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....
可以对 DataFrame 的多列使用apply函数,并传递多个参数。 importpandasaspd# 创建DataFramedf=pd.DataFrame({'A':range(1,6),'B':range(10,15)})# 定义一个处理多列的函数defsum_columns(x,y,factor):return(x+y)*factor# 使用 apply 函数df['C']=df.apply(lambdarow:sum_columns(row['A'],row['B...
In Pandas, the apply() function can indeed be used to return multiple columns by returning a pandas Series or DataFrame from the applied function. In this article, I will explain how to return multiple columns from the pandas apply() function....
To work with pandas, we need to importpandaspackage first, below is the syntax: import pandas as pd Let us understand with the help of an example, Python program to apply function that returns multiple values to rows in pandas DataFrame ...
运行apply函数,记录耗时: for col in md_data.columns: md_data[col] = md_data.apply(lambda x: apply_md5(x[col]), axis=1) 查看运行结果: 4. Pandarallel测试 Pandarallel特点: 非常简单实现Pandas并行; 没有自己的读取文件方式,依赖Pandas读取文件; 用户文档: 读取数据集,记录耗时: import pandas as...
import pandas as pd import numpy as np data = [(3,5,7), (2,4,6),(5,8,9)] df = pd.DataFrame(data, columns = ['A','B','C']) print(df) # Using Dataframe.apply() # To apply function to every row def add(row): return row[0]+row[1]+row[2] df['new_col'] = df...
#apply()函数使用案例# # 导入 numpy 库 import numpy as np # 导入 pandas 库 import pandas as pd # 定义 DataFrame # 数据为 3 行 4 列 s_data = pd.DataFrame([[5.1,3.5,1.4,0.2], [6.1,3.7,4.1,1.5], [5.8,2.7,5.1,1.9]], columns=['feature_one','feature_two','feature_three','fea...
df['修改的列'] = df['条件列'].apply(调用函数名) import pandas as pd def test(): # 读取Excel文件 df = pd.read_excel('测试数据.xlsx') def modify_value(x): if x < 5: return '是' elif x < 10: return '否' else: return 'x' # 插入列 for col_num in range(4, 9): df....
6 rows x 16 columns] Another aggregation example is to compute the number of unique values of each group. This is similar to thevalue_countsfunction, except that it only counts unique values. In [77]: ll = [['foo', 1], ['foo', 2], ['foo', 2], ['bar', 1], ['bar', 1]...
columns=['numbers', 'colors']) df['colName'] = 'colors' tic = time.perf_counter() enriched_df = df.apply(enrich_row, col_name='colors', axis=1) toc = time.perf_counter() print(f"{df.shape[0]} rows enriched in {toc - tic:0.4f} seconds") ...