示例代码 2: 使用 apply 返回多列 importpandasaspd# 创建一个 DataFramedf=pd.DataFrame({'A':range(1,6),'B':['pandasdataframe.com'for_inrange(5)]})# 定义一个函数,返回多个新的列值defmultiple_columns(row):returnpd.Series([row['A']*2,row['A']*3],index=['double','triple'])# 应用...
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 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...
20,30],'C':['pandasdataframe.com','modify','columns']})# 定义一个函数,如果数值大于10,加10defadd_ten(x):returnx+10ifx>10elsex# 对'A'和'B'列应用条件函数df[['A','B']]=df[['A','B']].applymap(add_ten)print(df)
使用Pandas apply()方法返回多列原文:https://www . geesforgeks . org/return-multi-columns-use-pandas-apply-method/传递给pants . apply()的对象是系列对象,其索引是数据框的索引(轴=0)或数据框的列(轴=1)。默认情况下(result_type=None),最终的返回类型是从应用的函数的返回类型推断出来的。否则,它...
hexdigest(), 16) h = k % 2147483648 return h 1. Pandas测试 读取数据集,记录该操作耗时: 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(...
return pd.DataFrame(array([[1,2]]), columns=['x1', 'x2']) df['size'].astype(int).apply(gimmeMultiple) df['size'].astype(int).apply(gimmeMultipleDf) 返回一个数据帧肯定有它的好处,但有时不是必需的。您可以查看apply()返回的内容,并对函数进行一些操作;) ...
Yields below output. When you apply count on the entire DataFrame, pretty much all columns will have the same values. So when you want togroup by countjustselect a column, you can even select from your group columns. # Group by multiple columns and get ...
Getting value counts for multiple columns at onceFor this purpose, we will use the pandas apply() method inside which we will use the series value_counts() method. This method returns a Series that contain counts of unique values.Let us understand with the help of an example,Python...
评论 In [23]: #行列聚合,这里使用groupby数据分组内容,详细学习groupby函数可参考第三节内容,groupby函数指定分类对象分组 df_group = DP_table.groupby(['区域']).apply(lambda x: x['商品品类'].unique()).reset_index() df_group.rename(columns={0:'商品品类'},inplace=True)#重命名 df_group ....