SEABORN PPT - PowerPoint PPT Presentation
Title:
SEABORN PPT
Description:
Seaborn is a Python data visualization library based on matplotlib. Some basics of seaborn are highlighted in this tutorial along with some characteristics. Moreover, various plots that can be plotted to observe the distribution patterns using seaborn are also named. Distplot function is discussed in detail. To learn more about trending data science courses, artificial intelligence or machine learning courses visit: – PowerPoint PPT presentation
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Title: SEABORN PPT
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Seaborn
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Introduction
- Seaborn is a Python statistical graphics library.Â
- It builds on top of matplotlib and incorporates da
ta structures closely with pandas. - Seaborn allows you to explore and understand yourÂ
data. - Its plotting functions work on data frames and arr
ays containing entire datasets and internally per
form the necessary semantic mapping and statistic
al aggregationfor the development of informative p
lots. - It makes the charts more appealing and promotes s
ome of the common needs for data visualisation (li
ke mapping a color to a variable). - Importing seaborn import seaborn as sns
- To check the version of seaborn on your system
- print(sns.__version__)
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Various Plots in Seaborn
- Wide range of plots can be plotted using seaborn
for visualization. - Here are some of them
- Univariate Data Plotting a graph for single
variable - Distplot
- Rug Plot
- 2. Bivariate Data Plotting a graph for 2
variables - Scatter Plot
- Hexbin Plot
- Kde Plot
- Co-relation
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Distplot
- Distplot stands for distribution plot, it takes
as input an array and plots a curve corresponding
to the distribution of points in the array. - We will be using distplot() function for this
purpose. - The output plot will have basically 2
graphs 1.Histogram 2.kde curve-kernel
density - Eg import matplotlib.pyplot as pltimport seabo
rn as snssns.distplot(0, 1, 2, 3, 4, 5)plt.sho
w()
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- O/P
Histogram
Kde curve
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- We can also separately visualize both the plots.
- sns.distplot(0, 1, 2, 3, 4, 5,kdeFalse)only
histogram curve will be observed. - sns.distplot(0, 1, 2, 3, 4, 5,histFalse) only
kde curve will be observed.
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