Vis u ali z ing regressions IN TR OD U C TION TO DATA VISU AL - - PowerPoint PPT Presentation

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Vis u ali z ing regressions IN TR OD U C TION TO DATA VISU AL - - PowerPoint PPT Presentation

Mar 11, 2023 316 likes •754 views

Vis u ali z ing regressions IN TR OD U C TION TO DATA VISU AL IZATION IN P YTH ON Br y an Van de Ven Core De v eloper of Bokeh Seaborn h p :// seaborn . p y data . org / INTRODUCTION TO DATA VISUALIZATION IN PYTHON Recap : pandas

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Visualizing regressions

IN TR OD U C TION TO DATA VISU AL IZATION IN P YTH ON

Bryan Van de Ven

Core Developer of Bokeh

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INTRODUCTION TO DATA VISUALIZATION IN PYTHON

Seaborn

hp://seaborn.pydata.org/

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INTRODUCTION TO DATA VISUALIZATION IN PYTHON

Recap: pandas DataFrames

Labelled tabular data structure Labels on rows: index Labels on columns: columns Columns are pandas Series

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Tips DataFrame

total_bill tip sex smoker day time size 16.99 1.01 Female No Sun Dinner 2 1 10.34 1.66 Male No Sun Dinner 3 2 21.01 3.5 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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Linear regression plots

95% condence interval highlighted

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Using lmplot()

import pandas as pd import matplotlib.pyplot as plt import seaborn as sns tips = sns.load_dataset('tips') sns.lmplot(x='total_bill', y='tip', data=tips) plt.show()

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Factors

total_bill tip sex smoker day time size 16.99 1.01 Female No Sun Dinner 2 1 10.34 1.66 Male No Sun Dinner 3 2 21.01 3.5 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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Grouping factors (same plot)

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Using hue

sns.lmplot(x='total_bill', y='tip', data=tips, hue='sex', palette='Set1') plt.show()

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Grouping factors (subplots)

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Using col

sns.lmplot(x='total_bill', y='tip', data=tips, col='sex') plt.show()

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Residual plots

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Using residplot()

sns.residplot(x='age',y='fare', data=tips, color='indianred') plt.show()

Similar arguments as lmplot() but more exible

x , y can be arrays or strings data is DataFrame (optional)

Optional arguments (e.g., color ) as in

matplotlib

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Let's practice!

IN TR OD U C TION TO DATA VISU AL IZATION IN P YTH ON

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Visualizing univariate distributions

IN TR OD U C TION TO DATA VISU AL IZATION IN P YTH ON

Bryan Van de Ven

Core Developer of Bokeh

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INTRODUCTION TO DATA VISUALIZATION IN PYTHON

Visualizing data

Univariate → "one variable" Visualization techniques for sampled univariate data Strip plots Swarm plots Violin plots

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Strip plot

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Using stripplot()

sns.stripplot(y='tip', data=tips) plt.ylabel('tip ($)') plt.show()

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Grouping with stripplot()

sns.stripplot(x='day', y='tip', data=tip) plt.ylabel('tip ($)') plt.show()

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Spreading out strip plots

sns.stripplot(x='day', y='tip', data=tip, size=4, jitter=True) plt.ylabel('tip ($)') plt.show()

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Swarm plot

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Using swarmplot()

sns.swarmplot(x='day', y='tip', data=tips) plt.ylabel('tip ($)') plt.show()

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More grouping

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More grouping with swarmplot()

sns.swarmplot(x='day', y='tip', data=tips, hue='sex') plt.ylabel('tip ($)') plt.show()

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Changing orientation

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Changing orientation

sns.swarmplot(x='tip', y='day', data=tips, hue='sex',

  • rient='h')

plt.xlabel('tip ($)') plt.show()

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Violin plot

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Using violinplot()

plt.subplot(1,2,1) sns.boxplot(x='day', y='tip', data=tips) plt.ylabel('tip ($)') plt.subplot(1,2,2) sns.violinplot(x='day', y='tip', data=tips) plt.ylabel('tip ($)') plt.tight_layout() plt.show()

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Combining plots

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Combining plots

sns.violinplot(x='day', y='tip', data=tips, inner=None, color='lightgray') sns.stripplot(x='day', y='tip', data=tips, size=4, jitter=True) plt.ylabel('tip ($)') plt.show()

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Let's practice!

IN TR OD U C TION TO DATA VISU AL IZATION IN P YTH ON

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Visualizing multivariate distributions

IN TR OD U C TION TO DATA VISU AL IZATION IN P YTH ON

Bryan Van de Ven

Core Developer of Bokeh

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Visualizing data

Bivariate → "two variables" Multivariate → "multiple variables" Visualizing relationships in multivariate data Joint plots Pair plots Heat maps

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Joint plot

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Using jointplot()

sns.jointplot(x= 'total_bill', y= 'tip', data=tips) plt.show()

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Joint plot using KDE

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Using kde=True

sns.jointplot(x='total_bill', y= 'tip', data=tips, kind='kde') plt.show()

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Pair plot

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Using pairplot()

sns.pairplot(tips) plt.show()

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Using pairplot() with hue

sns.pairplot(tips, hue='sex') plt.show()

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Covariance heat map of tips data

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Using heatmap()

print(covariance) 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 sns.heatmap(covariance) plt.title('Covariance plot') plt.show()

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Let's practice!

IN TR OD U C TION TO DATA VISU AL IZATION IN P YTH ON