Getting Started with Matplotlib and Seaborn

Selayang Pandang Matplotlib

In [1]:

import matplotlib.pyplot as plt

import numpy as np

import seaborn as sns

%matplotlib inline

​

x1_values = [2012, 2013, 2014, 2015]

y1_values = [4.3, 2.5, 3.5, 4.5]

​

x2_values = [2012, 2013, 2014, 2015]

y2_values = [2.4, 4.4, 1.8, 2.8]

​

x3_values = [2012, 2013, 2014, 2015]

y3_values = [2, 2, 3, 5]

Line Plot

In [7]:

plt.figure()

plt.plot(x1_values, y1_values, label='Python')

plt.plot(x2_values, y2_values, label='JavaScript')

plt.plot(x3_values, y3_values, label='R')

​

plt.xlim(2012, 2015)

plt.ylim(0, 6)

plt.xticks([2012, 2013, 2014, 2015], ['2012', '2013', '2014', '2015'])

plt.yticks([1, 2, 3, 4, 5])

​

plt.xlabel('')

plt.ylabel('Web Searches')

​

plt.legend(loc='upper center', ncol=3)

plt.grid(True)

​

plt.savefig('web-searches.png', dpi=150)

Histogram

In [8]:

column_data = np.random.normal(42, 3, 1000)

​

plt.figure()

​

plt.hist(column_data)

​

Out[8]:

(array([  12.,   25.,  115.,  197.,  237.,  218.,  130.,   50.,   11.,    5.]),
 array([ 33.47322037,  35.3363003 ,  37.19938024,  39.06246018,
         40.92554011,  42.78862005,  44.65169999,  46.51477992,
         48.37785986,  50.2409398 ,  52.10401973]),
 <a list of 10 Patch objects>)

In [12]:

sns.distplot(column_data)

Out[12]:

<matplotlib.axes._subplots.AxesSubplot at 0x7fbdab50cac8>

Boxplot

In [13]:

two_columns_data = [np.random.normal(57, 3, 1000),

                    np.random.normal(42, 7, 1000)]

​

plt.boxplot(two_columns_data)

Out[13]:

{'boxes': [<matplotlib.lines.Line2D at 0x7fbdab4609e8>,
  <matplotlib.lines.Line2D at 0x7fbdab400f60>],
 'caps': [<matplotlib.lines.Line2D at 0x7fbdab46be10>,
  <matplotlib.lines.Line2D at 0x7fbdab477cf8>,
  <matplotlib.lines.Line2D at 0x7fbdab410f98>,
  <matplotlib.lines.Line2D at 0x7fbdab419828>],
 'fliers': [<matplotlib.lines.Line2D at 0x7fbdab400dd8>,
  <matplotlib.lines.Line2D at 0x7fbdab422908>],
 'means': [],
 'medians': [<matplotlib.lines.Line2D at 0x7fbdab477ef0>,
  <matplotlib.lines.Line2D at 0x7fbdab419a20>],
 'whiskers': [<matplotlib.lines.Line2D at 0x7fbdab460be0>,
  <matplotlib.lines.Line2D at 0x7fbdab46bc18>,
  <matplotlib.lines.Line2D at 0x7fbdab40aeb8>,
  <matplotlib.lines.Line2D at 0x7fbdab410748>]}

In [16]:

sns.boxplot(data=two_columns_data)

Out[16]:

<matplotlib.axes._subplots.AxesSubplot at 0x7fbdab294d68>

Vertical Bar Chart

In [19]:

years = np.arange(2012, 2015)

values = [2, 5, 9]

​

plt.figure()

​

plt.bar(years, values)

Out[19]:

<Container object of 3 artists>

In [20]:

sns.barplot(years, values)

Out[20]:

<matplotlib.axes._subplots.AxesSubplot at 0x7fbdab107f60>

Pie Chart

In [3]:

counts = [17, 14]

​

plt.figure()

​

plt.pie(counts)

Out[3]:

([<matplotlib.patches.Wedge at 0x7f8941b91828>,
  <matplotlib.patches.Wedge at 0x7f8941ba14a8>],
 [<matplotlib.text.Text at 0x7f8941b91f98>,
  <matplotlib.text.Text at 0x7f8941ba1c18>])

In [4]:

counts = [17, 14]

​

plt.figure(figsize=(4, 4))

​

plt.pie(counts,

        colors=['blue', 'orange'],

        labels=['Category A', 'Other categories'],

        startangle=90,

        autopct='%1.1f%%')

Out[4]:

([<matplotlib.patches.Wedge at 0x7f8941b44860>,
  <matplotlib.patches.Wedge at 0x7f8941acfa58>],
 [<matplotlib.text.Text at 0x7f8941b44f60>,
  <matplotlib.text.Text at 0x7f8941ad6240>],
 [<matplotlib.text.Text at 0x7f8941acf518>,
  <matplotlib.text.Text at 0x7f8941ad6710>])

Scatter Plot

In [6]:

x = range(20)

y = np.arange(50, 70) + (np.random.random(20) * 10.)

​

plt.figure()

​

plt.scatter(x, y)

Out[6]:

<matplotlib.collections.PathCollection at 0x7f894198c4e0>

In [11]:

x = np.arange(20)

y = np.arange(50, 70) + (np.random.random(20) * 10.)

​

plt.figure()

​

plt.scatter(x,

            y,

            c='red',

            s=40,

            marker='s',

            edgecolor='none')

Out[11]:

<matplotlib.collections.PathCollection at 0x7f8941892a90>