Python Numpy + Matplotlib 再探

为什么是再探？因为之前咕了不知道多少次了，看了忘忘了看.

Numpy

ndarray

NumPy’s array class is called ndarray. It is also known by the alias array.

import numpy as np

li = np.arange(20).reshape((4,5))
print(li)
print(li.ndim)
print(li.dtype)
print(li.size)
print(type(li))

[[ 0  1  2  3  4]
 [ 5  6  7  8  9]
 [10 11 12 13 14]
 [15 16 17 18 19]]
2
int64
20
<class 'numpy.ndarray'>

arange(lower_bound, upper_bound, step), zeros, ones, linspace(lo,hi,count)

import math
import numpy as np

x = np.linspace(0, math.pi*2, 10)
y = np.sin(x)
print(x, y)

[0.         0.6981317  1.3962634  2.0943951  2.7925268  3.4906585
 4.1887902  4.88692191 5.58505361 6.28318531]
 
[ 0.00000000e+00  6.42787610e-01  9.84807753e-01  8.66025404e-01      
  3.42020143e-01 -3.42020143e-01 -8.66025404e-01 -9.84807753e-01
 -6.42787610e-01 -2.44929360e-16]

reshape()

Basic operations

import numpy as np

a = np.array([10, 20, 30, 40, 50])
b = np.arange(1, 6)

print(a)
print(b)
print(a+b)
print(a/b)
print(b**2)
print(a < 30)
print(a*b)
print(a.dot(b))

c = np.array([[1, 2], [3, 4]])
d = np.array([[5, 6], [7, 8]])
print(c@d)

[10 20 30 40 50]
[1 2 3 4 5]
[11 22 33 44 55]
[10. 10. 10. 10. 10.]
[ 1  4  9 16 25]
[ True  True False False False]
[ 10  40  90 160 250]
550
[[19 22]
 [43 50]]

exp() …

Indexing and Slicing

One-dimensional arrays can be indexed, sliced and iterated over, much like lists and other Python sequences.
Multidimensional arrays can have one index per axis. These indices are given in a tuple separated by commas.
The dots (...) represent as many colons as needed to produce a complete indexing tuple. For example, if x is an array with 5 axes, then
- x[1, 2, ...] is equivalent to x[1, 2, :, :, :],
- x[..., 3] to x[:, :, :, :, 3] and
- x[4, ..., 5, :] to x[4, :, :, 5, :].

Iterating

Iterating over multidimensional arrays is done with respect to the first axis.
But you can use .flat attribute which could serve as an iterator to iterate over all the elements over the array.

Matplotlib

Goals

画不同图像
- 散点图、折线图
- 饼图
- 柱状图
更改表格的 style
子图

图像的绘制

散点图与折线图

from matplotlib import pyplot as plt
import numpy as np
import math

x = np.linspace(0, 2*math.pi, 5)
y = np.sin(x)

plt.plot(x, y)
plt.savefig("1.png")

And when we changed 5 points to 500…

Markers and line styles

Markers

character	description
`'.'`	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
`'8'`	octagon marker
`'s'`	square marker
`'p'`	pentagon marker
`'P'`	plus (filled) marker
`'*'`	star marker
`'h'`	hexagon1 marker
`'H'`	hexagon2 marker
`'+'`	plus marker
`'x'`	x marker
`'X'`	x (filled) marker
`'D'`	diamond marker
`'d'`	thin_diamond marker
	vline marker
`'_'`	hline marker

Example

from matplotlib import pyplot as plt
import numpy as np
import math

x = np.linspace(0, 2*math.pi, 8)
y = np.sin(x)

plt.plot(x, y, 's')
plt.savefig("1.png")

Line styles

character	description
`'-'`	solid line style
`'--'`	dashed line style
`'-.'`	dash-dot line style
`':'`	dotted line style

Example

from matplotlib import pyplot as plt
import numpy as np
import math

x = np.linspace(0, 2*math.pi, 20)
y = np.sin(x)

plt.plot(x, y, 's-')
plt.savefig("1.png")

饼图

pyplot.pie(x, explode=None, labels=None, colors=None, autopct=None, pctdistance=0.6, shadow=False, labeldistance=1.1, startangle=0, radius=1, counterclock=True, wedgeprops=None, textprops=None, center=0, 0, frame=False, rotatelabels=False, *, normalize=None, data=None)[source]

from matplotlib import pyplot as plt
import numpy as np
import math

x = ["CA", "LA", "FOP", "DM"]
y = [90, 90, 60, 50]

plt.pie(y, labels=x)

plt.savefig("1.png")

参数的使用

from matplotlib import pyplot as plt
import numpy as np
import math

x = ["CA", "LA", "FOP", "DM"]
y = [80, 100, 60, 50]

plt.pie(y, labels=x, explode=(0, 0.2, 0, 0), autopct='%.2f%%')
# %d%% 整数百分比，%0.1f%% 一位小数百分比， %0.2f%% 两位小数百分比
plt.savefig("1.png")

柱形图

matplotlib.pyplot.bar(x, height, width=0.8, bottom=None, *, align='center', data=None, **kwargs)

Prettify

设置标题

plt.title(title)

设置图例

https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.legend.html#matplotlib.pyplot.legend

Subplots

plt.subplot(nrows, ncols, index)

https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.subplot.html#matplotlib.pyplot.subplot