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Data Visualization Articles
Page 2 of 68
Explain the concept of Report View, Data View, and Model View in Power BI
Power BI is a good tool with three major views Report View, Data View, and Model View which we will discuss in this article. Power BI can be useful for an individual who wants to derive some useful insights from the data and permits users to visualize. In short, it can be defined as a tool used to connect different data sources, by transforming and modeling data, creating an interactive visual report, and sharing these reports with others. Power BI is developed to help organizations and individuals make data-driven decisions by turning raw data into actionable insights. The first window ...
Read MoreHow to compute the average monthly income for employees and illustrate it in a card visualization
Introduction Power BI is an awesome tool for creating interactive reports and modeling data. Various built-in visuals are exclusive in the Visualization pane where measures, aggregation functions, and so on can be applied to construct the specific model. A card visual is one of the crucial visuals where a computed solitary value like average salary, maximum salary, or total production in a specific year is to be highlighted on a card. In this article, we will explore how to compute the average monthly income for employees and illustrate it in card visualization. Computation of Average Monthly Salary Step 1 You ...
Read MoreAdvanced Filtering: How to create a Slicer in Power BI?
Introduction Users may populate the selected employee data through the slicer on the dashboard. Two options Filter and Slicer assist users in filtering data. Slicers are more flexible and dynamic, provide quick accessibility of data, are easily embedded into the Report editor, and are also compatible with other charts. The disadvantage of slicers is that a Visual filter is not permitted in slicer visual, and choices like drill down, and input fields can’t be endorsed. In this article, we will learn the fundamental concepts of creating a slicer to shortlist the employees’ names based on age categories. To create a ...
Read MoreHow to merge tables using Inner Join and Left Outer Join in Power BI?
Introduction In this article, we will gain deep insights into Inner Join and Left Outer Join in Power BI. A wide variety of joins, like full join, inner join, and left anti-join are available in Power BI to join tables. The inner join merges two tables and extracts only those records that contain the matching values in the common column of both tables. The left-outer join also matches the common values from the two tables but retrieves all the first table records along with the corresponding records of the matched values from the second table. Implementation of Inner Join in ...
Read MoreHow To Visualize Sparse Matrix in Python using Matplotlib?
Sparse matrices are a specialized type of matrix that contain mostly zero values. These matrices are commonly encountered in applications such as graph theory, machine learning, and network analysis. Visualizing sparse matrices can provide valuable insights into the distribution and patterns of non-zero values. In this article, we will understand how to visualize sparse matrices in Python using the popular data visualization library, Matplotlib. Understanding Sparse Matrices A sparse matrix is a matrix in which most of the elements are zero. These matrices are typically large and inefficient to store in memory if all the zeros are explicitly represented. ...
Read MoreDraw a Unstructured Triangular Grid as Lines or Markers in Python using Matplotlib
Python is a popularly used programming language. It offers a wide range of tools and libraries which can be used for solving different problems, one of them is Matplotlib. This library provides various functions for data visualization and creating different plots. In this article, we will be using Matplotlib for drawing an unstructured triangular grid as liners or markers in python. What is Matplotlib and How to Install it? Matplotlib is one of the libraries of python. This library is very strong tool for serving the purpose of plotting graphs for visualizing data. It has a module named “pyplot” ...
Read MoreHow to Calculate and Plot the Derivative of a Function Using Python – Matplotlib?
The Derivative of a function is one of the key concepts used in calculus. It is a measure of how much the function changes as we change the output. Whereas Matplotlib is a plotting library for python, since it does not provide a direct method to calculate the derivative of a function you need to use NumPy, which is also one of the python libraries and you can use it to calculate the derivative of a function and Matplotlib for visualizing the results. In this article, we will be calculating the derivative of a function using the NumPy ...
Read MoreHow to Change the Line Width of a Graph Plot in Matplotlib?
Matplotlib one of the libraries of python, which plays an important role in beautifying plots and making the data analysis and data visualization an easier task. You can use Matplotlib for experimenting, by using different options available in it and creating a more appealing, informative plot. One common customization in Matplotlib is changing the line width of a graph plot. Since, line width controls the thickness of the lines, which are used in the plots at various points such as in connecting the plot points, etc. In this article, we will be learning how to change the line ...
Read MoreHow to write text in subscript in the axis labels and the legend using Matplotlib?
To write text in subscript in the axis labels and the legend, we can take the following steps −Create x and y data points using NumPy.Plot x and y data points with a super subscript texts label.Use xlabel and ylabel with subscripts in the text.Use the legend() method to place a legend in the plot.Adjust the padding between and around subplots.To display the figure, use the show() method.Exampleimport numpy as np import matplotlib.pyplot as plt plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True x = np.linspace(1, 10, 1000) y = np.exp(x) plt.plot(x, y, label=r'$e^x$', c="red", lw=2) plt.xlabel("$X_{axis}$") plt.ylabel("$Y_{axis}$") plt.legend(loc='upper left') plt.show()Output
Read MoreDifference Between Abstraction and Encapsulation
Abstraction is a process of hiding the implementation details of a system from the user, and only the functional details will be available to the user end. On the other hand, Encapsulation is a method of wrapping up the data and code acting on the data into a single unit. Read this article to find out more about abstraction and encapsulation and how they are different from each other. What is Abstraction? Abstraction is defined as a process of hiding the implementation details of a system from the user. Thus, by using abstraction, we provided only the functionality of the ...
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