importpandasaspd# 创建 DataFramedf=pd.DataFrame({'A':range(1,6),'B':[10*xforxinrange(1,6)],'C':['pandasdataframe.com'for_inrange(5)]})# 定义一个函数,操作多列defmodify_columns(row):row['A']=row['A']*100row['B']=row['B']+5returnrow# 应用函数到 DataFramedf=df.apply(mod...
The following syntax shows to apply a function to multiple columns of DataFrame:df[['column1','column1']].apply(anyFun); Where, column1 and column2 are the column names on which we have to apply the function, and "function" has some operations that will be performed on the columns....
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...
DataFrame(data) print("Original DataFrame:\n", df) # applying function to each row in the dataframe # and storing result in a new column df['add'] = df.apply(np.sum, axis = 1) print('\nAfter 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...
import pandas as pd # 定义一个函数,该函数将在每一行中应用 def my_function(row): return pd.Series([row['column1'] * 2, row['column2'] * 3]) # 创建一个DataFrame data = {'column1': [1, 2, 3], 'column2': [4, 5, 6]} df = pd.DataFrame(data) # 使用apply函数将my_fu...
import pandas as pd df_data = pd.read_csv(data_file, names=col_list) 显示原始数据,df_data.head() 运行apply函数,并记录该操作耗时: for col in df_data.columns: df_data[col] = df_data.apply(lambda x: apply_md5(x[col]), axis=1) 显示结果数据,df_data.head() 2. Polars测试 Polars...
is inferred from the return type of the applied function. Otherwise, it depends on the `result_type` argument. """ 通过函数介绍,我们知道了以下信息: apply会将自定义的func函数应用在dataframe的每列或者每行上面。 func接收的是每列或者每行转换成的一个Series对象,此对象的索引是行索引(对df每列操作...
applymap() (elementwise):接受一个函数,它接受一个值并返回一个带有 CSS 属性值对的字符串。apply()(column-/ row- /table-wise): 接受一个函数,它接受一个 Series 或 DataFrame 并返回一个具有相同形状的 Series、DataFrame 或 numpy 数组,其中每个元素都是一个带有 CSS 属性的字符串-值对。此方法根据axi...
# 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....