数据分析 -MATPLOTLIB Tutorial 教程

KevinZhang13579
123 阅读3分钟

MATPLOTLIB Tutorial

from matplotlib import pylab
print(pylab.__version__)

1.20.3

"""Use NumPy to generate random data
"""
import numpy as np

x = np.linspace(0, 10, 25)
y = x * x + 2
print(x)
print(y)

[ 0.          0.41666667  0.83333333  1.25        1.66666667  2.08333333  2.5         2.91666667  3.33333333  3.75        4.16666667  4.58333333  5.          5.41666667  5.83333333  6.25        6.66666667  7.08333333  7.5         7.91666667  8.33333333  8.75        9.16666667  9.58333333 10.        ]
[  2.           2.17361111   2.69444444   3.5625       4.77777778   6.34027778   8.25        10.50694444  13.11111111  16.0625  19.36111111  23.00694444  27.          31.34027778  36.02777778  41.0625      46.44444444  52.17361111  58.25        64.67361111  71.44444444  78.5625      86.02777778  93.84027778 102.        ]

"""It takes only one command to draw
"""
pylab.plot(x, y, 'r') # 'r' stands for red

[<matplotlib.lines.Line2D at 0x2068905ebb0>]

output_3_1.png

"""Drawing a subgraph
一个绘图区域可以画多个子图，下面是画2个1x2的子图，1图起点在从左上角起(1, 2)，2图起点在从左上角起(2, 2)
"""
pylab.subplot(1, 2, 1) # The contents of the brackets represents (rows, columns, indexes)
pylab.plot(x, y, 'r--') # The third parameter here determines color and line style
pylab.subplot(1, 2, 2)
pylab.plot(y, x, 'g*-')

[<matplotlib.lines.Line2D at 0x24316f90730>]

output_4_1.png

#(1, 2, 1) and (1, 2, 2) are signoficant if we want tiled subplots, without which both the graphs will in the same plot
#for example
pylab.subplot(1, 2, 1) # The contents of the brackets represents (rows, columns, indexes)
pylab.plot(x, y, 'r--') # The third parameter here determines color and line style
pylab.subplot(1, 2, 1)
pylab.plot(y, x, 'g*-')

[<matplotlib.lines.Line2D at 0x20689802fd0>]

output_5_1.png

Operator Description

fig.add_axes() = Initialize subplot a = fig.add_subplot(222)

fig, b = plt.subplots(nrow = 3, ncols = 2) = Adds subplot

ax = plt.subplots(2, 2) = Create subplot

from matplotlib import pyplot as plt

fig = plt.figure()
axis = fig.add_axes([0.5, 0.1, 0.8, 0.8]) # Control the left, right, width, height of the canvas (from 0 to 1) 

axis.plot(x, y, 'r')

[<matplotlib.lines.Line2D at 0x2068ae1efd0>]

output_8_1.png

# again we can draw subgraphs
fig, axes = plt.subplots(nrows = 1, ncols = 2) # Submap is of 1 row, 2 columns
for ax in axes:
    ax.plot(x, y, 'r')

output_9_0.png

# we can also draw a picture, or graph, inside another graph
fig = plt.figure()
# Control the left, right, width, height of the canvas (from 0 to 1) 
axes1 = fig.add_axes([0.1, 0.1, 0.8, 0.8]) # big axes
axes2 = fig.add_axes([0.2, 0.5, 0.4, 0.3]) # small canvas

axes1.plot(x, y, 'r')
axes2.plot(y, x, 'g')

[<matplotlib.lines.Line2D at 0x2068b16cdf0>]

output_10_1.png

fig = plt.figure(figsize=(16, 9), dpi=100) # Create graphic object
ax1 = fig.add_subplot()
plt.plot(x, y, 'r')

[<matplotlib.lines.Line2D at 0x20690ca8c10>]

output_11_1.png

ax.legend(['label1', 'label2'])

fig, axes = plt.subplots()
axes.set_xlabel('x-label')
axes.set_ylabel('y-label')

axes.set_title('title of graph')

axes.plot(x, x**2)
axes.plot(x, x**3)

axes.legend(['y = x**2', 'y = x**3'], loc=2)

<matplotlib.legend.Legend at 0x20696b2dee0>

output_12_1.png

# In matplotlab, you can set other properties such as line color, transparency, and more
fig, axes = plt.subplots(dpi=150)
axes.plot(x, x**2, color = 'red', alpha = .5)
axes.plot(x, x + 2, color = '#1155dd', alpha = .5)
axes.plot(x, x + 3, color = '#15cc55', alpha = .5)

[<matplotlib.lines.Line2D at 0x20699171b20>]

output_13_1.png

fig, axes = plt.subplots(dpi=100)
# line width
axes.plot(x, x + 1, 'b', linewidth=.25)
axes.plot(x, x + 2, 'b', linewidth=1)
axes.plot(x, x + 3, 'b', linewidth=1.5)
axes.plot(x, x + 4, 'b', linewidth=2)

[<matplotlib.lines.Line2D at 0x20696d37160>]

output_14_1.png

fig, axes = plt.subplots(dpi=100)
# line width
axes.plot(x, x + 1, 'b', lw=2, linestyle='-')
axes.plot(x, x + 2, 'b', lw=2, linestyle='-.')
axes.plot(x, x + 3, 'b', lw=2, linestyle=':')

line, = axes.plot(x, x + 4, 'black', lw=1.50)
line.set_dashes([5, 10, 15, 10])

line, = axes.plot(x, x + 5, 'black', lw=1.50)
line.set_dashes([5, 10, 15, 3])

line, = axes.plot(x, x + 6, 'black', lw=1.50)
line.set_dashes([5, 10, 4, 10])

output_15_0.png

fig, ax = plt.subplots(dpi=100)

ax.plot(x, x + 1, color='blue', marker='o')
ax.plot(x, x + 2, color='blue', marker='+')
ax.plot(x, x + 3, color='blue', marker='s')
ax.plot(x, x + 4, color='blue', marker='1')

ax.plot(x, x + 5, color='blue', marker='o', markersize=2)
ax.plot(x, x + 6, color='blue', marker='s', markerfacecolor='red')
ax.plot(x, x + 7, color='blue', marker='o', markersize=8, markerfacecolor='green')

[<matplotlib.lines.Line2D at 0x2069bf32af0>]

output_16_1.png

"""Set the canvas grid and axis range
"""

fig, axes = plt.subplots(1, 2, figsize=(10, 5))
axes[0].plot(x, x**2, x, x**3, lw=2)
axes[0].grid(True)

axes[1].plot(x, x**2, x, x**3, lw=2)
axes[1].set_ylim([0, 60])
axes[1].set_xlim([2, 5])
axes[1].grid(True)

output_17_0.png

Other 2D Graphics

n = np.array([0, 1, 2, 3, 4, 5])

fig, axes = plt.subplots(1, 4, figsize = (16, 5))
axes[0].set_title('scatter')
axes[0].scatter(x, x + 0.25*np.random.randn(len(x)))
axes[1].set_title('step')
axes[1].step(n, n**2, lw=2)
axes[2].set_title('bar')
axes[2].bar(n, n**2, align='center', width=0.5, alpha=0.5)
axes[3].set_title('fill_between')
axes[3].fill_between(x, x**2, x**3, color="green", alpha=0.5)

<matplotlib.collections.PolyCollection at 0x1d490472c40>

output_19_1.png

"""Draw a radar chart
"""
fig = plt.figure(figsize=(6, 6))
ax = fig.add_axes([0, 0, 0.6, 0.6], polar = True)

t = np.linspace(0, 2*np.pi, 100)
ax.plot(t, t, color='blue', lw=3)

[<matplotlib.lines.Line2D at 0x1d48e95b700>]

output_20_1.png

"""Draw a histogram
"""
n = np.random.randn(100000)
fig, axes = plt.subplots(1, 2, figsize = (12, 4))
axes[0].set_title('Default histogram')
axes[0].hist(n)

axes[1].set_title('Cumulative detailed histogram')
axes[1].hist(n, cumulative=True, bins=50)

(array([1.0000e+00, 2.0000e+00, 2.0000e+00, 3.0000e+00, 7.0000e+00,        1.6000e+01, 3.6000e+01, 6.5000e+01, 1.2500e+02, 2.2900e+02,        4.3100e+02, 7.3900e+02, 1.2210e+03, 1.9490e+03, 3.0650e+03,        4.6220e+03, 6.7240e+03, 9.6150e+03, 1.3206e+04, 1.7836e+04,        2.3198e+04, 2.9459e+04, 3.6171e+04, 4.3526e+04, 5.1027e+04,        5.8458e+04, 6.5785e+04, 7.2550e+04, 7.8415e+04, 8.3632e+04,        8.7903e+04, 9.1318e+04, 9.3883e+04, 9.5843e+04, 9.7335e+04,        9.8310e+04, 9.8956e+04, 9.9380e+04, 9.9650e+04, 9.9819e+04,        9.9887e+04, 9.9935e+04, 9.9966e+04, 9.9981e+04, 9.9988e+04,        9.9995e+04, 9.9997e+04, 9.9998e+04, 9.9999e+04, 1.0000e+05]),
 array([-4.74285772, -4.55213386, -4.36140999, -4.17068613, -3.97996227,        -3.7892384 , -3.59851454, -3.40779067, -3.21706681, -3.02634294,        -2.83561908, -2.64489521, -2.45417135, -2.26344748, -2.07272362,        -1.88199975, -1.69127589, -1.50055203, -1.30982816, -1.1191043 ,        -0.92838043, -0.73765657, -0.5469327 , -0.35620884, -0.16548497,         0.02523889,  0.21596276,  0.40668662,  0.59741049,  0.78813435,         0.97885821,  1.16958208,  1.36030594,  1.55102981,  1.74175367,         1.93247754,  2.1232014 ,  2.31392527,  2.50464913,  2.695373  ,         2.88609686,  3.07682073,  3.26754459,  3.45826845,  3.64899232,         3.83971618,  4.03044005,  4.22116391,  4.41188778,  4.60261164,         4.79333551]),
 <BarContainer object of 50 artists>)

output_21_1.png

# Draw contour image

import matplotlib
import numpy as np
import matplotlib.cm as cm
import matplotlib.pyplot as plt

delta = 0.025
x = np.arange(-3.0, 3.0, delta)
y = np.arange(-2.0, 2.0, delta)
X, Y = np.meshgrid(x, y)
Z1 = np.exp(-X**2 - Y**2)
Z2 = np.exp(-(X - 1)**2 - (Y - 1)**2)
Z = (Z1 - Z2) * 2

fig, ax = plt.subplots()
CS = ax.contour(X, Y, Z)
ax.clabel(CS, inline=1, fontsize=10)
ax.set_title('contour map')

Text(0.5, 1.0, 'contour map')

output_23_1.png

# Draw 3D surface image
from mpl_toolkits.mplot3d.axes3d import Axes3D

fig = plt.figure(figsize=(14, 6))

# Specify the 3D graphics to draw, with projection='3d'
ax = fig.add_subplot(1, 2, 1, projection='3d')
ax.plot_surface(X, Y, Z, rstride=4, cstride=4, lw=0)

<mpl_toolkits.mplot3d.art3d.Poly3DCollection at 0x1d4908799d0>

output_24_1.png

# heatmap.... color map

Practice Example

Write a Python programming to create a pie chart of the popularity of programming languages

import matplotlib .pyplot as plt
# Data to plot
languages = 'Python', 'Java', 'PHP', 'JavaScript', 'C#', 'C++'
popularity = [22.2, 17.6, 8.8, 8, 7.7, 6.7]
colors = ["#1f77b4", "#ff7f0e", "#2ca02c", "#d62728", "#9467bd", "#8c564b"]

# explode 1st slice
explode = (0.1, 0, 0, 0, 0, 0)
plt.pie(popularity, explode=explode, labels=languages, colors=colors,
       autopct='1%.1f%%', shadow=True, startangle=140)

([<matplotlib.patches.Wedge at 0x1d491d87340>,
  <matplotlib.patches.Wedge at 0x1d491d87cd0>,
  <matplotlib.patches.Wedge at 0x1d491d926d0>,
  <matplotlib.patches.Wedge at 0x1d491d9f0a0>,
  <matplotlib.patches.Wedge at 0x1d491d9fa30>,
  <matplotlib.patches.Wedge at 0x1d491dad400>],
 [Text(-1.1518739051683529, -0.33643202373170245, 'Python'),
  Text(0.5025192070582963, -0.978506232242545, 'Java'),
  Text(1.0971674240514186, 0.07889007288863878, 'PHP'),
  Text(0.754341041824552, 0.8006057660415953, 'JavaScript'),
  Text(0.06701830757132049, 1.0979565321315212, 'C#'),
  Text(-0.5993297985645449, 0.9223902604389219, 'C++')],
 [Text(-0.6719264446815391, -0.19625201384349306, '131.3%'),
  Text(0.2741013856681616, -0.5337306721322972, '124.8%'),
  Text(0.5984549585735011, 0.043030948848348426, '112.4%'),
  Text(0.41145875008611926, 0.4366940542045065, '111.3%'),
  Text(0.036555440493447534, 0.5988853811626479, '110.8%'),
  Text(-0.3269071628533881, 0.5031219602394119, '19.4%')])

output_28_1.png

avatar
文章
阅读
粉丝