我有一个数据框，它包含整数、字符串、数字等数据类型。 像下面这样的东西。我想排除所有非数字变量。 Python 有什么自动化的方法吗？
'data.frame': 891 obs. of 12 variables:
$ PassengerId: int 1 2 3 4 5 6 7 8 9 10 ...
$ Survived : int 0 1 1 1 0 0 0 0 1 1 ...
$ Pclass : int 3 1 3 1 3 3 1 3 3 2 ...
$ Name : Factor w/ 891 levels "Abbing, Mr. Anthony",..: 109 191 358 277 16 559 520 629 417 581 ...
$ Sex : Factor w/ 2 levels "female","male": 2 1 1 1 2 2 2 2 1 1 ...
$ Age : num 22 38 26 35 35 NA 54 2 27 14 ...
$ SibSp : int 1 1 0 1 0 0 0 3 0 1 ...
$ Parch : int 0 0 0 0 0 0 0 1 2 0 ...
$ Ticket : Factor w/ 681 levels "110152","110413",..: 524 597 670 50 473 276 86 396 345 133 ...
$ Fare : num 7.25 71.28 7.92 53.1 8.05 ...
$ Cabin : Factor w/ 148 levels "","A10","A14",..: 1 83 1 57 1 1 131 1 1 1 ...
$ Embarked : Factor w/ 4 levels "","C","Q","S": 4 2 4 4 4 3 4 4 4 2 ...
排除数字变量后，我的数据框应如下所示:
'data.frame': 891 obs. of 12 variables:
$ PassengerId: int 1 2 3 4 5 6 7 8 9 10 ...
$ Survived : int 0 1 1 1 0 0 0 0 1 1 ...
$ Pclass : int 3 1 3 1 3 3 1 3 3 2 ...
$ Age : num 22 38 26 35 35 NA 54 2 27 14 ...
$ SibSp : int 1 1 0 1 0 0 0 3 0 1 ...
$ Parch : int 0 0 0 0 0 0 0 1 2 0 ...
$ Fare : num 7.25 71.28 7.92 53.1 8.05 ...
最佳答案
我们可以使用._get_numeric_data()
import pandas as pd #import the pandas library
#creating a small dataset for testing
df1 = pd.DataFrame({'PassengerId' : [1, 2, 3],
'Name' : ['Abbing, Mr. Anthony', 'Ann, C', 'John, H'],
'Fare' : [7.25, 71.28, 7.92]})
#extract only the numeric column types
df2 = df1._get_numeric_data()
print(df2)
或者另一种选择是 select_dtypes()
df3 = df1.select_dtypes(include = ['int64', 'float64'])
print(df3)
关于python - 如何从 Python 中的数据框中排除非数字整数，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/33705180/