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 […]
Continue readingYou 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 […]
Continue readingDefinition: 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 […]
Continue readingYou 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. import pandas as pd import numpy as np import seaborn as sns import matplotlib.pyplot as plt %matplotlib inline Now […]
Continue readingThe 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 […]
Continue readingOne 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 spread shit style pivot table, you will need two dependencies with is Numpy and Pandas. However, in newer iterations, you don’t need Numpy. […]
Continue readingPareto 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 […]
Continue readingEXCLUDE: “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 higher granularity level. “EXCLUDE” level of detail expression is majorly used to calculate ‘difference from overall […]
Continue readingINCLUDE: As the name suggests, “INCLUDE” level of detail expression compute aggregations considering dimensions which are specified in the calculation and also take into consideration those dimensions which are present in the view. “INCLUDE” level of detail expression is useful when the user wants to calculate values at the lower level of granularity and then […]
Continue readingFIXED: “FIXED” level of detail expression aggregates the value only at the dimensions which are specified by the user in the calculation. “FIXED” expression does not take into consideration those dimensions in the view. The difference between FIXED and INCLUDE/EXCLUDE is unlike INCLUDE/EXCLUDE, FIXED calculations are not relative to the dimensions in view. Example: Requirement: […]
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