![]() HistogramĪ histogram is used to visualize the distribution of a single continuous variable. Finally, we use show() to display the plot. We add labels to the x-axis and y-axis and provide a title for the plot. The categories list represents the x-axis categories, and the values list represents the height of each bar. We use the bar() function to create a bar plot. Plt.title( 'Bar Plot of Categories and Values') Here's an example of creating a bar plot using Matplotlib: import matplotlib.pyplot as plt Bar plots are commonly used to compare categorical data or to show the distribution of a continuous variable across categories. Bar PlotĪ bar plot represents data as rectangular bars, with the length of each bar proportional to the value it represents. The rest of the code is similar to the line plot example. We use the scatter() function to create a scatter plot. Let's create a scatter plot using Matplotlib: import matplotlib.pyplot as plt It is useful for examining the relationship between two continuous variables. Scatter PlotĪ scatter plot displays individual data points as markers on a two-dimensional plane. We import matplotlib.pyplot as plt, create lists listOne and listTwo representing the data points, and then use the plot() function to create the line plot. Here's an example of creating a simple line plot using Matplotlib: import matplotlib.pyplot as plt It is useful for visualizing trends and changes over time or any continuous variable. Now that we have everything set up, let's dive into the tutorial! Visualization with Matplotlib Line PlotĪ line plot is a basic plot type that displays data points connected by lines. Make sure you have an up-to-date version of both libraries. You can install them using pip, the Python package installer, by running the following commands in your terminal: pip install matplotlib Installationīefore we start, let's make sure Matplotlib and Seaborn are installed. ![]() Seaborn also offers themes and color palettes that make plots visually appealing with minimal customization. It simplifies the process of creating attractive statistical graphics by providing high-level functions for common plot types. ![]() Seaborn is a higher-level data visualization library built on top of Matplotlib. Matplotlib can be used in interactive environments like Jupyter or Google Colab notebooks. It provides a wide variety of plot types, including line plots, scatter plots, bar plots, histograms, and more. Matplotlib is a versatile plotting library that offers a high degree of control over plot customization. Matplotlib and Seaborn are two popular Python libraries that provide powerful tools for creating a wide range of static, animated, and interactive visualizations. ![]() It allows us to visually explore and communicate data patterns, trends, and relationships effectively. Data visualization is a crucial step in the data analysis process. ![]()
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