seaborn_cheat_sheet_3


Seaborn - matrix and regression plots


In [1]:
import seaborn as sns
%matplotlib inline
flights = sns.load_dataset('flights')
flights.head()


Out[1]:
year month passengers
0 1949 January 112
1 1949 February 118
2 1949 March 132
3 1949 April 129
4 1949 May 121

In [2]:
flights.shape


Out[2]:
(144, 3)

Let us pivot this flights data such that it becomes a 2D matrix. Lets make the Month as row indices


In [3]:
flights_pv = flights.pivot_table(index='month', columns='year', values='passengers')
flights_pv.head()


Out[3]:
year 1949 1950 1951 1952 1953 1954 1955 1956 1957 1958 1959 1960
month
January 112 115 145 171 196 204 242 284 315 340 360 417
February 118 126 150 180 196 188 233 277 301 318 342 391
March 132 141 178 193 236 235 267 317 356 362 406 419
April 129 135 163 181 235 227 269 313 348 348 396 461
May 121 125 172 183 229 234 270 318 355 363 420 472

Using pivot_tables we have also aggregated the data by month and years.

Heatmap

Heatmaps are a great way to represent continually variying data. However, you need to run this on a matrix kind of dataset, one where the row indexes are values themselves instead of serials.


In [4]:
sns.heatmap(flights_pv)


Out[4]:
<matplotlib.axes._subplots.AxesSubplot at 0x117e0ed68>

From the heatmap above, we see there are more passengers in summer (June, July, August) and the number of passengers increases by the year as well.

Heatmap for null data visualization

SNS Heatmap is great to view how many nulls are in your data.


In [2]:
#from ml chapter, read titanic data
import pandas as pd
titanic = pd.read_csv('../udemy_ml_bootcamp/Machine Learning Sections/Logistic-Regression/titanic_train.csv')
titanic.head()


Out[2]:
PassengerId Survived Pclass Name Sex Age SibSp Parch Ticket Fare Cabin Embarked
0 1 0 3 Braund, Mr. Owen Harris male 22.0 1 0 A/5 21171 7.2500 NaN S
1 2 1 1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 0 PC 17599 71.2833 C85 C
2 3 1 3 Heikkinen, Miss. Laina female 26.0 0 0 STON/O2. 3101282 7.9250 NaN S
3 4 1 1 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 0 113803 53.1000 C123 S
4 5 0 3 Allen, Mr. William Henry male 35.0 0 0 373450 8.0500 NaN S

In [5]:
titanic.shape


Out[5]:
(891, 12)

In [3]:
titanic.isnull().head()


Out[3]:
PassengerId Survived Pclass Name Sex Age SibSp Parch Ticket Fare Cabin Embarked
0 False False False False False False False False False False True False
1 False False False False False False False False False False False False
2 False False False False False False False False False False True False
3 False False False False False False False False False False False False
4 False False False False False False False False False False True False

In [4]:
sns.heatmap(titanic.isnull(), yticklabels=False, cbar=False, cmap='viridis')


Out[4]:
<matplotlib.axes._subplots.AxesSubplot at 0x115ae64a8>

You can see Age and Cabin columns have lots of null while others have none or very few.

Cluster plot

Cluster plots are useful to auto group datasets. More of this in machine learning section


In [5]:
sns.clustermap(flights_pv)


/Users/atma6951/anaconda/envs/pychakras/lib/python3.6/site-packages/matplotlib/cbook.py:136: MatplotlibDeprecationWarning: The axisbg attribute was deprecated in version 2.0. Use facecolor instead.
  warnings.warn(message, mplDeprecation, stacklevel=1)
Out[5]:
<seaborn.matrix.ClusterGrid at 0x11a438470>

Cluster map rearranges the data to show cells of similar values close by.

Regression linear model plot

You can do regression plots in two ways. You can decorate a scatter plot to have a fit or you can make a regression plot with scatter on it. We will see the latter.


In [6]:
tips = sns.load_dataset('tips')
tips.head()


Out[6]:
total_bill tip sex smoker day time size
0 16.99 1.01 Female No Sun Dinner 2
1 10.34 1.66 Male No Sun Dinner 3
2 21.01 3.50 Male No Sun Dinner 3
3 23.68 3.31 Male No Sun Dinner 2
4 24.59 3.61 Female No Sun Dinner 4

In [7]:
#regressin total bill to the tip
sns.lmplot(x='total_bill', y='tip', data=tips)


Out[7]:
<seaborn.axisgrid.FacetGrid at 0x11aa258d0>

You can decorate this by splitting it by sex and assigning a different color for males and females


In [9]:
sns.lmplot(x='total_bill', y='tip', data=tips, hue='sex')


Out[9]:
<seaborn.axisgrid.FacetGrid at 0x11aa4c630>

You can bring in factors like day of week and create a regression for each day


In [10]:
sns.lmplot(x='total_bill', y='tip', data=tips, hue='sex', col='day')


Out[10]:
<seaborn.axisgrid.FacetGrid at 0x11af086a0>