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__ == '__...
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': [1,2,3],'B': [4,5,6]})print("Original DataFrame:")print(df)# 应用函数到每一行result = df.apply(sum...
Function to apply to each column or 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. broadcast : bool, optional Only relevant for aggreg...
apply()将一个函数作用于DataFrame中的每个行或者列 axis参数:axis=0 按照列 ;axis=1 按照行 例子1: 我们现在用apply来对列data1,data2进行相加 #axis =1 ,apply function to each row.#axis =0,apply function to each column,default 0df['total']=df[['data1','data2']].apply(lambdax:x.sum(...
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...
func : function Function to apply to each column or row. # 解释 函数能用于行或列而已 If you are just applying a NumPy reduction function this will achieve much better performance. # 这儿就说如果用numpy的函数,能有更好表现,可以说明np.sum 与 sum 是调用不同模块的函数 找了半天,虽然猜测...
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 ...
(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...
# Example 2: Pandas apply function to every row # Using lambda function df['new_col'] = df.apply(lambda row : row[0]+row[1]+row[2], axis=1) # Example 3: Add 3 to each column of a row df2 = df.apply(lambda row : pd.Series([row[0]+3,row[1]+3,row[2]+3]), axis=...