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__ == '__...
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...
Python program to apply function to all columns on a pandas dataframe# Importing pandas package import pandas as pd # Creating two dictionaries d1 = { 'A':[1,-2,-7,5,3,5], 'B':[-23,6,-9,5,-43,8], 'C':[-9,0,1,-4,5,-3] } # Creating DataFrame df = pd.DataFrame(d...
applied on the DataFrame columns. Theapply()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 insideapply()method, and if our column name matches then only the function specified will ...
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...
apply(func, axis=0, broadcast=None, raw=False, reduce=None, result_type=None, args=(), **kwds) 重点参数 func : function 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': ...
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=1) # Example 4: Apply function NumPy.sum() to each row df['...
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...
(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...
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 ...