In this blog we will see scatter plotting and different type of category plotting.
Seaborn is a Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics.
seaborn can be installed from PyPI.
Open command prompt in your system and install seaborn library.
pip install seaborn
The library is also included as part of the Anaconda distribution:
conda install seaborn
import seaborn as sns
import matplotlib.pyplot as plt
dir(sns)
sns.get_dataset_names()
Some sample datasets are available with seaborn library. Let us take one database "tips" and plot some graph.
tips = sns.load_dataset('tips')
tips
sns.set(color_codes=True)
seaborn.scatterplot
seaborn.scatterplot(*, x=None, y=None, hue=None, style=None, size=None, data=None, palette=None, hue_order=None, hue_norm=None, sizes=None, size_order=None, size_norm=None, markers=True, style_order=None, x_bins=None, y_bins=None, units=None, estimator=None, ci=95, n_boot=1000, alpha=None, x_jitter=None, y_jitter=None, legend='auto', ax=None, **kwargs)
Draw a scatter plot with possibility of several semantic groupings.
The relationship between x and y can be shown for different subsets of the data using the hue, size, and style parameters. These parameters control what visual semantics are used to identify the different subsets.
Parameters
x, y: vectors or keys in data
Variables that specify positions on the x and y axes.
hue: vector or key in data
Grouping variable that will produce points with different colors. Can be either categorical or numeric, although color mapping will behave differently in latter case.
size: vector or key in data
Grouping variable that will produce points with different sizes. Can be either categorical or numeric, although size mapping will behave differently in latter case.
style: vector or key in data
Grouping variable that will produce points with different markers. Can have a numeric dtype but will always be treated as categorical.
data: pandas.DataFrame, numpy.ndarray, mapping, or sequence
Input data structure. Either a long-form collection of vectors that can be assigned to named variables or a wide-form dataset that will be internally reshaped.
palette: string, list, dict, or matplotlib.colors.Colormap
Method for choosing the colors to use when mapping the hue semantic. String values are passed to color_palette(). List or dict values imply categorical mapping, while a colormap object implies numeric mapping.
hue_orde: rvector of strings
Specify the order of processing and plotting for categorical levels of the hue semantic.
hue_norm: tuple or matplotlib.colors.Normalize
Either a pair of values that set the normalization range in data units or an object that will map from data units into a [0, 1] interval. Usage implies numeric mapping.
sizes: list, dict, or tuple
An object that determines how sizes are chosen when size is used. It can always be a list of size values or a dict mapping levels of the size variable to sizes. When size is numeric, it can also be a tuple specifying the minimum and maximum size to use such that other values are normalized within this range.
size_order: list
Specified order for appearance of the size variable levels, otherwise they are determined from the data. Not relevant when the size variable is numeric.
size_norm: tuple or Normalize object
Normalization in data units for scaling plot objects when the size variable is numeric.
markers: boolean, list, or dictionary
Object determining how to draw the markers for different levels of the style variable. Setting to True will use default markers, or you can pass a list of markers or a dictionary mapping levels of the style variable to markers. Setting to False will draw marker-less lines. Markers are specified as in matplotlib.
style_order: list
Specified order for appearance of the style variable levels otherwise they are determined from the data. Not relevant when the style variable is numeric.
{x,y}_bins: lists or arrays or functions
Currently non-functional.
units: vector or key in data
Grouping variable identifying sampling units. When used, a separate line will be drawn for each unit with appropriate semantics, but no legend entry will be added. Useful for showing distribution of experimental replicates when exact identities are not needed. Currently non-functional.
estimatorname of pandas method or callable or None
Method for aggregating across multiple observations of the y variable at the same x level. If None, all observations will be drawn. Currently non-functional.
ci: int or “sd” or None
Size of the confidence interval to draw when aggregating with an estimator. “sd” means to draw the standard deviation of the data. Setting to None will skip bootstrapping. Currently non-functional.
n_boot: int
Number of bootstraps to use for computing the confidence interval. Currently non-functional.
alpha: float
Proportional opacity of the points.
{x,y}_jitter: booleans or floats
Currently non-functional.
legend: “auto”, “brief”, “full”, or False
How to draw the legend. If “brief”, numeric hue and size variables will be represented with a sample of evenly spaced values. If “full”, every group will get an entry in the legend. If “auto”, choose between brief or full representation based on number of levels. If False, no legend data is added and no legend is drawn.
ax: matplotlib.axes.Axes
Pre-existing axes for the plot. Otherwise, call matplotlib.pyplot.gca() internally.
kwargs: key, value mappings
Other keyword arguments are passed down to matplotlib.axes.Axes.scatter().
Returns
matplotlib.axes.Axes
The matplotlib axes containing the plot.sns.scatterplot(x = 'total_bill', y = 'tip', data = tips)
hue : It will produce data points with different colors.
sns.scatterplot(x = "total_bill", y = "tip", hue = "day", data = tips)
style: Pass value as a name of variables or vector from DataFrame, it will group variable and produce points with different markers.
sns.scatterplot(x = "total_bill", y = "tip", hue = "day", style = "time", data = tips)
sns.catplot(x = "day", y = "total_bill", data = tips)
The jitter parameter controls the magnitude of jitter or disables it altogether:
sns.catplot(x = "day", y = "total_bill", jitter = False, data = tips)
The second approach adjusts the points along the categorical axis using an algorithm that prevents them from overlapping. It can give a better representation of the distribution of observations.
This kind of plot is sometimes called a “beeswarm” and is drawn in seaborn by swarmplot(), which is activated by setting kind="swarm" in catplot():
sns.catplot(x = "day", y = "total_bill", kind = "swarm", data = tips)
sns.catplot(x = "day", y = "total_bill", hue = "sex", kind = "swarm", data = tips)
sns.catplot(x = "day", y = "total_bill", hue = "smoker", kind = "swarm", data = tips);
Filter size column data and then plot
sns.catplot(x = "size", y = "total_bill", kind = "swarm", data = tips.query("size != 3"));
sns.catplot(x = "day", y = "total_bill", kind = "box", data = tips)
g = sns.catplot(x = "day", y = "total_bill", hue = "time", kind = 'box', data = tips)
g.add_legend(title = "Meal")
plt.show()
g = sns.catplot(x = "day", y = "total_bill", hue = "time", kind = 'box', data = tips)
g.add_legend(title = "Meal")
g.set_axis_labels("", "Total bill ($)")
To increase or decrease the size of a matplotlib plot, you set the width and height of the entire figure, either in the global rcParams, while setting up the plot (e.g. with the figsize parameter of matplotlib.pyplot.subplots()), or by calling a method on the figure object (e.g. matplotlib.Figure.set_size_inches()). m
g = sns.catplot(x = "day", y = "total_bill", hue = "time", kind = 'box', data = tips)
g.add_legend(title = "Meal")
g.fig.set_size_inches(10.5, 5.5)
g.set_axis_labels("", "Total bill ($)")
g = sns.catplot(x = "day", y = "total_bill", hue = "time",
height = 3.5, aspect = 1.5, kind = 'box', data = tips)
g.add_legend(title="Meal")
g.set_axis_labels("", "Total bill ($)")
g.set(ylim = (0, 60), xticklabels = ["Thursday", "Friday", "Saturday", "Sunday"])
g.fig.set_size_inches(12.5, 8.5)
g.ax.set_yticks([5, 15, 25, 35, 45, 55], minor = True);
plt.setp(g.ax.get_xticklabels(), rotation=30);
sns.catplot(x = "day", y = "total_bill", hue = "smoker", kind = "violin", data = tips);
sns.catplot(x = "day", y = "total_bill", hue = "smoker", kind = "bar", data = tips);
g = sns.catplot(x = "day", y = "total_bill", hue = "time", kind = "boxen", data = tips);
seaborn.lmplot: Plot data and regression model fits across a FacetGrid.
to enhance a scatterplot to include a linear regression model (and its uncertainty) using lmplot():
sns.lmplot(x = "total_bill", y = "tip", data = tips)
sns.lmplot(x = "total_bill", y = "tip", data = tips, hue = "time")
sns.lmplot(x = "total_bill", y = "tip", data = tips, hue="day")
