importpandasaspd# 创建 DataFramedf=pd.DataFrame({'A':range(1,6),'B':[10*xforxinrange(1,6)],'C':['pandasdataframe.com'for_inrange(5)]})# 定义一个函数,操作多列defmodify_columns(row):row['A']=row['A']*100row['B']=row['B']+5returnrow# 应用函数到 DataFramedf=df.apply(mod...
20,30],'C':['pandasdataframe.com','modify','columns']})# 定义一个函数,如果数值大于10,加10defadd_ten(x):returnx+10ifx>10elsex# 对'A'和'B'列应用条件函数df[['A','B']]=df[['A','B']].applymap(add_ten)print(df)
How to Apply a Function to Multiple Columns of DataFrame?To apply a function to multiple columns of a Pandas DataFrame, you can simply use the DataFrame.apply() method by specifying the column names. The method itself takes a function as a parameter that has to be applied on the columns....
1 or ‘columns’:函数按行处理( apply function to each row) # 只处理指定行、列,可以用行或者列的 name 属性进行限定df5=df.apply(lambdad:np.square(d)ifd.name=="a"elsed,axis=1)print("-"*30,"\n",df5)# 仅对行"a"进行操作df6=df.apply(lambdad:np.square(d)ifd.namein["x","y"]e...
Whenever we want to perform some operation on the entire DataFrame, we either use apply method. It is used on the grouped objects in pandas DataFrame. The apply() method Theapply()method passes the columns of each group in the form of a DataFrame inside the function which is descri...
apply() 最后的是经过函数处理,数据以 Series 或 DataFrame 格式返回。 下面用几个例子来介绍一下 apply() 的具体使用; DataFrame 使用apply() 1,计算每个元素的平方根 这里为了方便,直接用到 numpy 的 sqrt 函数; >>>df=pd.DataFrame([[4,9]]*3,columns=['A','B'])>>>dfAB049149249>>>df...
DataFrame.apply() DataFrame.apply() 函数则会遍历每一个元素,对元素运行指定的 function。比如下面的示例: 代码语言:javascript 代码运行次数:0 运行 AI代码解释 import pandas as pd import numpy as np matrix = [ [1,2,3], [4,5,6], [7,8,9] ] df = pd.DataFrame(matrix, columns=list('xyz'...
2 James, William Pandas 22000 James William 3 Addem, Smith Hadoop 25000 Addem Smith 6. Use apply() Function Split Column into Two Columns In Pandas In Pandas, theapply()function is used to execute a function that can be used to split one column value into multiple columns. For that, ...
# Using lambda function df['new_col'] = df.apply(lambda row : row[0]+row[1]+row[2], axis=1) print("Use the apply() function to every row:\n", df) Yields the same output as above. Apply Lambda Function to Update Each Row (all columns) ...
根据Businessbroadway 的一项分析,数据专业人员将会花高达 60% 的时间用于收集、清理和可视化数据。 资料来源:Businessbroadway 清理和可视化数据的一个关键方面是如何处理丢失的数据。Pandas 以 fillna 方法的形式提供了一些基本功能。虽然 fillna 在最简单的情况下工作得很好,但只要数据中的组或数据顺序变得相关,它就会出...