Python, like every other object-oriented language, allows you to define classes to create objects. In-built Python classes are the most common data types in Python, such as strings, lists, dictionaries, and so on.
A class is a collection of instance variables and related methods that define a particular object type. You can think of a class as an object’s blueprint or template. Attributes are the names given to the variables that make up a class. Click here for more info.
Whether you need Python tools for data science, machine learning, web development, or anything in between, this list has you covered. Here’s the link to the article.
Data visualization has gained massive popularity in recent years owing to the demand for data. In a business setup, these business intelligence tools can help in analyzing all the data and monitor performance to enhance growth for the firm, and productivity for the employees. With the world switching to digital means all together in the year that went by, data is now considered fuel for every small, medium, or big firm.
In such a scenario, what sounds better- a spreadsheet that mentions the date, time, sales, and profit OR a colorful, descriptive bar chart interactively explaining all the details? Our vote goes to the latter.
An essential part of any business strategy, data visualization is the process of collecting data and transforming it into a meaningful visualization to support decision-making. These visualizations could be in the form of bar charts, maps, or anything that is visually appealing and interactive. They convey the information to the viewer by simply looking at them, whereas normally one needs to read spreadsheets or text reports to understand the data.
Talking of the best data visualization tools used by analysts in various industries according to their specifications and applications, they comprise Power BI, Tableau and Python Dash. All these software programs help businesses make decisions questions faster.
Power BI is a Data Visualization and Business Intelligence tool provided by Microsoft. It can collect data from different data sources like Excel spreadsheets, on-premise database, cloud database and convert them into meaningful reports and dashboards. Its features such as creating quick insights, Q&A, Embedded Report, and Self Service BI made it top among all BI tools. It is also robust and always ready for extensive modeling and real-time analytics, as well as custom visual development.
Tableau offers business analysts to take business decisions by its feature, data visualization available to all business users of any background. It can establish a connection with any data source (Excel, local/on-premise database, cloud database).
Tableau is the fastest growing Data Visualization Tool among all visualization tools. Its visualizations are created as worksheets and dashboards. The beauty of tableau is that it does not require any technical or programming knowledge to create or develop reports and dashboards.
Python Dash
Dash is a python framework created by plotly for creating interactive web applications. With Dash, you don’t have to learn HTML, CSS and Javascript in order to create interactive dashboards, you only need python. Dash is open source and the application build using this framework are viewed on the web browser.
Dash is Downloaded 600,000 times per month, it’s the original low-code framework for rapidly building data apps in Python, R, Julia and F#(experimental).
It’s written on top of Plotly.js and React.js. Dash is ideal for building and deploying data apps with customized user interfaces. It’s particularly suited for anyone who works with data.
Through a couple of simple patterns, Dash abstracts away all of the technologies and protocols that are required to build a full-stack web app with interactive data visualization.
Dash is simple enough that you can bind a user interface to your code in less than 10 minutes.
Dash apps are rendered in the web browser. You can deploy the apps to VMs or kubermetes clusters and then share them through URLs. Since Dash apps are viewed in the web browser, Dash is inherently cross-platform and mobile ready.
There is a lot behind the framework. To learn more about how it’s built and what motivated Dash, read announcement letter or Dash is React for Python post.
Dash is an open source library released under the permissive MIT license. Plotly develops Dash and also offers a platform for writing and deploying Dash apps in an enterprise environment.
Python Dash is mostly suited for the quick and easy representation of big data which helps in analyzing and resolving issues. Power BI, on the other hand, has its data models focused on ingestion and building relatively complex models. Python is the best when it comes to handling streaming data.
Power BI vs. Tableau vs, Python Dash:
| Power BI | Tableau | Python Dash |
| It is provided by Microsoft | It is provided by Tableau | It is a Python library, provided by the Python Software Foundation |
| It is available at a moderate price | It is expensive than Power BI | It is an open-source programming language that is freely available for everyone to use. |
| Need a business/private email to open an account | Need a business/private email to open an account | Any email address is acceptable |
| Uses DAX for Measures and Calculated columns | Uses MDX for Measures and Dimensions | Uses dynamic, interpretive script programming language |
| Connect limited Data Sources but increases it Data Source connections in Monthly updates | It can connect to numerous Data Sources | Python has an ecosystem of modules and tools to collect data from multiple sources. |
| Can handle large Datasets using Premium capacity | Can handle large Datasets | Can handle large Datasets |
| It provides Account base subscription | It provides Key base subscription | No subscription necessary |
| Embedding report is easy | Embedding report is a Real time challenge | Dash is simple enough that you can bind a user interface to your code in less than 10 minutes |
| It is integrated with Microsoft Azure, which helps in analyzing the data and understanding the patterns of the product | Tableau has built-in machine learning capabilities which makes it suitable for doing ML operations on datasets | Dash is integrated with Python which offers multiple libraries in graphics that are packed with different features. Python is preferred for data analysis of the highest levels |
| It supports R and Python language-based visualizations | It provides full integrated support for R and Python | Dash is ideal for building and deploying data apps with customized user interfaces. |
Data Analytics field has been changed over time from traditional bi practice, embedded bi and collaborative bi. Initially, data analytics was led by companies like IBM, Oracle, SAP but now this is not the situation. Now, this led by companies like Microsoft, Tableau and Python because of their features like Embedded BI Collaborative BI, Data Blending, Data Binding and Multi Data Source Connection.
Power BI, Tableau and Python Dash have their own Pros and Cons. The right product can be chosen based on touchstones & priority.
| Touchstones | Power BI | Tableau | Python Dash |
| Description | A cloud-based business intelligence platform which offers an overview of critical data | A collection of intuitive business intelligence tools used for data discovery | A python framework created by Plotly for creating interactive web applications. It is best when it comes to handling Streaming Data |
| Visualization | Provides various visualizations | Provides a larger set of visualizations than Power BI | Provides numerous set of visualizations |
| OS Support | Only Windows | Windows and Macintosh OS | Mac OS, Windows, Linux, AWS, and others |
| Graphical features | Regular charts, graphs, and maps | Any category of charts, bars, and graphs | Dash is ideal for building and deploying data apps with customized user interfaces. Any category of charts, bars and graphs |
| Cost | Cheaper | Costly | Free |
| Organization | Suitable for Small, Medium & Large type of Organization | Suitable for Medium & Large type of Organization | Suitable for Small, Medium & Large type of Organization |
Data Analysis Expressions (DAX) is a programming language that is used throughout Microsoft Power BI for creating calculated columns, measures, and custom tables. It is a collection of functions, operators, and constants that can be used in a formula, or expression, to calculate and return one or more values.
In Multidimensional Expressions (MDX), a measure is a named DAX expression that is resolved by calculating the expression to return a value in a Tabular Model. This innocuous definition covers an incredible amount of ground.
Power BI is a Data Visualization and Business Intelligence tool provided by Microsoft. It can collect data from different data sources like Excel spreadsheets, on-premise database, cloud database and convert them into meaningful reports and dashboards. Its features such as creating quick insights, Q&A, Embedded Report, and Self Service BI made it top among all BI tools. It is also robust and always ready for extensive modeling and real-time analytics, as well as custom visual development.
Tableau offers business analysts to take business decisions by its feature, data visualization available to all business users of any background. It can establish a connection with any data source (Excel, local/on-premise database, cloud database).
Tableau is the fastest growing Data Visualization Tool among all visualization tools. Its visualizations are created as worksheets and dashboards. The beauty of tableau is that it does not require any technical or programming knowledge to create or develop reports and dashboards.
Power BI vs. Tableau:
| Power BI | Tableau |
| It is provided by Microsoft | It is provided by Tableau |
| It is available at a moderate price | It is expensive than Power BI |
| Need a business/private email to open an account | Need a business/private email to open an account |
| Uses DAX for Measures and Calculated columns | Uses MDX for Measures and Dimensions |
| Connect limited Data Sources but increases it Data Source connections in Monthly updates | It can connect to numerous Data Sources |
| Can handle large Datasets using Premium capacity | Can handle large Datasets |
| It provides Account base subscription | It provides Key base subscription |
| Embedding report is easy | Embedding report is a Real time challenge |
| It is integrated with Microsoft Azure, which helps in analyzing the data and understanding the patterns of the product | Tableau has built-in machine learning capabilities which makes it suitable for doing ML operations on datasets |
| It supports R and Python language-based visualizations | It provides full integrated support for R and Python |
Data Analytics field has been changed over time from traditional bi practice embedded bi and collaborative bi. Initially, data analytics led by companies like IBM, Oracle, SAP but now this is not a situation. Now, this led by companies like Microsoft & Tableau because of their features like Embedded BI Collaborative BI, Data Blending, Multi Data Source Connection.
Both Power BI and Tableau have their own Pros and Cons. The right product can be chosen based on touchstones & priority.
| Touchstones | Power BI | Tableau |
| Description | A cloud-based business intelligence platform which offers an overview of critical data | A collection of intuitive business intelligence tools used for data discovery |
| Visualization | Provides various visualizations | Provides a larger set of visualizations than Power BI |
| OS Support | Only Windows | Windows and Macintosh OS |
| Graphical features | Regular charts, graphs, and maps | Any category of charts, bars, and graphs |
| Cost | Cheaper | Costly |
| Organization | Suitable for Small, Medium & Large type of Organization | Suitable for Medium & Large type of Organization |
Power BI is a Data Visualization & Business Intelligence tools that offer us to connect to single or multiple data sources and convert that connected raw data into impressive visual and share insights across an organization. It also offers us to embed the report into our application or website.
e.g.: – If you are using Power BI as well as Power App’s, a single gateway can be used for both which is dependent on the account you signed with it.
Tableau is a Business Intelligence & Data Visualization Tool that used to analyze our data visually. Users can create and share interactive reports & dashboards using it. It offers Data Blending to users to Connect multiple data sources.
Here is a link to Tableau
Power BI desktop is the best tool to analyze your data while you connect using Direct query (or) Live connections and might struggle handle huge if you import data into the application and at times it might get hung or simply crashes. However, in future monthly updates, Microsoft Product team will surely resolve this problem.
Tableau Creator – $70.00 & Tableau Online – $35 Monthly
Power BI & Tableau are most happening BI tools among all tools in business intelligence because of their features and capabilities like Embedded BI, Data Blending, Multi Data Source connection like Cloud databases and on-premise databases. They make sharing of reports and dashboards for the users, easy. Business Analyst without even having to access these tools can access reports & dashboards and take critical business decisions.
These two tools stood top in the BI market because of the attractive visualizations available. Power BI offers a feature of import of custom visual and creation of custom visual which is its beauty. These facts have made these BI tools most happening BI tools in the market till the date.
According to Gartner Magic Quadrant for Analytics and Business Intelligence Platforms report, the 1st choice is Power BI and 2nd top choice is Tableau in BI Tool in the present market.
Data Analysis Expressions (DAX) is a programming language that is used throughout Microsoft Power BI for creating calculated columns, measures, and custom tables. It is a collection of functions, operators, and constants that can be used in a formula, or expression, to calculate and return one or more values.
In Multidimensional Expressions (MDX), a measure is a named DAX expression that is resolved by calculating the expression to return a value in a Tabular Model. This innocuous definition covers an incredible amount of ground.
In this tutorial, I will discuss and go through a practical example on how to create a Python Dash Plotly App. I will create multiple charts for Data Visualization using Dynamic Callbacks which is also known as Pattern Matching Callbacks from Plotly.com. I will use data of The World Population to create the Dashboard App.
Pattern Matching Callbacks – Creating different charts for Data Visualization with callbacks. The users get much more power and control over the App. It gives the users much more flexibility to create callbacks for every set of inputs and outputs that doesn’t yet exist in the App.
MATCH will fire the callback when any of the component’s properties change. However, instead of passing all of the values into the callback, MATCH will pass just a single value into the callback. Instead of updating a single output, it will update the dynamic output that is “matched” with.
Let’s get started. Import the following libraries as listed below: I’m using Anaconda Jupyter Notebook, launch the CMD Prompt and install the following libraries if you don’t currently have them installed on your computer.
import dash #pip install dash from dash import dcc from dash import html from dash.dependencies import Input, Output, ALL, State, MATCH, ALLSMALLER import plotly.express as px #pip install plotly==5.2.2 import pandas as pd #pip install pandas import numpy as np #pip install numpy
We then read in the Panda data frame file. I have download the file to my computer but you can get it from my Github repository link.
df = pd.read_csv("Documents/Data Science/population.csv") #https://github.com/Valnjee/datascience/blob/master/population.csv
print(df)
country year population 0 China 2020.0 1.439324e+09 1 China 2019.0 1.433784e+09 2 China 2018.0 1.427648e+09 3 China 2017.0 1.421022e+09 4 China 2016.0 1.414049e+09 ... ... ... ... 4180 United States 1965.0 1.997337e+08 4181 United States 1960.0 1.867206e+08 4182 United States 1955.0 1.716853e+08 4183 India 1960.0 4.505477e+08 4184 India 1955.0 4.098806e+08 [4185 rows x 3 columns]
Make sure to clean the data by dropping all the null values.
# dropping null values df = df.dropna()
print(df.head(10))
country year population 0 China 2020.0 1.439324e+09 1 China 2019.0 1.433784e+09 2 China 2018.0 1.427648e+09 3 China 2017.0 1.421022e+09 4 China 2016.0 1.414049e+09 5 China 2015.0 1.406848e+09 6 China 2010.0 1.368811e+09 7 China 2005.0 1.330776e+09 8 China 2000.0 1.290551e+09 9 China 1995.0 1.240921e+09
Here we design the layout in HTML with the button. Every option will go into the children.
app = dash.Dash(__name__)
app.layout = html.Div([
html.H1("The World Population Dashboard with Dynamic Callbacks", style={"textAlign":"center"}),
html.Hr(),
html.P("Add as many charts for Data Visualization:"),
html.Div(children=[
html.Button('Add Chart', id='add-chart', n_clicks=0),
]),
html.Div(id='container', children=[])
])
The new child is append to the div_children. Every click triggers the callback, then you get another child to append to the div_children with everything created in it. The dcc.RadioItems have options of 4 charts.
Output – displays the chart.
State – saves the input of the children.
@app.callback(
Output('container', 'children'),
[Input('add-chart', 'n_clicks')],
[State('container', 'children')]
)
def display_graphs(n_clicks, div_children):
new_child = html.Div(
style={'width': '45%', 'display': 'inline-block', 'outline': 'thin lightgrey solid', 'padding': 10},
children=[
dcc.Graph(
id={
'type': 'dynamic-graph',
'index': n_clicks
},
figure={}
),
dcc.RadioItems(
id={
'type': 'dynamic-choice',
'index': n_clicks
},
options=[{'label': 'Bar Chart', 'value': 'bar'},
{'label': 'Line Chart', 'value': 'line'},
{'label': 'Scatter Chart', 'value': 'scatter'},
{'label': 'Pie Chart', 'value': 'pie'}],
value='bar',
),
dcc.Dropdown(
id={
'type': 'dynamic-dpn-s',
'index': n_clicks
},
options=[{'label': s, 'value': s} for s in np.sort(df['country'].unique())],
multi=True,
value=["United States", "China"],
),
dcc.Dropdown(
id={
'type': 'dynamic-dpn-ctg',
'index': n_clicks
},
options=[{'label': c, 'value': c} for c in ['country']],
value='country',
clearable=False
),
dcc.Dropdown(
id={
'type': 'dynamic-dpn-num',
'index': n_clicks
},
options=[{'label': n, 'value': n} for n in ['population']],
value='population',
clearable=False
)
]
)
div_children.append(new_child)
return div_children
html.Br()
display_dropdowns callback returns two elements with the same index: a dropdown and a div.MATCH selector. With this selector, we’re asking Dash to:value property of any component with the id 'type': 'dynamic-dropdown' changes: Input({'type': 'dynamic-dropdown', 'index': MATCH}, 'value')'type': 'dynamic-output' and the index that matches the same index of the input: Output({'type': 'dynamic-output', 'index': MATCH}, 'children')id of the dropdown into the callback: State({'type': 'dynamic-dropdown', 'index': MATCH}, 'id')MATCH selector, only a single value is passed into the callback for each Input or State. MATCH contract is that Dash will update whichever output has the same dynamic ID as the id. In this case, the “dynamic ID” is the value of the index and we’ve designed our layout to return dropdowns & divs with identical values of index.id as State in the callback.dash.callback_context to access the inputs and state and to know which input changed. outputs_list is particularly useful with MATCH because it can tell you which dynamic component this particular invocation of the callback is responsible for updating. Here is what that data might look like with two dropdowns rendered on the page after we change the first dropdown.The second callback renders the chart interactively. It uses a dictionary of ‘type and ‘index’. The dynamic part of the callback is the input – component_id and the component_property which is the value. Input will trigger when the value of the component_id is changed which refers to the dynamic-dpn-s. The index is going to be matched with the ‘index’ : MATCH = 1.
dff – Always make a copy of the data frame.
Sometimes the user wants to see the data in different charts. With the multiple charts and dropdown options, the user gets to select the different countries he/she is interested in.
@app.callback(
Output({'type': 'dynamic-graph', 'index': MATCH}, 'figure'),
[Input(component_id={'type': 'dynamic-dpn-s', 'index': MATCH}, component_property='value'),
Input(component_id={'type': 'dynamic-dpn-ctg', 'index': MATCH}, component_property='value'),
Input(component_id={'type': 'dynamic-dpn-num', 'index': MATCH}, component_property='value'),
Input({'type': 'dynamic-choice', 'index': MATCH}, 'value')]
)
def update_graph(s_value, ctg_value, num_value, chart_choice):
print(s_value)
dff = df[df['country'].isin(s_value)]
if chart_choice == 'bar':
dff = dff.groupby([ctg_value], as_index=False)[['population']].sum()
fig = px.bar(dff, x='country', y=num_value)
return fig
elif chart_choice == 'line':
if len(s_value) == 0:
return {}
else:
dff = dff.groupby([ctg_value, 'year'], as_index=False)[['population']].sum()
fig = px.line(dff, x='year', y=num_value, color=ctg_value)
return fig
elif chart_choice == 'scatter':
if len(s_value) == 1:
return {}
else:
dff = dff.groupby([ctg_value, 'year'], as_index=False)[['population']].sum()
fig = px.scatter(dff, x='year', y=num_value, color=ctg_value)
return fig
elif chart_choice == 'pie':
fig = px.pie(dff, names=ctg_value, values=num_value)
return fig
Here is the link on how to setup a development server.
if __name__ == '__main__':
app.run_server(debug=False)
Dash is running on http://127.0.0.1:8050/ * Serving Flask app "__main__" (lazy loading) * Environment: production WARNING: This is a development server. Do not use it in a production deployment. Use a production WSGI server instead. * Debug mode: off
CONGRATULATIONS! You have just learnt how to develop Web apps. Dash Plotly gives data scientists the power to build web apps to interact with data, deep learning, artificial intelligence and machine learning models.
In this introductory article, we’ve explored how to develop dashboard apps using Dash Plotly. Although it’s a trivial application, it illustrates the core concepts of this technology. Besides development, we’ve also seen how effortless it is to code in Plotly.
Dash is the original low-code framework for rapidly building data apps in Python, R, Julia, and F# (experimental).
Written on top of Plotly.js and React.js, Dash is ideal for building and deploying data apps with customized user interfaces. It’s particularly suited for anyone who works with data.
Through a couple of simple patterns, Dash abstracts away all of the technologies and protocols that are required to build a full-stack web app with interactive data visualization.
Dash is simple enough that you can bind a user interface to your code in less than 10 minutes.
Dash apps are rendered in the web browser. You can deploy your apps to VMs or Kubernetes clusters and then share them through URLs. Since Dash apps are viewed in the web browser, Dash is inherently cross-platform and mobile ready.
There is a lot behind the framework. To learn more about how it is built and what motivated Dash, read their announcement letter or their post Dash is React for Python.
Dash is an open source library released under the permissive MIT license. Plotly develops Dash and also offers a platform for writing and deploying Dash apps in an enterprise environment. If you’re interested, please get in touch.
Web Apps are great for Data Visualization and gives the clients more flexibilities to navigate and maneuver the data. It’s very user friendly and aid in simplifying the understanding of the DATA.
Data Visualization Using Python
Web2goTech 