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import seaborn as sns
%matplotlib inline
flights = sns.load_dataset('flights')
flights.head()
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flights.shape
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Let us pivot this flights data such that it becomes a 2D matrix. Lets make the Month as row indices
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flights_pv = flights.pivot_table(index='month', columns='year', values='passengers')
flights_pv.head()
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Using pivot_tables we have also aggregated the data by month and years.
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sns.heatmap(flights_pv)
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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.
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()
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In [5]:
titanic.shape
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titanic.isnull().head()
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sns.heatmap(titanic.isnull(), yticklabels=False, cbar=False, cmap='viridis')
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You can see Age and Cabin columns have lots of null while others have none or very few.
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sns.clustermap(flights_pv)
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Cluster map rearranges the data to show cells of similar values close by.
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tips = sns.load_dataset('tips')
tips.head()
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#regressin total bill to the tip
sns.lmplot(x='total_bill', y='tip', data=tips)
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You can decorate this by splitting it by sex and assigning a different color for males and females
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sns.lmplot(x='total_bill', y='tip', data=tips, hue='sex')
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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')
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