LEFT JOIN: blog on analytics, visualisation & data science

Pandas Profiling in action: reviewing a new EDA library on Superstore Sales dataset

Estimated read time – 8 min

Before moving directly to data analysis we need to understand what type of data we are going to work with. In today’s material, we will take a closer look at the SuperStore Sales dataset, specifically at the Orders column. It includes customer shopping data of a Canadian online supermarket, such as order, product and customer ids, type of shipping, prices, product categories, names and etc. You can find more information about this dataset on GitHub. After creating a pandas DataFrame we can simply use the describe() method to get a sense of our data.

import pandas as pd

df = pd.read_csv('superstore_sales_orders.csv', decimal=',')

And oftentimes it leads to such a mess:

The source code of this library is available on GitHub

If we spend some time trying to get a grasp of this descriptive table, we can find out that customers are more likely to choose “Regular air” as a shipping type or that the majority of orders were made from Ontario. Nevertheless, there is a better tool to describe the dataset in more detail  – the pandas-profiling library. Just pass a DataFrame to it and we will get a generated HTML page with a detailed description of our dataset:

import pandas_profiling
profile = pandas_profiling.ProfileReport(df)

As you see, it returned a page with 6 sections, namely: overview, variables, interactions and correlations, number of missing values, and dataset samples.

View a full version of the Pandas Profiling Report

Data overview

Let’s move to the first subsection called “Overview”. Pandas Profiling provided the following stats: number of variables, number of observations, missing cells, duplicates, and file size. The  Variable types column shows that our DataFrame consists of 12 categorical and 9 numerical variables.

The  “Reproduction” subsection stores technical information, showing how long it took to analyze the dataset, currently installed version , configuration info and etc.

The  “Warnings” subsection informs about possible issues in the dataset structure. Now, it warns us that the “Order Date” column has too many distinct values.


Moving further, this subsection contains a detailed description of each variable, displaying the number of duplicates and missing values stored, memory size, maximum and minimal values. Right next to the stats you can see the distribution of column values.

Clicking on  Toggle details you will see more expanded information: quartiles, median and other useful descriptive statistical indicators. The remaining tabs contain a histogram displayed on the main screen, top 10 frequent values and extremes.


This section displays how variables are interconnected on a hexbin plot: The graph looks not very obvious and clear, since the legend is lacking.


The section represents correlations between variables calculated in a variety of ways. For example, the first tab shows Pearson’s r-value. It is noticeable that Profit is positively correlated with Sales. You can get a detailed explanation to each coefficient by clicking on the Toggle correlation descriptions button.

Missing values

This section includes a bar chart, matrix, and dendrogram with the number of fields in each variable. For instance, the  Product Base Margin column is missing three values.


And the final section show the first and last 10 rows as chunks of a dataset, pretty similar to the  head() method in Pandas.

Key Takeaways

The library is definitely more focused on statistics than Pandas, one can get useful descriptive stats for each variable and see their correlation. It provides a comprehensive report on a dataset in a user-friendly way, allowing to undertake an initial investigation and get a sense of data.
Still, the library has its shortfalls. If your dataset is fairly large the report generation time may be extended up to several hours. It’s a great tool for automating EDA tasks, however, it can’t do all the work for you and some details may be overlooked. If you are just getting started with data analysis, we would highly recommend to start it with pandas. It will solidify your knowledge and boost confidence in working with data.

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

Estimated read time – 13 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 draw.io 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 application.py. 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:

- application.py
- 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='ec2-3-16-148-63.us-east-2.compute.amazonaws.com',

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__':
    application.run(debug=True, 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    4   6 d   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('https://raw.githubusercontent.com/codeforamerica/click_that_hood/master/public/data/russia.geojson') 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    492   1 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: application.py – creates a dashboard on a local server, and get_plots.py – returns a scatter plot with Untappd breweries from Building a scatter plot for Untappd Breweries. Let’s modify the application.py 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__':
   application.run(debug=True, 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 application.py get_plots.py 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="http://dashboard1-env.eba-fvfdgmks.us-east-2.elasticbeanstalk.com/" height="1100" width="800"></iframe>

As a result, you can see the following dashboard:

View the code on Github

 No comments    316   1 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 app.py 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 = ['https://codepen.io/chriddyp/pen/bWLwgP.css']
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    215   1 mon   dash   data analytics   plotly   python   untappd
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