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How to Visualize Data Using Boxplot in Python

Boxplot Visualization in Python

Condition for Visualizing Data Using Boxplot in Python

  • Description:
    A box plot is a graphical representation that shows the distribution of a dataset based on five summary statistics: 1) the minimum, 2) first quartile (Q1), 3) median, 4) third quartile (Q3), 5) and maximum.

    It also highlights outliers. Box plots are very useful for visualizing the spread and skewness of the data, as well as for comparing different datasets.
Step-by-Step Process
  • Install Required Libraries:
    Make sure you have `matplotlib` and `seaborn` installed.
  • Prepare the Data:
    Use a numeric dataset (can be random or real).
  • Create the Plot:
    Use `matplotlib` or `seaborn` to plot the box plot.
  • Interpret the Results:
    Use the plot to understand the distribution and detect outliers.
Sample Source Code
  • # Boxplot

    import pandas as pd

    import matplotlib.pyplot as plt

    df = pd.read_csv('/home/soft23/Downloads/company_sales_data.csv')

    plt.boxplot(df['facewash'])
    plt.title('Box Plot of Facewash')
    plt.xlabel('Facewash')
    plt.ylabel('Frequency')

    plt.show()
Screenshots
  • Boxplot Output