How to create a map visualization in Power BI with example

Map visualization in Power BI

Map visualization in Power BI is a very useful feature to show location-wise values. In this article, I will demonstrate how to use this feature with a practical example.

I have used maps a lot with ArcGIS to create boundary maps and display locations as supplementary information. It certainly requires expertise and considerable time.

Being data analytics guy, I always wondered if there is any tool which will make the mapping task easier and I can focus more on the analysis part. And here comes the Map feature of Power BI to make my life easier.

This feature has been well described by the Microsoft in its Power BI help document. So no point to repeat the same thing here. Rather I will discuss the map feature with some real-world data so that you can apply the same with yours.

I will also discuss some common problems a Power BI user may face while using this feature for the first time (which I have also faced). Certain things you need to keep in mind while using the map in Power BI which are:

  • The data you want to map needs to be correctly geocoded. Otherwise, it will cause ambiguity while mapping the locations
  • The purpose of map visualization is to show the geospatial information or the distance between the locations. So, your project should have variables assigned to locations
  • To remove any ambiguity regarding any locations you may have to produce the latitude and longitude of locations in decimal values.

Types of Map visualization in Power BI

Power BI offers three types of Map visualizations.

  • A simple Map option creates a bubble map. The size of the bubble varies with the variable set in the value field.
  • A filled map option. It displays the area of particular locations.
  • The third option is to create Map using ArcGIS tool.

All these options are available in Power BI visualization pane as shown in the below image.

Different Map visualizations of Power BI
Different Map visualizations of Power BI

All of the above options are powered by Bing. So you need an active internet connection to use these feature. The map gets created dynamically with the help of internet.

Power BI is very much flexible in order to take location values either city name or PIN number or specific latitude or longitude.

Many a time you may face an error thrown by Power BI due to some ambiguity in the location name. For example, a state and district both have the same name( a good instance is Washington DC of US).

In such cases Power BI itself can not decide how to mark the particular location you want. And so you need to guide Power BI to mention if it is a state name or district name.

You can do that either by editing in Power query or providing specific latitude and longitude of the location.

Practical example: map visualization of crop production in India

Without much ado. Lets dive into the example where I tried this Map option to create some beautiful and informative maps.

The data I have used here contains the crop production statistics of different states of India. The data I have collected from website. And it is in a very refined state. So you don’t need to do any serious transformation.

Just some removal of blank values, filtering as I have shown in this article is enough to start with. Also, I have made custom keys from the main table for the purpose of data modelling (here is the process).

The data set contains following columns

  • State_Name” containing different states of India.
  • District_Name” containing state-wise different districts name
  • Crop_Year” has the year of production
  • Season” has the particular season of crop production
  • Crop” is the particular crop name
  • Area_ha” this column contains a total area of under the crop in Hectare
  • Production_tonnes” has a total production of the season in Tonnes

Creating the Bubble map

This is the most interesting Map visualization offered by Power BI and here we will create this map first. Bubble map is very frequently used in data analytics. The size of bubble helps us to compare the magnitude of any parameter across different locations.

Here we need to click the Map icon from the visualization pane as shown in the first image.

Then as shown in the below figure I have selected “India_states” for the location field and “Crop_productivity” for the Size field. The purpose is to show the crop productivity of different states on the map of India.

Selecting location and size for bubble in map visualization
Selecting location and size for bubble in map visualization

So here is the report output. See the below screenshot of my Power BI desktop report page. Different states of India has been located with the bubbles. The bubbles are of different size and proportional to values of crop productivity.

You can see now how it has become to visually compare the crop productivity of different states of India. Which is otherwise a tough call in a table form visualization.

The bubble map with different state wise crop productivity
The bubble map with different state wise crop productivity

Changing the fields to add more information in the map visualization

Lets make little changes in the fields of the map we created. And try to add some more information. Suppose we need the district level information. We also want the map to show different states separately.

To do that we can use the field “Legend” which we have left blank earlier. See that we have added “India_states” in that field. Also this time I have replaced “Crop_productivity” with “Area_ha“.

Here I want to mention one important point. You should drag the particular field from the right pane to the left drop-down fields instead of selecting them with left click (see the figure below).

If you select them with a click then Power BI automatically assigns them field which may not lead to something you want.

Selecting the fields
Selecting the fields

See the result below. All the states are shown with different colour shades. Different districts within states have bubbles with size proportional to the cropped area. Such a map can quickly provide an overview of the crop production scenario with a glance.

You can see the “Bing” icon at the extreme bottom-left corner. It suggests that all these map functionalities are provided by Microsoft Bing. So you need to be online to create the maps.

Formatting the Map visualization in Power BI

The basic map feature has several formatting options. You have all these formatting options available under the visualization pane with a brush icon as shown in the below figure.

Format option for basic map visualization
Format option for basic map visualization

With the help of this format option, you can change the category, data colors, bubble size, map styles, map controls and many other visual appeal of the map you created.

You can also change the themes of your map if you are using Power BI “online service”. By default the “Road” theme is applied. The other themes available to opt from are Aerial, Dark, Light and Grey scale. Power BI desktop does not have this option.

Filled map

This is another basic map visualization option where the territory of the particular location is displayed as a filled one. For example, if we apply the same criteria as we have set for the map option, then it looks like as in the below image.

To create this map I have just clicked the “Filled map” icon in the visualization selecting the existing map. And the old map has been replaced by this new filled map in the report.

Filled map visualization in Power BI
Filled map visualization in Power BI

Here we can see that the selected states are simple filled with different shades as shown in the legend. The same format options are also available for filled map also. If you want some variations in its appearance other than the default one, you can apply them.

ArcGIS map visualization in Power BI

The last and third map visualization option available in Power BI is ArcGIS map. The process of creating such map is also same. You just need to select the ArcGIS option while selecting the already created filled map.

See the below image the same map with the same options has been converted to an ArcGIS map.

ArcGIS map visualization in Power BI
ArcGIS map visualization in Power BI

An ArcGIS map has some advanced features which other map visualizations do not offer. These features are as given below:

  • When you zoom in and zoom out in the map the clustering features
  • It has advanced options like drivetime and distance radii
  • You can have heatmap visualization
  • You can add reference layers from ArcGIS online repositories
  • Some infographics options are also available as default in the report

All these advanced features come with some limitations like:

  • This map visualization option is not available for Power BI report server
  • In case you want to publish or embed the map in the web then this map option is not visible
  • The custom shape options are available only if the maps are added to the ArcGIS online and publicly shared

Final words

So, we have completed all the map visualization options available with Power BI. A practical data set I have used to demonstrate all of them.

I have kept this as simple as possible and discussed all the important functions so that you do not need to waste time. Otherwise, all the first time users invariably waste a lot of valuable time looking for the correct steps.

Hope you will find the article helpful. I would appreciate if you point out any other queries regarding the topic and suggestions by commenting below. I would like to answer them and also try to improve the content further.

How to use Measures in Power BI? a practical example

Measures in Power BI

Measures in Power BI are really a beautiful feature. They are fast in the calculation, has the benefit of reusability. Measures can be applied to multiple tables. We create measures to obtain counts, averages, sums, ranking, percentiles, aggregating year to dates and many more handy calculations.

Measures are dynamically calculated. And most importantly it gets imported in other applications like MS Excel through its report format. A good data modelling and creating useful measures are among the core skills Power BI Pro users have.

So, in nutshell, measures in Power BI are one of the most important features you must know and use. In this article, I am going to discuss how to use them with a simple application using real-world data.

I would suggest going through this article for in-depth knowledge of Power BI measures. It is from the creator Microsoft itself. This article will give you the overview and the necessary idea about measures to apply the steps described here.

NB: Being a non-native English speaker, I always take extra care to proofread my articles with Grammarly. It is the best grammar and spellchecker available online. Read here my review of using Grammarly for more than two years. 

The data set used

The data set used here is the same used to demonstrate the data modelling process in Power BI. For the first time readers, a brief description of the data is given below.

The data I used here is crop production data of different states of India. This data is accessible at This is real-world data and without any garbage.

The columns are

  • State_Name” containing different states of India.
  • District_Name” containing state-wise different districts name
  • Crop_Year” has the year of production
  • Season” has the particular season of crop production
  • Crop” is the particular crop name
  • Area_ha” this column contains a total area of under the crop in Hectare
  • Production_tonnes” has a total production of the season in Tonnes

Use of DAX (Data Analysis Expressions) to create Measures

We use Data Analysis Expressions (DAX) in Power BI to create measures. The same DAX is used for Excel formulas. The only difference is, in Excel, DAX is applied on cells and columns whereas, in Power BI, DAX is applied on tables and columns of the data model. And they are very fast in calculations.

Implicit and explicit measures: which one you should use?

Measures are of two kind one is implicit measures which gets calculated by default and the other one is explicit measure. Now both of these measures yield the same result.

But there are subtle differences between the calculation approaches of these two. Which you should know and use accordingly.

Implicit measures are easy to calculate and you need not write any DAX expressions for it. But it has its own share of disadvantages. Which is Implicit measures are not reusable.

On the other hand, explicit measures require some knowledge of writing DAX expressions to achieve desires result. But they can be reused. Also, you can make little changes in the DAX expression you wrote to use it for other calculations.

An example of limitations of Implicit Measures is its inability to handle division by 0(zero). If there are some calculations have divisions and your data contains some zeros in the numerator, then Implicit Measures will give garbage values.

But such a situation is well handled by Explicit Measures. Where these measures through a tiny error displaying “NAN” i.e. “Not A Number”. In this way, you can understand the possible problem and can correct it.

New Measure and Quick Measure

These two are another options provided by Power BI when you need to create measures. Now again, creating a Quick Measure is easy and does not require writing DAX expressions.

Where creating New Measure needs writing few lines of DAX expressions. But it has some advantages over Quick Measures. I will discuss it with example here in a bit.

The figure below displays the options for creating “New measure”, “Quick measure”, “New column” and “New table”.

Different measures option in Power BI
Different measures option in Power BI

While in case of “Quick measure” a wizard opens which help us to define the calculation. Again it is easy as we don’t have to bother about writing DAX at the cost of losing the flexibility and reusability offered by it.

Common mistake while creating new measure

Here I would like to mention a very common mistake while you are in the process of creating you first ever New measure. It may sound silly but related to very basic concept of measure in Power BI.

The role of a measure in Power BI is to apply some aggregate functions of the column values in the report. So, it is important to remember that measures can not be created using single row values.

See the error thrown by Power BI in the below figure when I have tried to create a new measure using single row values. My intention was to calculate productivity by dividing crop production with the area.

Common mistake while creating measures for the first time
Common mistake while creating measures for the first time

And the error clearly directed me that a “single value” can not be determined. We need to create “New column” for that purpose.

Creating a “New column”

For creating a column to calculate the productivity from the crop production and area values, we will create a “New column” here. Simply click the “New column” option. A formula bar will appear where we need to write the DAX for the calculation.

Creating "New column"
Creating “New column”

Now see in the below figure where the DAX has been written for crop productivity calculation. Important to note that I have mentioned an alternate value as 0 at the end of the expression. It avoids the problem that arises whiling dividing by 0.

Creating new column with crop productivity
Creating new column with crop productivity

Creating “New measure”: an Explicit measure

While we create new measure by clicking the “New measure” option, a formula bar appears in the window. See the below figure. The “Fields” pane at the right also starts to display the Measure with a calculator icon. By default, a new measure has the name “Measure”.

Creating a "New measure"
Creating a “New measure”

Now we have written a DAX for calculating the average crop production. Its very simple and remember selecting the relevant table while writing the DAX. It will help you select the right column by prompting from the selected table columns.

Creating a new measure

As I have already said that explicit measures are reusable, it means that one explicit measure can be used to create another explicit measure.

Application of an Explicit measure

Use of explicit measure

Implicit measures

Here we will see how a measure can be created implicitly. It is an easier option compare to explicit measures. But it is always advisable to go for an explicit measure for its several advantages as I have discussed before.

In the below figure you can see that a bar plot has been created for the Area_ha column. By default it is the sum which is selected. You can change to other options as available in the figure.

And all these options create an implicit measure for each of them. You will not get them in the field options there. Which means that implicit measures are not reusable. Every time you want to do the same calculation, you have to go through the same process.

Creating an implicit measure
Creating an implicit measure

The bar plot takes only the measure “Area_ha” which gives the sum of all the cropped area from all the states. So, it gives a single bar for a single value. You can drill down further to any level adding them as values.

Like in the below image we can see that the cropped area has been displayed district wise.

Application of implicit measure
Application of implicit measure

Final words

In this article, we have mainly focused on creating measures both implicit and explicit. Both measures have been shown using practical examples.

As I have discussed the merits and demerits of both the measures, it is prudent that creating explicit measures always has the edge. Implicit measures are easy to apply. That’s why any new Power BI users always prone to use the implicit measures.

Although in the long run, when he delves into more advanced use of Power BI, the data size and complexity increases, he finds that using implicit measures are not helpful in terms of their reusability.

Also, as the Explicit measures get dynamically calculated, it puts less burden on the computer memory and quick to give result.

Hope this article will help you to get a good grip and understanding on the use of measures. If you have any queries or issues regarding application of measures let me know through comments below.

I would like to address them which will enrich my knowledge too.

How to create data model relationships in Power BI

data model relationships in Power BI

Data model relationships are the core of Power BI. The report preparation and visualization becomes very easy if data modelling is done well. So here in this article, we will discuss data modelling relationship with a simple example.

A common misconception among Power BI users is that Power BI is all about visualization. But that is not the case. Visualization is only the icing on the cake. Whereas the data modeling part is the cake itself.

Interested in Machine Learning, Deep Learning, Artificial Intelligence? visit here.

You should give 75% of your time in data transformation and modeling. For data visualization rest 25% is enough. A good data model will enable you to dig into the data insight and to know the relationship between all the variables.

You can get a good detailed discussion on data modeling in Power BI here. The document is prepared by Microsoft itself. So no point to discuss the same thing here again. My target will be here to present you a small practical example of data modeling.

Use of multiple narrow tables

I will encourage you to use multiple narrow tables instead of a big flat table with several columns. And here lies the beauty of data modeling and relationships. Power BI can work with big wide table. But it is a kind of constrain while scaling the model.

With time your project is going to increase in size. The tables will increase in rows. Chances are there that table number will also increase as well as the complexity of your model. Fetching required information from a big table with lots of columns is time-consuming. It occupies a good amount of computer memory too.

In this scenario use of multiple tables with few columns can make your life easier.

Data model relationships in Power BI: from the Business Intelligence point of view

Business Intelligence or BI is of very critical importance in industries. Almost all businesses have a well-developed database to store all kind of transactions. Over time such databases get huge and extracting information becomes complex.

Extracting information from such big data requires experts in the field. Business houses appoint software developers to extract data, transform and load in a well-maintained data warehouse.

Creating a data warehouse is essential because no industry wants to disturb its always busy database. It may lead to jeopardies the real-time data transaction process. That’s why software developers keep their required information in a data warehouse of their own.

Now for a person without software development background it is tough to handle a data warehouse. Here comes the power of Power BI. It makes the whole ETL (Extraction, Transform and Load) process of data a cake walk for anyone with zero knowledge in software development.

A practical example of data model relationships in Power BI

Here is a simple example of how you can model relationships in Power BI. The data I used here is crop production data of different states of India. This data is accessible at This is a real world data and without any garbage.

Data world already has provided the data in a refined manner. So, very little left for us on the part of data cleaning. And we can straight way jump into the modeling process.

Below is a glimpse of the data file imported into Power BI. This is the screenshot of the data transformation window of Power BI.

Glimpse of the crop production data
Glimpse of the crop production data

The columns are

  • State_Name” containing different states of India.
  • District_Name” containing state-wise different districts name
  • Crop_Year” has the year of production
  • Season” has the particular season of crop production
  • Crop” is the particular crop name
  • Area_ha” this column contains a total area of under the crop in Hectare
  • Production_tonnes” has a total production of the season in Tonnes

Now this is not a very big table. Still we can reduce its size and create a custom key to further improve the data model.

Creating a custom key

Using a column to create a custom key needs selecting a variable to create a separate table. Let’s select the “District_Name” to create a new table.

Now, why should I select this column? the answer is easy this column has some redundant values. The same way the “State_Name” also does not have unique values so either of them can be selected.

We will create a separate table with this column where they will be unique with a far lesser number of rows. In this way, these tables will help us data munging, data wrangling and whatever fancy term you want to use 🙂

Trimming the column

The first step is to trim the column to remove any unwanted white spaces from the column. Just select the column in question, right-click and select transform to get the trimming option. See the below image.

Trimming the column
Trimming the column

Removing the blank values

The second most important step is to replace any blank values. Blank values are a real problem in data analytics. Blank values can lead to spurious result as tools generally not capable to handle them. So, we should filter them out carefully in data transformation stage itself.

We can easily inspect the column quality and distribution from the view tab as shown in the below image.

Checking column quality and distribution from the view tab
Checking column quality and distribution from the view tab

So we can conclude that this particular variable is already free from any white space. See that data quality option shows “Empty as 0%”

But if your data has blank values then it is a convenient option to check it. For example, if we check the “Crop_production” variable we can see that the empty as <1%. That means it has some blank rows.

Checking for rows with "null" values
Checking for rows with “null” values

To check for the blank values we need to click the filter icon at the left of the column name. Uncheck the “Select All” option and check only the null values as shown in the above figure. Now Empty fields are 100%.

Replacing null values with 0
Replacing null values with 0

In order to replace the null values, you have to right click on the column name and select the “Replace Values…” option. In the new window replace the null with 0 and click OK. Now you can check the Empty has become 0% and all null values have been replaced by 0.

As no other column shows any value as Empty, so we have made our data completely free from any blank values. And we can now proceed for next step.

Creating new query and converting to table

In the next step we will create new query using the “Add as New Query” option. See the below image for the steps you should follow to create new query and then converting to a new separate table.

Creating new query and converting to new table
Creating new query and converting to new table

Removing duplicate entries

The newly created column has several duplicate entries. To make it a table with only unique entries, we need to remove the duplicates. As given in the below image, click the option “Remove Duplicates“. Now you have a column with all different districts of India.

Creating table with "District_Name"
Creating table with “District_Name”

Creating IDs for the column

It is very helpful creating a custom ID for the values in newly created column. Since the IDs are created by you, you have full control over it. Creating ID has a simple option in Power BI. See the following image to understand the steps.

Creating index column
Creating index column

The “Add Column” tab has options for both custom column and default column IDs starting from 0 or1. You can choose any of them. For example, here I have selected column ID starting from 0.

Merging the tables

The next step is to merge queries. As we have completed creating custom keys, now we need to merge the designated tables. See the following images to complete the tasks. Go to the “Home” tab and select “Merge Queries” option.

Selecting merge queries option
Selecting merge queries option

Now a “Merge” window will open and you need to verify that both the tables have exact same number of rows match. Here you can see that in both tables we have 246091 number of matching rows.

Merging the tables
Merging the tables

Now the newly created column will appear in the table. You can see in the below image that the whole table is displayed as a column. So you need to select the particular column and deselect the option “Use original column as prefix“.

Selecting particular column from merged table
Selecting particular column from merged table

Data model relationships in Power BI: task completed

With the last step, we have completed creating a data model relationship between the two tables. You can verify that in the “Model” view of Power BI. It displays a “One to Many” relationship between the tables.

Final data model relationship
Final data model relationship

Final words

So, here is a simple example of data model relationships in Power BI with practical data. It also displays how can you create custom keys to join or merge into different tables. It is an effective way of creating a relationship as you have full control over the keys.

I hope this article will help you to start with data model relationships in Power BI with ease. In case of any doubt you can comment below. Also suggest if there any interesting topic regarding data modeling you have.

With this article, I am planning to start a series of articles over the Power BI application to solve real-world problems. So, keep visiting the website for new and interesting articles.

An introduction to Power BI for data visualization

An introduction to Power BI

Microsoft’s Power BI is a very popular and most frequently used data visualization business intelligence tool. This is an introductory article on Power BI which will be followed by a series of practical problem-solving articles. So it will be “learning by doing”.

Data exploration and visualization is the most basic yet very important step in data analytics. It reveals important information to understand the data and relationships between the variables. Without a thorough understanding of these variables, we can not approach for in-depth analysis of the data.

Even in data science tasks for advance application of different Machine Learning algorithms, deep learning and artificial intelligence applications, Exploratory Data Analysis (EDA) plays a very important role.

Need for data exploration tools

Although there are programming tools like R and Python which are the most capable of advance data science jobs, using them for data exploration every time is tedious.

For example, we can use Python for EDA but it takes time. In such cases tools like Microsoft Power BI, Tableau etc. comes very handy.

They prepare exhaustive analytics on the data with some beautiful, professional-looking charts, interactive dashboard and real-time reports really quickly. Another added advantage is collaboration with your team by publishing the report through their cloud service.

For industry leaders, it does not matter if you have created a report writing a thousand lines of codes or used a tool like Power BI. They need just a good analytics and insights from the data. So, besides tools like Python and R a data analyst should have a working knowledge of these tools too.

But why Microsoft Power BI for data visualization?

Gartner Inc. renowned research and advisory firm for providing information, advice and tools for different industries across the business sectors in its report on Magic Quadrant for Analytics and Business Intelligence Platforms has ranked Power BI at the top for the consecutive 13 years.

See the below figure where the different Analytics and Business Intelligence Platforms are compared with respect to two different parameters. On the X-axis it shows the completeness of vision and on the Y-axis it has the ability to execute. We can see that with respect to both these parameters, Microsoft finds its place at the top.

Analytics and Business Intelligence Platforms comparison

Increasing popularity: Google trends

Microsoft Power BI has a close competitor as Tableau analytical software for Business Intelligence. Tableau has been a popular choice of business houses since long. Compare to Tableau, Power BI came very late for public use.

Tableau made its presence in the field of business intelligence way back in 2004. Whereas Microsoft Power BI was designed by Ron George only 10 years back in 2010 and made available for public download on July,11 of 2011.

Soon after its availability, it rapidly became popular mainly because of its more affordability, user friendly interface and most importantly its compatibility with hugely popular widely used Microsoft office products.

Below is an example (Google trends) how Power BI, since its inception, gradually overtake Tableau in popularity and became the most Google-searched term in business intelligence tool in recent past.

Installation of Power BI for data visualization

I will discuss the free version of Power BI which is Power BI desktop here and you can install it from the Microsoft store itself and the process is well documented here. Just search Power BI in the windows store and click download. It will start downloading a file sized nearly 500 MB.

Below is the screenshot of my computer when the installation was complete and Power BI was ready to launch.

Power BI desktop in Microsoft store
Power BI desktop in Microsoft store

When it is opened for the first time, you will get to see a window like below.

First screen after Power BI gets installed
First screen after Power BI gets installed

Just click “get started” and you are ready to work with the Power BI. The first step is to bring the data in Power BI. Here you will get an idea why Power BI is so popular. It has the capacity to load from any kind of data source you can name.

Compatible data sources for Power BI
Compatible data sources for Power BI

Power query

Power query is the most powerful feature of Power BI. It helps us clean the messy raw data. In real-world the data, we have to deal is never clean. Sometimes the data is arranged in a fashion which is only human friendly.

A human-friendly data means lots of tags and long strings within the data file for the ease of human understanding. But a machine does not have anything to do with these texts and arrangements. Rather they create problems to get read by a computer.

Power query helps to clean the data and also transform to create desired information with the available data. Below is an image showing how you can create a custom column from the columns available.

Custom column in Power BI
Custom column in Power BI

Query editor

So in this data transformation step, we need to convert a human-friendly data to a machine-friendly data. Don’t just click on “Load”, spend some time in data cleaning before loading it. And here comes the most powerful part of Power BI “Query editor”.

In the below image a query editor has been used to modify the data. According to your transform action, the query is built by Power BI by default. The query used the popular “M language”. You can make necessary changes in the query to get the desired result.

The syntax of the query is very easy and in case any problem a quick Google search is always helpful.

Use of query editor
Use of query editor

You need to master the use of Power BI query editor in order to dig deep into the analysis. It is a common misconception that Power BI is only about visualization. Many Power BI users directly jump into the visualization without proper data transformation and data modelling.

But it is a wrong practice. Query editor is refered as the “Kitchen” of Power BI. The better you will able to use it the more information you can extract from the data.

Use narrow tables

A good practice for data modelling is to use narrow tables. A wider table will create a problem when you attempt some complex calculations. Always break down a wider table identifying variables which are redundant and place them in a separate table.

Using tables with lesser columns instead of wide tables
Using tables with lesser columns instead of wide tables

As your table size grows with time i.e. number of rows increases. And there are chances that you need to add more tables in data modelling. In both cases narrow tables will help you to create relationships with ease and fetch required data.

Create as many tables as required for analysis. And later link them using primary key and foreign keys. We will discuss this process in a separate article dedicated to data modelling.

Power query is the built-in data tool for both M.S Excel & Power BI. It helps us to transform the data as we wish in order to build a meaningful model between the dimension tables and fact tables.

Star schema: Fact tables & dimension tables

Fact tables and dimension tables are components of dimensional modelling with a star schema.

Power BI for data visualization: Star schema
Power BI for data visualization: Star schema

Fact tables

These tables are generally long tables containing numeric values.

Dimension table

Now dimension tables are those table which generally contains information related to Who, What, Where, When and How about the fact tables. These are short tables compare to fact tables and contains mainly string variables.

Query settings

It is the particular part of Power BI data transformation window where all the magics happen. It automatically keeps track of all the data transformation steps you perform. You can go to any of these steps any time and make any kind of changes you want also replicate them further.

It kind of helps you to time travel to see the changes, how the data was cleaned and what are the data transformation steps.

DAX: Data Analytics Expressions

These are functions and operators available in Power BI which enable us to perform analytical services, building formulas, data modelling and information extraction (source: Microsoft document).

DAX functions are almost 250 in numbers used in different data transformation applications.

Measures Vs calculated columns

Implicit measures

Implicit measures are already defined measures in Power BI.

Explicit measures

These are measures defined by the Power BI user and has many benefits over the implicit measures. Explicit measures give the Power BI user more freedom, reusability and flexible applicability through connected reports.

Measures and calculated columns
Measures and calculated columns

Both of these options yield the same result and may appear as with the same functionalities. But it is always a good practice to use Measures over calculated columns for the reusability and flexibility of Measures.

Measures also have a plus side when you want to publish your analytical report with Excel or other applications. If you open any Power BI published file with excel, you can find the Measures you created but not Calculated columns. Hence you can reuse only the Measures not the calculated columns.

Measures make your application more light and memory friendly

Measures are being calculated dynamically and do not consume any space in the file. Hence use of measures instead of calculated columns keeps the Power BI application file very light in size even lighter than an excel file.

Use of Explicit measures also makes your data model execute very fast. You should always keep the scalability factor in mind while building models. Your model should be flexible so that it remains equally efficient with the increasing data size.

Hybrid measures

These measures are to calculate some statistics using variables from different tables. Once such measures are created they become a part of the model and can be used everywhere.

Unpivot columns

This is also a beautiful feature of Power BI. With a click it can make the rows converted to columns and vise versa in a table.

Model & relationship view

As soon as you complete your transformation/editing the data file the files are saved as Power BI files. Power BI by default identifies the relationship between the files and displays them in the Model and relationship view. You can change the model relationship and build your own.

Below is an example of such modelling. A typical star schema consists of one to many relationships from dimension tables to fact tables. In this image, I have one many to many relationship also. But keep in mind you should have a clear idea of why you are creating it.

Data relationship view
Data relationship view

This modelling part is so important. An efficient data modelling leads to easy and effecitive data analysis. A basic idea of data warehousing can be helpful here.

Once you are done with data transformation and model building, click “Close and apply” to exit the query editor.

Here we begin…

Power BI is such an interesting and powerful analytical platform and I wish to devote a complete blog category on this. As I am learning it the interest continues to grow.

It may appear overwhelming at first. With more practice, things become easier. Even you will become hungry for a more complex problem with messier data.

This is just starting off learning Power BI and here is only a glimpse of some important features. I will be covering more advanced applications in coming articles. I will update you about different analytical techniques as I learn them.

Let me know how you find this article and which topics you want to be covered in coming blogs by commenting below. Your suggestion will help me to improve the quality of articles as well as to introduce new interesting topics.

How to do Exploratory Data Analysis (EDA) with python?

Exploratory Data Analysis

This article presents a thorough discussion on how to perform Exploratory Data Analysis (EDA) to extract meaningful insights from a data set. And to do this I am going to use Python programming language and its four very popular libraries for data handling.

EDA is considered a basic and one of the most important steps in data science. It helps us planning advance data analytics by revealing the nature of feature and target variables and their interrelationships.

Every advanced application of data science like machine learning, deep learning or artificial intelligence requires a thorough knowledge of the variables you have in your data. Without a good exploratory data analysis, you can not have that sufficient information about the variables.

In this article, we will discuss four very popular and useful libraries of Python namely Pandas, NumPy, Matplotlib and Seaborn. The first two are to handle arrays and matrices whereas the last two are for creating beautiful plots.

I have created this exploratory data analysis code file in Jupyter notebook with a common data file name and use it anytime a new data set is to be analyzed. The variable names just need to be changed. It saves my considerable time and I am thorough with all the variables with a good enough idea for further data science tasks.

NB: Being a non-native English speaker, I always take extra care to proofread my articles with Grammarly. It is the best grammar and spellchecker available online. Read here my review of using Grammarly for more than two years. 

Data structures for exploratory data analysis

Pandas and NumPy provide us with data structures for data handling. Pandas has two main data structures called series and data frame as data container. Series contains data of mixed type in one-dimensional array whereas data frame is a two-dimensional array having columns with the same kind of data so, it can be considered as a dictionary of series.

Lets first import all the required libraries.

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

The data set used here is the very popular Titanic data set from Kaggle ( It contains the details of the passengers travelled in the ship and evidenced the disaster. The data frame contains 12 variables in total which are as below.

Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',  'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'],       dtype='object')

Feature and target variables

The target variable here is the ‘Survived‘ which contains the information if the passenger survived the disaster or not. It is a binary variable having ‘1’ representing the passenger has survived and ‘0’ indicating the passenger has not survived.

The other variables are all feature variables. Among the feature variables, ‘Pclass‘ contains the class information which has three classes like Upper, Middle and Lower; ‘SibSp‘ contains the number of passengers in a relationship in terms of sibling or spouse, the variable ‘Parch‘ also displays the number of relationships in terms of ‘parent‘ or ‘child‘, the ‘Embarked‘ variable displays the name of the particular port of embarkation, all other variables carry information as the variable names suggest.

Here is the shape of the data set.

(891, 12)

It shows that the data set has 891 rows and 12 columns.

Basic information

The info() function displays some more basic information like the variable names, their variable type and if they have null values or not.
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 891 entries, 0 to 890
Data columns (total 12 columns):
 #   Column       Non-Null Count  Dtype  
---  ------       --------------  -----  
 0   PassengerId  891 non-null    int64  
 1   Survived     891 non-null    int64  
 2   Pclass       891 non-null    int64  
 3   Name         891 non-null    object 
 4   Sex          891 non-null    object 
 5   Age          714 non-null    float64
 6   SibSp        891 non-null    int64  
 7   Parch        891 non-null    int64  
 8   Ticket       891 non-null    object 
 9   Fare         891 non-null    float64
 10  Cabin        204 non-null    object 
 11  Embarked     889 non-null    object 
dtypes: float64(2), int64(5), object(5)
memory usage: 83.7+ KB

Displaying the data set

The head() function prints few starting rows of the data set for our understanding.

Sample of the data set
Sample of the data set

Summary statistics

The describe() function prints some basic statistics of the data in hand.

Summary statistics
Summary statistics

To get some idea about the non-numeric variables in the data set

Boolean, object count
Boolean, object count

Inspecting any particular variable more closely


What if we take any categorical variable for inspection? Lets consider the target variable “Survived” here. It is a binary variable as I have mentioned before.

PassengerId    444.368421
Survived         1.000000
Pclass           1.950292
Age             28.343690
SibSp            0.473684
Parch            0.464912
Fare            48.395408
dtype: float64

So, it reveals important information that those who survived the disaster has an average age of 28 and they have spent on an average $48 for the fare.

Let’s find out some more critical information using logical operators. Like if we want to know what is the maximum age of a survivor travelling in Class I.

Use of logical operators

df[(df['Survived'] == 1) & (df['Pclass'] == 1)]['Age'].max()

So, the maximum age of a passenger travelling in class I is 80.

df[(df['Survived'] == 0) & (df['Pclass'] == 1)]['Age'].max()

Similarly, the youngest passenger from class I was 71 years old. Such queries can retrive very interesting information.

Suppose we want to inspect the passenger details whose names start with ‘A’. Here I have used the ‘lambda‘ function for the purpose. It makes the task very easy.

df[df['Name'].apply(lambda P_name: P_name[0] == 'A')].head()
Few lines from the data set
Few lines from the data set

Another very useful function is ‘replace()‘ from Pandas. It allows us to replace a particular value of any variable with our desired character. For example, if we want to replace the ‘Pclass‘ variable values 1,2 and 3 with ‘Class I’, ‘Class II’ and ‘Class III’ respectively then we can use the following piece of code.

x = {1 : 'Class I', 2 : 'Class II', 3:'Class III'}
df_new=df.replace({'Pclass': x})
Use of replace() function
Use of replace() function

Application of ‘groupby’

This is another important function frequently used to get summary statistics. Below is an example of its application to group the variables ‘Fare‘ and ‘Age‘ with respect to the target variable ‘Survived‘.

var_of_interest = ['Fare', 'Age']

df.groupby(['Survived'])[var_of_interest].agg([np.mean, np.std, np.min, np.max])
Application of groupby() function
Application of groupby() function

Contingency table

Contingency table or cross-tabulation is a very popular technique to create a table for multivariate data set in order to display the frequency distribution of variables corresponding to other variables. Here we will use the crosstab() function of Pandas to perform the task

pd.crosstab(df['Survived'], df['Pclass'])
Contingency table
Contingency table

So, you can see how quickly we can get the passenger class wise tally of passenger’s survival and death count through a contingency table.

Pivot table

The ‘Pivot_table()‘ function does the job by providing a summary of some variables corresponding to any particular variable. Below is an example of how we get the mean ‘Fare‘ and ‘Age’ of all passengers either survived or died.

Pivot table
Pivot table

Sorting the data set

We can sort the data set with respect to any of the variables. For example below we have sort the data set with respect to the variable “Fare“. The parameter “ascending=False” specifies that the table will be arranged in descending order with respect to variable ‘Fare‘.

df.sort_values(by=["Fare"], ascending=False)
Sorted with respect to 'Fare'
Sorted with respect to ‘Fare’

Visualization using different plots

Visualization is the most important part in case of performing exploratory data analysis. It reveals many interesting pattern among the variables which otherwise tough to recognise using numerals.

Here we will use two very capable python libraries called matplotlib and seaborn to create different plots and charts.

Check for missing values in the data set

A heat map created using the seaborn library is helpful to find out missing values easily. This is quite useful as if the data frame is a big one and missing values are few, locating them is not always easy. In this case, such a heatmap is quite helpful to find out missing values.

import seaborn as sns
plt.rcParams['figure.dpi'] = 100# the dpi can be set to enhance the resolution of the image
# Congiguring retina format
%config InlineBackend.figure_format = 'retina'
sns.heatmap(df.isnull(), cmap='viridis',yticklabels=False)
Heatmap to locate missing values
Heatmap to locate missing values

So, we can notice from here that out of total 12 variables, the variables “Age” and “Cabin” only have the missing values. We have used the ‘retina’ format of seaborn library to make the plot more sharp and legible.

Also, see the code to create these two plots as subplots and how the figure size has been mentioned. You can create separate plots without specifying all these details and see the effect. These specifications will help you adjust the plots and make them more legible.

Plotting the variable “Survived” to compare the dead and alive passengers of Titanic with a bar chart

Bar plot for variable 'Survived'
Bar plot for variable ‘Survived’

The above plot displays how many people survived out of all passengers. Again if we want these comparison according to the sex of the passengers, then we should incorporate another variable in the chart.

Bar plot showing the survival according to passengers' sex
Bar plot showing the survival according to passengers’ sex

The above plot reveals an important information regarding the survival of the passengers. From the plot we have drawn before it was evident that the death was higher than the number of people survived the disaster.

Now if we group this survival according to the sex, it further reveals that the number of male passengers survived the accident was much more than that of female passengers. Also, the death count for female passengers was also higher than male passengers.

Lets inspect the same information with a contingency table

pd.crosstab(df['Survived'], df['Sex'], margins=True)
Contingency table for count of passengers survived according to their sex
Contingency table for count of passengers survived according to their sex

Again if we want to categorize the plot of survival of the passenger depending on the class of the passengers, then we can have the information about how many passengers of a particular class have survived.

Bar plot with two categorical variables

There were three classes which have been represented as class 1,2 and 3. Let’s prepare a count plot with passenger class as the subcategory in case of survival of the passengers

sns.countplot(df.Survived, hue=df.Pclass)
Count plot for passenger class wise survival
Count plot for passenger class wise survival

The above plot clearly shows that the death toll was much higher in case of passenger of class 3 and class 1 passengers had the highest survival. Passengers of class 2 have almost equal no. of death and survival rate.

The highest no. of passengers were in class 3 and so the death toll too. We can see the below count plot where it is evident that class 3 has a much higher number of passengers compared to the other classes.

Again we can check the exact figure of passenger survival according to the passenger class with a contingency table too as below.

pd.crosstab(df['Survived'], df['Pclass'], margins=True)

Creating distribution plot

Below a seaborn distribution plot has been created with simple “distplot()” function all other parameters are set to the default one. By default, it calculates the standard normal values to display its distribution pattern.

sns.distplot(df.Age, color='red')
Distribution plot-1
Distribution plot-1

If we want the original ‘Age’values to be displayed, we need to set the ‘kde’ as ‘False’.

Distribution plot-2
Distribution plot-2
Distribution plot-3
Distribution plot-3
sns.distplot(df.Fare, color='green')
Distribution plot-4
Distribution plot-4

Box plot and violin plot

Box plot and violin plots are also very good visualization method to determint the distribution of any variable. See the application of these two plots for the variable ‘Fare‘ below,

Box plot and violin plot
Box plot and violin plot

The whiskers in the boxplot above, display the interval of the point scatter which is (Q1−1.5⋅IQR, Q3+1.5⋅IQR) where Q1 is the first quartile, Q3 is the third quartile and IQR is the Inter Quartile range i.e. the difference between 1st and 3rd quartile.

The black dots represent outliers which are beyond the normal scatter marked by the whiskers. On the other hand the violin plot, the kernel density estimate has been displayed on both sides.

Creating a boxplot to inspect the distribution

Below is a boxplot created to see the distribution of different passenger class with respect to the fare and as expected the highest fare class is the first class. Another boxplot has been created with the same ‘Pclass‘ variable against the “Age” variable.

These two boxplots side by side let us understand the relation between passengers’ age group and their choice of classes. We can clearly see that senior passengers are more prone to spend higher and travel in higher classes.

sns.boxplot(x=df.Pclass, y=df.Age)

Correlation plot

Here we will inspect the relationship between the numerical variables using the correlation coefficient. Although the data set is not ideal to do this correlation study as it lacks numerical variables having a meaningful interrelation.

But for the sake of complete EDA steps, we will perform this correlation study with the numerical variables we have in our hand. We will produce a heatmap to display the correlation with different colour shades.

# Considering only numerical variables
scatter_var = list(set(df.columns)-set(['Name', 'Survived', 'Ticket','Cabin','Embarked','Sex','SibSp','Parch']))

# Creating heatmap
corr_matrix = df[scatter_var].corr()
Heat map showing correlation coefficients
Heat map showing correlation coefficients

Scatter plot

Scatter plots are very handy in displaying the relationship between two numeric variables. The scatter() function of matplotlib library does this very quick to give us the first-hand idea about the variables.

Below is a scatterplot created between the ‘Fare‘ and ‘Age‘ variables. Here the two variables are taken as Cartesian coordinates in the 2D space. But even 3D scatterplots are also possible.

plt.scatter(df['Age'], df['Fare'])
plt.title("Age Vs Fare")
Scatter plot
Scatter plot

Creating a scatterplot matrix

If we want a glimpse of the joint distribution and one to one scatterplots among all combinations of the variables, a scatterplot matrix can be a good solution. The pairplot() function of the seaborn library does the job for us.

Below is an example with the scatter_var variable we created before with all the numerical variables in the data set.

Scatter plot matrix
Scatter plot matrix

See the above scatterplot matrix, the diagonal plots are the distribution plot for the corresponding variables while the rest of the scatterplots are for each pair of the variables.

To conclude with I will discuss a very handy and useful function from Pandas. Pandas profiling can create a summary from the data set in a jiffy.

Pandas profiling

First of all you need to install the library using the pip command.

pip install pandas-profiling

It will take some time to install all its module. Once it gets installed then to execute it run the below line of codes. The ProfileReport() function creates the EDA_report and finally an interactive HTML file is created for the user.

from pandas_profiling import ProfileReport
EDA_report = ProfileReport(df)

It is a very helpful process to perform exploratory data analysis specially for those who does not very familiar to coding and statistical analysis and just want some basic idea about his data. The interactive report allows them to dig further to get a particular information.


The main demerit of pandas profiler is it takes too much time to generate report when the data set is huge one. And many a time the practical real world data set has thousands of records. If you through the entire data set to the profiler you might get fustrated.

In this situation ideally you should use only a part of the data and generate the report. The random sample part from the whole dat set may also help you to have some idea about the variables of interest.


Exploratory data analysis is the key to know your data. Any data science task starts with data exploration. So, you need to be good at exploratory data analysis and it needs a lot of practice.

Although there are a lot of tools which can prepare a summary report from the data at once. Here I have also discussed Pandas profiling function which does all data exploration on your behalf. But my experience is, these are not that effective and may result in some misleading result in case the data is not filtered properly.

If you do the exploration by hand step by step, you may need to devote some more time, but in this way you become more familiar to the data. You get a good grasp about the variables which helps you in advance data science applications.

So, that’s all about exploratory data analysis using four popular python libraries. I have discussed every function with example which are generally required to explore any data set. Please let me know how you find this article and if I have missed anything here. I will certainly improve it according to your suggestions.

How to change the default working folder of Jupyter notebook in windows PC?

How to change the default working folder of Jupyter notebook

This article discussed two very easy fixes for this problem faced by almost all Jupyter notebook users while doing data science projects. I have faced this issue myself while working folder of Jupyter notebook, the most preferred IDE of data scientists.

Read more about Data Science, Machine Learning(ML), Artificial Intelligence (AI), Deep Learning (DL)

Although at the start it did not seem a big problem, as you start using Jupyter on a daily basis, you want it should start from your directory of choice. It helps you being organized, all your data science files at one place.

While I searched the internet thoroughly and got many suggestions, very few of them were really helpful. And it took quite a lot of my time to figure out the process which is really helpful. I thought to write it down as a blog so that in future I don’t have to waste time again to fix the issue and so my readers.

So, without any further ado, lets jump to the solutions…

NB: Being a non-native English speaker, I always take extra care to proofread my articles with Grammarly. It is the best grammar and spellchecker available online. Read here my review of using Grammarly for more than two years. 

The easiest way: using anaconda powershell

The first and the quickest solution is to run your Jupyter notebook right from the Anaconda PowerShell. You need to just change the directory to the desired one there and run Jupyter notebook. It is that simple. See the below image

Running the jupyter notebook with anaconda powershell
Running the jupyter notebook with anaconda powershell

Here you can see that the default working folder of Jupyter notebook was c:\user\Dibyendu as in the PowerShell. I have changed the directory to E: and simply run the command jupyter notebook. Consequently, PowerShell has run the Jupyter notebook with the start folder as mentioned.

This is very effective and changes the start folder for jupyter notebook very easily. But the problem is that this change is temporary and you have to go through this process every time you open the notebook.

To fix this problem one solution can be to create a batch file with these commands and just run this batch file while you need to work in jupyter notebook.

Creating shortcut with target as the working folder of Jupyter notebook

This solution is my favourite and I personally follow this procedure. Here the steps are explained with screenshots from my system.

You need to first locate the jupyter notebook app in your computer by right clicking the application in your menu as shown in the below image.

Jupyter notebook app in my start menu
Jupyter notebook app in my start menu

Now navigate to the file location and select the application file like the below image. Copy the file in your desktop or any location you want a shortcut of the application.

Location the jupyter notebook application in your computer
Location the jupyter notebook application in your computer

Now right-click the application and go to the shortcut tab. The target file you can see here is mentioned as “%USERPROFILE%”, which is indeed the default installation folder for jupyter notebook. That’s why it is the default start folder for the notebook.

Property dialog for Jupyter notebook app
Property dialog for Jupyter notebook app

Now you need to replace the “%USERPROFILE%” part with the exact location of your desired directory.

Changed target folder
Changed target folder

In the above image you can see that I have replaced the “%USERPROFILE%” with the data science folder which contains all of my data science projects. Now just click Apply and then OK. Now to open jupyter notebook click the shortcut and jupyter will open with your mentioned directory as the start folder as in the below image.

jupyter notebook with the data science folder as the start folder
jupyter notebook with the data science folder as the start folder

So, the problem is solved. You can use this trick and create multiple shortcuts with different folders as the start folder of jupyter notebook.