back to the matplotlib-gallery at https://github.com/rasbt/matplotlib-gallery
In [1]:
%load_ext watermark
In [2]:
%watermark -u -v -d -p matplotlib,numpy
[More info](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/ipython_magic/watermark.ipynb) about the `%watermark` extension
In [3]:
%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 [19]:
from matplotlib import pyplot as plt
import numpy as np
# input data
X1 = np.array([1, 2, 3])
X2 = np.array([2, 2, 3])
bar_labels = ['bar 1', 'bar 2', 'bar 3']
fig = plt.figure(figsize=(8,6))
# plot bars
y_pos = np.arange(len(X1))
y_pos = [x for x in y_pos]
plt.yticks(y_pos, bar_labels, fontsize=10)
plt.barh(y_pos, X1,
align='center', alpha=0.4, color='g')
# we simply negate the values of the numpy array for
# the second bar:
plt.barh(y_pos, -X2,
align='center', alpha=0.4, color='b')
# annotation and labels
plt.xlabel('measurement x')
t = plt.title('Bar plot with standard deviation')
plt.ylim([-1,len(X1)+0.1])
plt.xlim([-max(X2)-1, max(X1)+1])
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 [17]:
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()*0.08, b.get_y() + b.get_height()/2,
'{0:.2%}'.format(d/min(data)),
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(min(data), -1, len(data)+0.5, linestyles='dashed')
plt.grid()
plt.show()
In [21]:
import matplotlib.pyplot as plt
import matplotlib.colors as col
import matplotlib.cm as cm
# input data
mean_values = range(10,18)
x_pos = range(len(mean_values))
# create colormap
cmap1 = cm.ScalarMappable(col.Normalize(min(mean_values), max(mean_values), cm.hot))
cmap2 = cm.ScalarMappable(col.Normalize(0, 20, cm.hot))
# plot bars
plt.subplot(121)
plt.bar(x_pos, mean_values, align='center', alpha=0.5, color=cmap1.to_rgba(mean_values))
plt.ylim(0, max(mean_values) * 1.1)
plt.subplot(122)
plt.bar(x_pos, mean_values, align='center', alpha=0.5, color=cmap2.to_rgba(mean_values))
plt.ylim(0, max(mean_values) * 1.1)
plt.show()