# 2.7 - Intro to Seaborn

Last updated: February 16th, 2019

# Intro to Seaborn - Exercises¶

In [ ]:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

%matplotlib inline


### Exercise 1¶

Import the data/tips.csv dataset.

In [ ]:
# your code goes here

In [ ]:
df = pd.read_csv('data/tips.csv')



### Exercise 2¶

Plot an histogram of the total_bill column. Add title, xlabel and ylabel.

In [ ]:
# your code goes here

In [ ]:
plot = sns.distplot(df['total_bill'])

plot.set(title='Total bill',
xlabel='Value',
ylabel='Frequency')


### Exercise 3¶

Plot a scatter showing the relationship between total_bill and tip.

In [ ]:
# your code goes here

In [ ]:
sns.jointplot(x='total_bill', y='tip', data=df,
kind='scatter')


### Exercise 4¶

Plot a scatter showing all the relationships between total_bill, tip and size.

In [ ]:
# your code goes here

In [ ]:
sns.pairplot(df, vars=['total_bill', 'tip', 'size'])


### Exercise 5¶

Draw a scatter plot showing the relationship between total_bill and the categorical variabledays.

In [ ]:
# your code goes here


Remember to use stripplot() as one variable is categorical.

In [ ]:
sns.stripplot(data=df, x='day', y='total_bill')


### Exercise 6¶

Draw a scatter plot showing the relationship between tip and the categorical variable day, differ the dots by sex.

In [ ]:
# your code goes here


Remember that we can use hue parameter indicating by which column we want to differ the dots.

In [ ]:
sns.stripplot(data=df, x='tip', y='day', hue='sex')


### Exercise 7¶

Draw a boxplot of total_bill values per day .

In [ ]:
# your code goes here

In [ ]:
sns.boxplot(data=df, x='day', y='total_bill')


### Exercise 8¶

Draw two histograms plot of the tip values per day.

In [ ]:
# your code goes here


Remember that we can create multigraphic plots in Seaborn using FacetGrid() function.

In [ ]:
grid = sns.FacetGrid(df, col='time')

grid.map(plt.hist, 'tip')


### Exercise 9¶

Draw two scatter plots of the relation between total_bill and tip per sex. Also, differing the dots by smoker.

In [ ]:
# your code goes here

In [ ]:
grid = sns.FacetGrid(df, col='sex', hue='smoker')

grid.map(plt.scatter, 'total_bill', 'tip', alpha=0.8)