In [1]:
import numpy as np
import pandas as pd

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

In [2]:
data = pd.read_table('/Users/spardy/Data/SameStatsDataAndImages/datasets/BoxPlots.tsv')

In [5]:
data['left'].to_csv('/Users/spardy/Data/SameStatsDataAndImages/datasets/BoxPlots_left.csv', index=False)

In [6]:
data['normal'].to_csv('/Users/spardy/Data/SameStatsDataAndImages/datasets/BoxPlots_normal.csv', index=False)

In [20]:
plt.boxplot([data[n] for n in ['left', 'right', 'normal']])


Out[20]:
{'boxes': [<matplotlib.lines.Line2D at 0x117b9ad68>,
  <matplotlib.lines.Line2D at 0x117bbd908>,
  <matplotlib.lines.Line2D at 0x117bdb400>],
 'caps': [<matplotlib.lines.Line2D at 0x117bad828>,
  <matplotlib.lines.Line2D at 0x117bada20>,
  <matplotlib.lines.Line2D at 0x117bc4c88>,
  <matplotlib.lines.Line2D at 0x117bcbb70>,
  <matplotlib.lines.Line2D at 0x117be4dd8>,
  <matplotlib.lines.Line2D at 0x117be4fd0>],
 'fliers': [<matplotlib.lines.Line2D at 0x117bb5b00>,
  <matplotlib.lines.Line2D at 0x117bd2c50>,
  <matplotlib.lines.Line2D at 0x117bf0748>],
 'means': [],
 'medians': [<matplotlib.lines.Line2D at 0x117bb52b0>,
  <matplotlib.lines.Line2D at 0x117bcbd68>,
  <matplotlib.lines.Line2D at 0x117bec860>],
 'whiskers': [<matplotlib.lines.Line2D at 0x117b9af60>,
  <matplotlib.lines.Line2D at 0x117ba5f98>,
  <matplotlib.lines.Line2D at 0x117bbdba8>,
  <matplotlib.lines.Line2D at 0x117bc4a90>,
  <matplotlib.lines.Line2D at 0x117bdbcf8>,
  <matplotlib.lines.Line2D at 0x117bdbef0>]}

In [10]:
sns.stripplot(data['right'])


Out[10]:
<matplotlib.axes._subplots.AxesSubplot at 0x117122240>

In [11]:
sns.stripplot(data['left'])


Out[11]:
<matplotlib.axes._subplots.AxesSubplot at 0x1171619b0>

In [12]:
sns.stripplot(data['lines'])


Out[12]:
<matplotlib.axes._subplots.AxesSubplot at 0x11725f7b8>

In [13]:
sns.stripplot(data['normal'])


Out[13]:
<matplotlib.axes._subplots.AxesSubplot at 0x1172fdac8>

In [14]:
sns.stripplot(data['split'])


Out[14]:
<matplotlib.axes._subplots.AxesSubplot at 0x11748f9b0>

In [ ]: