grouped_single = df.groupby('Team').agg({'Age': ['mean', 'min', 'max']}) grouped_single.columns = ['age_mean', 'age_min', 'age_max'] grouped_single = grouped_single.reset_index() # 聚合多列 grouped_multiple = df.groupby(['Team', 'Pos']).agg({'Age': ['mean', 'min'...
#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...
20,30,40,50],'value2':[100,200,300,400,500],'value3':[1,2,3,4,5],'website':['pandasdataframe.com']*5})result=df.groupby('group').agg({'value1':'sum','value2':'mean','value3':['min','max']})print(result)
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 ....
columns=['user', 'another_user', 'mate_type']) result = (pairs_df.groupby(['user', 'anoth...
returnarr.max()-arr.min() grouped.agg(peak_to_peak)# 计算各组类的极差, 类似apply 1. 2. 3. 4. 5. size tip tip_pct total_bill day smoker Fri No 1 2.00 0.067349 10.29 Yes 3 3.73 0.159925 34.42 Sat No 3 8.00 0.235193 41.08 ...
The result index has the name 'key1' because the DataFrame columns df['key1'] did. If instead we had passed multiple arrays as list, we'd get something different: "多个键进行分组索引"means = df['data1'].groupby([df['key1'], df['key2']]).mean() ...
我想创建两个新列。当小时=16时,一列将存储索引。当湿度达到最大值时,另一列将存储索引。对于每个日期,这两个操作都需要单独完成。我可以使用groupby和transform函数找到每个日期的最大湿度,如下所示: >>> df["max_humidity"] = "" >>> df["max_humidity"] = df["humidity"].groupby(df["dates"]).tr...
return dates And then: my_dataframe = my_dataframe.groupby('id').apply(generate_date_ranges('date_columns', my_dataframe)) 但我得到了以下信息: Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/anaconda/envs/scoring_env/lib/python3.9/site-packages/pandas/...
top_5_subcategories_chained=(df[df['Category']=='Electronics']#1.筛选.groupby('Sub-Category')#2.分组.agg(#3.聚合 TotalSales=('Sales','sum'),AverageProfit=('Profit','mean')).sort_values(by='TotalSales',ascending=False)#4.排序.head(5)#5.取前5)print(top_5_subcategories_chained) ...