![]() The y-axis is used to measure the minimum, first quartile, median, third quartile, and maximum value in a set of numbers. The x-axis assigns one box for each Category or Numeric field variable. Variablesīox plots are composed of an x-axis and a y-axis. Box plots also illustrate the minimum and maximum data values through whiskers, or lines, extending from the box, and optionally, outliers as points extending beyond the whiskers. A large IQR indicates a large spread in values, while a smaller IQR indicates most values fall near the center. The IQR illustrates the variability in a set of values. The median of the values is depicted as a line splitting the box in half. The box portion of the diagram below illustrates the middle 50 percent of the data values, also known as the interquartile range (IQR). ![]() Quartiles are a method of splitting numeric values into four equal groups based on five key values: minimum, first quartile, median, third quartile, and maximum. You can find out more about box plot on the matplotlib documentation page.Box plots allow you to visualize and compare the distribution and central tendency of numeric values through their quartiles. patch_artist equals true, which fills the box plot with colors, so that we can see different colors to different boxes. We are using just a few of them such as notch equals True attribute, which will create the notch format to the box plot. The boxplot function provides different customization possibilities to the box plot. Here x-axis denotes the data to be plotted, while the y-axis shows the frequency distribution. ![]() A vertical line is also there, which goes through the box at the median. In the box plot, a box is created from the first quartile to the third quartile. Just press Shift plus Enter around the code. Let's open our Jupyter Notebook 03_01 and take a look at our box plot. With the help of numpy and matplotlib, we can effortlessly create box plots. Now let's see an example of the box plot. Outliers are the data points that differ significantly from the most of other points in the dataset, and to determine if our data is skewed. We use box plots to understand the distribution of the data, identify outliers or anomalous data points. It shows the minimum, maximum, median, first quartile, and the third quartile in the data set. Box plot or a whisker plot is a chart that is used to visualize how given data is distributed using quartiles. In order to represent our data set split into quartiles easily, we use box plots. So in Q3, we have 75% of the data points below that point. Q3 or the end of the third quartile is at the 75th percentile. 50% of the data points are below that point and 50% of the data points are above that point. Q2 or the end of the second quartile is at the 50th percentile or median. So at Q1, we have 25% of the data points below that point. Q1 or the end of the first quartile is the 25th percentile. ![]() ![]() We can represent their connection visually with the following table. We can break every set of numerical data into quartiles, which are just a fancy name for four equal sizes segments that each contain exactly a quarter or 25% of the data. Quartiles are connected with percentiles. For example, if your result is in 80th percentile on the test, it means you're better than 80% of other students who took the test. We have explored percentiles on SAT and IQ examples. ![]()
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