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import time
print('Last updated:', time.strftime('%m/%d/%Y'))


    

    




Last updated: 06/20/2014

In [2]:

    

%matplotlib inline


    

In [6]:

    

import matplotlib.pyplot as plt

# input data
mean_values = [1, 2, 3]
variance = [0.2, 0.4, 0.5]
bar_labels = ['bar 1', 'bar 2', 'bar 3']

# plot bars
x_pos = list(range(len(bar_labels)))
plt.bar(x_pos, mean_values, yerr=variance, align='center', alpha=0.5)

plt.grid()

# set height of the y-axis
max_y = max(zip(mean_values, variance)) # returns a tuple, here: (3, 5)
plt.ylim([0, (max_y[0] + max_y[1]) * 1.1])

# set axes labels and title
plt.ylabel('variable y')
plt.xticks(x_pos, bar_labels)
plt.title('Bar plot with error bars')

plt.show()
#plt.savefig('./my_plot.png')


    

In [20]:

    

from matplotlib import pyplot as plt
import numpy as np

# input data
mean_values = [1, 2, 3]
std_dev = [0.2, 0.4, 0.5]
bar_labels = ['bar 1', 'bar 2', 'bar 3']

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

# plot bars
y_pos = np.arange(len(mean_values))
y_pos = [x for x in y_pos]
plt.yticks(y_pos, bar_labels, fontsize=10)
plt.barh(y_pos, mean_values, xerr=std_dev, 
         align='center', alpha=0.4, color='g')

# annotation and labels
plt.xlabel('measurement x')
t = plt.title('Bar plot with standard deviation')
plt.ylim([-1,len(mean_values)+0.5])
plt.xlim([0, 4])
plt.grid()

plt.show()


    

In [39]:

    

import matplotlib.pyplot as plt

# Input data
green_data = [1, 2, 3]
blue_data = [3, 2, 1]
red_data = [2, 3, 3]
labels = ['group 1', 'group 2', 'group 3']

# Setting the positions and width for the bars
pos = list(range(len(green_data))) 
width = 0.2 
    
# Plotting the bars
fig, ax = plt.subplots(figsize=(8,6))

plt.bar(pos, green_data, width,
                 alpha=0.5,
                 color='g',
                 label=labels[0])

plt.bar([p + width for p in pos], blue_data, width,
                 alpha=0.5,
                 color='b',
                 label=labels[1])
    
plt.bar([p + width*2 for p in pos], red_data, width,
                 alpha=0.5,
                 color='r',
                 label=labels[2])

# Setting axis labels and ticks
ax.set_ylabel('y-value')
ax.set_title('Grouped bar plot')
ax.set_xticks([p + 1.5 * width for p in pos])
ax.set_xticklabels(labels)

# Setting the x-axis and y-axis limits
plt.xlim(min(pos)-width, max(pos)+width*4)
plt.ylim([0, max(green_data + blue_data + red_data) * 1.5])

# Adding the legend and showing the plot
plt.legend(['green', 'blue', 'red'], loc='upper left')
plt.grid()
plt.show()


    

In [36]:

    

import matplotlib.pyplot as plt

blue_data = [100,120,140]
red_data = [150,120,190]
green_data = [80,70,90]

f, (ax1, ax2) = plt.subplots(1, 2, figsize=(10,5))

bar_width = 0.5

# positions of the left bar-boundaries
bar_l = [i+1 for i in range(len(blue_data))] 

# positions of the x-axis ticks (center of the bars as bar labels)
tick_pos = [i+(bar_width/2) for i in bar_l] 

###################
## Absolute count
###################

ax1.bar(bar_l, blue_data, width=bar_width,
        label='blue data', alpha=0.5, color='b')
ax1.bar(bar_l, red_data, width=bar_width,
        bottom=blue_data, label='red data', alpha=0.5, color='r')
ax1.bar(bar_l, green_data, width=bar_width,
        bottom=[i+j for i,j in zip(blue_data,red_data)], label='green data', alpha=0.5, color='g')

plt.sca(ax1)
plt.xticks(tick_pos, ['category 1', 'category 2', 'category 3'])

ax1.set_ylabel("Count")
ax1.set_xlabel("")
plt.legend(loc='upper left')
plt.xlim([min(tick_pos)-bar_width, max(tick_pos)+bar_width])
plt.grid()

# rotate axis labels
plt.setp(plt.gca().get_xticklabels(), rotation=45, horizontalalignment='right')

############
## Percent
############

totals = [i+j+k for i,j,k in zip(blue_data, red_data, green_data)]
blue_rel = [i / j * 100 for  i,j in zip(blue_data, totals)]
red_rel = [i / j * 100 for  i,j in zip(red_data, totals)]
green_rel = [i / j * 100 for  i,j in zip(green_data, totals)]

ax2.bar(bar_l, blue_rel, 
        label='blue data', alpha=0.5, color='b', width=bar_width
        )
ax2.bar(bar_l, red_rel, 
        bottom=blue_rel, label='red data', alpha=0.5, color='r', width=bar_width
        )
ax2.bar(bar_l, green_rel, 
        bottom=[i+j for i,j in zip(blue_rel, red_rel)], 
        label='green data', alpha=0.5, color='g', width=bar_width
        )

plt.sca(ax2)
plt.xticks(tick_pos, ['category 1', 'category 2', 'category 3'])
ax2.set_ylabel("Percentage")
ax2.set_xlabel("")

plt.xlim([min(tick_pos)-bar_width, max(tick_pos)+bar_width])
plt.grid()

# rotate axis labels
plt.setp(plt.gca().get_xticklabels(), rotation=45, horizontalalignment='right')

plt.show()


    

In [11]:

    

from matplotlib import pyplot as plt
import numpy as np

data = range(200, 225, 5)

bar_labels = ['a', 'b', 'c', 'd', 'e']

fig = plt.figure(figsize=(10,8))

# plot bars
y_pos = np.arange(len(data))
plt.yticks(y_pos, bar_labels, fontsize=16)
bars = plt.barh(y_pos, data,
         align='center', alpha=0.4, color='g')

# annotation and labels

for b,d in zip(bars, data):
    plt.text(b.get_width() + b.get_width()*0.09, b.get_y() + b.get_height()/2,
        '{0:.2%}'.format(max(data)/d), 
        ha='center', va='bottom', fontsize=12)

plt.xlabel('X axis label', fontsize=14)
plt.ylabel('Y axis label', fontsize=14)
t = plt.title('Bar plot with plot labels/text', fontsize=18)
plt.ylim([-1,len(data)+0.5])
plt.vlines(max(data), -1, len(data)+0.5, linestyles='dashed')
plt.grid()

plt.show()


    

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