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(完整word版)python图形绘制源代码.doc
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(完整word版)python图形绘制源代码.doc
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(完整 word 版)python 图形绘制源代码
饼图
import matplotlib。pyplot as plt
# Pie chart, where the slices will be ordered and plotted counter-clockwise:
labels = ’Frogs', 'Hogs’, ’Dogs’, ’Logs’
sizes = [15, 30, 45, 10]
explode = (0, 0.1, 0, 0) # only "explode" the 2nd slice (i。e。 ’Hogs’)
fig1, ax1 = plt。subplots()
ax1。pie(sizes, explode=explode, labels=labels, autopct='%1.1f%%’,
shadow=True, startangle=90)
ax1.axis('equal') # Equal aspect ratio ensures that pie is drawn as a circle。
plt.show()
条形图 1
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.ticker import MaxNLocator
from collections import namedtuple
n_groups = 5
means_men = (20, 35, 30, 35, 27)
std_men = (2, 3, 4, 1, 2)
means_women = (25, 32, 34, 20, 25)
std_women = (3, 5, 2, 3, 3)
fig, ax = plt。subplots()
index = np。arange(n_groups)
bar_width = 0。35
opacity = 0。4
error_config = {'ecolor’: '0。3’}
rects1 = ax.bar(index, means_men, bar_width,
alpha=opacity, color=’b’,
yerr=std_men, error_kw=error_config,
label='Men')
(完整 word 版)python 图形绘制源代码
rects2 = ax.bar(index + bar_width, means_women, bar_width,
alpha=opacity, color='r',
yerr=std_women, error_kw=error_config,
label='Women’)
ax。set_xlabel('Group’)
ax。set_ylabel('Scores')
ax。set_title(’Scores by group and gender’)
ax.set_xticks(index + bar_width / 2)
ax.set_xticklabels(('A', 'B', ’C’, ’D', 'E’))
ax。legend()
fig.tight_layout()
plt。show()
表格图
import numpy as np
import matplotlib.pyplot as plt
data = [[ 66386, 174296, 75131, 577908, 32015],
[ 58230, 381139, 78045, 99308, 160454],
[ 89135, 80552, 152558, 497981, 603535],
[ 78415, 81858, 150656, 193263, 69638],
[139361, 331509, 343164, 781380, 52269]]
columns = (’Freeze', 'Wind’, ’Flood', 'Quake’, ’Hail’)
rows = [’%d year' % x for x in (100, 50, 20, 10, 5)]
values = np.arange(0, 2500, 500)
value_increment = 1000
# Get some pastel shades for the colors
colors = plt。cm。BuPu(np。linspace(0, 0.5, len(rows)))
n_rows = len(data)
index = np.arange(len(columns)) + 0。3
bar_width = 0。4
# Initialize the vertical—offset for the stacked bar chart.
y_offset = np。zeros(len(columns))
# Plot bars and create text labels for the table
cell_text = []
(完整 word 版)python 图形绘制源代码
for row in range(n_rows):
plt。bar(index, data[row], bar_width, bottom=y_offset, color=colors[row])
y_offset = y_offset + data[row]
cell_text.append(['%1。1f’ % (x / 1000.0) for x in y_offset])
# Reverse colors and text labels to display the last value at the top。
colors = colors[::—1]
cell_text。reverse()
# Add a table at the bottom of the axes
the_table = plt.table(cellText=cell_text,
rowLabels=rows,
rowColours=colors,
colLabels=columns,
loc='bottom’)
# Adjust layout to make room for the table:
plt.subplots_adjust(left=0.2, bottom=0。2)
plt。ylabel("Loss in ${0}'s"。format(value_increment))
plt。yticks(values * value_increment, ['%d' % val for val in values])
plt.xticks([])
plt。title('Loss by Disaster’)
plt.show()
散点图
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.cbook as cbook
# Load a numpy record array from yahoo csv data with fields date, open, close,
# volume, adj_close from the mpl—data/example directory。 The record array
# stores the date as an np.datetime64 with a day unit (’D’) in the date column.
with cbook.get_sample_data(’goog。npz') as datafile:
price_data = np.load(datafile)['price_data’]。view(np.recarray)
(完整 word 版)python 图形绘制源代码
price_data = price_data[—250:] # get the most recent 250 trading days
delta1 = np.diff(price_data.adj_close) / price_data.adj_close[:—1]
# Marker size in units of points^2
volume = (15 * price_data。volume[:—2] / price_data。volume[0])**2
close = 0。003 * price_data。close[:—2] / 0。003 * price_data.open[:-2]
fig, ax = plt。subplots()
ax。scatter(delta1[:-1], delta1[1:], c=close, s=volume, alpha=0。5)
ax。set_xlabel(r'$\Delta_i$’, fontsize=15)
ax.set_ylabel(r'$\Delta_{i+1}$’, fontsize=15)
ax。set_title(’Volume and percent change’)
ax。grid(True)
fig.tight_layout()
plt。show()
平滑图
import numpy as np
import matplotlib.pyplot as plt
from matplotlib。widgets import Slider, Button, RadioButtons
fig, ax = plt.subplots()
plt。subplots_adjust(left=0.25, bottom=0.25)
t = np。arange(0。0, 1。0, 0。001)
a0 = 5
f0 = 3
delta_f = 5。0
s = a0*np。sin(2*np。pi*f0*t)
l, = plt。plot(t, s, lw=2, color=’red’)
plt.axis([0, 1, -10, 10])
axcolor = ’lightgoldenrodyellow'
axfreq = plt.axes([0.25, 0.1, 0。65, 0.03], facecolor=axcolor)
axamp = plt.axes([0.25, 0。15, 0。65, 0.03], facecolor=axcolor)
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