df=df.apply(process_row_based_on_column_a,axis=1)print(df) Python Copy Output: 示例代码 6: 添加新列,其中包含每行数据的描述 importpandasaspd data={'A':[10,20,30],'B':[40,50,60],'C':[70,80,90]}df=pd.DataFrame(data)defadd_description(row):returnf"Row with A={row['A']},...
Use the apply() function when you want to update every row in the Pandas DataFrame by calling a custom function. In order to apply a function to every
DataFrame(data) print("Original DataFrame:\n", df) # applying function to each row in the dataframe # and storing result in a new column df['add'] = df.apply(np.sum, axis = 1) print('\nAfter Applying Function: ') # printing the new dataframe print(df) if __name__ == '__...
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...
运行apply函数,记录耗时: for col in ps_data.columns: ps_data[col] = ps_data[col].apply(apply_md5) 查看运行结果: 总结 a. 读取数据速度排名:Polars > pySpark >> Pandarallel > Pandas > Modin b. Apply函数处理速度排名: pySpark > Polars > Pandarallel >> Modin > Pandas c. 在处理Apply函数...
print(df)# 定义一个计算平方的函数defsquare(x):returnx **2# 应用函数到每一列result = df.apply(square) print("\nDataFrame after applying square function to each column:") print(result) 2)应用函数到每一行 计算每一行的和。 importpandasaspd# 创建一个 DataFramedf = pd.DataFrame({'A': [1...
使用pandas 将函数应用于数据框中的每一行或每一列。apply() 原文:https://www . geesforgeks . org/apply-a-function-to-每行或每列-in-data frame-use-pandas-apply/ 对数据框中的每一行或每一列应用函数有不同的方法。我们将在这篇文章中了解各种方法。让我们先创建一个小的数据帧,看看这个。
在操作DataFrame的函数中,通常有沿着轴来进行操作,沿着axis=0,表示对一列(column)的数据进行操作;沿着axis=1,表示对一行(row)的数据进行操作。 axis{0 or ‘index’, 1 or ‘columns’}, default 0 Axis along which the function is applied: 0 or ‘index’: apply function to each column. ...
"""You may then apply this function as follows:""" df.apply(subtract_and_divide, args=(5,), divide=3) 按照group的size排序 代码语言:python 代码运行次数:0 运行 AI代码解释 """sort a groupby object by the size of the groups""" dfl = sorted(dfg, key=lambda x: len(x[1]), reverse...
Object to compute the transform on.func:string,function,list,or dictionaryFunction(s)to compute the transformwith.axis:{0or'index',1or'columns'}Axis along which thefunctionis applied:*0or'index':applyfunctionto each column.*1or'columns':applyfunctionto each row.Returns---DataFrame or Series ...