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Seaborn Articles
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How are Outliers Determined in Seaborn using Boxplot?
What are Outliers? Outliers are the data points or observations which are far from the other points in a dataset. Outliers are caused due to measurement error, data entry error or experimental error etc. Outliers can skew the dataset which effects in statistical analysis and can increase the standard deviation of the dataset further effecting model prediction. An outlier may be a valid data point or can be noise. For a better understanding let us look at an example scenario. Imagine you are collecting data of student's height (age between 9 to 12 years). Most of the students are around ...
Read MoreWhat are the seaborn compatible IDLEs?
Integrated Development Environments (IDEs) are software applications that provide comprehensive tools and features to facilitate software development. The following are the IDLEs that are compatible with seaborn library. Jupyter Notebook/JupyterLab Jupyter Notebook and JupyterLab are widely used interactive computing environments for data analysis and visualization. They provide a web-based interface where we can write and execute Python code in cells. Seaborn integrates seamlessly with Jupyter Notebook and JupyterLab, allowing us to create and visualize plots directly within the notebook environment. The inline plotting feature in Jupyter Notebook displays Seaborn plots directly in the notebook, making it easy to iterate on ...
Read MoreWhich way the pandas data can be visualized using seaborn?
Seaborn offers various ways to visualize pandas data, allowing you to gain insights and communicate patterns or relationships effectively. Here are some common ways to visualize pandas data using Seaborn. Scatter Plots The `scatterplot()` function can be used to create scatter plots that show the relationship between two numeric variables. You can use Seaborn to enhance the scatter plot with additional visual cues, such as color-coding points based on a categorical variable using the `hue` parameter. Line Plots The `lineplot()` function can be used to create line plots to represent trends or changes over time or any other continuous numeric ...
Read MoreWhat are the main components of a Seaborn plot?
A Seaborn plot consists of several main components that work together to create informative and visually appealing visualizations. Understanding these components can help you customize and interpret Seaborn plots effectively. The below are the main components of a Seaborn plot. Figure and Axes Seaborn plots are created using Matplotlib's figure and axes framework. The figure represents the entire canvas or window on which the plot is displayed. The axes represent the individual subplots or regions within the figure where the actual data is plotted. Seaborn functions typically create a figure with a single set of axes by default, but you ...
Read MoreData Pre-Processing with Sklearn using Standard and Minmax scaler
Introduction Data pre-processing is required for producing trustworthy analytical results. Data preparation includes eliminating duplicates, identifying and fixing outliers, normalizing measurements, and filing away categories of information. Popular for its ability to scale features, handle missing data, and encode categorical variables, the Python-based Sklearn toolkit is an essential resource for pre-processing data. With Sklearn, preprocessing data is a breeze, and you have access to trustworthy methodologies for effective data analysis. Data Pre-Processing Techniques Standard Scaling Data can be transformed using standard scaling so that it is normally distributed around zero and one. It ensures that everything is uniform in size. This ...
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