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![20
Histogram
import seaborn as sns
from matplotlib import pyplot as plt
df = sns.load_dataset('iris')
sns.distplot(df['petal_length'],kde = False)](https://image.slidesharecdn.com/seaborn-241028044933-f3cf9876/85/Seaborn-for-data-visualization-using-python-pptx-20-320.jpg)
![29
KDE plot
import seaborn as sns
import matplotlib.pyplot as plt
sns.set_style("dark")
iris = sns.load_dataset("iris")
# Plotting the KDE Plot
sns.kdeplot(iris.loc[(iris['species']=='setosa'),
'sepal_length'], color='b', shade=True, Label='setosa')
sns.kdeplot(iris.loc[(iris['species']=='virginica'),
'sepal_length'], color='r', shade=True, Label='virginica')](https://image.slidesharecdn.com/seaborn-241028044933-f3cf9876/85/Seaborn-for-data-visualization-using-python-pptx-29-320.jpg)
![31
Violin Plot
import seaborn as sns
tips = sns.load_dataset("tips")
ax = sns.violinplot(x=tips["total_bill"])](https://image.slidesharecdn.com/seaborn-241028044933-f3cf9876/85/Seaborn-for-data-visualization-using-python-pptx-31-320.jpg)
![20
Histogram
import seaborn as sns
from matplotlib import pyplot as plt
df = sns.load_dataset('iris')
sns.distplot(df['petal_length'],kde = False)](https://image.slidesharecdn.com/seaborn-241028044933-f3cf9876/75/Seaborn-for-data-visualization-using-python-pptx-20-2048.jpg)
![29
KDE plot
import seaborn as sns
import matplotlib.pyplot as plt
sns.set_style("dark")
iris = sns.load_dataset("iris")
# Plotting the KDE Plot
sns.kdeplot(iris.loc[(iris['species']=='setosa'),
'sepal_length'], color='b', shade=True, Label='setosa')
sns.kdeplot(iris.loc[(iris['species']=='virginica'),
'sepal_length'], color='r', shade=True, Label='virginica')](https://image.slidesharecdn.com/seaborn-241028044933-f3cf9876/75/Seaborn-for-data-visualization-using-python-pptx-29-2048.jpg)
![31
Violin Plot
import seaborn as sns
tips = sns.load_dataset("tips")
ax = sns.violinplot(x=tips["total_bill"])](https://image.slidesharecdn.com/seaborn-241028044933-f3cf9876/75/Seaborn-for-data-visualization-using-python-pptx-31-2048.jpg)
![python libray for data analytics seaborn[1].pptx](https://cdn.slidesharecdn.com/ss_thumbnails/pythonseaborn1-241222125910-e118d8f2-thumbnail.jpg?width=600ounds&width=560&fit=bounds)
Seaborn for data visualization using python.pptx
- 1. Dr. M. Kriushanth AssistantProfessor Department of Data Science St. Joseph’s College (Autonomous) Tiruchirappalli - 2 Data Visualization using Seaborn
- 2. 2 Agenda • Seaborn Intro •Installation • Load Datasets • Seaborn Color Palette • Seaborn Plotting Functions
- 3. 3 • Seaborn isan amazing data visualization library for statistical graphics plotting in Python. • It provides beautiful default styles and colour palettes to make statistical plots more attractive. • It is built on the top of the matplotlib library and also closely integrated to the data structures from pandas. Seaborn
- 4. 4 Installing Seaborn • Usingpip installer – pip install seaborn • Anaconda – conda install seaborn • Also, make sure you have the following dependencies installed on your computer: • Python 3.6, NumPy, SciPy, Pandas, Matplotlib and Statsmodels(optional,but recommended)
- 5. 5 Load Data ToConstruct Seaborn Plots import pandas import matplotlib import scipy import seaborn as sns print(sns.get_dataset_names())
- 6. 6 • Let usimport any one of those datasets import seaborn as sns df = sns.load_dataset('car_crashes') print(df.head())
- 7. 7 Styling and Themesin Seaborn • Matplotlib library is highly customizable, but it may be hard for us to tweak the right setting to get an attractive and good looking plot. • A simple Matplotlib plot using Seaborn’s set() function.
- 8. 8 from matplotlib importpyplot as plt import seaborn as sns plt.scatter(df.speeding,df.alcohol) plt.show()
- 9. 9 Plot using theset() function from matplotlib import pyplot as plt import seaborn as sns plt.scatter(df.speeding,df.alcohol) sns.set() plt.show()
- 10. 10 Here are afew of the popular themes: Darkgrid Whitegrid Dark White Ticks
- 11. 11 from matplotlib importpyplot as plt import seaborn as sns plt.scatter(df.speeding,df.alcohol) sns.set_style("whitegrid") plt.show()
- 12. 12 We can removethe top and right axis spines using the despine() function. from matplotlib import pyplot as plt import seaborn as sns plt.scatter(df.speeding,df.alcohol) sns.set_style("ticks") sns.despine() plt.show()
- 13. 13 Axes Style import seabornas sns param=sns.axes_style() param
- 14. 14 from matplotlib importpyplot as plt import seaborn as sns plt.scatter('speeding','alcohol',data=df) sns.set_style("darkgrid", {'grid.color': '.5'}) sns.despine() plt.show()
- 15. 15 Seaborn has areputation for making plots and graphs more attractive using attractive colors and color combinations. Some of the color palettes out of the 170 palettes offered by Seaborn. Seaborn Color Palette
- 16. 16 • sns.palplot(sns.color_palette("deep", 10)) •sns.palplot(sns.color_palette("PiYG", 10)) • sns.palplot(sns.color_palette("GnBu", 10))
- 17. 17 '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',
- 18. 18 Seaborn’s plotting functions importseaborn as sns tips = sns.load_dataset("tips") tips.head() sns.relplot(data=tips, x="total_bill", y="tip") sns.relplot(data=tips, x="total_bill", y="tip", hue="day")
- 19. 19 sns.relplot(data=tips, x="total_bill", y="tip",hue="sex", col="day", col_wrap=2) sns.relplot(data=tips, x="size", y="tip",kind="line",ci=None)
- 20. 20 Histogram import seaborn assns from matplotlib import pyplot as plt df = sns.load_dataset('iris') sns.distplot(df['petal_length'],kde = False)
- 21. 21 Bar Plot import matplotlib.pyplotas plt import seaborn as sns sns.set_context('paper') # load dataset titanic = sns.load_dataset('titanic') # create plot sns.barplot(x = 'embark_town', y = 'age', data = titanic, palette = 'PuRd',ci=None ) plt.legend() plt.show() print(titanic.columns)
- 22. 22 Bar Plot Horizontal barplot importmatplotlib.pyplot as plt import seaborn as sns sns.set_context('paper') sns.barplot(x = 'age', y = 'embark_town', data = titanic, palette = 'PuRd', orient = 'h', ) plt.show()
- 23. 23 Horizontal barplot import matplotlib.pyplotas plt import seaborn as sns sns.set_context('paper') sns.barplot(x = 'age', y = 'embark_town', data = titanic, palette = 'PuRd', orient = 'h', ) plt.show()
- 24. 24 Count plot import matplotlib.pyplotas plt import seaborn as sns sns.set_context('paper') # load dataset titanic = sns.load_dataset('titanic') # create plot sns.countplot(x = 'class', hue = 'who', data = titanic, palette = 'magma') plt.title('Survivors') plt.show()
- 25. 25 Point Plot # importingrequired packages import seaborn as sns import matplotlib.pyplot as plt # loading dataset data = sns.load_dataset("tips") sns.pointplot(x="day", y="tip", data=data) plt.show()
- 26. 26 Joint Plot import seabornas sns import matplotlib.pyplot as plt sns.set_style("dark") tips=sns.load_dataset('tips') sns.jointplot(x='total_bill', y='tip',data=tips) Replace with sns.jointplot(x='total_bill', y='tip', data=tips, kind='reg’) sns.jointplot(x='total_bill', y='tip', data=tips, kind='kde’) sns.jointplot(x='total_bill', y='tip', data=tips, kind='hex’)
- 27. 27 Regplot import seaborn assns tips = sns.load_dataset("tips") ax = sns.regplot(x="total_bill", y="tip", data=tips)
- 28. 28 Lm Plot import seabornas sns tips = sns.load_dataset("tips") sns.lmplot(x="total_bill", y="tip", data=tips) sns.lmplot(x="total_bill", y="tip", col="day", hue="day", data=tips, col_wrap=2, height=3)
- 29. 29 KDE plot import seabornas sns import matplotlib.pyplot as plt sns.set_style("dark") iris = sns.load_dataset("iris") # Plotting the KDE Plot sns.kdeplot(iris.loc[(iris['species']=='setosa'), 'sepal_length'], color='b', shade=True, Label='setosa') sns.kdeplot(iris.loc[(iris['species']=='virginica'), 'sepal_length'], color='r', shade=True, Label='virginica')
- 30. 30 Box Plot import seabornas sns tips = sns.load_dataset("tips") sns.boxplot(x="day", y="total_bill", data=tips)
- 31. 31 Violin Plot import seabornas sns tips = sns.load_dataset("tips") ax = sns.violinplot(x=tips["total_bill"])
- 32. 32 Heatmap flights=sns.load_dataset("flights") flights = flights.pivot("month","year", "passengers") print(flights) sns.heatmap(flights,linewidths=.5,cmap="YlGnBu")
- 33. 33 Cluster map import seabornas sns flights=sns.load_dataset("flights") flights = flights.pivot("month", "year", "passengers") sns.clustermap(flights,linewidths=.5,cmap="coolwarm") import seaborn as sns flights=sns.load_dataset("flights") flights = flights.pivot("month", "year", "passengers") sns.clustermap(flights,linewidths=.5,cmap="coolwarm",col_cluster= False)
- 34. 34 Pair Plot import seabornas sns from matplotlib import pyplot as plt df = sns.load_dataset('iris') sns.set_style("ticks") sns.pairplot(df,hue = 'species',diag_kind = "kde",kind = "scatter",palette = "husl") plt.show()
- 35.