0 or ‘index’:函数按列处理(apply function to each column) 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...
DataFrame(data) print('Before applying function: ') print(df) # applying function to each row in # dataframe and storing result in a new column df = df.apply(lambda row : replace(row)) print('After Applying Function: ') # printing the new dataframe print(df) if __name__ == '__...
The apply() method takes the function which has to be applied on the DataFrame columns. The apply() method traverses each column one by one and then performs the specified operation inside it. To operate on a single column, we will check the column name inside apply() method, and if ...
Python program to apply function to all columns on a pandas dataframe # Importing pandas packageimportpandasaspd# Creating two dictionariesd1={'A':[1,-2,-7,5,3,5],'B':[-23,6,-9,5,-43,8],'C':[-9,0,1,-4,5,-3] }# Creating DataFramedf=pd.DataFrame(d1)# Display the DataFr...
# Using Dataframe.apply() to apply function# To every rowdefadd(row):returnrow[0]+row[1]+row[2]df['new_col']=df.apply(add,axis=1)print("Use the apply() function to every row:\n",df) Yields below output. This creates a new column by adding values from each column of a row....
axis : {0 or 'index', 1 or 'columns'}, default 0 Axis along which the function is applied: * 0 or 'index': apply function to each column. * 1 or 'columns': apply function to each row. 另一个例子:编辑于 2020-09-07 22:35 Pandas(Python) 数据分析 函数...
简而言之：如果你真的很关心apply函数的执行速度，并且你有一个巨大的数据集要处理，你可以使用swifter来...
raw : boolean, default False|If False, convert each row or column into a Series. If raw=True the passed function will receive ndarray objects instead. reduce : boolean or None, default None|Try to apply reduction procedures. args : tuple|函数的参数 ...
(9,10,11,12), (13,14,15,16) ] # Creating a Dataframe object df = pd.DataFrame(matrix, columns = list('abcd')) # Applying a user defined function to each # column which will add value in each # column by given number new_df = df.apply(addData, args = [1]) # Output print...
In general, the output column names should be unique. You can’t apply the same function (or two functions with the same name) to the same column. In [86]:grouped["C"].agg(["sum","sum"])Out[86]:sum sumAbar 0.392940 0.392940foo -1.796421 -1.796421 ...