DataFrame.boxplot([column, by, ax, …]) #Make a box plot from DataFrame column optionally grouped by some columns or DataFrame.hist(data[, column, by, grid, …]) #Draw histogram of the DataFrame’s series using matplotlib / pylab. 转换为其他格式 DataFrame.from_csv(path[, header, sep,...
grouped = self.df.groupby('category', as_index=False) print(grouped.sum()) 1. 2. as_index为False的输出结果如下,与SQL的groupby输出风格相似 category price count 0 水果 14.7 10 1 米面 11.8 9 2 粮油 18.0 2 3 蔬菜 11.5 13 1. 2. 3. 4. 5. sort bool类型,默认为True。是否对分组名进行...
Female No54 0.156921 0.252672 54 18.105185 35.83Yes33 0.182150 0.416667 33 17.977879 44.30Male No97 0.160669 0.291990 97 19.791237 48.33Yes60 0.152771 0.710345 60 22.284500 50.81grouped.agg({'tip':np.max,'size':'sum'})#对不同列应用不同的函数,想agg传入一个从列名映射到函数的字典#返回tip size s...
grouped.last()#---617283dtype:int64 grouped.sum()data=pd.DataFrame({'X':['A','B','A','B'],'Y':[4,3,2,1]})data.groupby(['X']).get_group('A')#根据X列groupby, 取出A组的值#---X Y0A42A2 importnumpyasnpdata=[['bar','bar','foo','foo','tax','tax','cat','cat']...
result = (data_frame.groupby('category')['value'].sum() / data_frame['value'].sum()).reset_index().sort_values(by='value', ascending=False)我们可以将其拆分成多个短语句，每个短语句只完成一个任务：python total_value = data_frame['value'].sum()grouped_data = data_frame....
如果传入一组函数或函数名,得到的DataFrame的列就会以相应的函数命名 print(grouped.agg(['mean','sum']))data1data2meansummeansumkey1a1.666667511.66666735b2.500000512.50000025print(grouped.agg([(
grouped.agg({"tip_pct": ["min","max","mean","std"],"size":"sum"}) 7、”无索引“形式返回聚合数据 tips.groupby(["day","smoker"], as_index=False).mean() 8、分组级运算和转换 k1_means = df.groupby('key1').mean().add_prefix('mean_')'''mean_data1 mean_data2 ...
sum() # 数据分组 grouped_data = data.groupby('category').mean() # 透视表 pivot_table = pd.pivot_table(data, values='value', index='category', columns='type', aggfunc=np.sum) 数据可视化是数据分析中不可或缺的一部分。Python的matplotlib和seaborn库提供了丰富的图表类型和强大的绘图功能,可以...
colums = ['user_id', 'order_dt', 'order_products', 'order_amount'] data = pd.read_table('/Users/liuxiaohui/pythonProject/pythonProject/pythonProject/数据分析/data/CDNOW_master.txt', names=colums, sep='\s+') user_grouped = data.groupby(by='user_id').sum() print(user_grouped.descri...
grouped = df['data'].groupby(df['key']) 2. split by column importpandasaspd traffic=pd.read_csv("test.csv")col_mapping={'PC_UV':'UV','M_UV':'UV','APP_UV':'UV','WQ_UV':'UV','PC_PV':'PV','APP_sumbit':'Submit','PC_Visits':'Visit','Total':'Total'}by_column=traffi...