Tips and Tricks for Data Visualization in Python
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This document provides summaries of various data visualization techniques in Python including: Seaborn is a Python library for statistical graphics and is built on matplotlib. It supports NumPy and Pandas data structures. FacetGrid allows plotting multiple axes showing the same relationship conditioned on different levels of variables from a Pandas DataFrame. It can condition on up to three variables. Kdeplot fits and plots univariate or bivariate kernel density estimates and allows customizing colors, shading, and adding a colorbar. Jointplot provides a wrapper for the JointGrid class to create scatter plots, regression plots, residual plots, and histograms of two variables jointly. Heatmap plots a hierarchically clustered heatmap of a matrix dataset
Tips and Tricks for Data Visualization in Python
- 1. Tips and Tricks for Data Visualization in Python #seaborn by Jacqueline Carvalho
- 2. Seaborn is a library for making attractive and informative statistical graphics in Python. It is built on top of matplotlib and integrated with the PyData stack, including support for numpy and pandas data structures and statistical routines.
- 3. FacetGrid Matplotlib is a Python 2D plotting library which produces publication quality figures in a variety of hardcopy formats and interactive environments across platforms. Matplotlib can be used in Python scripts, the Python and IPython shells, the Jupyter notebook, web application servers, and four graphical user interface toolkits. matplotlib.pyplot is a collection of command style functions that make matplotlib work like MATLAB. Each pyplot function makes some change to a figure: e.g., creates a figure, creates a plotting area in a figure, plots some lines in a plotting area, decorates the plot with labels, etc. The FacetGrid is an object that links a Pandas DataFrame to a matplotlib figure with a particular structure. In particular, FacetGrid is used to draw plots with multiple Axes where each Axes shows the same relationship conditioned on different levels of some variable. It’s possible to condition on up to three variables by assigning variables to the rows and columns of the grid and using different colors for the plot elements. hue allows to choose which logic of colors the grid will follow. For example: if you choose ‘genre’, it means that all the data related to that genre will have the same color; if you choose ‘year’, it means that all the data related to the same year will have the same color.
- 4. FacetGrid
- 5. Kdeplot Kdeplot fits and plots a univariate or bivariate kernel density estimate. If True, shade in the area under the KDE curve, or draw with filled contours when data is bivariate. The determination whether to issue a warning message is controlled by the warning filter, which is a sequence of matching rules and actions. Rules can be added to the filter by calling filterwarnings() and reset to its default state by calling resetwarnings(). The colormap or cmap is used to map normalized data values to RGBA (red, green, blue, alpha) colors.
- 6. Kdeplot
- 7. Kdeplot cbar. If True and drawing a bivariate KDE plot, add a colorbar. If True, shade the lowest contour of a bivariate KDE plot. Not relevant when drawing a univariate plot or when shade=False. Setting this to False can be useful when you want multiple densities on the same Axes. Subplots. This utility wrapper makes it convenient to create common layouts of subplots, including the enclosing figure object, in a single call.
- 8. Kdeplot
- 9. Jointplot Jointplot function provides a convenient interface to the JointGrid class, with several canned plot kinds. This is intended to be a fairly lightweight wrapper; if you need more flexibility, you should use JointGrid directly. Kind defines which plot to draw. It may be: Scatter, Reg, Resid, Kde or hex. xlim and ylim define the axis limits before plotting. Set aesthetic parameters in one step to the background of the chart. The style may be darkgrid, whitegrid, dark, white or ticks
- 10. Jointplot
- 11. Heatmap Clustermap plots a matrix dataset as a hierarchically- clustered heatmap.. Standard_scale. Either 0 (rows) or 1 (columns). Whether or not to standardize that dimension, meaning for each row or column, subtract the minimum and divide each by its maximum. cyl.unique sets standard colors to the left bar chart
- 12. Heatmap