INTRODUCTION TO DATA VISUALIZATION WITH PYTHON. Visualizing Regressions
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1 INTRODUCTION TO DATA VISUALIZATION WITH PYTHON Visualizing Regressions
2 Seaborn
3 Recap: Pandas DataFrames Labelled tabular data structure Labels on rows: index Labels on columns: columns Columns are Pandas Series
4 Recap: Pandas DataFrames total_bill tip sex smoker day time size Female No Sun Dinner Male No Sun Dinner Male No Sun Dinner Male No Sun Dinner Female No Sun Dinner 4
5 Linear regression plots 95% confidence
6 Using lmplot() In [1]: import pandas as pd In [2]: import matplotlib.pyplot as plt In [3]: import seaborn as sns In [4]: tips =sns.load_dataset('tips') In [5]: sns.lmplot(x= 'total_bill', y='tip', data=tips) In [6]: plt.show()
7 Factors total_bil tip sex smoker day time size 0 l Female No Sun Dinner Male No Sun Dinner Male No Sun Dinner Male No Sun Dinner Female No Sun Dinner 4
8 Grouping factors (same plot) Introduction to Data Visualization with Python
9 Using hue= In [7]: sns.lmplot(x='total_bill', y='tip', data=tips, hue='sex',...: palette='set1') In [8]: plt.show()
10 Grouping factors (subplots) Introduction to Data Visualization with Python
11 Using col= In [9]: sns.lmplot(x='total_bill', y='tip', data=tips, col='sex') In [10]: plt.show()
12 Residual plots Introduction to Data Visualization with Python
13 Using residplot() In [11]: sns.residplot(x='age',y='fare',data=tips,color='indianred') In [12]: plt.show() Similar arguments as lmplot() but more flexible x, y can be arrays or strings data is DataFrame (optional) Optional arguments (e.g., color) as in Matplotlib
14 INTRODUCTION TO DATA VISUALIZATION WITH PYTHON Let s practice!
15 INTRODUCTION TO DATA VISUALIZATION WITH PYTHON Visualizing univariate distributions
16 Visualizing data Univariate one variable Visualization techniques for sampled univariate data Strip plots Swarm plots Violin plots
17 Strip plot Introduction to Data Visualization with Python
18 Using stripplot() In [1]: sns.stripplot(y= 'tip', data=tips) In [2]: plt.ylabel('tip ($)') In [3]: plt.show()
19 Grouping with stripplot() In [4]: sns.stripplot(x='day', y='tip', data=tip) In [5]: plt.ylabel('tip ($)') In [6]: plt.show()
20 Grouped strip plot Introduction to Data Visualization with Python
21 Spreading out strip plots Introduction to Data Visualization with Python
22 Spreading out strip plots In [7]: sns.stripplot(x='day', y='tip', data=tip, size=4,...: jitter=true) In [8]: plt.ylabel('tip ($)') In [9]: plt.show()
23 Swarm plot Introduction to Data Visualization with Python
24 Using swarmplot() In [10]: sns.swarmplot(x='day', y='tip', data=tips) In [11]: plt.ylabel('tip ($)') In [12]: plt.show()
25 More grouping Introduction to Data Visualization with Python
26 More grouping with swarmplot() In [13]: sns.swarmplot(x='day', y='tip', data=tips, hue='sex') In [14]: plt.ylabel('tip ($)') In [15]: plt.show()
27 Changing orientation Introduction to Data Visualization with Python
28 Changing orientation In [16]: sns.swarmplot(x='tip', y='day', data=tips, hue='sex',...: orient='h') In [17]: plt.xlabel('tip ($)') In [18]: plt.show()
29 Violin plot Introduction to Data Visualization with Python
30 Using violinplot() In [19]: plt.subplot(1,2,1) In [20]: sns.boxplot(x='day', y='tip', data=tips) In [21]: plt.ylabel('tip ($)') In [22]: plt.subplot(1,2,2) In [23]: sns.violinplot(x='day', y='tip', data=tips) In [24]: plt.ylabel('tip ($)') In [25]: plt.tight_layout() In [26]: plt.show()
31 Combining plots Introduction to Data Visualization with Python
32 Combining plots In [27]: sns.violinplot(x='day', y='tip', data=tips, inner=none,...: color='lightgray') In [28]: sns.stripplot(x='day', y='tip', data=tips, size=4,...: jitter=true) In [29]: plt.ylabel('tip ($)') In [30]: plt.show()
33 INTRODUCTION TO DATA VISUALIZATION WITH PYTHON Let s practice!
34 INTRODUCTION TO DATA VISUALIZATION WITH PYTHON Visualizing Multivariate Distributions
35 Visualizing data Bivariate two variables Multivariate multiple variables Visualizing relationships in multivariate data Joint plots Pair plots Heat maps
36 Joint plot Introduction to Data Visualization with Python
37 Using jointplot() In [1]: sns.jointplot(x= 'total_bill', y= 'tip', data=tips) In [2]: plt.show()
38 Joint plot using KDE Introduction to Data Visualization with Python
39 Using kde=true In [3]: sns.jointplot(x='total_bill', y= 'tip', data=tips,...: kind='kde') In [4]: plt.show()
40 Pair plot Introduction to Data Visualization with Python
41 Using pairplot() In [5]: sns.pairplot(tips) In [6]: plt.show()
42 Using pairplot() with hue In [7]: sns.pairplot(tips, hue='sex') In [8]: plt.show()
43 Using pairplot() with hue Introduction to Data Visualization with Python
44 Covariance heat map of tips data
45 Using heatmap() In [9]: print(covariance) total_bill tip size total_bill tip size In [10]: sns.heatmap(covariance) In [11]: plt.title('covariance plot') In [12]: plt.show()
46 INTRODUCTION TO DATA VISUALIZATION WITH PYTHON Let s practice!
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