The purpose of a plotting package is to assist you in visualizing your data as easily as possible, with all the necessary control – that is, by using relatively high-level commands most of the time, and still have the ability to use the low-level commands when needed.
Therefore, everything in matplotlib is organized in a hierarchy.
- At the top of the hierarchy is the matplotlib “state-machine environment” which is provided by the matplotlib.pyplot module. At this level, simple functions are used to add plot elements (lines, images, text, etc.) to the current axes in the current figure.
- The next level down in the hierarchy is the first level of the object-oriented interface, in which pyplot is used only for a few functions such as figure creation, and the user explicitly creates and keeps track of the figure and axes objects. At this level, the user uses pyplot to create figures, and through those figures, one or more axes objects can be created. These axes objects are then used for most plotting actions.
- For even more control – which is essential for things like embedding matplotlib plots in GUI applications – the pyplot level may be dropped completely, leaving a purely object-oriented approach.
Matplotlib is the foundational library for 2d-plotting in the Python world.
Matplotlib and the Future of Visualization in Python
integration of mpl with Pandas is very useful: Plotting with matplotlib — pandas 0.13.1 documentation
look at beautifiers:
In [1]:
%matplotlib inline
import numpy as np
import pandas as pd
from pandas import DataFrame, Series
import matplotlib.pyplot as plt
plt.plot(np.arange(10))
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In [2]:
import matplotlib.pyplot as plt
ca = plt.gca()
ca.set_xlabel("hello")
lines = plt.plot([1,2,3,4], 'r')
label = plt.ylabel('some numbers')
plt.show()
In [3]:
line= lines[0]
line.get_color()
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Usage — Matplotlib 1.3.1 documentation
Pyplot provides the state-machine interface to the underlying plotting library in matplotlib. This means that figures and axes are implicitly and automatically created to achieve the desired plot. For example, calling plot from pyplot will automatically create the necessary figure and axes to achieve the desired plot. Setting a title will then automatically set that title to the current axes object
In [4]:
import numpy as np
import pandas as pd
from pandas import DataFrame, Series
# using the pyplot interface
import matplotlib.pyplot as plt
In [5]:
# plot different types of graphs to show distribution of populations
# Chap 8 of PfDA
# http://my.safaribooksonline.com/book/programming/python/9781449323592/8dot-plotting-and-visualization/id2802076
# hello world of mpl
plt.plot(np.arange(10))
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In [6]:
x = np.arange(0.001, 1, 0.001)
y = np.apply_along_axis(lambda x:-x*np.log(x),0,np.arange(0.001, 1, 0.001))
plt.plot(x,y)
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In [7]:
x = np.arange(0, 10, 0.2)
y = np.sin(x)
fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot(x, y)
# plt.show() done implicitly
#plt.show()
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In [8]:
x = np.arange(0.001, 1, 0.001)
y = np.apply_along_axis(lambda x:-(x*np.log(x)+(1-x)*np.log(1-x)),0,np.arange(0.001, 1, 0.001))
plt.plot(x,y)
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In [9]:
plt.plot(np.random.random_sample(10000), 'o', ms=1)
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In [10]:
fig = plt.figure()
In [11]:
# set up a 2x2 grid of subplots and instantiate 3 of them
ax1 = fig.add_subplot(2, 2, 1)
ax2 = fig.add_subplot(2, 2, 2)
ax3 = fig.add_subplot(2, 2, 3)
fig
Out[11]:
PfDA:
When you issue a plotting command like plt.plot([1.5, 3.5, -2, 1.6]), matplotlib draws on the last figure and subplot used (creating one if necessary), thus hiding the figure and subplot creation
In [12]:
plt.plot([1.5, 3.5, -2, 1.6])
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In [13]:
import math
options = [None, 'k--', 'ro', 'g+']
fig = plt.figure()
# let's plot subplot 2 columns wide
# try different options
num_row = math.ceil(len(options)/2.0)
for (i, option) in enumerate(options):
ax = fig.add_subplot(num_row,2, i+1)
if option is not None:
ax.plot([1.5, 3.5, -2, 1.6], option)
else:
ax.plot([1.5, 3.5, -2, 1.6])
In [14]:
from numpy.random import randn
fig = plt.figure()
ax1 = fig.add_subplot(2, 2, 1)
ax2 = fig.add_subplot(2, 2, 2)
ax3 = fig.add_subplot(2, 2, 3)
ax1.hist(randn(100), bins=20, color='k', alpha=0.3)
ax1.set_xlabel('ax1')
ax2.scatter(np.arange(30), np.arange(30) + 3 * randn(30))
ax3.plot(randn(50).cumsum(), 'k--')
fig.tight_layout()
fig.show()
In [15]:
#http://matplotlib.org/examples/text_labels_and_annotations/unicode_demo.html
from __future__ import unicode_literals
import matplotlib.pyplot as plt
plt.title('Développés et fabriqués')
plt.xlabel("réactivité nous permettent d'être sélectionnés et adoptés")
plt.ylabel('André was here!')
plt.text( 0.2, 0.8, 'Institut für Festkörperphysik', rotation=45)
plt.text( 0.4, 0.2, 'AVA (check kerning)')
plt.show()
In [16]:
#http://matplotlib.org/examples/api/barchart_demo.html
import numpy as np
import matplotlib.pyplot as plt
N = 5
menMeans = (20, 35, 30, 35, 27)
menStd = (2, 3, 4, 1, 2)
ind = np.arange(N) # the x locations for the groups
width = 0.35 # the width of the bars
fig, ax = plt.subplots()
rects1 = ax.bar(ind, menMeans, width, color='r', yerr=menStd)
womenMeans = (25, 32, 34, 20, 25)
womenStd = (3, 5, 2, 3, 3)
rects2 = ax.bar(ind+width, womenMeans, width, color='y', yerr=womenStd)
# add some
ax.set_ylabel('Scores')
ax.set_title('Scores by group and gender')
ax.set_xticks(ind+width)
ax.set_xticklabels( ('G1', 'G2', 'G3', 'G4', 'G5') )
ax.legend( (rects1[0], rects2[0]), ('Men', 'Women') )
def autolabel(rects):
# attach some text labels
for rect in rects:
height = rect.get_height()
ax.text(rect.get_x()+rect.get_width()/2., 1.05*height, '%d'%int(height),
ha='center', va='bottom')
autolabel(rects1)
autolabel(rects2)
plt.show()
going back to our calculation from Day_01_B_World_Population.ipynb
In [23]:
# https://gist.github.com/rdhyee/8511607/raw/f16257434352916574473e63612fcea55a0c1b1c/population_of_countries.json
# scraping of https://en.wikipedia.org/w/index.php?title=List_of_countries_by_population_(United_Nations)&oldid=590438477
# read population in
import json
import requests
import settings
# url = "http://api.census.gov/data/2010/sf1/variables.json"
# var_json = requests.get(url).json()
pop_json_url = "https://gist.github.com/rdhyee/8511607/raw/f16257434352916574473e63612fcea55a0c1b1c/population_of_countries.json"
# print pop_json_url
pop_list= requests.get(pop_json_url,verify=False).json()
pop_list
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In [24]:
country_num = range(len(pop_list))
country_names = [c[1] for c in pop_list]
country_pops = [int(c[2]) for c in pop_list]
In [25]:
plt.plot(country_pops)
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In [29]:
from itertools import izip, islice
sampled_country_tuples = list(islice(izip(country_num, country_names),0,None,10))
print sampled_country_tuples
sampled_i = [s[0] for s in sampled_country_tuples]
sampled_countries = [s[1] for s in sampled_country_tuples]
sampled_countries
Out[29]:
In [30]:
# bar charts
# can find barh: http://matplotlib.org/examples/lines_bars_and_markers/barh_demo.html
# http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.bar
plt.bar(left=map(lambda x: x, range(len(country_pops))),height=country_pops, width=2, log='x')
#plt.xticks(range(len(country_pops)), country_names, rotation='vertical')
plt.xticks(sampled_i, sampled_countries, rotation='vertical')
plt.ylabel('Population')
plt.show()
In [31]:
# what if we make a DataFrame from pop_list
df = DataFrame(pop_list)
In [32]:
plt.plot(df[2])
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In [33]:
plt.plot(df[2].cumsum())
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