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Bar Graph01:07

Bar Graph

16.5K
A bar graph is also called a bar chart and consists of bars that are separated from each other. It either uses horizontal or vertical bars to show comparisons among categories. The bars can be rectangles, or they can be rectangular boxes (used in three-dimensional plots). One axis of the graph represents the specific categories being compared, and the other axis shows a discrete value. In this graph, the length of the bar for each category is proportional to the number or percent of individuals...
16.5K
Multiple Bar Graph01:07

Multiple Bar Graph

5.2K
As the name suggests, a multiple bar graph is the same as a bar graph but has multiple bars to depict relationships between different data values. One can include as many parameters as possible. However, each parameter must have the same unit of measurement.
Each bar or column in the multiple bar graph represents a data value. These graphs are used primarily in interrelating two or more sets of data. The categories of different kinds of data are listed along the horizontal or x-axis, whereas...
5.2K
Pie Chart01:04

Pie Chart

14.2K
A pie chart (or a pie graph) is a circular graphical chart or a pictorial representation of categorical data. It is divided into slices of pie each indicating numerical proportions. It is also used to show the relative sizes of data in a single chart.
In a pie chart, the central angle, the arc length of each slice, and the area are directly proportional to the quantity or percentage it represents. Some real-world examples that can be depicted using pie charts include marks obtained by students...
14.2K
Time-Series Graph00:54

Time-Series Graph

4.4K
A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct a time-series graph, one must look at both pieces of a paired data set. The horizontal axis is used to plot the time increments, and the vertical axis is used to plot the values of the variable that one is measuring. By using the axes in this way, each point on the graph will correspond to time and a measured quantity. The points on the graph...
4.4K
Boxplot01:12

Boxplot

8.3K
Box plots (also called box-and-whisker plots or box-whisker plots) give an excellent graphical image of the concentration of the data. They also show how far the extreme values are from most data. A box plot is constructed from five values: the minimum value, the first quartile, the median, the third quartile, and the maximum value. We use these values to compare how close other data values are to them. To construct a box plot, use a horizontal or vertical number line and a rectangular box. The...
8.3K
Modified Boxplots00:57

Modified Boxplots

9.7K
A standard box and whisker plot informs us about the spread of the data in a given sample. One can identify the minimum value, maximum value, first quartile value, second quartile or median value, and third quartile.
However, the box plot does not tell the reader about outliers - values that lie far from the center of the data. We can modify the standard box and whisker plot to identify the outliers and visualize the actual spread of the data in a sample.
Initially, we calculate the adjusted...
9.7K

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Related Experiment Video

Updated: Jul 8, 2025

Facilitating the Analysis of Immunological Data with Visual Analytic Techniques
10:58

Facilitating the Analysis of Immunological Data with Visual Analytic Techniques

Published on: January 2, 2011

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Mastering data visualization with Python: practical tips for researchers.

Soyul Han1, Il-Youp Kwak1

  • 1Department of Applied Statistics, Chung-Ang University, Seoul, Korea.

Journal of Minimally Invasive Surgery
|December 15, 2023
PubMed
Summary
This summary is machine-generated.

Big data visualization transforms research by enabling insight discovery from large datasets. This study provides Python techniques and GitHub resources for researchers to effectively apply data visualization in their work.

Keywords:
Big dataData visualizationMatplotlibPythonSeaborn

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Area of Science:

  • Data Science
  • Bioinformatics
  • Computational Biology

Background:

  • The paradigm shift from hypothesis confirmation to insight discovery in research is driven by big data.
  • Effective data visualization is essential for interpreting complex, large-scale datasets.
  • Visualizing big data aids in identifying patterns, particularly for infectious disease characteristics and trends.

Purpose of the Study:

  • To provide a comprehensive overview of data visualization techniques.
  • To equip researchers with practical Python programming skills for data visualization.
  • To facilitate the application of data visualization in research through accessible resources.

Main Methods:

  • Review of data visualization principles and methodologies.
  • Development of practical Python code examples for data visualization.
  • Creation of hands-on exercises hosted on GitHub for researcher accessibility.

Main Results:

  • Demonstration of how visual formats clarify complex data patterns.
  • Provision of actionable Python scripts for diverse data visualization tasks.
  • Establishment of a GitHub repository with practical visualization exercises.

Conclusions:

  • Data visualization is a critical tool for extracting meaningful insights from big data.
  • Accessible Python programming resources empower researchers to implement advanced visualization techniques.
  • The study facilitates the integration of big data visualization into scientific research workflows.