Shulou(Shulou.com)06/02 Report--

This article mainly explains "how to use Python to display ordered takeout". Interested friends might as well take a look. The method introduced in this paper is simple, fast and practical. Let's let the editor take you to learn "how to use Python to display ordered takeout".

Log in to your ele.me account through your mobile phone & verification code. After success, you will return the user_id and login Cookie of the current user. These two pieces of information provide the necessary information for subsequent requests.

At the beginning of accessing the order, the request is like this.

H6.ele.me/restapi/bos/v2/users/26803312/orders?limit=8&offset=0

H6.ele.me/restapi/bos/v2/users/26803312/orders?limit=8&offset=8

H6.ele.me/restapi/bos/v2/users/26803312/orders?limit=8&offset=16

When the drop-down continues and the button "View takeout orders from three months ago" appears, the request looks like this

H6.ele.meUniverse restapimax bosqqqv2Universe users 26803312Universe ordersencoding eight steps from scratch time =

H6.ele.me/restapi/bos/v2/users/26803312/old_orders?limit=8&from_time=1557718107

The value of from_time can be seen in the response to the last request:

The code for getting the order is as follows:

"" get orders for nearly 3 months "" def get_new_order (): num = 0 while 1: # offset offset= num * limit response = requests.get (url + favored orders = {limit} & offset= {offset}', headers=headers Verify=False) resp_json = response.json () insert_mongo (resp_json) # jump out of the loop if len (resp_json) < 8: print ('=') break num + = 1 "" Historical order "def history_order (): from_time=''while 1: response = requests.get (old_url + favored orders = {limit} & from_time= {from_time}', headers=headers) Verify=False) resp_json = response.json () from_time = resp_json ['from_time'] orders = resp_json [' orders'] # tested On the last order, the response to the request is empty if not orders: break insert_mongo (orders)

After operation, it was found that takeout alone cost a little more than 1W for more than a year. You can choose to save the crawled data in a csv file or choose Mongod. Here I am plugged into the MongoDB.

Def insert_mongo (resp_json): if not resp_json: return for i in resp_json: # dishes foods_group = I ['basket'] [' group'] [0] for j in foods_group: J ['name'] = clean_data (j [' name']) with open ('foods_name_banxia.txt',' asides') as f: f.write (j ['name'] +'\ n') # write dishes to the file Convenient to handle # Distribution fee deliver_price = 0 if 'deliver_fee' in I [' basket'] .keys (): deliver_price = I ['basket'] [' deliver_fee'] ['price'] # calculate the total cost global total total + = I [' total_amount'] # Restaurant name restaurant_name = clean_data (I ['restaurant_name']) with open (' restaurant_name_banxia.txt') 'a restaurant') as f: f.write (restaurant_name +'\ n') # record the restaurant name clo.insert_one ({# restaurant name 'restaurant_name': restaurant_name, # order time formatted_created_at can also be taken But the recent one will show xx hours before 'created_timestamp': time_convert (I [' created_timestamp']), # total_amount': I ['total_amount'],' foods_group': foods_group, 'deliver_price': deliver_price})

In the process of looking at the data, we found that some dishes and store names are followed by special characters such as parentheses or square brackets, and the information inside is of little value to us. It can be dealt with simply.

Import redef clean_data (data): a = re.sub ("\ (. *?\) |\ (. *?\) |\ [. *?\] |\\ [. *?\] | [A-Za-z0-9\ @\\!\]", ", data) a = a.replace ('box',''). Replace ('gram','). Replace ('',')\ .replace ('large') ''). Replace ('small','). Replace ('bottle','). Replace ('group','). Replace ('',') return a

In this way, we store the information in the order in the database. In order to facilitate statistics, I put the dishes of each order, as well as the name of the restaurant in the txt file.

You can make a simple visualization of the meal through wordcloud.

From random import randintfrom matplotlib import pyplot as pltfrom wordcloud import WordClouddef random_color (word=None, font_size=None, position=None, orientation=None, font_path=None, random_state=None): "" Random Color func "" r = randint (30,255) g = randint (30180) b = int (100.0 * float (randint (60,120)) / 255.0) return "rgb ({: .0f}, {: .0f}, {: .0f})" .format (r, g, b) content = open ('foods_name.txt' Encoding='utf-8') .read () wordcloud = WordCloud (background_color= "white", width=1000, height=600, max_font_size=50, font_path='/System/Library/Fonts/PingFang.ttc', # needs to be changed according to the actual operating system path color_func=random_color) .generate (content) plt.imshow (wordcloud) plt.axis ("off") plt.savefig ('ele_wordcloud.png', format='png', dpi=200)

The results are as follows: are there any students with the same taste?

Similarly, we can count the restaurants that are frequented by sort command at the terminal.

Sort-n sorts ascending order by numerical value

Sort-r descending permutation

Uniq-c deduplication and statistics

Head-5 shows the first 5 items

Cat mao_out.txt | sort | uniq-c | sort-rg | head-5

The results are as follows: the most visited are Gonggong Rice Noodle and Zhang Liang Spicy Hot Pot, which have been visited 14 times in the statistical range.

Python3XXXAir:$ cat restaurant_name.txt | sort | uniq-c | sort-rg | head-5 14 Gonggong Rice Noodle 14 Zhang Liang Spicy Hot Pot 13 Jingkelong 11 Qinxiangyuan Home Cuisine 11 Lane Spicy Spicy Pot

You can count the price of each takeout order through matplotlib and show it.

Import pymongoimport matplotlib.pyplot as pltclient = pymongo.MongoClient ('mongodb://localhost:27017/') db = client [' ele'] clo = db ['info_banxia'] result = clo.find ({}) y = [I [' total_amount'] for i in result] x = [i for i in range (len (y))] plt.ylabel ("The unit price") plt.xlabel ("Times") plt.plot (x, y) plt.show ()

Result picture:

It can be seen that most of the prices are between 20 and 40, because sometimes in order to fully reduce, most of them work together with colleagues and friends. Occasionally beyond this range is the purchase of fruit, medicine and other goods.

From these data, we also know which month orders takeout most frequently.

# count = [] for i in data: ele_count = clo.count ({'created_timestamp': re.compile (I)}) count.append (ele_count) plt.scatter (data, count) plt.xticks (rotation=45) plt.show ()

It can be seen that 22 takeouts were ordered in March 18. Orders for takeout are the fewest in November.

At this point, I believe you have a deeper understanding of "how to use Python display point takeout". You might as well do it in practice. Here is the website, more related content can enter the relevant channels to inquire, follow us, continue to learn!

Welcome to subscribe "Shulou Technology Information " to get latest news, interesting things and hot topics in the IT industry, and controls the hottest and latest Internet news, technology news and IT industry trends.