记录了几个好入的可视化库,学习还是要从基础—— Matplotlib 开始学习。
import numpy as np
import matplotlib.pyplot as plt
plt.plot(np.random.rand(10))
# 创建图表
plt.show()
与 Emacs org mode 交互使用:
import matplotlib.pyplot as plt
import matplotlib
import numpy
matplotlib.use('Agg')
fig = plt.figure(figsize=(4, 2))
x = numpy.linspace(-15, 15)
plt.plot(numpy.sin(x)/x)
fig.tight_layout()
plt.savefig('images/python-matplot-fig.png')
return '/images/python-matplot-fig.png' # return filename to org-mode
plt.close()
: 关闭窗口
plt.gcf().clear()
: 每次清空图标内的内容
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib
import numpy as np
df = pd.DataFrame(np.random.rand(10, 2), columns=['A', 'B'])
f = plt.figure(figsize=(10, 10))
fig = df.plot(figsize=(8, 6))
# 表头
plt.title('aa')
plt.xlabel('x')
plt.xlabel('y')
# 图例位置
# best 自适应位置
# upper right
# upper left
# lower left
# lower right
# right
# center left
# center right
# lower center
# upper center
# center
plt.legend(loc='best')
# x 轴边界
plt.xlim([0, 10])
# y 轴边界
plt.ylim([0, 1.1])
# 设置 x 刻度
plt.xticks(range(10))
# 设置 y 刻度
plt.yticks([0, 0.2, 0.4, 0.6, 0.8, 1.0, 1.2])
# x 轴刻度标签
fig.set_xticklabels('%.1f' % i for i in range(10))
# y 轴刻度标签
fig.set_yticklabels('%.2f' % i for i in [0, 0.2, 0.4, 0.6, 0.8, 1.0, 1.2])
# 边界限定了值的范围,刻度表示显示的标尺,这里 x 轴是 0 - 10 ,但是刻度只有 0.0 - 9.0
plt.savefig('./images/matplotlib02.png')
return '/images/matplotlib02.png'
import numpy as np
import matplotlib.pyplot as plt
import matplotlib
x = np.linspace(-np.pi,np.pi ,256, endpoint=True)
c, s = np.cos(x), np.sin(x)
plt.plot(c)
plt.plot(s)
# 显示网格
# linestyle: 线型
# color: 颜色
# linewidth: 宽度
# axis: x,y,both 显示 x,y,两者
plt.grid(True, linestyle='--', color='gray', linewidth='0.5', axis='both')
plt.tick_params(bottom='on', top='on', left='on',right='on')
# 显示刻度的方向 in, out, inout
matplotlib.rcParams['xtick.direction']='out'
matplotlib.rcParams['ytick.direction']='in'
# 返回当前 axes 对象,gcf() 返回当前 figure 对象
frame = plt.gca()
plt.axis('on')
frame.axes.get_xaxis().set_visible(True)
frame.axes.get_yaxis().set_visible(False)
plt.savefig('./images/matplotlib03.png')
return '/images/matplotlib03.png'
-
: 直线--
: 虚线-.
: 点横线:
: 全点线在 matplotlib 中,整个图像为 Figure ,而一个 Figure 中可以有多个 axes。
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib
matplotlib.style.use('bmh')
fig = plt.figure(figsize=(10, 6), facecolor='gray')
# 创建图表,在2行2列的第一个位置
ax1 = fig.add_subplot(2, 2, 1)
plt.plot(np.random.rand(50).cumsum(), '--g')
ax2 = fig.add_subplot(2, 2, 4)
ax2.hist(np.random.rand(50).cumsum(), alpha=0.5, color='b')
ax4 = fig.add_subplot(2, 2, 2)
df2 = pd.DataFrame(np.random.rand(10, 4), columns=['a', 'b', 'c', 'd'])
ax4.plot(df2, linestyle='--', marker='.')
plt.savefig('./images/matplotlib04.png')
return '/images/matplotlib04.png'
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib
df = pd.DataFrame(np.random.randn(1000, 4), columns=list('ABCD'))
df = df.cumsum()
df.plot(style='--', alpha=0.5, grid=True,
figsize=(8, 6), subplots=True, layout=(2, 2))
plt.subplots_adjust(wspace=0, hspace=0.2)
plt.savefig('./images/matplotlib05.png')
return '/images/matplotlib05.png'
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib
ts = pd.Series(np.random.randn(12),
index=pd.date_range('1/1/2019', periods=12))
ts = ts.cumsum()
ts.plot(
# kind 包括,line, bar, barh
kind='line',
color='r',
# linestyle -, marker . color g
style='-gx',
# alpha 透明度,0-1
alpha=0.5,
use_index=True,
rot=0,
ylim=[-50, 50],
yticks=list(range(-50, 50, 10)),
title='Time Series',
legend=True,
label='test')
plt.grid(True, linestyle=':', color='gray', linewidth='0.5', axis='both')
plt.savefig('./images/matplotlib06.png')
return '/images/matplotlib06.png'
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib
df = pd.DataFrame(np.random.randn(12, 4),
index=pd.date_range('1/1/2019', periods=12), columns=list('abcd'))
df = df.cumsum()
df.plot(
style='--.',
alpha=0.8,
ylim=[-100, 100],
figsize=(10, 8),
grid=True,
yticks=list(range(-100, 125, 25)),
title='test',
subplots=True)
plt.grid(True, linestyle=':', color='gray', linewidth='0.5', axis='both')
plt.savefig('./images/matplotlib07.png')
return '/images/matplotlib07.png'
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib
fig, axes = plt.subplots(4,1, figsize=(12,12))
s = pd.Series(np.random.randint(0,10,16), index=list('abcdefghijklmnop'))
df=pd.DataFrame(np.random.rand(10,3), columns=list('ABC'))
# 单系列柱状图
s.plot(kind='bar', ax=axes[0], grid=True,legend=True,label='s',alpha=0.6)
# 多系列柱状图
df.plot(kind='bar',ax=axes[1],colormap='Reds_r')
# 多系列堆叠图
df.plot(kind='bar',ax=axes[2], colormap='Blues_r', stacked=True)
df.plot.barh(ax=axes[3],grid=True,stacked=True,colormap='BuGn_r')
plt.savefig('./images/matplotlib08.png')
return '/images/matplotlib08.png'
import numpy as np
import matplotlib.pyplot as plt
category_names = ['Strongly disagree', 'Disagree',
'Neither agree nor disagree', 'Agree', 'Strongly agree']
results = {
'Question 1': [10, 15, 17, 32, 26],
'Question 2': [26, 22, 29, 10, 13],
'Question 3': [35, 37, 7, 2, 19],
'Question 4': [32, 11, 9, 15, 33],
'Question 5': [21, 29, 5, 5, 40],
'Question 6': [8, 19, 5, 30, 38]
}
def survey(results, category_names):
labels = list(results.keys())
data = np.array(list(results.values()))
data_cum = data.cumsum(axis=1)
category_colors = plt.get_cmap('RdYlGn')(
np.linspace(0.15, 0.85, data.shape[1]))
fig, ax = plt.subplots(figsize=(9.2, 5))
ax.invert_yaxis()
ax.xaxis.set_visible(False)
ax.set_xlim(0, np.sum(data, axis=1).max())
for i, (colname, color) in enumerate(zip(category_names, category_colors)):
widths = data[:, i]
starts = data_cum[:, i] - widths
ax.barh(labels, widths, left=starts, height=0.5,
label=colname, color=color)
xcenters = starts + widths / 2
r, g, b, _ = color
text_color = 'white' if r * g * b < 0.5 else 'darkgrey'
for y, (x, c) in enumerate(zip(xcenters, widths)):
ax.text(x, y, str(int(c)), ha='center', va='center',
color=text_color)
ax.legend(ncol=len(category_names), bbox_to_anchor=(0, 1),
loc='lower left', fontsize='small')
return fig, ax
survey(results, category_names)
plt.savefig('./images/matplotlib09.png')
return '/images/matplotlib09.png'
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib
data = {'a': np.arange(50),
'c': np.random.randint(0, 50, 50),
'd': np.random.randn(50)}
data['b'] = data['a'] + 10 * np.random.randn(50)
data['d'] = np.abs(data['d']) * 100
plt.scatter('a', 'b', c='c', s='d', data=data)
plt.xlabel('entry a')
plt.ylabel('entry b')
plt.savefig('./images/matplotlib010.png')
return '/images/matplotlib010.png'
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
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
y_offset = np.zeros(len(columns))
cell_text = []
for row in range(n_rows):
plt.bar(index, data[row], bar_width, bottom=y_offset,
color=colors[row], edgecolor='black')
y_offset = y_offset+data[row]
cell_text.append(['%1.1f' % (x/1000.0) for x in y_offset])
colors_col = plt.cm.Reds(np.linspace(0, 0.5, len(rows)))
colors = colors[::-1]
cell_text.reverse()
the_table = plt.table(cellText=cell_text,
rowLabels=rows,
rowColours=colors,
colLabels=columns,
colColours=colors_col,
loc='bottom')
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.savefig('./images/matplotlib011.png')
return '/images/matplotlib011.png'
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
fig, axes = plt.subplots(2, 1, figsize=(10, 8))
df1 = pd.DataFrame(np.random.rand(10, 4), columns=list('abcd'))
df2 = pd.DataFrame(np.random.randn(10, 4), columns=list('abcd'))
df1.plot.area(colormap='Greens_r', alpha=0.8, ax=axes[0])
df2.plot.area(stacked=False, colormap='Set2', alpha=0.8, ax=axes[1])
plt.savefig('./images/matplotlib012.png')
return '/images/matplotlib012.png'
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
import numpy as np
np.random.seed(196608081)
def randrange(n, vmin, vmax):
return (vmax-vmin)*np.random.rand(n)+vmin
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
n = 100
for m, zlow, zhigh in [('o', -50, -25), ('^', -30, -5)]:
xs = randrange(n, 23, 32)
ys = randrange(n, 0,100)
zs = randrange(n, zlow, zhigh)
ax.scatter(xs, ys, zs, marker=m)
ax.set_xlabel('X Label')
ax.set_ylabel('Y Label')
ax.set_zlabel('Z Label')
plt.savefig('./images/matplotlib013.png')
return '/images/matplotlib013.png'
更多内容内 Matplotlib Gallery,可以从中找到想使用的图例进行使用。