'sales':[100,200,300,150,250]}df=pd.DataFrame(data)# 按name分组并应用多个聚合函数grouped=df.groupby('name')['sales'].agg(['sum','mean','max','min'])print("pandasdataframe.com - Multiple aggregations:")print(grouped
2.1 创建多列分组 要创建多列分组,我们只需要在groupby()函数中传入一个包含多个列名的列表即可。例如: importpandasaspd# 创建示例数据data={'website':['pandasdataframe.com']*8,'year':[2021,2021,2021,2021,2022,2022,2022,2022],'quarter':[1,2,3,4,1,2,3,4],'revenue':[1000,1200,1100,1300,...
PYTHON # 按城市统计销售指标 city_stats = df.groupby('city')['sales'].agg(['sum', 'mean', 'count']) # 添加分层统计 df.groupby(['province', 'city'])['gmv'].sum().unstack() # 转换为宽表 1.2 时间序列分析 PYTHON # 重采样(周粒度统计) df.set_index('order_date').resample('W'...
1. 2. 3. 输出: DataFrame multiple aggregations by columns: A B C sum 6.0 15.0 24.0 mean 2.0 5.0 8.0 1. 2. 3. 4. ⑶.案例:按照 city 列对数据进行分组,并对 price 列进行统计,计算数据的数量、总和和均值。 #对 city 字段进行汇总,并分别计算 price 的合计和均值 agg_price = df_inner.gro...
# 还是上面的例子,求股票月度平均价格 # 方法一、用groupby,string来做 (df_cls_price #用function作为grouper时,会取日期索引字符串前7位,比如2021-07 .groupby(lambda x: str(x)[:7]) .mean() ) #方法二、用resample来操作 (df_cls_price .resample('1M') .mean() ) 方法2更直观且速度快,而且...
By default, pandas sorts the group keys during the call to .groupby(). If you don’t want to sort, then pass sort=False. This parameter can lead to performance gains.You can also group by multiple columns:Python >>> nba[ ... (nba["fran_id"] == "Spurs") & ... (nba["...
In this section, To get multiple stats, collapse the index, and retain column names. For example- # Using groupby() and agg() function. df2 = df.groupby(['Courses','Duration']).agg(['mean', 'count']) df.columns = [ ' '.join(str(i) for i in col) for col in df.columns] ...
与SQL 的比较,对于熟悉 SQL 但仍在学习 pandas 的人来说应该很有用。 与R 的比较,从 R 到 pandas 的成语翻译。 性能增强,使用eval/query提高 pandas 性能的方法。 警告 在0.13.0 中,Series在内部已经进行了重构,不再是子类ndarray,而是子类NDFrame,类似于其他 pandas 容器。这应该是一个透明的改变,只有非常...
This is an example of the apply in split-apply-combine: you're applying the .describe() method to each group in the groupby. Do this and print the first 5 rows of the result: # Summary stats over years df_by_year.describe().head() Powered By ratingdescriptionuser_rating_scoreuser...
Tasks such as merging, joining, or concatenating multiple DataFrames are straightforward with pandas. The concat method, combined with tools like pandas append, enables combining disparate data sources. The library also provides GroupBy functionality to aggregate and transform data, supporting advanced spl...