Alberto

Alberto

Experience is not what
happens to a man;
it is what a man does
with what happens to him.
-Aldous Huxley

Python: Seaborn

Utilizzo l’environment conda py3

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~$ conda activate py3

Versione modulo installato

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~$ pip show seaborn
Name: seaborn
Version: 0.10.1
Summary: seaborn: statistical data visualization
Home-page: https://seaborn.pydata.org
Author: Michael Waskom
Author-email: mwaskom@nyu.edu
License: BSD (3-clause)
Location: /home/user/miniconda3/envs/py3/lib/python3.7/site-packages
Requires: scipy, matplotlib, pandas, numpy
Required-by:

Seaborn

Documentazione Seaborn

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import seaborn as sns
%matplotlib inline

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tips = sns.load_dataset('tips') # default df in sns
tips.head()
total_bill tip sex smoker day time size
0 16.99 1.01 Female No Sun Dinner 2
1 10.34 1.66 Male No Sun Dinner 3
2 21.01 3.50 Male No Sun Dinner 3
3 23.68 3.31 Male No Sun Dinner 2
4 24.59 3.61 Female No Sun Dinner 4 
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# dist plot (histogram e kernel density estimation)
sns.distplot(tips['total_bill'],kde=False,bins=30,color='purple')

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<matplotlib.axes._subplots.AxesSubplot at 0x7f7ddeea0c50>

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# kde plot
sns.kdeplot(tips['total_bill'])

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<matplotlib.axes._subplots.AxesSubplot at 0x7f7de3ca07d0>

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# jointplot
sns.jointplot(x='total_bill',y='tip',data=tips,kind='kde') # kind default è scatter, hex è scatterplot esagonali, reg è scatter con regression, kde è 2dimensianl density
# from scipy import stats
# sns.jointplot(x='total_bill',y='tip',data=tips,color='red',marginal_kws=dict(bins=15, rug=True),annot_kws=dict(stat="r"),s=40, edgecolor="black", alpha=0.4, linewidth=1).annotate(stats.pearsonr) # invece di annotate si può mettere dentro il jointplot "stat_func=stats.pearsonr"

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<seaborn.axisgrid.JointGrid at 0x7f7de3c96ed0>

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# pairplot, come jointplot per tutto il df
sns.pairplot(tips,hue='sex') # hue specifico le categoriche per fare stratificazione
# sns.pairplot(tips,hue='sex',diag_kind='hist',diag_kws=dict(edgecolor='black',alpha=0.6))

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<seaborn.axisgrid.PairGrid at 0x7f7de3bfe690>

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# rug plot, distribuzione univariata
sns.rugplot(tips['total_bill'])

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<matplotlib.axes._subplots.AxesSubplot at 0x7f7dedf8dad0>

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# Don't worry about understanding this code!
# It's just for the diagram below
import numpy as np
import matplotlib.pyplot as plt
from scipy import stats

#Create dataset
dataset = np.random.randn(25)

# Create another rugplot
sns.rugplot(dataset);

# Set up the x-axis for the plot
x_min = dataset.min() - 2
x_max = dataset.max() + 2

# 100 equally spaced points from x_min to x_max
x_axis = np.linspace(x_min,x_max,100)

# Set up the bandwidth, for info on this:
url = 'http://en.wikipedia.org/wiki/Kernel_density_estimation#Practical_estimation_of_the_bandwidth'

bandwidth = ((4*dataset.std()**5)/(3*len(dataset)))**.2


# Create an empty kernel list
kernel_list = []

# Plot each basis function
for data_point in dataset:
    
    # Create a kernel for each point and append to list
    kernel = stats.norm(data_point,bandwidth).pdf(x_axis)
    kernel_list.append(kernel)
    
    #Scale for plotting
    kernel = kernel / kernel.max()
    kernel = kernel * .4
    plt.plot(x_axis,kernel,color = 'grey',alpha=0.5)

plt.ylim(0,1)

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(0.0, 1.0)

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# To get the kde plot we can sum these basis functions.

# Plot the sum of the basis function
sum_of_kde = np.sum(kernel_list,axis=0)

# Plot figure
fig = plt.plot(x_axis,sum_of_kde,color='indianred')

# Add the initial rugplot
sns.rugplot(dataset,c = 'indianred')

# Get rid of y-tick marks
plt.yticks([])

# Set title
plt.suptitle("Sum of the Basis Functions")

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Text(0.5, 0.98, 'Sum of the Basis Functions')

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Categorical Plots

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import numpy as np

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# barplot with function
sns.barplot(x='sex',y='total_bill',data=tips,estimator=np.std) # di default l'estimator è la media

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<matplotlib.axes._subplots.AxesSubplot at 0x7f7de1d7cfd0>

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# countplot, barplot
sns.countplot(x='sex',data=tips)

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<matplotlib.axes._subplots.AxesSubplot at 0x7f7ded503450>

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# boxplot
sns.boxplot(x='day',y='total_bill',data=tips,hue='sex') # con hue stratifico

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<matplotlib.axes._subplots.AxesSubplot at 0x7f7de1d530d0>

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# violin plot
sns.violinplot(x='day',y='total_bill',data=tips,hue='sex',split=True)

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<matplotlib.axes._subplots.AxesSubplot at 0x7f7dec907f90>

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# strip plot
sns.stripplot(x='day',y='total_bill',data=tips,hue='sex',jitter=True, split=True) # con jitter migliora la vista delle sovrapposizioni dei punti

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<matplotlib.axes._subplots.AxesSubplot at 0x7f7decc7df90>

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# swarplot (combina stripplot con violin plot)
sns.violinplot(x='day',y='total_bill',data=tips,palette='rainbow')
sns.swarmplot(x='day',y='total_bill',data=tips,color='black',alpha=0.5)

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<matplotlib.axes._subplots.AxesSubplot at 0x7f7ddf32bb10>

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# factor o cat plot
sns.catplot(x='day',y='total_bill',data=tips,kind='bar') # kind puoi usare anche violin, ma è analogo ai singoli comandi

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<seaborn.axisgrid.FacetGrid at 0x7f7dec4a5290>

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Matrix Plots

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tips = sns.load_dataset('tips')
flights = sns.load_dataset('flights')

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tips.head()
total_bill tip sex smoker day time size
0 16.99 1.01 Female No Sun Dinner 2
1 10.34 1.66 Male No Sun Dinner 3
2 21.01 3.50 Male No Sun Dinner 3
3 23.68 3.31 Male No Sun Dinner 2
4 24.59 3.61 Female No Sun Dinner 4 
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flights.head()
year month passengers
0 1949 January 112
1 1949 February 118
2 1949 March 132
3 1949 April 129
4 1949 May 121 
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# la heat map necessita la struttura matriciale
tc = tips.corr()
tc
total_bill tip size
total_bill 1.000000 0.675734 0.598315
tip 0.675734 1.000000 0.489299
size 0.598315 0.489299 1.000000 
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# heat map
sns.heatmap(tc,annot=True,cmap='coolwarm')
plt.title('tips.corr()')

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Text(0.5, 1.0, 'tips.corr()')

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# per il df flights la struttura matriciale si può anche costruire rendendo il df wide
fp = flights.pivot_table(index='month',columns='year',values='passengers')
fp
year 1949 1950 1951 1952 1953 1954 1955 1956 1957 1958 1959 1960
month
January 112 115 145 171 196 204 242 284 315 340 360 417
February 118 126 150 180 196 188 233 277 301 318 342 391
March 132 141 178 193 236 235 267 317 356 362 406 419
April 129 135 163 181 235 227 269 313 348 348 396 461
May 121 125 172 183 229 234 270 318 355 363 420 472
June 135 149 178 218 243 264 315 374 422 435 472 535
July 148 170 199 230 264 302 364 413 465 491 548 622
August 148 170 199 242 272 293 347 405 467 505 559 606
September 136 158 184 209 237 259 312 355 404 404 463 508
October 119 133 162 191 211 229 274 306 347 359 407 461
November 104 114 146 172 180 203 237 271 305 310 362 390
December 118 140 166 194 201 229 278 306 336 337 405 432 
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sns.heatmap(fp,cmap='magma',linecolor='white',linewidths=1)

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<matplotlib.axes._subplots.AxesSubplot at 0x7f7de7ea59d0>

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# cluster map (hierarchical)
sns.clustermap(fp,cmap='coolwarm',standard_scale=1)

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<seaborn.matrix.ClusterGrid at 0x7f7de1cf2b10>

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Grids

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iris = sns.load_dataset('iris')
iris.head()
sepal_length sepal_width petal_length petal_width species
0 5.1 3.5 1.4 0.2 setosa
1 4.9 3.0 1.4 0.2 setosa
2 4.7 3.2 1.3 0.2 setosa
3 4.6 3.1 1.5 0.2 setosa
4 5.0 3.6 1.4 0.2 setosa 
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iris['species'].unique()

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array(['setosa', 'versicolor', 'virginica'], dtype=object)

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sns.pairplot(iris)

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<seaborn.axisgrid.PairGrid at 0x7f7de1aed490>

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# pairgrid, per personalizzare il pairplot
g = sns.PairGrid(iris)
# g.map(plt.scatter)
g.map_diag(sns.distplot)
g.map_upper(plt.scatter)
g.map_lower(sns.kdeplot)

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<seaborn.axisgrid.PairGrid at 0x7f7de1ac7c10>

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# facegrid, plot condizionali, mix matplotlib
g = sns.FacetGrid(data=tips,col='time',row='smoker')
# g.map(sns.distplot,'total_bill')
g.map(plt.scatter,'total_bill','tip') # invece di plt.scatter si possono usare altri grafici sia di plt che sns

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<seaborn.axisgrid.FacetGrid at 0x7f7de4d73290>

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Regression Plots

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# lm plot
sns.lmplot(x='total_bill',y='tip',data=tips,hue='sex',markers=['o','v'],scatter_kws={'s':50}) # scatter_kws per modificare i parametri di matplotlib sottostante tramite dizionario (es s è il size del marker)
# fit_reg=False per lasciare solo lo scatterplot

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<seaborn.axisgrid.FacetGrid at 0x7f7de4ad89d0>

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# multiplo by categorical
sns.lmplot(x='total_bill',y='tip',data=tips,col='day',row='time',hue='sex',aspect=0.6,height=8)

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<seaborn.axisgrid.FacetGrid at 0x7f7de25eaa90>

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Style and Color

Styles

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sns.set_style('ticks')
# sns.set_style('whitegrid')
# sns.set_style('darkgrid')
# sns.reset_orig() # resetta style

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sns.countplot(x='sex',data=tips)
sns.despine(bottom=True)

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plt.figure(figsize=(12,3))
sns.countplot(x='sex',data=tips)

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<matplotlib.axes._subplots.AxesSubplot at 0x7f7ddf8ddc90>

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sns.set_context('poster',font_scale=1.2)
sns.countplot(x='sex',data=tips)

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<matplotlib.axes._subplots.AxesSubplot at 0x7f7de56ed810>

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Palette

Colors

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# sns.set_palette("GnBu_d")
# sns.set_palette("bwr")

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sns.set_context('notebook')
sns.lmplot(x='total_bill',y='tip',data=tips,hue='sex',palette='seismic')

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<seaborn.axisgrid.FacetGrid at 0x7f7ddf3dbf90>

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Altro

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# df utile
titanic = sns.load_dataset('titanic')
titanic.head()
survived pclass sex age sibsp parch fare embarked class who adult_male deck embark_town alive alone
0 0 3 male 22.0 1 0 7.2500 S Third man True NaN Southampton no False
1 1 1 female 38.0 1 0 71.2833 C First woman False C Cherbourg yes False
2 1 3 female 26.0 0 0 7.9250 S Third woman False NaN Southampton yes True
3 1 1 female 35.0 1 0 53.1000 S First woman False C Southampton yes False
4 0 3 male 35.0 0 0 8.0500 S Third man True NaN Southampton no True

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