Using What If parameter in Power Bi can easily give you the ability to dynamically transform your data. For example, using this parameter will allow to demonstrates how your data change under various scenarios. For example how much revenue would you have if your products were at 10%, 20% or 30% of the retail price. […]
It took me a long time to learn Python. The reasons are aplenty on why it was such a long journey. However, hands down, I can tell you the main reasons are due to the vast amount of flexibility with Python. I tried to learn too much at one time instead of focusing on key […]
NBA All Time Point Leaders This Plotly chart was created using a dataset of NBA players stats from basketball-reference.com. It contains player points, rebounds, assists, starts and etc. I create this visualization by building it in Plotly, based on an initial Python plot created using Matplotlib. I wanted to explore using Plotly cause I thought […]
You can easily create and style a histogram in Seaborn with just a few steps. Let’s get started. You will need a few dependencies to ensure that the plot is shown. The dependencies that you essentially need to load are Matplotlib and Seaborn. However, let’s load the standards such as Pandas and Numpy also in case […]
Definition: Waffle chart is a 10 X 10 cell grid in which each cell represents 1 percentage point summing up to total 100%. Waffle charts can be represented with conditional formatting where cells are highlighted with different colors based on the percentage value of that KPI. There are following used cases of Waffles chart: To […]
You can easily create a heatmap using the Seaborn library in Python. For this tutorial, I’m going to create this using Jupyter Notebooks. The first step is to load the dependencies which are the essential library. You can also Learn Python Data Insights on YouTube import pandas as pd import numpy as np import seaborn […]
The Google Apps store has a plethora of different apps ranging across a ton of different categories. Here is a visualization created in Tableau that give you an idea of what are the most popular apps on the platform. The dataset was sourced a Kaggle.com in the form a CSV. I did some light […]
One of the key actions for any data analyst is to be able to pivot data tables. Luckily Pandas has an excellent function that will allow you to pivot. To create this spreadsheet style pivot table, you will need two dependencies with is Numpy and Pandas. However, in newer iterations, you don’t need Numpy. Let’s […]
Pareto chart is basically based on the theory of “80-20” phenomena, where it means that 80% of the output is being generated by the 20% of the input. In terms of retail data, we can also say like this that 80% of revenue is from 20% of customers. Pareto chart is a combination of both […]
EXCLUDE: “EXCLUDE” level of detail expression is used to omit specified dimensions from the aggregations. Using “EXCLUDE”, a user can omit the lower level granularity dimension which is present in the view and can directly calculate the value at a higher granularity level. Tableau Training and Certification at Edureka “EXCLUDE” level of detail expression is […]