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...
lambda x: x**2 represents the function that takes x as an input and returns x**2 as output.Example Codes: Apply Function to Each Column With DataFrame.apply()import pandas as pd import numpy as np df = pd.DataFrame({'X': [1, 2, 3,], 'Y': [4, 1, 8]}) print("Original ...
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=1) # Example 4: Apply function NumPy.sum() to each row...
apply(lambda row : normalize(row['X'], row['Y']), axis = 1) print('\nNormalized:') print(df) if __name__ == '__main__': main() Python Copy输出:例子#4:生成范围import pandas as pd import numpy as np pd.options.mode.chained_assignment = None # Function to generate range ...
("ABC"))print("Original Dataframe before applying lambda function: ", sep="\n")display(dataframe)# Apply a lambda function to each row by adding 10new_dataframe=dataframe.apply(lambdax: x+10, axis=1)print("Modified New Dataframe by applying lambda function on each row:")display(new_...
需要注意的是,不能在lambda表达式里面单独使用 if apply 在Series的值上调用函数。func既可以是Numpy的一元通用函数(ufunc),也可以是只用于单个值的python函数。 DataFrame对象的函数 apply 在DataFrame的行或列上应用函数 Apply a function along an axis of the DataFrame. Objects passed to the function are Serie...
(1,2,3,4), (5,6,7,8,), (9,10,11,12), (13,14,15,16) ] # Creating a Dataframe object df = pd.DataFrame(matrix, columns = list('abcd')) # Applying a lambda function to each # row which will add 5 to the value new_df = df.apply(lambda x: x + 5, axis = 1) # ...
dataframe.apply()method applies a Lambda function to a single row. For example, we applied the lambda function to a single rowaxis=1. Using the lambda function, we increased each person's月收入value by 1000. Sample code: importpandasaspddf=pd.DataFrame({"ID": [1,2,3,4,5],"Names":...
result = s.apply(lambda x: x ** 2) 2. pandas.DataFrame.apply Apply a function along an axis of the DataFrame. axis=0oraxis='index': apply function to each column, which is the default value. axis=1oraxis='column': apply function to each row, which is similar to Series.apply()....
理解 pandas 的函数，要对函数式编程有一定的概念和理解。函数式编程，包括函数式编程思维，当然是一个...