seaborn facetgrid user-defined plot function

Just change the my_errorbar function so that it pops the data out of the keyword dict:

def my_errorbar( * args, ** kwargs):
   data = kwargs.pop('data')
errors = np.vstack([data['error_min'],
   data['error_max']
])

print(errors)

plt.errorbar(data[args[0]],
   data[args[1]],
   yerr = errors, **
   kwargs);

Suggestion : 2

Multi-plot grid for plotting conditional relationships.,Initialize the matplotlib figure and FacetGrid object.,This class maps a dataset onto multiple axes arrayed in a grid of rows and columns that correspond to levels of variables in the dataset. The plots it produces are often called “lattice”, “trellis”, or “small-multiple” graphics.,The FacetGrid constructor accepts a hue parameter. Setting this will condition the data on another variable and make multiple plots in different colors. Where possible, label information is tracked so that a single legend can be drawn:

tips = sns.load_dataset("tips")
sns.FacetGrid(tips)

sns.FacetGrid(tips, col = "time", row = "sex")

g = sns.FacetGrid(tips, col = "time", row = "sex")
g.map(sns.scatterplot, "total_bill", "tip")

g = sns.FacetGrid(tips, col = "time", row = "sex")
g.map_dataframe(sns.histplot, x = "total_bill")

g = sns.FacetGrid(tips, col = "time", row = "sex")
g.map_dataframe(sns.histplot, x = "total_bill", binwidth = 2, binrange = (0, 60))

g = sns.FacetGrid(tips, col = "time", hue = "sex")
g.map_dataframe(sns.scatterplot, x = "total_bill", y = "tip")
g.add_legend()

Suggestion : 3

Last Updated : 15 Jul, 2020

FacetGrid class helps in visualizing distribution of one variable as well as the relationship between multiple variables separately within subsets of your dataset using multiple panels.
A FacetGrid can be drawn with up to three dimensions ? row, col, and hue. The first two have obvious correspondence with the resulting array of axes; think of the hue variable as a third dimension along a depth axis, where different levels are plotted with different colors.
FacetGrid object takes a dataframe as input and the names of the variables that will form the row, column, or hue dimensions of the grid. The variables should be categorical and the data at each level of the variable will be used for a facet along that axis.

                        seaborn.FacetGrid(data, \*\ * kwargs)

Suggestion : 4

To plot multiple pairwise bivariate distributions in a dataset, you can use the pairplot() function. This shows the relationship for (n,2) combination of variable in a DataFrame as a matrix of plots and the diagonal plots are the univariate plots.,A special case in barplot is to show the no of observations in each category rather than computing a statistic for a second variable. For this, we use countplot().,It is also possible to plot a different function on the diagonal to show the univariate distribution of the variable in each column.,Jointplot creates a multi-panel figure that projects the bivariate relationship between two variables and also the univariate distribution of each variable on separate axes.

To install the latest release of Seaborn, you can use pip −

pip install seaborn

It is also possible to install the released version using conda −

conda install seaborn

The following command will help you import Pandas −

# Pandas
for managing datasets
import pandas as pd

We will import the Seaborn library with the following command −

# Seaborn
for plotting and styling
import seaborn as sb

You can use any of these datasets for your learning. With the help of the following function you can load the required dataset

load_dataset()

Suggestion : 5

Data Science Tutorial For Beginners | Learn Data Science Complete Tutorial,Python Tutorial For Beginners,This brings us to the end of this article where we covered some basics of Seaborn and learned to plot various plots. You can get a free course on Machine learning on great learning academy, click the banner below to know more.,Before using Seaborn, we need to install it and here I am going to show various ways of installing it on your computer.

import pandas
import matplotlib
import scipy
import seaborn as sns
print(sns.get_dataset_names())

import seaborn as sns
df = sns.load_dataset('car_crashes')
print(df.head())

from matplotlib
import pyplot as plt
import seaborn as sns
plt.scatter(df.speeding, df.alcohol)
plt.show()

from matplotlib
import pyplot as plt
import seaborn as sns
plt.scatter(df.speeding, df.alcohol)
sns.set()
plt.show()

from matplotlib
import pyplot as plt
import seaborn as sns
plt.scatter(df.speeding, df.alcohol)
sns.set_style("whitegrid")
plt.show()

from matplotlib
import pyplot as plt
import seaborn as sns
plt.scatter(df.speeding, df.alcohol)
sns.set_style("dark")
plt.show()