#!/usr/bin/env python
# coding: utf-8

# [Sebastian Raschka](http://www.sebastianraschka.com)
# 
# [back](https://github.com/rasbt/matplotlib-gallery) to the `matplotlib-gallery` at [https://github.com/rasbt/matplotlib-gallery](https://github.com/rasbt/matplotlib-gallery)

# In[1]:


get_ipython().run_line_magic('load_ext', 'watermark')


# In[2]:


get_ipython().run_line_magic('watermark', '-u -v -d -p matplotlib,numpy')


# <font size="1.5em">[More info](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/ipython_magic/watermark.ipynb) about the `%watermark` extension</font>

# In[3]:


get_ipython().run_line_magic('matplotlib', 'inline')


# <br>
# <br>

# # Boxplots in matplotlib

# # Sections

# - [Simple Boxplot](#Simple-Boxplot)
# 
# - [Black and white Boxplot](#Black-and-white-Boxplot)
# 
# - [Horizontal Boxplot](#Horizontal-Boxplot)
# 
# - [Filled and cylindrical boxplots](#Filled-and-cylindrical-boxplots)
# 
# - [Boxplots with custom fill colors](#Boxplots-with-custom-fill-colors)
# 
# - [Violin plots](#Violin-plots)

# <br>
# <br>

# <br>
# <br>

# ![](../images/intro_boxplot.png)

# <br>
# <br>

# # Simple Boxplot

# [[back to top](#Sections)]

# In[7]:


import matplotlib.pyplot as plt
import numpy as np

all_data = [np.random.normal(0, std, 100) for std in range(1, 4)]

fig = plt.figure(figsize=(8,6))

plt.boxplot(all_data, 
            notch=False, # box instead of notch shape 
            sym='rs',    # red squares for outliers
            vert=True)   # vertical box aligmnent

plt.xticks([y+1 for y in range(len(all_data))], ['x1', 'x2', 'x3'])
plt.xlabel('measurement x')
t = plt.title('Box plot')
plt.show()


# <br>
# <br>

# # Black and white Boxplot

# [[back to top](#Sections)]

# In[8]:


import matplotlib.pyplot as plt
import numpy as np

all_data = [np.random.normal(0, std, 100) for std in range(1, 4)]

fig = plt.figure(figsize=(8,6))

bplot = plt.boxplot(all_data, 
            notch=False, # box instead of notch shape 
            sym='rs',    # red squares for outliers
            vert=True)   # vertical box aligmnent

plt.xticks([y+1 for y in range(len(all_data))], ['x1', 'x2', 'x3'])
plt.xlabel('measurement x')

for components in bplot.keys():
    for line in bplot[components]:
        line.set_color('black')     # black lines

t = plt.title('Black and white box plot')

plt.show()


# <br>
# <br>

# # Horizontal Boxplot

# [[back to top](#Sections)]

# In[13]:


import matplotlib.pyplot as plt
import numpy as np

all_data = [np.random.normal(0, std, 100) for std in range(1, 4)]

fig = plt.figure(figsize=(8,6))

plt.boxplot(all_data, 
            notch=False,  # box instead of notch shape 
            sym='rs',     # red squares for outliers
            vert=False)   # horizontal box aligmnent

plt.yticks([y+1 for y in range(len(all_data))], ['x1', 'x2', 'x3'])
plt.ylabel('measurement x')
t = plt.title('Horizontal Box plot')
plt.show()


# <br>
# <br>

# # Filled and cylindrical boxplots

# [[back to top](#Sections)]

# In[10]:


import matplotlib.pyplot as plt
import numpy as np

all_data = [np.random.normal(0, std, 100) for std in range(1, 4)]

fig = plt.figure(figsize=(8,6))

plt.boxplot(all_data, 
            notch=True,  # notch shape 
            sym='bs',     # blue squares for outliers
            vert=True,   # vertical box aligmnent
            patch_artist=True)   # fill with color

plt.xticks([y+1 for y in range(len(all_data))], ['x1', 'x2', 'x3'])
plt.xlabel('measurement x')
t = plt.title('Box plot')
plt.show()


# <br>
# <br>

# # Boxplots with custom fill colors

# [[back to top](#Sections)]

# In[31]:


import matplotlib.pyplot as plt
import numpy as np

all_data = [np.random.normal(0, std, 100) for std in range(1, 4)]

fig = plt.figure(figsize=(8,6))

bplot = plt.boxplot(all_data, 
            notch=False,  # notch shape 
            vert=True,   # vertical box aligmnent
            patch_artist=True)   # fill with color

colors = ['pink', 'lightblue', 'lightgreen']
for patch, color in zip(bplot['boxes'], colors):
    patch.set_facecolor(color)

plt.xticks([y+1 for y in range(len(all_data))], ['x1', 'x2', 'x3'])
plt.xlabel('measurement x')
t = plt.title('Box plot')
plt.show()


# <br>
# <br>

# # Violin plots

# [[back to top](#Sections)]

# Violin plots are closely related to Tukey's (1977) box plots but add useful information such as the distribution of the sample data (density trace).   
# 
# Violin plots were added in [matplotlib 1.4](http://matplotlib.org/1.4.0/users/whats_new.html).

# In[9]:


import matplotlib.pyplot as plt
import numpy as np

fig, axes = plt.subplots(nrows=1,ncols=2, figsize=(12,5))

all_data = [np.random.normal(0, std, 100) for std in range(6, 10)]

#fig = plt.figure(figsize=(8,6))

axes[0].violinplot(all_data,
               showmeans=False,
               showmedians=True 
               )
axes[0].set_title('violin plot')

axes[1].boxplot(all_data,
               )
axes[1].set_title('box plot')

# adding horizontal grid lines 
for ax in axes:
    ax.yaxis.grid(True) 
    ax.set_xticks([y+1 for y in range(len(all_data))], )
    ax.set_xlabel('xlabel')
    ax.set_ylabel('ylabel')

plt.setp(axes, xticks=[y+1 for y in range(len(all_data))], 
        xticklabels=['x1', 'x2', 'x3', 'x4'],
        )    
    
plt.show()


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