Data Visualization with Python and Seaborn — Part 6: Additional Linear Data (Regression) Plots
In this last article on visualizing Regression plots, we shall be discussing few other types of plots like Pair plot and revisit additional use cases of plots that we’ve already covered similar to LM plot. Since Regression is no new topic for us, let us quickly get into exploring but before doing that, as always we shall make few necessary imports:
# Importing intrinsic libraries:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
np.random.seed(0)
sns.set(style='ticks',color_codes=True)
import warnings
warnings.filterwarnings('ignore')# Let us also get Tableau colors we defined earlier:
tableau_20 = [(31, 119, 180), (174, 199, 232), (255, 127, 14), (255, 187, 120),(44, 160, 44), (152, 223, 138), (214, 39, 40), (255, 152, 150),(148, 103, 189), (197, 176, 213), (140, 86, 75), (196, 156, 148),(227, 119, 194), (247, 182, 210), (127, 127, 127), (199, 199, 199),(188, 189, 34), (219, 219, 141), (23, 190, 207), (158, 218, 229)]# Scaling over RGB values to [0,1] range as Matplotlib acceptable format:
for i in range(len(tableau_20)):
r,g,b = tableau_20[i]
tableau_20[i] = (r/255., g/255., b/255.)# Loading built-in Seaborn dataset:
tips = sns.load_dataset('tips')
# Plotting a simple Jointplot:
sns.pairplot(data=tips, hue='sex', palette='icefire', x_vars=['total_bill','size'], y_vars=['tip'], size=6, aspect=.85, kind='reg')
Let us begin our discussion with parameters of this Pair plot:
sns.pairplot(data, hue=None, hue_order=None, palette=None, vars=None, x_vars=None, y_vars=None, kind='scatter', diag_kind='hist', markers=None, size=2.5, aspect=1, dropna=True, plot_kws=None, diag_kws=None, grid_kws=None)
While data parameter helps us in specifying the dataset we intend to use, like a Pandas DataFrame for instance. On the other hand, variables x_vars and y_vars are used separately for the rows and columns of our desired figure; i.e. to make a non-square plot. size determines height, aspect determines width & finally kind determines whether to get Scatter or regression in grids.
And before I proceed further, let me giveaway a set of palette options that we can chose from for our Seaborn plots. It is not so easy to find all of them listed for us anywhere on internet (at least I couldn't find), so I have compiled a list for you guys to play around with and use at your workplace or in your project/assignments for your plots to look awesome. If I have missed any, feel free to let me know in Comments sections for everyone to benefit from.
Accent, Accent_r, Blues, Blues_r, BrBG, BrBG_r, BuGn, BuGn_r, BuPu, BuPu_r, CMRmap, CMRmap_r, Dark2, Dark2_r, GnBu, GnBu_r, Greens, Greens_r, Greys, Greys_r, OrRd, OrRd_r, Oranges, Oranges_r, PRGn, PRGn_r, Paired, Paired_r, Pastel1, Pastel1_r, Pastel2, Pastel2_r, PiYG, PiYG_r, PuBu, PuBuGn, PuBuGn_r, PuBu_r, PuOr, PuOr_r, PuRd, PuRd_r, Purples, Purples_r, RdBu, RdBu_r, RdGy, RdGy_r, RdPu, RdPu_r, RdYlBu, RdYlBu_r, RdYlGn, RdYlGn_r, Reds, Reds_r, Set1, Set1_r, Set2, Set2_r, Set3, Set3_r, Spectral, Spectral_r, Wistia, Wistia_r, YlGn, YlGnBu, YlGnBu_r, YlGn_r, YlOrBr, YlOrBr_r, YlOrRd, YlOrRd_r, afmhot, afmhot_r, autumn, autumn_r, binary, binary_r, bone, bone_r, brg, brg_r, bwr, bwr_r, cividis, cividis_r, cool, cool_r, coolwarm, coolwarm_r, copper, copper_r, cubehelix, cubehelix_r, flag, flag_r, gist_earth, gist_earth_r, gist_gray, gist_gray_r, gist_heat, gist_heat_r, gist_ncar, gist_ncar_r, gist_rainbow, gist_rainbow_r, gist_stern, gist_stern_r, gist_yarg, gist_yarg_r, gnuplot, gnuplot2, gnuplot2_r, gnuplot_r, gray, gray_r, hot, hot_r, hsv, hsv_r, icefire, icefire_r, inferno, inferno_r, jet, jet_r, magma, magma_r, mako, mako_r, nipy_spectral, nipy_spectral_r, ocean, ocean_r, pink, pink_r, plasma, plasma_r, prism, prism_r, rainbow, rainbow_r, rocket, rocket_r, seismic, seismic_r, spring, spring_r, summer, summer_r, tab10, tab10_r, tab20, tab20_r, tab20b, tab20b_r, tab20c, tab20c_r, terrain, terrain_r, viridis, viridis_r, vlag, vlag_r, winter, winter_r
Let us now view our Pair plots on a bigger scale, and to do that let us get our Iris dataset in action, just for a change:
iris = sns.load_dataset('iris')
sns.set_style('dark')
sns.pairplot(iris, hue='species', palette='gist_heat', dropna=True)
Here what we see in above plot, is a grid with 4*4 linear relationship between each feature of Iris dataset, with visual separation using huefactor on species feature. This plot is generally gonna be our first pick for any dataset to understand the correlation on a single grid. We may notice histograms being plotted diagonally when the features are correlated with each other. Rest of the individual plots remain to be Scatter plots that we discussed in detail in our previous article. There is a legend indicating the separation based on the color. The axes have ticks because earlier we had set the style accordingly.
Let me also give you a preview to something known as Pair grid which is actually the backbone of our Pair plot. So, we are going to just vaguely look at it as of now to feel the difference; we’re just going to explore the potential of Pair plot and then delve deeper into Pair Grid later in this Seaborn article series. And for achieving this, we shall continue with our Iris dataset:
from itertools import cycle
sns.set_style('dark')def make_kde(*args,**kwargs):
sns.kdeplot(*args, cmap=next(make_kde.cmap_cycle), **kwargs)make_kde.cmap_cycle = cycle(('coolwarm', 'Wistia', 'icefire'))
our_plot = sns.PairGrid(iris, vars=('sepal_length', 'sepal_width', 'petal_length'), hue='species', dropna=True)our_plot.map_diag(sns.kdeplot)
our_plot.map_lower(make_kde)
our_plot.map_upper(plt.scatter)
To give a fair bit of an idea, here we cycled through the list of color maps stored in the cmap_cycle attribute attached to the make_kde function. And Python programmers know this isn't actually an efficient way to use function attributes, so that bit of customization I shall leave as a homework. Overall it helps us achieve a pair-wise relationship between features using Pair Grid and Pair plot; and simultaneously allows us to visualize different types of plot like Kernel Density Contours, Scatter plot, etc. as per our needs.
Residual Plots:
So now let us quickly cover a very small topic here, i.e. Residplot and once done with this, I shall again show you a small demo of Pair Grid, and include Residplot in this one. So, let’s plot our first Residplot and understand associated use cases. And this time let us generate some random data using our function we previously created in the very first lecture of Linear Regression plots. If you can’t recollect, it is okay, as I am going to paste the code here as well for your reference:
def generatingData():
num_points = 1500
cat_points = []
for i in range(num_points):
if np.random.random()>0.5:
x,y = np.random.normal(0.0, 0.9),np.random.normal(0.0, 0.9)
cat_points.append([x,y])
else:
x,y = np.random.normal(3.0, 0.5),np.random.normal(1.0, 0.5)
cat_points.append([x, y])
df= pd.DataFrame({'x':[v[0] for v in cat_points], 'y':[v[1] for v in cat_points]})
sns.residplot('x','y',data=df, lowess=True, color='r')
plt.show()sns.set_style('whitegrid')
generatingData()
This Residplot is a plot of the residuals after fitting a linear model. This function is something we had established in a section of our previous article on Matplotlib v/s Seaborn demonstration, so all we need to alter here is the type of model we want, along with lowess parameter. If lowessparameter is set to TRUE, then it uses statsmodels to estimate a non-parametric lowess model, indicating a locally weighted linear regression model.
Important to note is that confidence intervals cannot currently be drawn for this kind of model or even for Regplot (as of version 0.8).
There isn’t anything more to discuss about Residplot and it isn’t quite often that we are going to use these unless we’re trying to assess our linear data models, but it is again good to know what Seaborn has in stock to offer. So as promised let me show another example of Pair Grid, but this time with reference to Residplot as it shall help us improvise if ever required at work. Again, we shall be not discussing in detail and instead just try to focus on visualization aspect of it. Let’s plot:
np.random.seed(42)
sample = 100
categories = pd.DataFrame(np.array(['p','q'])[np.random.randint(0,2,size=[sample,1])], columns=['cat'], dtype='object')
data = pd.DataFrame(np.random.rand(sample, 2),columns=list('AB'))
df = pd.concat([data,categories],axis=1)
g = sns.PairGrid(df, hue='cat')
g.map_diag(plt.hist)
g.map_upper(sns.regplot, scatter_kws={'s':10})
def func(*args, **kwargs):
if 'scatter_kws' in kwargs.keys():
kwargs['scatter_kws'].update({'color': kwargs.pop('color')})
sns.residplot(*args,**kwargs)
g.map_lower(func, scatter_kws={'s':10})
Here we generated some random data and drafted a pair plot out of it, ensuring a smaller than default size of our data points which also have separate color coding. Now, let us try to use built-in Anscombe’s dataset to create a linear regression plot on these quartet grid; and to achieve this we’re going to use what we are already familiar with, i.e. Lmplot():
df = sns.load_dataset('anscombe')
sns.lmplot(x='x', y='y', col='dataset', hue='dataset', data=df, col_wrap=2, ci=None, palette='spring', size=3.5, scatter_kws={'s': 60, 'alpha': 1})
With this quartet, we have an output of the results of a linear regression within each dataset. If having an urge to explore this dataset more, feel free to load it and check various other possibilities to wrangle; and I highly recommend it because there is no better way of learning.
We’re going to end this article here and this pretty much brings us to the end of Linear Regression possibilities in general with Seaborn. I hope you are enjoying the shine and mileage that Seaborn adds to our presentation and see you soon in our next article where we shall be discussing few other plots which deal heavily with categorical data, that is also commonly seen across. Till then, Happy Visualizing!
Data Visualization with Python and Seaborn — Part 5
Data Visualization with Python and Seaborn — Part 7
