data={'website':['pandasdataframe.com']*8,'category':['A','A','B','B','C','C','D','D'],'product':['X','Y','X','Y','X','Y','X','Y'],'sales':[100,150,200,120,80,250,300,180]}df=pd.DataFrame(data)# 筛选出平均销售额大于150的组filtered=df.groupby(['categor...
In Example 1, we have created groups and subgroups using two group columns. Example 2 demonstrates how to use more than two (i.e. three) variables to group our data set. For this, we simply have to specify another column name within the groupby function. ...
300],'quantity':[10,20,15,25,30],'pandasdataframe.com':[1,2,3,4,5]}df=pd.DataFrame(data)# 使用 agg 方法进行多列操作result=df.groupby(['category','subcategory']).agg({'sales':'sum','quantity':'mean','pandas
grouped = df.groupby('Group') # 定义一个函数来减去两列的值 def subtract_two_columns(group): group['Result'] = group['Column1'] - group['Column2'] return group # 使用transform方法将函数应用于每个组 df['Result'] = grouped.transform(subtract_two_columns)['Result'] # 打印结果 print(df...
columns = ['a','b','c','d'])print(df)print('---') mapping = {'a':'one','b':'one','c':'two','d':'two','e':'three'} by_column = df.groupby(mapping, axis = 1)print(by_column.sum())print('---')# mapping中,a、b列对应的为one,c、d列对应的为two,以字典来分组...
#A single group can be selected using get_group():grouped.get_group("bar")#Out:ABC D1barone0.2541611.5117633barthree0.215897-0.9905825bartwo -0.0771181.211526Orfor an object grouped onmultiplecolumns:#for an object grouped on multiple columns:df.groupby(["A","B"]).get_group(("bar","one...
默认情况下,NaN数据会被排除在groupby之外,通过设置 dropna=False 可以允许NaN数据: 代码语言:javascript 代码运行次数:0 运行 AI代码解释 In [27]: df_list = [[1, 2, 3], [1, None, 4], [2, 1, 3], [1, 2, 2]] In [28]: df_dropna = pd.DataFrame(df_list, columns=["a", "b", ...
1.462816 -0.441652 0.075531 0.592714 1.109898 1.627081 [6 rows x 16 columns] 通用聚合方法 下面是通用的聚合方法: 函数描述 同时使用多个聚合方法 可以同时指定多个聚合方法: In [81]: grouped = df.groupby("A") In [82]: grouped["C"].agg([np.sum, np.mean, np.std]) Out[82]: sum mean std...
grouped = df['data1'].groupby(df['key1']) grouped 1. 2. 变量grouped是一个GroupBy对象,它实际上还没有进行任何计算,只是含有一些有关分组键df['key1']的中间数据而已,然后我们可以调用GroupBy的mean方法来计算分组平均值: grouped.mean() 1. ...
map_series=pd.Series(mapping)df.groupby(map_series)['data1'].mean()# 输出结果同上 依据Function结果聚合 初始化样例数据 people=pd.DataFrame(np.random.randn(5,5),columns=['a','b','c','d','e'],index=['Joe','Steve','Wes','Jim','Travis'])people.head() ...