Python Seaborn Data Visualization

Jun 5, 2019Download as pptx, pdf

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This document discusses using the Seaborn library in Python for data visualization. It covers installing Seaborn, importing libraries, reading in data, cleaning data, and creating various plots including distribution plots, heatmaps, pair plots, and more. Code examples are provided to demonstrate Seaborn's functionality for visualizing and exploring data.

Introduction
• Data Visualization
• Built on the top of matplotlib

Most read

Vertical Distribution
• dst=sns.distplot(data['no2'],color='green',vertical=True)
• dst.set_title('Pollution statistics of NO2')
• dst.set_xlabel('Air Quality Index of No2')
• dst.set_ylabel('Density of AQI')
• plt.show()

$More Variant • dst=sns.distplot(data['no2'],color='green',vertical=True,kde_kws={"color":"b", • "lw":2, • "label":"KDE" • },hist_kws={ • "histtype":"step" , • "linewidth":2, • "alpha":0.7, • "color":"r", • "label":"HIST" • }) • dst.set_title('Pollution statistics of NO2') • dst.set_xlabel('Air Quality Index of No2') • dst.set_ylabel('Density of AQI') • plt.show()$

• dst=sns.distplot(data['no2'],color='green',kde=False,bins=500)
• dst.set_title('Pollution statistics of NO2')
• dst.set_xlabel('Air Quality Index of No2')
• dst.set_ylabel('Frequency')
• plt.show()

Sub Plots & heatmap
• plt.subplots(figsize=(30,10))
• cordata=data.corr()
• sns.heatmap(cordata,vmax=0.8,square=T
rue,annot=True,fmt=".2f",cmap="YlGnBu
")

PairPlot
• sns.pairplot(data)
• plt.show()

• Sns.set() //sets default theme
• Style=‘darkgrid’ palette=‘deep’ font=‘sans=serif’

Installation
•For Installation
•Pip install seaborn
•!pip install seaborn --upgrade
For Testing
Import seaborn as sns
Sns.__version__

Demo
• To disable warning
• Import warnings
• Warnings.filterwarnings(‘Ignore’)

Imports
• import pandas as pd
• import seaborn as sns
• import numpy as np
• import matplotlib.pyplot as plt

Read Data
• data = pd.read_csv('data.csv', encoding='ISO-8859–
1')
• Data.head(100)

• data.columns
• Index(['stn_code', 'sampling_date', 'state', 'location',
'agency', 'type', 'so2', 'no2', 'rspm', 'spm',
'location_monitoring_station', 'pm2_5', 'date'],
dtype='object')

Data Cleaning
• data['no2']=data['no2'].fillna(0)

Distribution Plot
• dst=sns.distplot(data['no2'])
• dst.set_title('Pollution statistics of NO2')
• dst.set_xlabel('Air Quality Index of No2')
• dst.set_ylabel('Density of AQI')
• plt.show()

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Python Seaborn Data Visualization

1. Seaborn Sourabh Sahu

2. Introduction • Data Visualization • Built on the top of matplotlib

3. Architecture

4. Installation •For Installation •Pip install seaborn •!pip install seaborn --upgrade For Testing Import seaborn as sns Sns.__version__

5. Demo • To disable warning • Import warnings • Warnings.filterwarnings(‘Ignore’)

6. Imports • import pandas as pd • import seaborn as sns • import numpy as np • import matplotlib.pyplot as plt

7. Read Data • data = pd.read_csv('data.csv', encoding='ISO-8859– 1') • Data.head(100)

8. • data.columns • Index(['stn_code', 'sampling_date', 'state', 'location', 'agency', 'type', 'so2', 'no2', 'rspm', 'spm', 'location_monitoring_station', 'pm2_5', 'date'], dtype='object')

9. Data Cleaning • data['no2']=data['no2'].fillna(0)

10. Distribution Plot • dst=sns.distplot(data['no2']) • dst.set_title('Pollution statistics of NO2') • dst.set_xlabel('Air Quality Index of No2') • dst.set_ylabel('Density of AQI') • plt.show()

11. Display

12. Vertical Distribution • dst=sns.distplot(data['no2'],color='green',vertical=True) • dst.set_title('Pollution statistics of NO2') • dst.set_xlabel('Air Quality Index of No2') • dst.set_ylabel('Density of AQI') • plt.show()

13. More Variant • dst=sns.distplot(data['no2'],color='green',vertical=True,kde_kws={"color":"b", • "lw":2, • "label":"KDE" • },hist_kws={ • "histtype":"step" , • "linewidth":2, • "alpha":0.7, • "color":"r", • "label":"HIST" • }) • dst.set_title('Pollution statistics of NO2') • dst.set_xlabel('Air Quality Index of No2') • dst.set_ylabel('Density of AQI') • plt.show()

14. • dst=sns.distplot(data['no2'],color='green',kde=False,bins=500) • dst.set_title('Pollution statistics of NO2') • dst.set_xlabel('Air Quality Index of No2') • dst.set_ylabel('Frequency') • plt.show()

15. Sub Plots & heatmap • plt.subplots(figsize=(30,10)) • cordata=data.corr() • sns.heatmap(cordata,vmax=0.8,square=T rue,annot=True,fmt=".2f",cmap="YlGnBu ")

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17. • Sns.set() //sets default theme • Style=‘darkgrid’ palette=‘deep’ font=‘sans=serif’

18. • Sns.displine() //removes lines

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