Python数据分析-数据可视化(二)

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前言

看到有些论文插图十分简洁美观，于是便摸索一下如何美化一下折线图绘图。本文将在前文Python数据分析-数据可视化的基础上，介绍折线图格式的调整。

本文使用的画图工具为matplotlib，相关API可访问python matplotlib文档。

Matplotlib 折线图格式调整

首先，贴一下文档中折线图绘制的附加参数表：

Property	Description
agg_filter	a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array
alpha	scalar or None
animated	bool
antialiased or aa	bool
clip_box	Bbox
clip_on	bool
clip_path	Patch or (Path, Transform) or None
color or c	color
dash_capstyle	CapStyle or {‘butt’, ‘projecting’, ‘round’}
dash_joinstyle	JoinStyle or {‘miter’, ‘round’, ‘bevel’}
dashes	sequence of floats (on/off ink in points) or (None, None)
data	(2, N) array or two 1D arrays
drawstyle or ds	{‘default’, ‘steps’, ‘steps-pre’, ‘steps-mid’, ‘steps-post’}, default: ‘default’
figure	Figure
fillstyle	{‘full’, ‘left’, ‘right’, ‘bottom’, ‘top’, ‘none’}
gid	str
in_layout	bool
label	object
linestyle or ls	{’-’, ‘–’, ‘-.’, ‘:’, ‘’, (offset, on-off-seq), …}
linewidth or lw	float
marker	marker style string, Path or MarkerStyle
markeredgecolor or mec	color
markeredgewidth or mew	float
markerfacecolor or mfc	color
markerfacecoloralt or mfcalt	color
markersize or ms	float
markevery	None or int or (int, int) or slice or list[int] or float or (float, float) or list[bool]
path_effects	AbstractPathEffect
picker	float or callable[[Artist, Event], tuple[bool, dict]]
pickradius	float
rasterized	bool
sketch_params	(scale: float, length: float, randomness: float)
snap	bool or None
solid_capstyle	CapStyle or {‘butt’, ‘projecting’, ‘round’}
solid_joinstyle	JoinStyle or {‘miter’, ‘round’, ‘bevel’}
transform	unknown
url	str
visible	bool
xdata	1D array
ydata	1D array
zorder	float

接下来，我将挑选几个常用的附加参数介绍使用方式与效果。

标签

1. 附加参数名：label
2. 功能：为绘制曲线命名，该名称会在图例显示
3. 使用方式：plt.plot(x,y,label=‘example’)

import random

import matplotlib.pyplot as plt

x = range(10)
y = [random.random() for _ in range(10)]

plt.figure()
plt.plot(x, y, label='example')
plt.legend()
plt.show()

线条颜色

1. 附加参数名：color
2. 功能：选择绘制线条的颜色
3. 使用方式：plt.plot(x,y,color=‘r’)
4. 颜色选取方式分为三种：

• 用全名或简称 ，如blue或b
• 16进制 ，如FF00FF
• (r, g, b) 或 (r, g, b, a)，如（1,0,1,1） ，其中 r g b a 取均为[0, 1]之间，[0, 1]之间的浮点数的字符串形式，表示灰度值。0表示黑色，1表示白色

import random

import matplotlib.pyplot as plt

x = range(10)
y = [random.random() for _ in range(10)]
y2 = [random.random() for _ in range(10)]
y3 = [random.random() for _ in range(10)]
y4 = [random.random() for _ in range(10)]

plt.figure()
plt.plot(x, y, label='example1', color='blue')
plt.plot(x, y2, label='example2', color='r')
plt.plot(x, y3, label='example3', color='#00FFFF')
plt.plot(x, y4, label='example4', color=(0.4, 0.5, 0.6))
plt.legend()
plt.show()

线条形状

1. 附加参数名：linestyle(或ls)
2. 功能：选择绘制线条的形状
3. 使用方式：plt.plot(x,y,linestyle=’:’)或者plt.plot(x,y,ls=’:’)
4. 常用形状：

• - 实线(solid)
• – 短线(dashed)
• -. 短点相间线(dashdot)
• ： 虚点线(dotted)
• ‘’, ’ ', None

import random

import matplotlib.pyplot as plt

x = range(10)
y = [random.random() for _ in range(10)]
y2 = [random.random() for _ in range(10)]
y3 = [random.random() for _ in range(10)]
y4 = [random.random() for _ in range(10)]

plt.figure()
plt.plot(x, y, label='example1', color='blue', linestyle='-')
plt.plot(x, y2, label='example2', color='r', ls='--')
plt.plot(x, y3, label='example3', color='#00FFFF', ls=':')
plt.plot(x, y4, label='example4', color=(0.4, 0.5, 0.6), ls='')
plt.legend()
plt.show()

折点样式

1. 附加参数名：
   (1)marker – 折点形状

(2)markeredgecolor 或 mec – 折点外边颜色

(3)markeredgewidth 或 mew – 折点线宽

(4)markerfacecolor 或 mfc --折点实心颜色

(5)markerfacecoloralt 或 mfcalt

(6)markersize 或 ms --折点大小

折点形状选择如下表:

character	description
'-'	solid line style
'--'	dashed line style
'-.'	dash-dot line style
':'	dotted line style
'.'	point marker
','	pixel marker
'o'	circle marker
'v'	triangle_down marker
'^'	triangle_up marker
'<'	triangle_left marker
'>'	triangle_right marker
'1'	tri_down marker
'2'	tri_up marker
'3'	tri_left marker
'4'	tri_right marker
's'	square marker
'p'	pentagon marker
'*'	star marker
'h'	hexagon1 marker
'H'	hexagon2 marker
'+'	plus marker
'x'	x marker
'D'	diamond marker
'd'	thin_diamond marker
``’	'``
'_'	hline marker

import random

import matplotlib.pyplot as plt

x = range(10)
y = [random.random() for _ in range(10)]
y2 = [random.random() for _ in range(10)]
y3 = [random.random() for _ in range(10)]
y4 = [random.random() for _ in range(10)]

plt.figure()
plt.plot(x, y, label='example1', color='blue', linestyle='-', marker='o')
plt.plot(x, y2, label='example2', color='r', ls='--', marker='1')
plt.plot(x, y3, label='example3', color='#00FFFF', ls=':', marker='2')
plt.plot(x, y4, label='example4', color=(0.4, 0.5, 0.6), marker='3')
plt.legend()
plt.show()

线条透明度

1. 附加参数名：alpha,值在[0,1]之间
2. 功能：选择绘制线条的透明度
3. 使用方式：plt.plot(x,y,alpha=‘0.9’)

import random

import matplotlib.pyplot as plt

x = range(10)
y = [random.random() for _ in range(10)]
y2 = [random.random() for _ in range(10)]
y3 = [random.random() for _ in range(10)]
y4 = [random.random() for _ in range(10)]

plt.figure()
plt.plot(x, y, label='example1', color='blue', linestyle='-', alpha=0.3)
plt.plot(x, y2, label='example2', color='r', ls='--', alpha=0.1)
plt.plot(x, y3, label='example3', color='#00FFFF', ls=':', alpha=0.5)
plt.plot(x, y4, label='example4', color=(0.4, 0.5, 0.6), ls='')
plt.legend()
plt.show()