Showing posts with the label Data visualization

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The Quick and Easy Way to Analyze Numpy Arrays

The quickest and easiest way to analyze NumPy arrays is by using the numpy.array() method. This method allows you to quickly and easily analyze the values contained in a numpy array. This method can also be used to find the sum, mean, standard deviation, max, min, and other useful analysis of the value contained within a numpy array. Sum You can find the sum of Numpy arrays using the np.sum() function.  For example:  import numpy as np  a = np.array([1,2,3,4,5])  b = np.array([6,7,8,9,10])  result = np.sum([a,b])  print(result)  # Output will be 55 Mean You can find the mean of a Numpy array using the np.mean() function. This function takes in an array as an argument and returns the mean of all the values in the array.  For example, the mean of a Numpy array of [1,2,3,4,5] would be  result = np.mean([1,2,3,4,5])  print(result)  #Output: 3.0 Standard Deviation To find the standard deviation of a Numpy array, you can use the NumPy std() function. This function takes in an array as a par

What is so Trendy in Data Visualization and Reporting

Data Visualization: Data visualization is the process that defines any effort to assist people to understand the importance of data by placing it in a visual context.  Patterns, trends, and correlations that might be missed in text-based data can be represented and identified with data visualization software. It is a graphical representation of numerical data. This is one of the Hot skills in the market, you will get the highest salary. Types of data visualization Visual Reporting Visual reporting uses charts and graphics to represent business performance, usually defined by metrics and time-series information. The best dashboards and scorecards enable the users to drill down one or more levels to view more detailed information about a metric A dashboard is a visual exception report that signifies the ambiguities in performances using visualization techniques Visual Analysis Visual-analysis allows users to visually explore the data to observe the data and discove

5 Emerging Trends in Data Visualization

Nowadays, we deeply drowned in data of diverse kinds due to the increased computational power and accessibility. Specifically, in addition to public data available on the Internet (e.g., census, demographics, environmental data), data pertaining personal daily activities are now more easily collected. For example, through mobile devices that can log people's running distances and time or their manual record of nutrition consumption. Due to such expanded sources of data, there appear new applications that involve data collection, visualization, exploration, and distribution in daily contexts. These applications do, not only display static information but also let users navigate the data in forms of interactive visualizations. 5 Emerging Trends in Data Visualization. #1: This emerging trend has brought both opportunities and challenges to interaction designers to develop new approaches to designing data-based applications. #2: Conveying information has been one of main functio