importpandasaspd# 创建DataFramedf=pd.DataFrame({'A':range(1,6),'B':range(10,15)})# 定义一个复杂的函数defcomplex_function(x,add,factor=1):return(x+add)*factor# 使用 apply 函数df['A']=df['A'].apply(complex_function,args=(10,),kwargs={'factor':2})print(df) Python Copy 6. 处...
importpandasaspd# 创建 DataFramedf=pd.DataFrame({'A':[1,2,3,4],'B':[10,20,30,40]})# 定义一个复杂的函数defcomplex_function(row,multiplier,divisor):return(row['A']*multiplier+row['B'])/divisor# 应用函数df['C']=df.apply(complex_function,axis=1,args=(5,2))print(df) Python Copy...
To apply a function that returns multiple values to rows in pandas DataFrame, we will define a function for performing some operations on the values, and then finally we will return all the values in the form of a series. Note To work with pandas, we need to importpandaspackage fi...
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
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...
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 ...
)df['Rolling_1']=df['A'].rolling(window=3).apply(custom_func)print("\nCase 2: With step...
Pandas DataFrame apply() function applies the input function to every element along row or column of Pandas DataFrame.
在Pandas数据帧上使用apply()时出现Numpy解包错误是因为apply()函数默认将数据帧的每一列作为参数传递给指定的函数,而Numpy解包错误通常是由于函数的参数数量与传递的列数量不匹配导致的。 解决这个问题的方法有两种: 确保传递给apply()函数的函数参数数量与数据帧的列数量匹配。例如,如果数据帧有3列,可以定义...
pandas的apply函数是自动根据function遍历每一个数据,然后返回一个数据结构为Series的结果 DataFrame.apply(func, axis=0, broadcast=False, raw=False, reduce=None,args=(), **kwds) 参数解释: 1.func:就是函数,不管是自定义的函数,还是匿名函数lambda ...