Seaborn Exercises - Solutions

Time to practice your new seaborn skills! Try to recreate the plots below (don't worry about color schemes, just the plot itself.

The Data

We will be working with a famous titanic data set for these exercises. Later on in the Machine Learning section of the course, we will revisit this data, and use it to predict survival rates of passengers. For now, we'll just focus on the visualization of the data with seaborn:



In [19]:

    
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline



In [27]:

    
sns.set_style('whitegrid')



In [28]:

    
titanic = sns.load_dataset('titanic')



In [40]:

    
titanic.head()









    Out[40]:






  
    
      
      survived
      pclass
      sex
      age
      sibsp
      parch
      fare
      embarked
      class
      who
      adult_male
      deck
      embark_town
      alive
      alone
    
  
  
    
      0
      0
      3
      male
      22.0
      1
      0
      7.2500
      S
      Third
      man
      True
      NaN
      Southampton
      no
      False
    
    
      1
      1
      1
      female
      38.0
      1
      0
      71.2833
      C
      First
      woman
      False
      C
      Cherbourg
      yes
      False
    
    
      2
      1
      3
      female
      26.0
      0
      0
      7.9250
      S
      Third
      woman
      False
      NaN
      Southampton
      yes
      True
    
    
      3
      1
      1
      female
      35.0
      1
      0
      53.1000
      S
      First
      woman
      False
      C
      Southampton
      yes
      False
    
    
      4
      0
      3
      male
      35.0
      0
      0
      8.0500
      S
      Third
      man
      True
      NaN
      Southampton
      no
      True

Exercises

Recreate the plots below using the titanic dataframe. There are very few hints since most of the plots can be done with just one or two lines of code and a hint would basically give away the solution. Keep careful attention to the x and y labels for hints.

Note! In order to not lose the plot image, make sure you don't code in the cell that is directly above the plot, there is an extra cell above that one which won't overwrite that plot!



In [42]:

    
# CODE HERE
# REPLICATE EXERCISE PLOT IMAGE BELOW
# BE CAREFUL NOT TO OVERWRITE CELL BELOW
# THAT WOULD REMOVE THE EXERCISE PLOT IMAGE!



In [41]:

    
sns.jointplot(x='fare',y='age',data=titanic)









    Out[41]:





<seaborn.axisgrid.JointGrid at 0x11d0389e8>



In [43]:

    
# CODE HERE
# REPLICATE EXERCISE PLOT IMAGE BELOW
# BE CAREFUL NOT TO OVERWRITE CELL BELOW
# THAT WOULD REMOVE THE EXERCISE PLOT IMAGE!



In [44]:

    
sns.distplot(titanic['fare'],bins=30,kde=False,color='red')









    Out[44]:





<matplotlib.axes._subplots.AxesSubplot at 0x11fc5ca90>



In [ ]:

    
# CODE HERE
# REPLICATE EXERCISE PLOT IMAGE BELOW
# BE CAREFUL NOT TO OVERWRITE CELL BELOW
# THAT WOULD REMOVE THE EXERCISE PLOT IMAGE!



In [45]:

    
sns.boxplot(x='class',y='age',data=titanic,palette='rainbow')









    Out[45]:





<matplotlib.axes._subplots.AxesSubplot at 0x11f23da90>



In [ ]:

    
# CODE HERE
# REPLICATE EXERCISE PLOT IMAGE BELOW
# BE CAREFUL NOT TO OVERWRITE CELL BELOW
# THAT WOULD REMOVE THE EXERCISE PLOT IMAGE!



In [46]:

    
sns.swarmplot(x='class',y='age',data=titanic,palette='Set2')









    Out[46]:





<matplotlib.axes._subplots.AxesSubplot at 0x11f215320>



In [ ]:

    
# CODE HERE
# REPLICATE EXERCISE PLOT IMAGE BELOW
# BE CAREFUL NOT TO OVERWRITE CELL BELOW
# THAT WOULD REMOVE THE EXERCISE PLOT IMAGE!



In [47]:

    
sns.countplot(x='sex',data=titanic)









    Out[47]:





<matplotlib.axes._subplots.AxesSubplot at 0x11f207ef0>



In [ ]:

    
# CODE HERE
# REPLICATE EXERCISE PLOT IMAGE BELOW
# BE CAREFUL NOT TO OVERWRITE CELL BELOW
# THAT WOULD REMOVE THE EXERCISE PLOT IMAGE!



In [48]:

    
sns.heatmap(titanic.corr(),cmap='coolwarm')
plt.title('titanic.corr()')









    Out[48]:





<matplotlib.text.Text at 0x11d72da58>



In [ ]:

    
# CODE HERE
# REPLICATE EXERCISE PLOT IMAGE BELOW
# BE CAREFUL NOT TO OVERWRITE CELL BELOW
# THAT WOULD REMOVE THE EXERCISE PLOT IMAGE!



In [49]:

    
g = sns.FacetGrid(data=titanic,col='sex')
g.map(plt.hist,'age')









    Out[49]:





<seaborn.axisgrid.FacetGrid at 0x11d81c240>

Seaborn Exercises - Solutions

The Data

Exercises

Great Job!

That is it for now! We'll see a lot more of seaborn practice problems in the machine learning section!

	survived	pclass	sex	age	sibsp	fare	embarked	class	who	adult_male	deck	embark_town	alive	alone
0	0	3	male	22.0	1	7.2500	S	Third	man	True	NaN	Southampton	no	False
1	1	1	female	38.0	1	71.2833	C	First	woman	False	C	Cherbourg	yes	False
2	1	3	female	26.0	0	7.9250	S	Third	woman	False	NaN	Southampton	yes	True
3	1	1	female	35.0	1	53.1000	S	First	woman	False	C	Southampton	yes	False
4	0	3	male	35.0	0	8.0500	S	Third	man	True	NaN	Southampton	no	True