Visualizing with the seaborn

In [1]:
%matplotlib inline

# standard
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np

# I've got style,
# miles and miles
import seaborn as sns
sns.set()
sns.set_context('notebook', font_scale=1.5)
cp = sns.color_palette()


Thing 1: Line Chart (with many lines)

In [2]:
ts = pd.read_csv('data/ts.csv')

# casting to datetime is important for
# ensuring plots "just work"
ts = ts.assign(dt = pd.to_datetime(ts.dt))
ts.head()
Out[2]:
dt kind value
0 2000-01-01 A 1.442521
1 2000-01-02 A 1.981290
2 2000-01-03 A 1.586494
3 2000-01-04 A 1.378969
4 2000-01-05 A -0.277937
In [3]:
# in matplotlib-land, the notion of a "tidy"
# dataframe matters not
dfp = ts.pivot(index='dt', columns='kind', values='value')
dfp.head()
Out[3]:
kind A B C D
dt
2000-01-01 1.442521 1.808741 0.437415 0.096980
2000-01-02 1.981290 2.277020 0.706127 -1.523108
2000-01-03 1.586494 3.474392 1.358063 -3.100735
2000-01-04 1.378969 2.906132 0.262223 -2.660599
2000-01-05 -0.277937 3.489553 0.796743 -3.417402
In [6]:
g = sns.FacetGrid(ts, hue='kind', size=5, aspect=1.5)
g.map(plt.plot, 'dt', 'value').add_legend()
g.ax.set(xlabel='Date',
         ylabel='Value',
         title='Random Timeseries')
g.fig.autofmt_xdate()
In [9]:
g = sns.FacetGrid(ts, row='kind', hue='kind', size=5, aspect=1.5)
g.map(plt.plot, 'dt', 'value').add_legend()

g.fig.autofmt_xdate()


Thing 2: Scatter

In [17]:
df = pd.read_csv('data/iris.csv')
df.head()
Out[17]:
petalLength petalWidth sepalLength sepalWidth species
0 1.4 0.2 5.1 3.5 setosa
1 1.4 0.2 4.9 3.0 setosa
2 1.3 0.2 4.7 3.2 setosa
3 1.5 0.2 4.6 3.1 setosa
4 1.4 0.2 5.0 3.6 setosa
In [20]:
g = sns.FacetGrid(df, hue='species', size=7.5)
g.map(plt.scatter, 'petalLength', 'petalWidth').add_legend()
g.ax.set_title('Petal Width v. Length -- by Species')
Out[20]:
<matplotlib.text.Text at 0x11c100410>


Thing 3: Trellising the Above

In [30]:
g = sns.FacetGrid(ts, hue='kind',
                  col='kind', col_wrap=2, size=5)

g.map(plt.plot, 'dt', 'value')
g.fig.autofmt_xdate()
g.fig.suptitle('Random Timeseries', y=1.01)
Out[30]:
<matplotlib.text.Text at 0x12223f290>
In [33]:
g = sns.FacetGrid(df, col='species', hue='species', size=5)
g.map(plt.scatter, 'petalLength', 'petalWidth')
Out[33]:
<seaborn.axisgrid.FacetGrid at 0x122682850>
In [34]:
tmp_n = df.shape[0] - df.shape[0]/2

df['random_factor'] = np.random.permutation(['A'] * tmp_n + ['B'] * (df.shape[0] - tmp_n))
df.head()
Out[34]:
petalLength petalWidth sepalLength sepalWidth species random_factor
0 1.4 0.2 5.1 3.5 setosa B
1 1.4 0.2 4.9 3.0 setosa A
2 1.3 0.2 4.7 3.2 setosa B
3 1.5 0.2 4.6 3.1 setosa B
4 1.4 0.2 5.0 3.6 setosa A
In [39]:
g = sns.FacetGrid(df.assign(tmp=df.species + df.random_factor).\
                      sort_values(['species', 'random_factor']),
                  col='species', row='random_factor', hue='tmp', size=5)
g.map(plt.scatter, 'petalLength', 'petalWidth')
Out[39]:
<seaborn.axisgrid.FacetGrid at 0x1252f1fd0>


Thing 4: Visualizing Distributions (Boxplot and Histogram)

In [45]:
fig, ax = plt.subplots(1, 1, figsize=(10, 10))

g = sns.boxplot('species', 'petalWidth', data=df, ax=ax)
g.set(title='Distribution of Petal Width by Species')
Out[45]:
[<matplotlib.text.Text at 0x12762b550>]
In [75]:
g = sns.FacetGrid(df, hue='species', size=7.5)

g.map(sns.distplot, 'petalWidth', bins=10,
      kde=False, rug=True).add_legend()

g.set(xlabel='Petal Width',
      ylabel='Frequency',
      title='Distribution of Petal Width by Species')
Out[75]:
<seaborn.axisgrid.FacetGrid at 0x12a793bd0>


Thing 5: Bar Chart

In [76]:
df = pd.read_csv('data/titanic.csv')
df.head()
Out[76]:
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
In [77]:
dfg = df.groupby(['survived', 'pclass']).agg({'fare': 'mean'})
dfg
Out[77]:
fare
survived pclass
0 1 64.684008
2 19.412328
3 13.669364
1 1 95.608029
2 22.055700
3 13.694887
In [78]:
died = dfg.loc[0, :]
survived = dfg.loc[1, :]
In [98]:
g = sns.factorplot(x='class', y='fare', hue='survived',
                   data=df, kind='bar',
                   order=['First', 'Second', 'Third'],
                   size=7.5, aspect=1.5, ci=None)
g.ax.set_title('Fare by survival and class')
Out[98]:
<matplotlib.text.Text at 0x1372c6490>
In [99]:
g = sns.factorplot(x='class', y='fare', hue='survived',
                   data=df, kind='bar',
                   order=['First', 'Second', 'Third'],
                   size=7.5, aspect=1.5)
g.ax.set_title('Fare by survival and class')
Out[99]:
<matplotlib.text.Text at 0x137f71610>