python数据分析-pandas相关操作复习5_frozen python pandas-优快云博客

本文通过使用Pandas和NumPy库，对BigMart销售数据进行了深入分析。从数据预处理到聚合操作，展示了如何按商品类型分组计算平均售价，以及如何创建交叉表和透视表来揭示销售趋势和模式。

#目录

5.Aggregating data

5.Aggregating data

#import required dictionary
import pandas as pd
import numpy as np

#read the dataset
data_BM = pd.read_csv('bigmart_data.csv')
#drop the null values
data_BM = data_BM.dropna(how = "any")
#reset index after dropping 
data_BM = data_BM.reset_index(drop=True)
#view the top result
#data_BM.head()

#group price based on item type
#sort by Item_Type values
#排序方式默认按照字母表顺序
price_by_item = data_BM.groupby("Item_Type")
# display first few rows
#price_by_item.head()

#mean price by item 
price_by_item.Item_MRP.mean()
#out<<
Item_Type
Baking Goods             125.795653
Breads                   141.300639
Breakfast                134.090683
Canned                   138.551179
Dairy                    149.481471
Frozen Foods             140.095830
Fruits and Vegetables    145.418257
Hard Drinks              140.102908
Health and Hygiene       131.437324
Household                149.884244
Meat                     140.279344
Others                   137.640870
Seafood                  146.595782
Snack Foods              147.569955
Soft Drinks              130.910182
Starchy Foods            151.256747
Name: Item_MRP, dtype: float64

#group on multiple columns
multiple_groups = data_BM[:10].groupby(['Item_Type','Item_Fat_Content'])
#multiple_groups.head()

#generate crosstab of Outlet_Size and Outlet_Location_Type
pd.crosstab(data_BM["Outlet_Location_Type"],data_BM["Outlet_Size"],margins = True)
#out<<
Outlet_Size	High	Medium	Small	All
Outlet_Location_Type				
Tier 1	0	930	930	1860
Tier 2	0	0	930	930
Tier 3	932	928	0	1860
All	932	1858	1860	4650

#crate pivot tablet
index = ['Outlet_Establishment_Year'],values = "Item_Outlet_Sales")
#out<<
	Item_Outlet_Sales
Outlet_Establishment_Year	
1987	2298.995256
1997	2277.844267
1999	2348.354635
2004	2438.841866
2009	1995.498739

#create pivot table
pd.pivot_table(data_BM,index = ['Outlet_Establishment_Year','Outlet_Size'],values = "Item_Outlet_Sales")
#out<<
		Item_Outlet_Sales
Outlet_Establishment_Year	Outlet_Size	
1987	High	2298.995256
1997	Small	2277.844267
1999	Medium	2348.354635
2004	Small	2438.841866
2009	Medium	1995.498739