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Seaborn scatter plot same variable by group11/11/2023 By using a color-coded system to display values on a 2D matrix, heat maps allow us to grasp complex patterns, trends, and relationships within the data at a glance. Heat maps are exceptionally powerful as they provide an intuitive and visually striking representation of data. Data points outside this range are considered outliers and are represented as individual points beyond the whiskers. ![]() Typically, the whiskers encompass data within 1.5 times the interquartile range (IQR), which is the difference between Q3 and Q1. The line inside the box represents the median (Q2), which is the middle value of the dataset.Īdditionally, the "whiskers" extend from the box and indicate the range of the data, excluding outliers. The "box" in the plot represents the middle 50% of the data, where the lower boundary of the box is the first quartile (Q1) and the upper boundary is the third quartile (Q3). For now, think of it this way:Ī box plot divides your data into four equal parts, with each part representing a quarter of the data points. We'll go deeper into quartiles in future posts about distributions as well. It also identifies outliers in your data. Typically when people use these libraries, they do the imports with the following aliases:Įnter fullscreen mode Exit fullscreen modeĪ box plot, also known as a box and whisker plot, shows the quartiles of the dataset and is useful to visualize the distribution and skewness of your data. The outputs, including all graphs and plots, are then displayed directly under each code cell, making it easy to view and interpret your results in a structured and clear manner. To use Matplotlib or Seaborn in Jupyter Notebooks, you simply need to import the required libraries and execute your code. Jupyter Notebooks provide an interactive and intuitive interface for conducting data analysis and visualization. Heat maps, violin plots, pair plots, and swarm plots are just a few of the more advanced visualizations available.īoth Matplotlib and Seaborn work exceptionally well in Jupyter Notebooks, a popular open-source web application that allows you to create and share documents that contain live code, equations, visualizations and narrative text. With Seaborn, you can create a range of informative and attractive statistical graphics. It's designed to work seamlessly with Pandas dataframes and makes creating complex plots from dataframes quite straightforward. Seaborn, while built on Matplotlib, enhances its capabilities and introduces more sophisticated visualization tools. Whether you're trying to spot trends over time, distributions of data, or relationships between variables, Matplotlib has the flexibility to meet your needs. Once imported, Matplotlib provides a wide variety of plots and charts to visualize data, from simple line and bar plots to more complex scatter plots and histograms. Its extensive functionality and versatility make it a powerful tool for any data scientist or analyst to perform Exploratory Data Analysis (EDA) ![]() ![]() Matplotlib is one of the most widely used libraries for creating static, animated, and interactive visualizations in Python. Trust me, it only gets better from here!ĭata Visualization with Matplotlib and Seaborn in Jupyter Notebooks ![]() These libraries gave us a solid foundation in handling and preparing data for further analysis or modeling, and as we delve into Matplotlib and Seaborn inside of Jupyter Notebooks, we're now stepping into the fascinating world of data visualization. So far we've covered Numpy and Pandas, where we learned how to manipulate, process, and analyze numerical and tabular data. Jokes aside, if you're like me, you're getting excited about learning new tools for your Data Science/ML/AI journey. You like python programs, don't you Squidward?
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