Actual Data Analysis—Visual Analysis of the Number of Starbucks Stores

Visual Analysis of Starbucks Store Distribution

Project introduction: Visual analysis of Starbucks store distribution using python

Data background: The data source comes from Kaggle: Starbucks Locations Worldwide | Kaggle, which includes the basic information of global Starbucks stores as of February 2017, including a series of detailed information such as brand name, house address, country, latitude and longitude.
Data introduction:

Field Name	explain
Brand	brand name
Store Number	store number
Store name	store name
Ownership Type	store ownership type
Street Address	State/Province
City	The city where the store is located
State/Province	The province where the store is located
Country	The country where the store is located
Postcode	The zip code where the store is located
Phone Number	Store contact number
Timezone	The time zone where the store is located
Longitude	The longitude of the store address
Latitude	Latitude of store address
centered text centered	right-aligned text right

task overview

• How many brands does Starbucks own?
• Statistics of how many countries have opened Starbucks stores in the world, showing the top five and bottom ten countries
• Display the top ten cities with the number of Starbucks stores
• According to the distribution of Starbucks in China, the top ten cities are counted
• There are several ways to use pie charts to show Starbucks' business

Import the necessary packages

import pandas as pd
import matplotlib.pyplot as plt
plt.rcParams['font.sans-serif'] = ['SimHei']  # 用来正常显示中文标签
plt.rcParams['axes.unicode_minus'] = False  # 用来正常显示负号

read data and view

data = pd.read_csv(r'./Desktop/directory.csv.csv')
data.head()

View missing values

data.isnull().sum()

Use the isnull() function to count the missing values ​​of the data, and you can find the missing data in each column. In this data, there are many missing values ​​in the city, postcode, and phone number fields, but the indicators processed by this task are not related to them. so don't process it

Count how many brands Starbucks owns

num = len(data['Brand'].unique())
print('星巴克旗下有%d个品牌'%num)
data['Brand'].value_counts()

Use the unique () function to de-duplicate the "Brand" field to get the number of brands under Starbucks, and then use the value_counts function to count the number of stores of each brand. It is found that Starbucks has a total of 4 brands , of which Starbucks has the largest number of stores , reached 25,249 .

Print out how many countries have opened Starbucks stores in the world, and display the top 5 and bottom 10 countries with the number of stores.

country_num = len(data['Country'].unique())
print('全国一共有%d个国家开设了星巴克门店'%country_num)

Then use the groupby function to group and aggregate 'country', count the top five countries with the number of stores, and sort them in descending order

dff = data.groupby(["Country"]).size().reset_index()
dff.columns = ['country','number']
dff.sort_values(by = ['number'],ascending = False).head()

Sure enough, Starbucks has the largest number of stores in the United States, reaching 13,608 , which is nearly 6 times that of the second-ranked China. We visualize it as a diagram to make it clearer.

plt.figure(figsize=(20,8))
plt.bar(top_5.index,top_5,width=0.5,color=['r','g','b','y','m'])
plt.xlabel('国家',fontsize = 20)
plt.ylabel('门店数量',fontsize = 20)
plt.xticks(fontsize = 20)
plt.yticks(fontsize = 20)
plt.grid(linestyle = '--',alpha = 0.5)
plt.title('星巴克全球门店数量排名前五的国家',fontsize = 20)
plt.show()

• The ten countries with the least number of stores in the world
  After exploring the countries with the largest number of stores in the world, let's take a look at which countries Starbucks has the least number of stores

plt.figure(figsize=(20,8))
plt.bar(tail_10.index,tail_10,color = 'brown')
plt.xlabel('国家',fontsize = 20)
plt.ylabel('门店数量',fontsize = 20)
plt.xticks(fontsize = 20)
plt.yticks(fontsize = 20)
plt.title('星巴克全球门店数量最少十个国家',fontsize = 20)
plt.show()

As can be seen from the figure above, the three countries AD, LU, and MC have the least number of stores, and there is only one store in AD country

Display the top 10 cities with the number of Starbucks stores

After looking at the countries with the largest number of stores, we will explore the 10 cities with the largest number of Starbucks stores in the world and visualize them

len(data['City'].unique())
city_count = data['City'].value_counts().head(10)
city_count

plt.figure(figsize=(20,8))
plt.bar(city_count.index,city_count)
plt.xlabel('城市',fontsize = 15)
plt.ylabel('门店数量',fontsize = 15)
plt.xticks(fontsize = 15)
plt.yticks(fontsize = 15)
plt.title('星巴克全球门店数量排名前十的城市',fontsize = 20)
plt.show()

We found that the city with the largest number of stores in the world is actually Shanghai, with 542 stores , which is worthy of being a magic city. The second and third places are Seoul and our capital Beijing, both of which have similar numbers. Seattle, the 10th place, is the headquarters of Starbucks. In addition to this reason, perhaps Seattle programmers also contributed a lot of turnover

According to the distribution of Starbucks stores in China, the top 10 cities are counted

df = data[data['Country'] =='CN'] #先把中国的门店数据提取出来

df2 = df.groupby(["City"]).size().reset_index()# 利用groupby分组聚合
df2.columns = ['city','number']
df2.sort_values(by = ['number'],ascending = False).head(10)# 按各城市门店数量降序排列 取前十

Not surprisingly, it is found that the cities with the largest number of Starbucks stores in China are cities with highly developed domestic GDP, mainly in the Pearl River Delta, Yangtze River Delta and Beijing. Only these developed cities can support Starbucks' high consumption. Next Let's visualize it:

plt.figure(figsize=(20,8))
plt.bar(china_city.index,china_city,color = 'c')
plt.xlabel('城市',fontsize = 15)
plt.ylabel('门店数量',fontsize = 15)
plt.xticks(fontsize = 15)
plt.yticks(fontsize = 15)
plt.title('星巴克在中国门店数量排名前十的城市',fontsize=20)
plt.show()

There are several ways to use pie charts to show the operation of Starbucks stores

#绘制饼图
plt.figure()
plt.pie(work_style,labels=data['Ownership Type'].value_counts().index,autopct='%1.2f%%')
plt.axis('equal')
plt.legend()
plt.title('星巴克的经营方式')
plt.show()

Starbucks operates in four main ways, of which company owned companies account for 46.6%, nearly half, followed by licensed operations, accounting for 36.6%, and the rest are joint ventures and franchises.

Summarize

This article sorts the countries and cities in China according to the number of Starbucks stores. It mainly uses the groupby method of DataFrame in pandas for grouping and aggregation, the value_counts function for value statistics, and uses the DataFrame.reset_index() method to re-specify the index and sort() method Sorting and matplotlib library for histogram, pie chart drawing.