資料採礦與大數據分析實務-資料型態(一維)

資料採礦與大數據分析實務-資料型態(一維)

Error_0x03F7 作者
  2024-03-07 13:29:44 2024-03-07 13:29:44 撰寫   2024-03-07 13:46:51 2024-03-07 13:46:51 更新    529 字數  3 分鐘

程式碼解釋筆記

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# D11016*** ***

## D11016*** ***

### D11016*** ***



# D11016*** ***

4+10



# 01_畫圖
import matplotlib.pyplot as plt          # pip install matplotlib
import numpy as np

# Fixing random state for reproducibility
np.random.seed(19680801)

fig, ax = plt.subplots()

# Example data
people = ('Tom', 'Dick', 'Harry', 'Slim', 'Jim')
y_pos = np.arange(len(people))
performance = 3 + 10 * np.random.rand(len(people))
error = np.random.rand(len(people))

ax.barh(y_pos, performance, xerr=error, align='center')
ax.set_yticks(y_pos, labels=people)
ax.invert_yaxis()  # labels read top-to-bottom
ax.set_xlabel('Performance')
ax.set_title('How fast do you want to go today?')

plt.show()



import matplotlib.pyplot as plt
import numpy as np

from matplotlib.patches import PathPatch
from matplotlib.path import Path

N = 400
t = np.linspace(0, 2 * np.pi, N)
r = 0.5 + np.cos(t)
x, y = r * np.cos(t), r * np.sin(t)

fig, ax = plt.subplots()
ax.plot(x, y, "k")
ax.set(aspect=1)

def draw_error_band(ax, x, y, err, **kwargs):
    # Calculate normals via centered finite differences (except the first point
    # which uses a forward difference and the last point which uses a backward
    # difference).
    dx = np.concatenate([[x[1] - x[0]], x[2:] - x[:-2], [x[-1] - x[-2]]])
    dy = np.concatenate([[y[1] - y[0]], y[2:] - y[:-2], [y[-1] - y[-2]]])
    l = np.hypot(dx, dy)
    nx = dy / l
    ny = -dx / l

    # end points of errors
    xp = x + nx * err
    yp = y + ny * err
    xn = x - nx * err
    yn = y - ny * err

    vertices = np.block([[xp, xn[::-1]],
                         [yp, yn[::-1]]]).T
    codes = np.full(len(vertices), Path.LINETO)
    codes[0] = codes[len(xp)] = Path.MOVETO
    path = Path(vertices, codes)
    ax.add_patch(PathPatch(path, **kwargs))


_, axs = plt.subplots(1, 2, layout='constrained', sharex=True, sharey=True)
errs = [
    (axs[0], "constant error", 0.05),
    (axs[1], "variable error", 0.05 * np.sin(2 * t) ** 2 + 0.04),
]
for i, (ax, title, err) in enumerate(errs):
    ax.set(title=title, aspect=1, xticks=[], yticks=[])
    ax.plot(x, y, "k")
    draw_error_band(ax, x, y, err=err,
                    facecolor=f"C{i}", edgecolor="none", alpha=.3)

plt.show()



# 02_資料型態 type()
# 02_1_布林 bool

x1 = False

type(x1)



# 02_2_整數 int

x2 = 3
type(x2)



# 02_3_浮點數 float

x3 = 3.3
type(x3)



# 02_4_字串 str

x4 = "名字"
type(x4)



# 03_sep + end
print(1, 2, 3, 4)




# " " 、 ' ' 或 ''' ''' 都是字串
# 程式加條件   ,

print(1, 2, 3, 4, end= "|", sep= '|')



print(1, 2, 3, 4, end= "|")



# 04_input + if + for in + 讀取相片
# 04_1 input

print("將華氏轉成攝氏溫度")

f = float(input("請輸入華氏溫度"))

c = (f-32)*(5/9)

print("攝氏溫度為:" , c)

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資料採礦與大數據分析實務-資料型態(一維)
  • 本文標題: 資料採礦與大數據分析實務-資料型態(一維)
  • 本文作者: Error_0x03F7
  • 撰寫於 : 2024-03-07 13:29:44
  • 更新於 : 2024-03-07 13:46:51
  • 本文連結: https://error0x03f7.vercel.app/2024/03/07/資料採礦與大數據分析實務-資料型態(一維)/
  • 版權聲明: 本文採用 CC BY-NC-SA 4.0 TW 進行許可。轉載請註明來自 Error_0x03F7 個人網站
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資料採礦與大數據分析實務-資料型態(一維)
  1. 程式碼解釋筆記
  2. Jupyter 實作檔案