In the previous tutorial, we learned why data visualization is important and how we can create plots using matplotlib. In this tutorial, we’ll learn about another data visualization library called Seaborn, which is built on top of matplotlib. But why do we need seaborn if we have matplotlib? Using seaborn you can make plots that are visually appealing and not just that seaborn is known for a range of plots that are not present in matplotlib that could be quite helpful in data analysis.
Before going into seaborn it is important that you know about matplotlib. If you don’t know matplotlib you can learn about it in our previous article.
Installing Seaborn
Unlike other libraries we’ve worked on until now seaborn doesn’t come pre-installed with anaconda but don’t worry we can install it using conda or pip.
conda install -c anaconda seaborn #Install using conda pip install seaborn # Install using pip
Importing Seaborn and Dataset
Conventionally seaborn is imported as sns, why you ask? Well, I had the same question! I mean sns is not an acronym for seaborn no matter how you see it. Turns out it’s actually an insider joke. You can read more about it here.
For this tutorial, we’ll be using Students Performance Dataset on Kaggle. Now that everything is cleared let’s start importing the dataset and libraries.
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
sns.set_style('darkgrid')
df = pd.read_csv('data.csv')
df.head()
But what’s this set_style() used for? Let’s understand.
Plot Styles in Seaborn
Using seaborn, you can actually set how you want your plot to be displayed. You can set these style using sns.set_style(). I really like the darkgrid style so let me show you how to set it.
sns.set_style('darkgrid')
Apart from darkgrid, there are other styles that you can use like:-
- darkgrid
- whitegrid
- dark
- white
- ticks
Now that we have everything set up, Let’s get plotting!
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Plotting in Seaborn
sns.scatterplot()
This is used to create scatter plots, we can pass data for the x and y-axis and it’ll create a scatter plot for them.
sns.scatterplot(x = df['math score'], y = df['reading score']) plt.show()
In seaborn, we can actually pass our data frame as value for data argument and then just pass labels to x and y, let’s see how.
plt.figure(figsize = (9,6)) sns.scatterplot(x = 'math score', y = 'reading score',data = df) plt.show()
Since Seaborn is built on top of matplotlib we can use plt.show() just like we did in matplotlib, in fact using matplotlib we can customize the seaborn plot. Now our plot is a bit boring let’s customize it a bit!
plt.figure(figsize = (9,6))
sns.scatterplot(x = 'math score',
y = 'reading score',
hue = 'gender',
data = df,
alpha = 0.8
)
plt.show()
Now let’s see what happened, We plotted a scatter plot between ‘math score’ and ‘reading score’, we set alpha = 0.8 to define the opacity of dots, and we passed hue as ‘gender’ with color the dot to its corresponding gender. For example, the bottom left dot is blue meaning that point had the gender Female in the data frame.
sns.countplot()
This creates a frequency plot that tells the no. of occurrence of the corresponding element in the column.
plt.figure(figsize = (9,6))
sns.countplot(x = 'race/ethnicity',
data = df
)
plt.show()
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sns.displot()
This creates a histogram for the corresponding distribution. By default, it displays the KDE of the distribution which you can turn off by setting kde = False.
plt.figure(figsize = (9,6)) sns.displot(x = df['math score'], kde = False) plt.show()
sns.kdeplot()
This creates a Kernel Density Estimate plot or KDE plot of the distribution. You can learn about KDE here.
plt.figure(figsize = (9,6)) sns.kdeplot(x = df['math score']) plt.show()
sns.regplot()
This creates a scatter plot that contains a regression line for the data. We’ll learn about regression lines in the next article but for now, think of it as a line that tells the relationship between 2 points.
plt.figure(figsize = (9,6))
sns.regplot(x = df['math score'],
y = df['reading score'],
scatter_kws = {'color':'pink'},
line_kws = {'color':'red'}
)
plt.show()
Using scatter_kws and line_kws we can set characteristics for lines and points in the plot.
sns.lmplot()
This is almost the same as regplot but it can create a regression line for all the categories of columns set as hue.
sns.lmplot(x = 'math score',
y = 'reading score',
hue = 'gender',
data = df
)
plt.show()
sns.pairplot()
This plots pairwise bivariate distributions in a dataset. pairplot() only works for numerical columns. You have to pass the subsetted data with columns you want to plot distribution in between.
sns.pairplot(df[['math score',
'reading score',
'writing score']]
)
plt.show()
sns.jointplot()
This plots both univariate and bivariate distribution between 2 columns.
sns.jointplot(x = 'math score',
y = 'reading score',
data = df
)
plt.show()
Let’s try adding a hue to see what happens.
sns.jointplot(x = 'math score',
y = 'reading score',
hue = 'gender',
data = df
)
plt.show()
sns.boxplot()
Creates a boxplot that gives 5 number summary of the distribution. You can create multiple box plots too. Boxplots also display the outliers in the dataset.
For Single Continuous Column
sns.boxplot(x = 'math score',
data = df
)
plt.show()
For All Numerical Columns in the Dataset
sns.boxplot(data = df) plt.show()
sns.swarmplot()
This plots a categorical scatterplot with non-overlapping points. A swarm plot can be drawn on its own, but it is also a good complement to a box or violin plot.
plt.figure(figsize = (12,8))
sns.swarmplot(x = 'race/ethnicity',
y = 'math score',
data = df,
alpha = 0.8
)
plt.show()
sns.heatmap()
Plots heatmap of the data. Most commonly used to create correlation heatmaps. annot = True is used to display the value of the cell.
sns.heatmap(df.corr(),
annot = True,
cmap = 'inferno')
plt.show()
Color pallets in Seaborn
Seaborn allows us to set custom color palettes. We can simply create an ordered Python list of color hex values.
plt.figure(figsize = (12,8))
colors = [
'#F8D030',
'#E0C068',
'#EE99AC',
'#C03028',
'#F85888'
]
sns.stripplot(x = 'race/ethnicity',
y = 'math score',
data = df,
palette = colors
)
plt.show()
Strip Plots are like swarm plots but with overlapping points.
Congratulations! Now you are capable to create plots in the seaborn. You can use these plots to gather insights into your data. There are various other plots that you can explore here, but now you know how to plot the important ones.
Thanks for reading the Seaborn: Create Elegant Plots article, hope you enjoyed and learned enough from it. But, still, if you found any problems, let us know in the comments.
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Credits to edureka!
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