11 posts tagged

Data engineering

Collecting Social Media Data for Top ML, AI & Data Science related accounts on Instagram

Estimated read time – 9 min

Instagram is in the top 5 most visited websites, perhaps not for our industry. Nevertheless, we are going to test this hypothesis using Python and our data analytics skills. In this post, we will share how to collect social media data using the Instagram API.

Data collection method
The Instagram API won’t let us collect data about other platform users for no reason, but there is always a way. Try sending the following request:


The request returns a JSON object with detailed user information, for instance, we can easily get an account name, number of posts, followers, subscriptions, as well as the first ten user posts with likes count, comments and etc. The pyInstagram library allows sending such requests.

SQL schema
Data will be collected into thee Clickhouse tables: users, posts, comments. The users table will contain user data, such as user id, username, user’s first and last name, account description, number of followers, subscriptions, posts, comments, and likes, whether an account is verified or not, and so on.

CREATE TABLE instagram.users
    `added_at` DateTime,
    `user_id` UInt64,
    `user_name` String,
    `full_name` String,
    `base_url` String,
    `biography` String,
    `followers_count` UInt64,
    `follows_count` UInt64,
    `media_count` UInt64,
    `total_comments` UInt64,
    `total_likes` UInt64,
    `is_verified` UInt8,
    `country_block` UInt8,
    `profile_pic_url` Nullable(String),
    `profile_pic_url_hd` Nullable(String),
    `fb_page` Nullable(String)
ENGINE = ReplacingMergeTree
ORDER BY added_at

The posts table will be populated with the post owner name, post id, caption, comments coun, and so on. To check whether a post is an advertisement, Instagram carousel, or a video we can use these fields: is_ad, is_album and is_video.

CREATE TABLE instagram.posts
    `added_at` DateTime,
    `owner` String,
    `post_id` UInt64,
    `caption` Nullable(String),
    `code` String,
    `comments_count` UInt64,
    `comments_disabled` UInt8,
    `created_at` DateTime,
    `display_url` String,
    `is_ad` UInt8,
    `is_album` UInt8,
    `is_video` UInt8,
    `likes_count` UInt64,
    `location` Nullable(String),
    `recources` Array(String),
    `video_url` Nullable(String)
ENGINE = ReplacingMergeTree
ORDER BY added_at

In the comments table, we store each comment separately with the comment owner and text.

CREATE TABLE instagram.comments
    `added_at` DateTime,
    `comment_id` UInt64,
    `post_id` UInt64,
    `comment_owner` String,
    `comment_text` String
ENGINE = ReplacingMergeTree
ORDER BY added_at

Writing the script
Import the following classes from the library: Account, Media, WebAgent and Comment.

from instagram import Account, Media, WebAgent, Comment
from datetime import datetime
from clickhouse_driver import Client
import requests
import pandas as pd

Next, create an instance of the WebAgent class required for some library methods and data updating. To collect any meaningful information we need to have at least account names. Since we don’t have them yet, send the following request to search for porifles by the  keywords specified in queries_list. The search results will be composed of Instagram pages that match any keyword in the list.

agent = WebAgent()
queries_list = ['machine learning', 'data science', 'data analytics', 'analytics', 'business intelligence',
                'data engineering', 'computer science', 'big data', 'artificial intelligence',
                'deep learning', 'data scientist','machine learning engineer', 'data engineer']
client = Client(host='', user='default', password='', port='9000', database='instagram')
url = 'https://www.instagram.com/web/search/topsearch/?context=user&count=0'

Let’s iterate the keywords collecting all matching accounts. Then remove duplicates from the obtained list by converting it to set and back.

response_list = []
for query in queries_list:
    response = requests.get(url, params={
        'query': query
instagram_pages_list = []
for item in response_list:
instagram_pages_list = list(set(instagram_pages_list))

Now we need to loop through the list of pages and request detailed information about an account if it’s not in the table yet. Create an instance of the Account class and pass username as a parameter.
Then update the account information using the agent.update()
method. We will collect only the first 100 posts to keep it moving. Next, create a list named media_list to store received post ids after calling the agent.get_media() method.

Collecting user media data

all_posts_list = []
username_count = 0
for username in instagram_pages_list:
    if client.execute(f"SELECT count(1) FROM users WHERE user_name='{username}'")[0][0] == 0:
        print('username:', username_count, '/', len(instagram_pages_list))
        username_count += 1
        account_total_likes = 0
        account_total_comments = 0
            account = Account(username)
        except Exception as E:
        except Exception as E:
        if account.media_count < 100:
            post_count = account.media_count
            post_count = 100
        print(account, post_count)
        media_list, _ = agent.get_media(account, count=post_count, delay=1)
        count = 0

Because we need to count the total number of likes and comments before adding a new user to our database, we’ll start with them first. Almost all required fields belong to the Media class:

Collecting user posts

for media_code in media_list:
            if client.execute(f"SELECT count(1) FROM posts WHERE code='{media_code}'")[0][0] == 0:
                print('posts:', count, '/', len(media_list))
                count += 1

                post_insert_list = []
                post = Media(media_code)
                post_insert_list.append(datetime.now().strftime('%Y-%m-%d %H:%M:%S'))
                if post.caption is not None:
                    post_insert_list.append(post.caption.replace("'","").replace('"', ''))
                post_insert_list.append(datetime.fromtimestamp(post.date).strftime('%Y-%m-%d %H:%M:%S'))
                except TypeError:
                    post_insert_list.append('cast(Null as Nullable(UInt8))')
                if post.location is not None:
                if post.video_url is not None:
                account_total_likes += post.likes_count
                account_total_comments += post.comments_count
                        INSERT INTO posts VALUES {tuple(post_insert_list)}
                except Exception as E:

Store comments in the variable with the same name after calling the get_comments() method:

Collecting post comments

comments = agent.get_comments(media=post)
                for comment_id in comments[0]:
                    comment_insert_list = []
                    comment = Comment(comment_id)
                    comment_insert_list.append(datetime.now().strftime('%Y-%m-%d %H:%M:%S'))
                    comment_insert_list.append(comment.text.replace("'","").replace('"', ''))
                            INSERT INTO comments VALUES {tuple(comment_insert_list)}
                    except Exception as E:

And now, when we have obtained user posts and comments new information can be added to the table.

Collecting user data

user_insert_list = []
        user_insert_list.append(datetime.now().strftime('%Y-%m-%d %H:%M:%S'))
        if account.fb_page is not None:
                INSERT INTO users VALUES {tuple(user_insert_list)}
        except Exception as E:

To sum up, we have collected data of 500 users, with nearly 20K posts and 40K comments. As the database will be updated, we can write a simple query to get the top 10 ML, AI & Data Science related most followed accounts for today.

FROM users
ORDER BY followers_count DESC

And as a bonus, here is a list of the most interesting Instagram accounts on this topic:

  1. @ai_machine_learning
  2. @neuralnine
  3. @datascienceinfo
  4. @compscistuff
  5. @computersciencelife
  6. @welcome.ai
  7. @papa_programmer
  8. @data_science_learn
  9. @neuralnet.ai
  10. @techno_thinkers

View the code on GitHub

 No comments    9   22 d   clickhouse   data analytics   Data engineering   instagram   python

Analyzing Business Intelligence (BI) and Analytics job market in Tableau

Estimated read time – 13 min


According to the SimilarWeb rating, hh.ru is the third among the most popular job search websites in the world. In one of the conversations with Roman Bunin, we came up with the idea of making a common project and collect data using the HeadHunter API for later analysis and visualization in Tableau Public. Our goal was to understand the dependency between salary and skills specified in a job posting and compare how things are in Moscow, Saint Petersburg, and other regions.

Data Collection Process

Our scheme is based on fetching a  brief job description, returned by the GET /vacancies method. According to the structure we need to create the following columns: vacancy type, id, vacancy rate (‘premium’), pre-employment testing (‘has_test’), company address, salary, work schedule, and so forth. We created a table using the following CREATE query down below:

Query for creating the vacancies_short table in ClickHouse

CREATE TABLE headhunter.vacancies_short
    `added_at` DateTime,
    `query_string` String,
    `type` String,
    `level` String,
    `direction` String,
    `vacancy_id` UInt64,
    `premium` UInt8,
    `has_test` UInt8,
    `response_url` String,
    `address_city` String,
    `address_street` String,
    `address_building` String,
    `address_description` String,
    `address_lat` String,
    `address_lng` String,
    `address_raw` String,
    `address_metro_stations` String,
    `alternate_url` String,
    `apply_alternate_url` String,
    `department_id` String,
    `department_name` String,
    `salary_from` Nullable(Float64),
    `salary_to` Nullable(Float64),
    `salary_currency` String,
    `salary_gross` Nullable(UInt8),
    `name` String,
    `insider_interview_id` Nullable(UInt64),
    `insider_interview_url` String,
    `area_url` String,
    `area_id` UInt64,
    `area_name` String,
    `url` String,
    `published_at` DateTime,
    `employer_url` String,
    `employer_alternate_url` String,
    `employer_logo_urls_90` String,
    `employer_logo_urls_240` String,
    `employer_logo_urls_original` String,
    `employer_name` String,
    `employer_id` UInt64,
    `response_letter_required` UInt8,
    `type_id` String,
    `type_name` String,
    `archived` UInt8,
    `schedule_id` Nullable(String)
ENGINE = ReplacingMergeTree
ORDER BY vacancy_id

The first script collects data from the HeadHunter website through API and inserts to our Database using the following libraries:

import requests
from clickhouse_driver import Client
from datetime import datetime
import pandas as pd
import re

Next, we create a DataFrame and connect to the Database in ClickHouse:

queries = pd.read_csv('hh_data.csv')
client = Client(host='1.234.567.890', user='default', password='', port='9000', database='headhunter')

The queries table stores a list of our search queries, having the following columns: query type, level, career field, and search phrase. The last column contains logical operators, for instance, we can get more results by putting logical ANDs between “Python”, “data” and “analysis”.


The search results may not always match the expectations, chiefs, marketers, and administrators can accidentally get into our database. To prevent this, we will write a function named check_name(name), it will accept a vacancy name and return a boolean value, depending on the match.

def check_name(name):
    bad_names = [r'курьер', r'грузчик', r'врач', r'менеджер по закупу',
           r'менеджер по продажам', r'оператор', r'повар', r'продавец',
          r'директор магазина', r'директор по продажам', r'директор по маркетингу',
          r'кабельщик', r'начальник отдела продаж', r'заместитель', r'администратор магазина', 
          r'категорийный', r'аудитор', r'юрист', r'контент', r'супервайзер', r'стажер-ученик', 
          r'су-шеф', r'маркетолог$', r'региональный', r'ревизор', r'экономист', r'ветеринар', 
          r'торговый', r'клиентский', r'начальник цеха', r'территориальный', r'переводчик', 
          r'маркетолог /', r'маркетолог по']
    for item in bad_names:
        if re.match(item, name):
            return True

Moving further, we need to create a while loop to collect data non-stop. Iterate over the Dataframe queries selecting the type, level, field, and search phrase columns. Send a GET request using a keyword to get the number of pages. Then we loop through the number of pages sending the same requests and populating vacancies_from_response with job descriptions. In the per_page parameter we specified 10, this is the max limit for the HH API. Since we didn’t pass any value to the area field, the results are collected worldwide.

while True:
   for query_type, level, direction, query_string in zip(queries['Query Type'], queries['Level'], queries['Career Field'], queries['Seach Phrase']):
           print(f'seach phrase: {query_string}')
           url = 'https://api.hh.ru/vacancies'
           par = {'text': query_string, 'per_page':'10', 'page':0}
           r = requests.get(url, params=par).json()
           added_at = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
           pages = r['pages']
           found = r['found']
           vacancies_from_response = []

           for i in range(0, pages + 1):
               par = {'text': query_string, 'per_page':'10', 'page':i}
               r = requests.get(url, params=par).json()
               except Exception as E:

Create a for loop to escape duplicate rows in our table. First, send a query to the database, verifying whether there is a vacancy with the same id and search phrase. If the verification was successful we then
pass the job title to check_name() and move on to the next one.

for item in vacancies_from_response:
               for vacancy in item:
                   if client.execute(f"SELECT count(1) FROM vacancies_short WHERE vacancy_id={vacancy['id']} AND query_string='{query_string}'")[0][0] == 0:
                       name = vacancy['name'].replace("'","").replace('"','')
                       if check_name(name):

Now we need to extract all the necessary data from a job description. The table will contain empty cells, since some data may be missing.

View the code for extracting job description data

vacancy_id = vacancy['id']
                       is_premium = int(vacancy['premium'])
                       has_test = int(vacancy['has_test'])
                       response_url = vacancy['response_url']
                           address_city = vacancy['address']['city']
                           address_street = vacancy['address']['street']
                           address_building = vacancy['address']['building']
                           address_description = vacancy['address']['description']
                           address_lat = vacancy['address']['lat']
                           address_lng = vacancy['address']['lng']
                           address_raw = vacancy['address']['raw']
                           address_metro_stations = str(vacancy['address']['metro_stations']).replace("'",'"')
                       except TypeError:
                           address_city = ""
                           address_street = ""
                           address_building = ""
                           address_description = ""
                           address_lat = ""
                           address_lng = ""
                           address_raw = ""
                           address_metro_stations = ""
                       alternate_url = vacancy['alternate_url']
                       apply_alternate_url = vacancy['apply_alternate_url']
                           department_id = vacancy['department']['id']
                       except TypeError as E:
                           department_id = ""
                           department_name = vacancy['department']['name']
                       except TypeError as E:
                           department_name = ""
                           salary_from = vacancy['salary']['from']
                       except TypeError as E:
                           salary_from = "cast(Null as Nullable(UInt64))"
                           salary_to = vacancy['salary']['to']
                       except TypeError as E:
                           salary_to = "cast(Null as Nullable(UInt64))"
                           salary_currency = vacancy['salary']['currency']
                       except TypeError as E:
                           salary_currency = ""
                           salary_gross = int(vacancy['salary']['gross'])
                       except TypeError as E:
                           salary_gross = "cast(Null as Nullable(UInt8))"
                           insider_interview_id = vacancy['insider_interview']['id']
                       except TypeError:
                           insider_interview_id = "cast(Null as Nullable(UInt64))"
                           insider_interview_url = vacancy['insider_interview']['url']
                       except TypeError:
                           insider_interview_url = ""
                       area_url = vacancy['area']['url']
                       area_id = vacancy['area']['id']
                       area_name = vacancy['area']['name']
                       url = vacancy['url']
                       published_at = vacancy['published_at']
                       published_at = datetime.strptime(published_at,'%Y-%m-%dT%H:%M:%S%z').strftime('%Y-%m-%d %H:%M:%S')
                           employer_url = vacancy['employer']['url']
                       except Exception as E:
                           employer_url = ""
                           employer_alternate_url = vacancy['employer']['alternate_url']
                       except Exception as E:
                           employer_alternate_url = ""
                           employer_logo_urls_90 = vacancy['employer']['logo_urls']['90']
                           employer_logo_urls_240 = vacancy['employer']['logo_urls']['240']
                           employer_logo_urls_original = vacancy['employer']['logo_urls']['original']
                       except Exception as E:
                           employer_logo_urls_90 = ""
                           employer_logo_urls_240 = ""
                           employer_logo_urls_original = ""
                       employer_name = vacancy['employer']['name'].replace("'","").replace('"','')
                           employer_id = vacancy['employer']['id']
                       except Exception as E:
                       response_letter_required = int(vacancy['response_letter_required'])
                       type_id = vacancy['type']['id']
                       type_name = vacancy['type']['name']
                       is_archived = int(vacancy['archived'])

The last field is the work schedule. If there is mentioned a fly-in-fly-out method, these kinds of job postings will be skipped.

    schedule = vacancy['schedule']['id']
except Exception as E:
    schedule = ''"
if schedule == 'flyInFlyOut':

Next, we create a list of obtained variables, replacing None values with empty strings to escape errors with Clickhouse and insert them into the table.

vacancies_short_list = [added_at, query_string, query_type, level, direction, vacancy_id, is_premium, has_test, response_url, address_city, address_street, address_building, address_description, address_lat, address_lng, address_raw, address_metro_stations, alternate_url, apply_alternate_url, department_id, department_name,
salary_from, salary_to, salary_currency, salary_gross, insider_interview_id, insider_interview_url, area_url, area_name, url, published_at, employer_url, employer_logo_urls_90, employer_logo_urls_240,  employer_name, employer_id, response_letter_required, type_id, type_name, is_archived, schedule]
for index, item in enumerate(vacancies_short_list):
    if item is None:
        vacancies_short_list[index] = ""
tuple_to_insert = tuple(vacancies_short_list)
client.execute(f'INSERT INTO vacancies_short VALUES {tuple_to_insert}')

Connecting Tableau to the data source

Unfortunately, we can’t work with databases in  Tableau Public, that’s why we decided to connect our Clickhouse Database to Google Sheets. With this in mind, we picked the following libraries: gspread and oauth2client for accessing Google Spreadsheets API, and schedule for task scheduling.

Refer to our previous article where we used  Google Spreadseets API for  Collecting Data on Ad Campaigns from VK.com

import schedule
from clickhouse_driver import Client
import gspread
import pandas as pd
from oauth2client.service_account import ServiceAccountCredentials
from datetime import datetime

scope = ['https://spreadsheets.google.com/feeds', 'https://www.googleapis.com/auth/drive']
client = Client(host='', user='default', password='', port='9000', database='headhunter')
creds = ServiceAccountCredentials.from_json_keyfile_name('credentials.json', scope)
gc = gspread.authorize(creds)

The update_sheet() function will transfer all data from Clickhouse to a Google Sheets table:

def update_sheet():
   print('Updating cell at', datetime.now())
   columns = []
   for item in client.execute('describe table headhunter.vacancies_short'):
   vacancies = client.execute('SELECT * FROM headhunter.vacancies_short')
   df_vacancies = pd.DataFrame(vacancies, columns=columns)
   df_vacancies.to_csv('vacancies_short.csv', index=False)
   content = open('vacancies_short.csv', 'r').read()
   gc.import_csv('1ZWS2kqraPa4i72hzp0noU02SrYVo0teD7KZ0c3hl-UI', content.encode('utf-8'))

Using schedule to run our function every day at 1:00 PM (UTC):

while True:

What’s the final point?

Roman created an informative dashboard based on this data.
3-4.jpg https://revealthedata.com/examples/hh/

And made a youtube video with a detailed explanation of the dashboard features.

Key Insights

  1. Data Analysts specializing in BI are most in-demand in the job market since the highest number of search results were returned with this query. However, the average salary is higher in Product Analyst and BI-analyst openings.
  2. Most of the postings were found In Moscow, where the average salary is 10-30K RUB higher than in Saint Petersburg and 30-40K higher than in other regions.
  3. Top highly paid positions: Head of Analytics (110K RUB per month on avg.), Database Engineer (138K RUB per month), and Head of Machine Learning (250K RUB per month).
  4. The most useful skills to have are a solid knowledge of Python with Pandas and Numpy, Tableau, Power BI, ETL, and Spark. Most of the posings found contained these requirements and were highly paid than any others. For Python programmers, it’s more valuable to have expertise with Matplotlib than Plotly.

View the code on  GitHub

 No comments    8   29 d   BI-tools   data analytics   Data engineering   headhunter

Sentiment analysis of Russians on Constitutional Amendments

Estimated read time – 11 min

In today’s article, we are going to use public data from vk.com to interpret and classify users’ attitudes about the 2020 amendments to the Constitution of Russia.

API Overview

First off, we need to receive data using the newsfeed.search method, this method allows us to get up to one thousand of the latest posts from the news feed by keyword.
The response data contains different fields, like post ids, user or community ids, text data, likes count, comments, apps, geolocation, and many more. We are only needed ids and text data.
Some expanded information about the author will also be useful for our analysis, this includes city, gender, age, and can be received with the users.get method.

Create Clickhouse Tables

The received data should be stored somewhere, we chose to use ClickHouse, an open-source column-oriented DBMS. Let’s create two tables to store users and their posts. The first table will be populated with ids and text data, the second one will hold user data, such as their ids, age, and city. The ReplacingMergeTree () engine will remove duplicates in our tables.

The article assumes that you’re familiar with how to install ClickHouse on AWS, create external dictionaries and  materialized views

CREATE TABLE vk_posts(
   post_id UInt64,
   post_date DateTime,
   owner_id UInt64,
   from_id UInt64,
   text String
) ENGINE ReplacingMergeTree()
ORDER BY post_date

CREATE TABLE vk_users(
   user_id UInt64,
   user_sex Nullable(UInt8),
   user_city String,
   user_age Nullable(UInt16)
) ENGINE ReplacingMergeTree()
ORDER BY user_id

Collecting user posts with the VK API

Let’s get to writing our script, import the libraries, and create several variables with constant values:

If you don’t have an access token yet and want to create one, refer to this step by step guide: “Collecting Data on Ad Campaigns from VK.com”

from clickhouse_driver import Client
from datetime import datetime
import requests
import pandas as pd
import time

token = 'your_token'
version = 5.103
client = Client(host='ec1-23-456-789-1011.us-east-2.compute.amazonaws.com', user='default', password='', port='9000', database='default')      
data_list = []
start_from = 0
query_string = 'конституция' #constitution

Define the get_and_insert_info_by_user function that will receive a list of user ids and expanded information about them, and send it to the vk_users table. Since the user_ids parameter takes a list as a string object, we need to change the structure and omit the square brackets.
Most users prefer to conceal their gender, age, and city. In such cases, we need to use Nullable values. To obtain user age we need to subtract the birth year from the current year, if the birth year is missing we can check it using the regular expression.

get_and_insert_info_by_user() function

def get_and_insert_info_by_user(users):
        r = requests.get('https://api.vk.com/method/users.get', params={
            'fields':'sex, city, bdate'
        for user in r:
            user_list = []
            if client.execute(f"SELECT count(1) FROM vk_users where user_id={user['id']}")[0][0] == 0:
                except Exception:
                    user_list.append('cast(Null as Nullable(UInt8))')
                except Exception:
                    now = datetime.now()
    			    year = item.split('.')[-1]
    			    if re.match(r'\d\d\d\d', year):
        		        age = now.year - int(year)
                except Exception:
                    user_list.append('cast(Null as Nullable(UInt16))')
                user_insert_tuple = tuple(user_list)
                client.execute(f'INSERT INTO vk_users VALUES {user_insert_tuple}')
    except KeyError:

Our script will work in a while loop to constantly update data, as we can only receive a thousand of the latest data points.The newsfeed.search method returns 200 posts per call, so we need to invoke it five times to collect all the posts.

While loop to collect new posts

while True:
    for i in range(5):
        r = requests.get('https://api.vk.com/method/newsfeed.search', params={
            'start_from': start_from
            start_from = r.json()['response']['next_from']
        except KeyError:

The data we received can be parsed, VK users always have a positive id, while for communities it’s negative. We need only users data for our analysis, where from_id > 0. The next step is to check whether a post contains any text data or not. Finally, we will collect and store unique entries by user id. Pause the script after each iteration for 180 seconds to wait for new user posts and not violate the VK API rules.

Adding new data to Clickhouse

user_ids = []
    for data in data_list:
        for data_item in data['items']:
            if data_item['from_id'] > 0:
                post_list = []
                if not data_item['text']:
                if client.execute(f"SELECT count(1) FROM vk_posts WHERE post_id={data_item['id']} AND from_id={data_item['from_id']}")[0][0] == 0:
                    date = datetime.fromtimestamp(data_item['date'])
                    date = datetime.strftime(date, '%Y-%m-%d %H:%M:%S')
                    post_tuple = tuple(post_list)
                        client.execute(f'INSERT INTO vk_posts VALUES {post_tuple}')
                    except Exception as E:
                        print('!!!!! try to insert into vk_post but got', E)
    except Exception as E:
        print("Try to insert user list:", user_ids, "but got:", E)

Dostoevsky for sentiment analysis

For one week our script collected almost 20000 posts from VK users that mention the keyword “constitution” (or “конституция” in Russian). It’s time to write our second script for data analysis and visualization. First, create a DataFrame with the data received, and evaluate the sentiment of each post, identifying whether it’s positive, negative, or neutral. We are going to use the Dostoevsky library to analyze the emotion behind a text.

from dostoevsky.tokenization import RegexTokenizer
from dostoevsky.models import FastTextSocialNetworkModel
from clickhouse_driver import Client
import pandas as pd
client = Client(host='ec1-23-456-789-1011.us-east-2.compute.amazonaws.com', user='default', password='', port='9000', database='default')

Assign all the contents of our table to the vk_posts variable with a simple query. Iterate through all the posts, select those with text data and populate our DataFrame.

vk_posts = client.execute('SELECT * FROM vk_posts')
list_of_posts = []
list_of_ids = []
for post in vk_posts:
    if str(post[-2]).replace(" ", ""):
df_posts = pd.DataFrame()
df_posts['post'] = list_of_posts
df_posts['id'] = list_of_ids

Instantiate our model and iterate through the posts to evaluate the sentiment of each entry.

tokenizer = RegexTokenizer()
model = FastTextSocialNetworkModel(tokenizer=tokenizer)
sentiment_list = []
results = model.predict(list_of_posts, k=2)
for sentiment in results:

Add several boolean columns to our DataFrame that will reflect whether it’s a  positive, negative, or neutral post.

neutral_list = []
negative_list = []
positive_list = []
speech_list = []
skip_list = []
for sentiment in sentiment_list:
    neutral = sentiment.get('neutral')
    negative = sentiment.get('negative')
    positive = sentiment.get('positive')
    if neutral is None:
    if negative is None:
    if positive is None:
df_posts['neutral'] = neutral_list
df_posts['negative'] = negative_list
df_posts['positive'] = positive_list

That’s how the DataFrame looks now:

Let’s examine the most negative posts:

df_posts[df_posts.negative > 0.9]

Now, let’s add data about the authors of these posts by merging two tables together on the id column.

vk_users = client.execute('SELECT * FROM vk_users')
vk_user_ids_list = []
vk_user_sex_list = []
vk_user_city_list = []
vk_user_age_list = []
for user in vk_users:
df_users = pd.DataFrame()
df_users['id'] = vk_user_ids_list
df_users['sex'] = vk_user_sex_list
df_users['city'] = vk_user_city_list
df_users['age'] = vk_user_age_list
df = df_posts.merge(df_users, on='id')

And the table now looks the following:

Analysing data with Plotly

Check out our previous article on data visualization with Plotly: Building an interactive waterfall chart in Python

Let’s find the percentage of posts for each group: positive, negative, neutral. Iterate through these three columns and calculate the values more than zero for each data point. Then do the same for different age categories and gender.

According to our chart, 45% of recent user posts relevant to the keyword “constitution” have a negative meaning, while the other 52% are neutral. Later it’ll be known how different the Internet opinions from the voting results.

It’s noticeable that among the men audience the proportion of positive posts is less than 2%, while for women it’s 3.5%. However, the number of negative posts for each group is almost the same, 47% and 43% respectively.

According to our analysis, posts made by younger audiences between 18-25 years have more positive sentiment, which is 6%. While users under 18 years leave mostly negative posts, this may be because most users under the age of 18 prefer to hide their real age, this makes it difficult to obtain accurate data for such a group.
The proportion of negative posts is almost equal for all groups and accounts for 44%.
As you can see, the data is distributed equally in all three charts. This means that half of all posts relevant to the keyword “constitution” and made by VK users over the past week mostly have a negative sentiment.

 No comments    57   3 mon   data analytics   Data engineering   plotly

Handling website buttons in Selenium

Estimated read time – 8 min

In our previous article, Parsing the data of site’s catalogue, using Beautiful Soup and Selenium we have addressed the problem of working with dynamic pages, but sometimes this method doesn’t work, as with “Show more” buttons. Today we will show how you can imitate button click with Selenium to load a whole page, collect beer IDs, ratings, and send the data to Clickhouse.

Webpage structure

Let’s take a random brewery that has 105 check-ins, or customer feedbacks. One page with check-ins displays up to 25 records and looks like this:

If we try to scroll down to the bottom, we will encounter the same button that prevents us from getting all 105 records at once:

First off, to address this task, let’s find out the button class and just click it until it works. Since Selenium launches the browser and the next “Show more” button may not be loaded in time, that’s why we set 2-second intervals between the clicks. As soon as the page is loaded we will take its content and parse the relevant data.
Let’s view the source code and  find the button, it’s assigned to the more_checkins class.

The button has style attributes, such as display. When the button is displayed this attribute takes the block value. But when we scroll the page to the buttom and there is nothing left to display, the attribute takes the none value and we can stop clicking.

Writing our code

Let’s import the necessary libraries

import time
from selenium import webdriver
from bs4 import BeautifulSoup as bs
import re
from datetime import datetime
from clickhouse_driver import Client

Chromedriver is used to run Selenium tests on Chrome and can be downloaded from the official website

Connect to the database and create cookies:

client = Client(host='ec1-23-456-789-10.us-east-2.compute.amazonaws.com', user='', password='', port='9000', database='')
count = 0
cookies = {

You can find out more about working with cookies in Selenium from Parsing the data of site’s catalogue, using Beautiful Soup and Selenium. We will need the untappd_user_v3_e parameter.

As we are going to work with pages that have more than hundreds of thousands of records, it’s pretty heavy and our instance may be overloaded. To prevent this, we will shut down unnecessary parts and then enable authentication cookie:

options = webdriver.ChromeOptions()
prefs = {'profile.default_content_setting_values': {'images': 2, 
                            'plugins': 2, 'fullscreen': 2}}
options.add_experimental_option('prefs', prefs)
driver = webdriver.Chrome(options=options)

We will need a function that would take a link, open it in the browser, load a whole page and return a soup object to be parsed. Get the  display attribute, assign it to the more_checkins: variable and click the button until the attribute is none. Let’s set 2-second intervals between the clicks, to wait for the page to load. As soon as we received the page, converth it into a soup object using the bs4 library.

def get_html_page(url):
    more_checkins = driver.execute_script("var more_checkins=document.getElementsByClassName('more_checkins_logged')[0].style.display;return more_checkins;")
    while more_checkins != "none":
        more_checkins = driver.execute_script("var more_checkins=document.getElementsByClassName('more_checkins_logged')[0].style.display;return more_checkins;")
    source_data = driver.page_source
    soup = bs(source_data, 'lxml')
    return soup

Write the following function that will take a page url, pass it in the get_html_page and receive a soup object to parse. The function returns zipped lists with beer IDs and ratings.

See how you can use Beautiful Soup to retrieve data from a website catalogue

def parse_html_page(url):
    soup = get_html_page(url)
    brewery_id = soup.find_all('a', {'class':'label',
    items = soup.find_all('div', {'class':'item',
    checkin_rating_list = []
    beer_id_list = []
    count = 0
    print('Filling the lists')
    for checkin in items:
        print(count, '/', len(items))
            checkin_rating_list.append(float(checkin.find('div', {'class':'caps'})['data-rating']))
        except Exception:
            checkin_rating_list.append('cast(Null as Nullable(Float32))')
            beer_id_list.append(int(checkin.find('a', {'class':'label'})['href'][-7:]))
        except Exception:
            beer_id_list.append('cast(Null as Nullable(UInt64))')
        count += 1 
    return zip(checkin_rating_list, beer_id_list)

Finally, write a function call for the breweries. We’ve covered how to receive a list of Russian brewery IDs in this article: Example of using dictionaries in Clickhouse with Untappd.
Let’s fetch it from the Clickhouse table.

brewery_list = client.execute('SELECT brewery_id FROM brewery_info')

If we print out the brewery_list, we will find out that the data is stored in a list of tuples.

Let’s make it a bit prettier with the lambda expression:

flatten = lambda lst: [item for sublist in lst for item in sublist]
brewery_list = flatten(brewery_list)

That’s much better:

Create a url for each brewery in the list, it includes a standard link and a brewery ID in the end. Pass it to the parse_html_page function that fetches the get_html_page and return lists with beer_id and rating_score. Since the lists are zipped, we can iterate throught them, create a tuple and send it to Clickhouse.

for brewery_id in brewery_list:
    print('Fetching the brewery with id', brewery_id, count, '/', len(brewery_list))
    url = 'https://untappd.com/brewery/' + str(brewery_id)
    returned_checkins = parse_html_page(url)
    for rating, beer_id in returned_checkins:
        tuple_to_insert = (rating, beer_id)
            client.execute(f'INSERT INTO beer_reviews VALUES {tuple_to_insert}')
        except errors.ServerException as E:
    count += 1

That’s it about the way we can handle “Show more” buttons. Over time we will form a large dataset for further analysis, to work with in our next series.

 No comments    107   4 mon   Amazon Web Services   AWS   clickhouse   Data engineering   python

Example of using dictionaries in Clickhouse with Untappd

Estimated read time – 12 min

In Clickhouse we can use internal dictionaries as well as external dictionaries, they can be an alternative to JSON that doesn’t always work fine. DIctionaries store information in memory and can be invoked with the dictGet method. Let’s review how we can create one in Clickhouse and use it for our queries.

We will illustrate an example of data using the Untappd API. Untappd is a social network for everyone who loves craft beer. We are going to use сheck-ins of Russian-based craft breweries and start collecting information about them to analyze this data later on and to draw some conclusions. in today’s article, we will analyze how to receive metadata on Russian breweries with Untappd and store it in a Clickhouse dictionary.

Collecting data with Untappd

First off, we need to create a new app to receive client_id and  client_secret_key to make API calls. Follow  this link and fill in the fields:

Usually, it takes about 1 to 3 weeks to wait for approval.

import requests
import pandas as pd
import time

We’ll be using the requests library to make API calls, view results in a Pandas DataFrame, and save them in a CSV file before sending it to a Clickhouse dictionary. Untappd has strict limits on the number of requests, prohibiting us to make more than 100 calls per hour. Therefore, we need to make our script wait for 38 seconds using the Python time module.

client_id = 'your_client_id'
client_secret = 'your_client_secret'
all_brewery_of_russia = []

We want to get data for one thousand Russian breweries. One request to the Brewery Search method enables us to view up to 50 breweries. The website gave us 3369 breweries when searching the word “Russia” manually.

Let’s check this, scroll down to the bottom, and open the page code.

Each brewery received is stored in the beer-item class. This means we can the number of references to beer-item:

And as it turned out, we have exactly 1000 breweries, not 3369. When searching the word “Russia” manually, the results also contain some American breweries. So, we need to make 20 calls, getting 50 breweries at a time:

for offset in range(0, 1000, 50):
        print('offset = ', offset)
        print('remained:', 1000 - offset, '\n')
        response = requests.get(f'https://api.untappd.com/v4/search/brewery?client_id={client_id}&client_secret={client_secret}',
        item = response.json()
        print(item, '\n')
    except Exception:

The Brewery Search method includes several parameters, q – a string with a country name (specify specify “Russia” to get all the breweries based in Russia), offset – allows us to shift by 50 lines in the search to get the next list of breweries, limit – restricts the number of breweries received and can not be more than 50. Convert the answer to JSON and append data sotred in the item object to the  all_brewery_of_russia list.

Our data may also include breweries from other countries. That’s why we need to filter the data. Iterate through the all_brewery_of_russia list and keep only those breweires, which country_name is Russia.

brew_list = []
for element in all_brewery_of_russia:
    brew = element['response']['brewery']
    for i in range(brew['count']):
        if brew['items'][i]['brewery']['country_name'] == 'Russia':

Print out the first element in our brew_list:


Create a DataFrame with the following columns: brewery_id, beer_count, brewery_name, brewery_slug, brewery_page_url, brewery_city, lat и  lng. And several lists to sort out the data stored in the brewery_list:

df = pd.DataFrame()
brewery_id_list = []
beer_count_list = []
brewery_name_list = []
brewery_slug_list = []
brewery_page_url_list = []
brewery_location_city = []
brewery_location_lat = []
brewery_location_lng = []
for brewery in brew_list:

Assign them as column values:

df['brewery_id'] = brewery_id_list
df['beer_count'] = beer_count_list
df['brewery_name'] = brewery_name_list
df['brewery_slug'] = brewery_slug_list
df['brewery_page_url'] = brewery_page_url_list
df['brewery_city'] = brewery_location_city
df['brewery_lat'] = brewery_location_lat
df['brewery_lng'] = brewery_location_lng

And view our DataFrame:


Let’s sort the values by brewery_id and store our DataFrame as a CSV file without index column and headings:

df = df.sort_values(by='brewery_id')
df.to_csv('brewery_data.csv', index=False, header=False)

Creating a Clickhouse dictionary

You can create Clickouse dictionaries in many different ways. We will try to structure it in an XML file, configure the server files, and access it through our client. The XML file structure will be the following:

Learn more about other ways you can create Clickhouse dictionaries in the documentation

                <flat />

name is a dictionary name, attribute holds the properties of the columns, id is a key field, file stores file path and format. We are going to store our file in this directory: /home/ubuntu.

Let’s upload our CSV and XML files to the server, it can be done using an FTP like FileZilla. We explained how to deploy Clickhouse on an Amazon instance in our previous article, this time need to do the same. Open your FileZilla client and go to SFTP settings to add a private key:

Connect to your server address, it can be found in the EC2 management console. Specify SFTP as a protocol, your Host, and Ubuntu as a username.

Your Public DNS may change in case of overload

After connecting we will wind up in this location /home/ubuntu. Let’s put the files in that folder and connect via SSH using Termius. Then we need to move the files to /etc/clickhouse-server to view them in Clickhouse:

Learn how you can connect to an AWS server using SSH client from our previous material Installing Clickhouse on AWS

sudo mv breweries_dictionary.xml /etc/clickhouse server/

Go to the config file:

cd /etc/clickhouse-server
sudo nano config.xml

We need the  tag, it’s the path to a file that describes the dictionaries structure. Specify the path to our XML file:


Save our file and run the Clickhouse client:

clickhouse client

Let’s check that the dictionary really loaded:

SELECT * FROM system.dictionaries\G

In case of success you will get the following:

Now, let’s write a query with the  dictGet function to get the name of the brewery with ID 999. Pass in the dictionary name, as the first argument, then the filed name and ID.

SELECT dictGet('breweries', 'brewery_name', toUInt64(999))

And our query returns this:

Similarly, we could use this function to get a beer name, when the table contains only IDs.

 No comments    144   4 mon   Amazon Web Services   clickhouse   data analytics   Data engineering   python
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