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How to build a dashboard with Bootstrap 4 from scratch (Part 1)

Estimated read time – 12 min

In previous articles we reviewed Plotly’s Dash Framework, learned to build scatter plots and  create a map visualization. This time we will summarize our knowledge and put all the pieces together to design a dashboard layout using the Bootstrap 4 grid system.
To facilitate the development, we’ll refer to the dash-bootstrap-components library. This is a great tool that integrates Bootstrap in Dash, allowing us to write web pages in pure Python, and add any Bootstrap components and styling.

Draft Layout

Before we begin coding it’s crucial to have a plan of our app, a rough layout that would help us to see the big picture and quickly modify the structure. We used to make a dashboard draft, this application enables to create diagrams, graphs, flowcharts, and forms at the click of a button. The dashboard will be built according to this template:


Like the dashboard itself, the top header will be colored in gold and white, the main colors of Untappd. Just below the header, there is a section with breweries, which includes a scatter plot and a control panel. And at the bottom of the page, there will be a map showing beverage rating across the regions of Russia.

All right, let’s get started, first create a new python file with the name The file will store all the front end components of the dashboard, and create a new directory named assets. The directory structure should be similar:

- assets/
    |-- typography.css
    |-- header.css
    |-- custom-script.js
    |-- image.png

Then we import the libraries and initialize our application:

import dash
import dash_bootstrap_components as dbc
import dash_html_components as html
import dash_core_components as dcc
import pandas as pd
from get_ratio_scatter_plot import get_plot
from get_russian_map import get_map
from clickhouse_driver import Client
from dash.dependencies import Input, Output

standard_BS = dbc.themes.BOOTSTRAP
app = dash.Dash(__name__, external_stylesheets=[standard_BS])

Main parameters of the app:
__name__ — to enable access to static elements stored in the assets folder (such as images, CSS and JS files)
external_stylesheets — external CSS styling, here we are using a standard Bootstrap theme, however you can create your own theme or use any of  the availables ones.

Hook up a few more things to work with local files and connect to the Clickhouse Database:

app.scripts.config.serve_locally = True
app.css.config.serve_locally = True

client = Client(host='',

Add a palette of colors:

colors = ['#ffcc00', 

Creating a layout

All the dashboard elements will be placed within a Bootstrap container, which is in the  <div> block:

- app 
    |-- div
     |-- container
      |-- logo&header
     |-- container
      |-- div
       |-- controls&scatter
       |-- map
app.layout = html.Div(

                                         < header>
                                    < body >
                            fluid=False, style={'max-width': '1300px'},
                    style={'background-color': colors[1], 'font-family': 'Proxima Nova Bold'},

Here we set a fixed container width, background color, and font style of the page that is stored in typography.css in the assets folder. Let’s take a closer look at the first element in the div block, that’s the top header with the Untappd logo:

logo = html.Img(src=app.get_asset_url('logo.png'),
                        style={'width': "128px", 'height': "128px",
                        }, className='inline-image')

and the header:

header = html.H3("Russian breweries stats from Untappd", style={'text-transform': "uppercase"})

We used Bootstrap Forms to position these two elements on the same level.

logo_and_header = dbc.FormGroup(

The class name ‘p-5’ allows to increase padding and vertically align the title while specifying ‘form-row’ as the form class name we put the logo and header in one row. At this point, the top header should look the following:


Now we need to center the elements and add some colors. Create a separate container that will take one row. Specify ‘d-flex justify-content-center’ in the className to achieve the same output.

                        style={'max-height': '128px',
                               'color': 'white',

                    className='d-flex justify-content-center',
                    style={'max-width': '100%',
                           'background-color': colors[0]},

And now the top header is done:


We’re approaching the main part, create the next Bootstrap Container and add a subheading:

                            html.H5("Breweries", style={'text-align':'center', 'text-transform': 'uppercase'}),
                            html.Hr(), # horizontal  break

The main body will consist of Bootstrap Cards, they can provide a structured layout of all parts, giving each element a clear border and saving the white space. Create the next element, a control panel with sliders:

slider_day_values = [1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100]
slider_top_breweries_values = [5, 25, 50, 75, 100, 125, 150, 175, 200]

controls = dbc.Card(
                        dbc.Label("Time Period", style={'text-align': 'center', 'font-size': '100%', 'text-transform': 'uppercase'}),
                            marks={i: i for i in slider_day_values}
                    ], style={'text-align': 'center'}
                        dbc.Label("Number of breweries", style={'text-align': 'center', 'font-size': '100%', 'text-transform': 'uppercase'}),
                            marks={i: i for i in slider_top_breweries_values}
                    ], style={'text-align': 'center'}
    style={'height': '32.7rem', 'background-color': colors[3]}


The control panel consists of two sliders that can be used to change the view on the scatter, they are positioned one below the other in a Bootstrap Form. The sliders were put inside the dbc.CardBody block, other elements will be added in the same way. It allows to eliminate alignment problem and achieve clear borders. By default, the sliders are painted in blue, but we can easily customize them by changing the properties of the class in sliders.css. Add the control panel with the scatter plot as follows:

                    dbc.Col(controls, width={"size": 4,
                                     "order": 'first',
                                             "offset": 0},
                                            html.H6("The ratio between the number of reviews and the average brewery rating",
                                                    style={'text-transform': 'uppercase'}), 
                                style={'background-color': colors[2], 'text-align':'center'}

And at the bottom of the page we will position the map:

html.H5("Venues and Regions", style={'text-align':'center', 'text-transform': 'uppercase',}),
                            html.Hr(), # horizontal  break
                                                        html.H6("Average beer rating across regions",
                                                                style={'text-transform': 'uppercase'},
                                        style={'background-color': colors[2], 'text-align': 'center'}

Callbacks in Dash

Callback functions allow making dashboard elements interactive through the  Input and Output properties of a particular component.

    Output('ratio-scatter-plot', 'figure'),
    [Input('slider-day', 'value'),
     Input('slider-top-breweries', 'value'),
def get_scatter_plots(n_days=100, top_n=200):
    if n_days == 100 and top_n == 200:
        df = pd.read_csv('data/ratio_scatter_plot.csv')
        return get_plot(n_days, top_n, df)
        return get_plot(n_days, top_n)

In this example, our inputs are the “value” properties of the components that have the ids “slider-day’” and  “slider-top-breweries”. Our output is the “children” property of the component with the id “ratio-scatter-plot”. When the input values are changed, the decorator function will be called automatically and the output on the scatter is updated. Learn more about callbacks from the examples in the docs.
It’s worth noting, that the scatter plot may not be displayed correctly when the page is loaded. To avoid this scenario we need to specify its initial state and produce a scatter plot from the saved CSV file stored in the data folder. Then, when changing the slider values, all data will be taken directly from the Clickhouse tables.

Add a few more lines responsible for deployment and our app is ready to run:

application = app.server

if __name__ == '__main__':, port=8000)

Next, we need to  deploy our app to AWS BeansTalk and the first part of our Bootstrap Dashboard is completed:

Thanks for reading the first part of our series about Bootstrap Dashboards, in the next one we are going to add more new components, improved callbacks, and talk about tables in Bootstrap.

View the code on Github

 No comments    128   7 mon   bootstrap   dash   data analytics   untappd

VIsualizing COVID-19 in Russia with Plotly

Estimated read time – 9 min

Maps are widely used in data visualization, it’s a great tool to display statistics for certain areas, regions, and cities. Before displaying the map we need to encode each region or any other administrative unit. Choropleth map gets divided into polygons and multipolygons with latitude and longitude coordinates. Plotly has a built-in solution for plotting choropleth map for America and Europe regions, however, Russia is not included yet. So we decided to use an existing GeoJSON file to map administrative regions of Russia and display the latest COVID-19 stats with Plotly.

from urllib.request import urlopen
import json
import requests
import pandas as pd
from selenium import webdriver
from bs4 import BeautifulSoup as bs
import plotly.graph_objects as go

Modifying GeoJSON

First, we need to download a public GeoJSON file with the boundaries for the Federal subjects of Russia. The file already contains some information, such as region names, but it’s still doesn’t fit the required format and missing region identifiers.

with urlopen('') as response:
    counties = json.load(response)

Besides that, there are slight differences in the namings. For example, Bashkortostan on стопкоронавирус.рф, the site we are going to scrape data from, it’s listed as “The Republic of Bashkortostan”, while in our GeoJSON file it’s simply named “Bashkortostan”. These differences should be eliminated to avoid possible confusion. Also, the names should start with a capital.

regions_republic_1 = ['Бурятия', 'Тыва', 'Адыгея', 'Татарстан', 'Марий Эл',
                      'Чувашия', 'Северная Осетия – Алания', 'Алтай',
                      'Дагестан', 'Ингушетия', 'Башкортостан']
regions_republic_2 = ['Удмуртская республика', 'Кабардино-Балкарская республика',
                      'Карачаево-Черкесская республика', 'Чеченская республика']
for k in range(len(counties['features'])):
    counties['features'][k]['id'] = k
    if counties['features'][k]['properties']['name'] in regions_republic_1:
        counties['features'][k]['properties']['name'] = 'Республика ' + counties['features'][k]['properties']['name']
    elif counties['features'][k]['properties']['name'] == 'Ханты-Мансийский автономный округ - Югра':
        counties['features'][k]['properties']['name'] = 'Ханты-Мансийский АО'
    elif counties['features'][k]['properties']['name'] in regions_republic_2:
        counties['features'][k]['properties']['name'] = counties['features'][k]['properties']['name'].title()

It’s time to create a DataFrame from the resulting GeoJSON file with the regions of Russia, we’ll take the identifiers and names.

region_id_list = []
regions_list = []
for k in range(len(counties['features'])):
df_regions = pd.DataFrame()
df_regions['region_id'] = region_id_list
df_regions['region_name'] = regions_list

As a result, our DataFrame looks like the following:

Data Scraping

We need to scrape the data stored in this table:

Let’s use the Selenium library for this task. We need to navigate to the webpage and convert it into a BeautifulSoup object

driver = webdriver.Chrome()
source_data = driver.page_source
soup = bs(source_data, 'lxml')

The region names are wrapped with <th> tags, while the latest data is stored in table cells, each one is defined with a <td> tag.

divs_data = soup.find_all('td')

The divs_data list should return something like this:

The data is grouped in one line, this includes both new cases and active ones. It is noticeable that each region corresponds to five values, for Moscow these are the first five, for Moscow Region the next five and so on. We can use this pattern to create five lists and populate with values according to the index. The first value will be appended to the list with active cases, the second value to the list of new ones, etc. After every five values, the index will be reset to zero.

count = 1
for td in divs_data:
    if count == 1:
    elif count == 2:
    elif count == 3:
    elif count == 4:
    elif count == 5:
        count = 0
    count += 1

The next step is to extract the region names from the table, they are stored within the col-region class. We also need to clean up the data by eliminating extra white spaces and line breaks.

divs_region_names = soup.find_all('th', {'class':'col-region'})
region_names_list = []
for i in range(1, len(divs_region_names)):
    region_name = divs_region_names[i].text
    region_name = region_name.replace('\n', '').replace('  ', '')

Create a DataFrame:

df = pd.DataFrame()
df['region_name'] = region_names_list
df['sick'] = sick_list
df['new'] = new_list
df['cases'] = cases_list
df['healed'] = healed_list
df['died'] = died_list

After reviewing our data once again we detected white space under the index 10. This should be fixed immediately, otherwise, we may run into problems.

df.loc[10, 'region_name'] = df[df.region_name == 'Челябинская область '].region_name.item().strip(' ')

Finally, we can merge our DataFrame on the region_name column, so that the resulted table will include a column with region id, which is required to make a choropleth map.

df = df.merge(df_regions, on='region_name')

Creating a choropleth map with Plotly

Let’s create a new figure and pass a choroplethmapbox object to it. The geojson parameter will accept the counties variable with the GeoJSON file, assign the region_id to locations. The z parameter represents the data to be color-coded, in this example we’re passing the number of new cases for each region. Assign the region names to text. The colorscale parameter accepts lists with values ranging from 0 to 1 and RGB color codes. Here, the palette changes from green to yellow and then red, depending on the number of active cases. By passing the values stored in customdata we can change our hovertemplate.

fig = go.Figure(go.Choroplethmapbox(geojson=counties,
                           colorscale=[[0, 'rgb(34, 150, 79)'],
                                       [0.2, 'rgb(249, 247, 174)'],
                                       [0.8, 'rgb(253, 172, 99)'],
                                       [1, 'rgb(212, 50, 44)']],
                           customdata=np.stack([df['cases'], df['died'], df['sick'], df['healed']], axis=-1),
                           hovertemplate='<b>%{text}</b>'+ '<br>' +
                                         'New cases: %{z}' + '<br>' +
                                         'Active cases: %{customdata[0]}' + '<br>' +
                                         'Deaths: %{customdata[1]}' + '<br>' +
                                         'Total cases: %{customdata[2]}' + '<br>' +
                                         'Recovered: %{customdata[3]}' +
                           hoverinfo='text, z'))

Let’s customize the map, we will use a ready-to-go neutral template, called carto-positron. Set the parameters and display the map:
mapbox_zoom: responsible for zooming;
mapbox_center: centers the map;
marker_line_width: border width (we removed the borders by setting this parameter to 0);
margin: usually accepts 0 values to make the map wider.

                  mapbox_zoom=1, mapbox_center = {"lat": 66, "lon": 94})

And here is our map. According to the plot, we can say that the highest number of cases per day is happening in Moscow – 608 new cases. It’s really high compared to the other regions, and especially to Nenets Autonomous Okrug, where this number is surprisingly low.

View the code on GitHub

 No comments    979   9 mon   dash   data analytics   plotly   python

Deploying Analytical Web App with AWS Elastic Beanstalk

Estimated read time – 6 min

If you need to deploy a web application and there’s an AWS EC2 Instance at hand, why not use Elastic Beanstalk? This is an AWS service that allows us to orchestrate many other ones, including EC2, S3, Simple Notification Service, CloudWatch, etc.

Setting things up

Previously, in our article “Building a Plotly Dashboard with dynamic sliders in Python” we created a project with two scripts: – creates a dashboard on a local server, and – returns a scatter plot with Untappd breweries from Building a scatter plot for Untappd Breweries. Let’s modify the script a bit to make it run with Elastic Beanstalk. Assign app.server to the application variable, it should look something like this:

application = app.server

if __name__ == '__main__':, port=8080)

Before deploying our app we need to create a compressed archive. This archive should contain all the necessary files, including requirements.txt that specifies what python packages are required to run the project. Just type pip freeze in your terminal window and save the output to a file:

pip freeze > requirements.txt

Now we can create a compressed archive. Unix-based systems have a built-in zip command for archiving and compression:

zip deploy_v0 requirements.txt

Application and Environment

Navigate to  Elastic Beanstalk, click the “Applications” section and then “Create a new application”.

Fill in the necessary fields by specifying your app name and its description. After this, we are suggested to assign metadata and tag our app. The format of the tag is similar to a dictionary in Python, it’s a key-value pair, where the value of a key is unique. Once you’re ready to continue click the orange “Create’” button.

After this step, you will see a list of environments available for your app, which is initially empty. Click “ Create a new environment”

Since we are working with a web app, we need to select a web server environment:

On the next step we need to specify our environment name and also choose a domain name, if available:

Next, we select the platform for our app, which is written in Python:

Now we can upload the file with our app, click “ Upload your code” and attach the compressed file. Afterward, click “Create environment”.

You will see a terminal window with event logs. We have a couple of minutes for a coffee break.

Now our app is up and running, if you need to upload a new version, just create a new archive with updated files and click the” Upload and deploy” button again. If everything’s done right, you will see something like this:

We can switch to the site with our dashboard by following the link above. Using the  <iframe> tag our dashboard can be embedded into any other site.

<iframe id="igraph" scrolling="no" style="border:none;" seamless="seamless" src="" height="1100" width="800"></iframe>

As a result, you can see the following dashboard:

View the code on Github

 No comments    580   9 mon   Amazon Web Services   AWS   dash   data analytics   python

Building a Plotly Dashboard with dynamic sliders in Python

Estimated read time – 2 min

Recently we discussed how to use Plotly and built a scatter plot to display the ratio between the number of reviews and the average rating for Russian Breweries registered on Untappd. Each marker on the plot has two properties, the registration period and the beer range. And today we are going to introduce you to Dash, a Python framework for building analytical web applications. First, create a new file name with a get_scatter_plot(n_days, top_n) function from the previous article.

import dash
import dash_core_components as dcc
import dash_html_components as html
from get_plots import get_scatter_plot

After importing the necessary libraries we need to load CSS styles and initiate our web app:

external_stylesheets = ['']
app = dash.Dash(__name__, external_stylesheets=external_stylesheets)

Create a dashboard structure:

app.layout = html.Div(children=[
       ]) ,
           html.H6('Time period (days)'),
               marks={i: str(i) for i in range(0, 100, 10)}
           html.H6('Number of breweries from the top'),
               marks={i: str(i) for i in range(0, 500, 50)})

Now we have a plot and two sliders, each with its id and parameters: minimum value, maximum value, step, and initial value. Since the sliders data will be displayed in the plot we need to create a callback. Output is the first argument that displays our plot, the following Input parameters accept values on which the plot depends.

   dash.dependencies.Output('fig1', 'figure'),
   [dash.dependencies.Input('slider-day1', 'value'),
    dash.dependencies.Input('slider-top1', 'value')])
def output_fig(n_days, top_n):
    get_scatter_plot(n_days, top_n)

At the end of our script we will add the following line to run our code :

if __name__ == '__main__':

Now, whenever the script is running our local IP address will be displayed in the terminal. Let’s open it in a web browser to view our interactive dashboard, it’s updated automatically when moving the sliders.

 No comments    457   9 mon   dash   data analytics   plotly   python   untappd

Sentiment analysis of Russians on Constitutional Amendments

Estimated read time – 11 min

In today’s article, we are going to use public data from 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 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”

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='', 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('', 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 =
    			    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 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('', 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='', 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.

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