Exploring Matplotlib and Seaborn

Matplotlib is a plotting library for the Python programming language and its numerical mathematics extension NumPy. Seaborn is a Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics.

Table of Content

1. Importing libraries
2. Line Graph
3. Multi-line Graph
4. Scatterplot
5. Histogram
6. Bar Graph
7. Pair Plot

Importing Libraries

import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np

Line Graph

apples = [11,45,23,76,34]
years = [2000,2001,2002,2003,2004]
plt.xlabel(years)
plt.ylabel(apples)
plt.plot(years,apples)

Multi-line Graph

apples = [11,45,23,76,34]
years = [2000,2001,2002,2003,2004]
plt.xlabel(years)
plt.ylabel(apples)
plt.plot(years,apples)
oranges = [23,34,54,22,67]
plt.plot(years,oranges)
plt.title('Graph')
plt.legend(['apples','oranges'])

apples = [11,45,23,76,34]
years = [2000,2001,2002,2003,2004]
plt.xlabel(years)
plt.ylabel(apples)
plt.plot(years,apples,marker='^')
oranges = [23,34,54,22,67]
plt.plot(years,oranges,marker='*')
plt.title('Graph')
plt.legend(['apples','oranges'])

sns.set_style('darkgrid')
apples = [11,45,23,76,34]
years = [2000,2001,2002,2003,2004]
plt.xlabel(years)
plt.ylabel(apples)
plt.plot(years,apples,marker='^')
oranges = [23,34,54,22,67]
plt.plot(years,oranges,marker='*')
plt.title('Graph')
plt.legend(['apples','oranges'])

plt.plot(flowers_df.sepal_length,flowers_df.sepal_width)

Scatterplot

apples = [11,45,23,76,34]
years = [2000,2001,2002,2003,2004]
plt.xlabel(years)
plt.ylabel(apples)
plt.plot(years,apples,'or',color='yellow')
oranges = [23,34,54,22,67]
plt.plot(years,oranges,'or')
plt.title('Graph')
plt.legend(['apples','oranges'])

flowers_df=sns.load_dataset("iris")
flowers_df

sns.scatterplot(x=flowers_df.sepal_length,y=flowers_df.sepal_width)

sns.scatterplot(x=flowers_df.sepal_length,y=flowers_df.sepal_width)

flowers_df.sepal_width

Histogram

plt.title('Distribution of sepal width')
plt.hist(flowers_df.sepal_width)

plt.title('Distribution of sepal width')
plt.hist(flowers_df.sepal_width,bins=15)

plt.title('Distribution of sepal width')
plt.hist(flowers_df.sepal_width,bins=[2,3,4])

plt.title('Distribution of sepal width')
plt.hist(flowers_df.sepal_width,bins=np.arange(2,5,0.25))

tips_df=sns.load_dataset('tips')
tips_df

Bar Graph

plt.bar(tips_df.size,tips_df.total_bill)

sns.barplot(x=tips_df.day,y=tips_df.total_bill)

sns.barplot(x=tips_df.day,y=tips_df.total_bill,data=tips_df)

sns.barplot(x=tips_df.day,y=tips_df.total_bill,data=tips_df,hue='sex')

Pair Plot

sns.pairplot(flowers_df, hue='species');

If you found this article useful, you can clap and to stay updated with my articles you can follow me.