 Marc 4 2 Scarlett 5 9 Kat 6 6 John Info of the entire dataframe:RangeIndex: 7 entries, 0 to 6 Data columns (total 2 columns): Roll Number 7 non-null int64 Student 7 non-null object dtypes: int64(1), object(1) memory usage: 184.0+ bytes None Column 1 with object type = Index([u'Student'], dtype='object') Column 2 with int64 type = Index([u'Roll Number'], dtype='object')