Python Numpy / Matplotlib - 資訊之芽 2023 python 語法班

--- type: slide tags: Sprout2023-Presentation title: Python Numpy / Matplotlib - 資訊之芽 2023 python 語法班 --- # Python Numpy / Matplotlib 資訊之芽 Python 語法班 2023/06/04 鄧人豪 --- ## 課程大綱 Outline ---- 1. Numpy - 建立矩陣的方法 - ndarray 基本性質與操作 - Matrix Maths 2. Matplotlib - 折線圖 Line Chart - 其他圖 ---- ## [教材連結](https://colab.research.google.com/drive/1pLagwtFbeMuHuALfNU9tAcEx-0X3s7es?usp=sharing)請點這 --- ## Numpy ---- ### Numpy 可以幹嘛? - 高維度矩陣運算 - 高速計算 ---- ### 安裝 numpy ``` pip install numpy --user python3 -m pip install numpy --user ``` ### import numpy ```python= import numpy as np print(np.__version__) ``` ---- ### 基本矩陣大小為 3 * 4，每格裡面是：值(Row, Col) | 1(0, 0) | 2(0, 1) | 3(0, 2) | 4(0, 3) | | ------- | -------- | -------- | -------- | | 5(1, 0) | 6(1, 1) | 7(1, 2) | 8(1, 3) | | 9(2, 0) | 10(2, 1) | 11(2, 2) | 12(2, 3) | - Row: 行（橫的）, Column: 列（直的） - Row0: 1, 2, 3, 4 - Row1: 5, 6, 7, 8 - Col0: 1, 5, 9 - Col1: 2, 6, 10 --- ## 建立矩陣的方法 ---- ### 建立基本矩陣用 list, tuple 或任何 array-like object 都可建立矩陣 ```python= import numpy as np arr_list = np.array([1, 2, 3, 4, 5]) # 由 list 建立 arr_tuple = np.array((1, 2, 3, 4, 5)) # 由 tuple 建立 arr_range = np.array(range(1, 6)) # 由 range 建立 print(arr_list) # [1 2 3 4 5] print(arr_tuple) # [1 2 3 4 5] print(arr_range) # [1 2 3 4 5] print(type(arr_list), type(arr_tuple), type(arr_range)) # <class 'numpy.ndarray'> <class 'numpy.ndarray'> <class 'numpy.ndarray'> ``` ---- ### 建立特殊矩陣 - 1 ```python= # np.zeros(shape): 建立 elements 都為 0 的矩陣 arr_0 = np.zeros((3, 4)) # shape = (3, 4) print(arr_0) # [[0. 0. 0. 0.] # [0. 0. 0. 0.] # [0. 0. 0. 0.]] ``` ---- ### 建立特殊矩陣 - 2 ```python= # np.ones(shape): 建立 elements 都為 1 的矩陣 arr_1 = np.ones((3, 4)) # shape = (3, 4) print(arr_1) # [[1. 1. 1. 1.] # [1. 1. 1. 1.] # [1. 1. 1. 1.]] ``` ---- ### 建立特殊矩陣 - 3 ```python= # np.full(shape, value): 建立 elements 都為 value 的矩陣are value arr_full = np.full((3, 4), 5) # shape = (3, 4), value = 5 print(arr_full) # [[5 5 5 5] # [5 5 5 5] # [5 5 5 5]] ``` ---- ### 建立特殊矩陣 - 4 ```python= # np.eye(n): 建立 n x n 的單位矩陣(identity matrix) arr_eye = np.eye(3) print(arr_eye) # [[1. 0. 0.] # [0. 1. 0.] # [0. 0. 1.]] ``` ---- ### 建立特殊矩陣 - 5 ```python= # np.random.random(shape): 建立 elements 都為在 [0, 1) 範圍內隨機值的矩陣 arr_random = np.random.random((3, 4)) # shape = (3, 4) print(arr_random) # [[0.09919389 0.51211242 0.58016963 0.17999457] # [0.49816204 0.72914405 0.01768119 0.86763625] # [0.89068661 0.0849361 0.72184463 0.75884479]] ``` ---- ### 等差數列 - 1 ```python= # np.arange(stop): 建立 elements 為從 0 到 stop - 1 的矩陣 arr_arange1 = np.arange(5) # stop = 5 print(arr_arange1) # [0 1 2 3 4] # np.arange(start, stop): 建立 elements 為從 start 到 stop - 1 的矩陣 arr_arange2 = np.arange(1, 5) # start = 1, stop = 5 print(arr_arange2) # [1 2 3 4] # np.arange(start, stop, step): 建立 elements 為從 start 到 stop - 1，間隔為 step 的矩陣 arr_arange3 = np.arange(1, 5, 2) # start = 1, stop = 5, step = 2 print(arr_arange3) # [1 3] ``` ---- ### 等差數列 - 2 ```python= # np.linspace(start, stop, num=50, endpoint=True) # 建立 elements 為從 start 到 stop、總共 num 個的矩陣 # endpoint: 假如為 True，stop 會是最後一個 sample，間隔為 (stop - start) / (num - 1) # 否則 stop 不會是最後一個 sample，間隔為 (stop - start) / num。預設為 True。 arr_linspace1 = np.linspace(1, 5, 5) # start = 1, stop = 5, num = 5 print(arr_linspace1) # [1. 2. 3. 4. 5.] arr_linspace2 = np.linspace(1, 5, 5, endpoint=False) # start = 1, stop = 5, num = 5, endpoint = False print(arr_linspace2) # [1. 1.8 2.6 3.4 4.2] ``` ---- ### Questions? --- ## ndarray 基本性質與操作 ---- ### ndarray 基本性質 - 1 ```python= arr = np.array([[1, 2, 3], [4, 5, 6]]) print(arr) print(arr.shape) # (2, 3): 代表 2 rows, 3 columns print(arr.ndim) # 2: 代表 2 dimensions print(arr.size) # 6: 代表 6 elements print(arr.dtype) # int64: 代表 elements are int64 type ``` ---- ### ndarray 基本性質 - 2 ```python= arr = np.array([[[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]]], dtype=np.float64) print(arr) print(arr.shape) # (2, 2, 3): 代表 2 layers, 2 rows, 3 columns print(arr.ndim) # 3: 代表 3 dimensions print(arr.size) # 12: 代表 12 elements print(arr.dtype) # float64: 代表 elements are float64 type ``` ---- ### Transpose Matrix 轉置矩陣 ```python= arr = np.array([[1, 2, 3], [4, 5, 6]]) print(arr) # [[1 2 3] # [4 5 6]] print(arr.T) # ndarray.T 代表 transpose # [[1 4] # [2 5] # [3 6]] ``` ---- ### 轉換型態 ```python= # change the type of elements in ndarray arr = np.array([[1, 2, 3], [4, 5, 6]]) print(arr) # [[1 2 3] # [4 5 6]] print(arr.dtype) # int64 arr = arr.astype(np.float64) # change type to float64 print(arr) # [[1. 2. 3.] # [4. 5. 6.]] print(arr.dtype) # float64 ``` ---- ### 改變形狀 ```python= # reshape ndarray: arr.reshape(new_shape) arr = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]) arr = arr.reshape((3, 4)) print(arr) # [[ 0 1 2 3] # [ 4 5 6 7] # [ 8 9 10 11]] arr = arr.reshape((2, -1)) # -1: 自動計算 print(arr) # [[ 0 1 2 3 4 5] # [ 6 7 8 9 10 11]] ``` ---- ### Array Indexing ```python= # one dimensional array indexing arr = np.arange(10) print(arr) # [0 1 2 3 4 5 6 7 8 9] print(arr[5]) # 5 ``` ```python= # two dimensional array indexing: arr[row, column] arr = np.arange(12).reshape((3, 4)) print(arr) # [[ 0 1 2 3] # [ 4 5 6 7] # [ 8 9 10 11]] print(arr[1, 2]) # 6 print(arr[2, 3]) # 11 ``` ---- ### One Dimensional Array Slicing ```python= # one dimensional array slicing: arr[start:stop:step]，不包括 stop arr = np.arange(10) print(arr) # [0 1 2 3 4 5 6 7 8 9] print(arr[2:5]) # [2 3 4] print(arr[2:5:2]) # [2 4] print(arr[2:]) # [2 3 4 5 6 7 8 9] print(arr[2:-1]) # [2 3 4 5 6 7 8] print(arr[:5]) # [0 1 2 3 4] print(arr[:]) # [0 1 2 3 4 5 6 7 8 9] print(arr[::2]) # [0 2 4 6 8] print(arr[::-1]) # [9 8 7 6 5 4 3 2 1 0] ``` ---- ### Two Dimensional Array Slicing - 1 ```python= # arr[start:stop:step, start:stop:step] arr = np.arange(12).reshape((3, 4)) print(arr) # [[ 0 1 2 3] # [ 4 5 6 7] # [ 8 9 10 11]] print(arr[1:3, 2:4]) # [[ 6 7] # [10 11]] print(arr[1:3, :]) # [[ 4 5 6 7] # [ 8 9 10 11]] ``` ---- ### Two Dimensional Array Slicing - 2 ```python= print(arr[:, 1:3]) # [[ 1 2] # [ 5 6] # [ 9 10]] print(arr[:, ::2]) # [[ 0 2] # [ 4 6] # [ 8 10]] print(arr[::-1, ::-1]) # [[11 10 9 8] # [ 7 6 5 4] # [ 3 2 1 0]] ``` ---- ### Array Filtering ```python= arr = np.arange(12) print(arr) # [0 1 2 3 4 5 6 7 8 9 10 11] print(arr > 5) # [False False False False False False True True True True True True] print(arr[arr > 5]) # [ 6 7 8 9 10 11] print(arr[(arr > 5) & (arr < 10)]) # [6 7 8 9] print(arr[(arr < 5) | (arr > 10)]) # [ 0 1 2 3 4 11] arr[(arr > 5)] = 0 print(arr) # [0 1 2 3 4 5 0 0 0 0 0 0] ``` ---- ### Array Copy ```python= arr = np.arange(10) print(arr) # [0 1 2 3 4 5 6 7 8 9] arr2 = arr arr2[0] = 100 print(arr) # [100 1 2 3 4 5 6 7 8 9] print(arr2) # [100 1 2 3 4 5 6 7 8 9] arr3 = arr.copy() arr3[0] = 200 print(arr) # [100 1 2 3 4 5 6 7 8 9] print(arr3) # [200 1 2 3 4 5 6 7 8 9] ``` ---- ### Array Iterating ```python= arr = np.arange(12).reshape((3, 4)) print(arr) # [[ 0 1 2 3] # [ 4 5 6 7] # [ 8 9 10 11]] for row in arr: for element in row: print(element) ``` ---- ### Questions? --- ## Matrix Maths ---- ### Math Operation ```python= arr = np.arange(12) print(arr) # [0 1 2 3 4 5 6 7 8 9 10 11] print(arr + 1) # [ 1 2 3 4 5 6 7 8 9 10 11 12] print(arr - 1) # [-1 0 1 2 3 4 5 6 7 8 9 10] print(arr * 2) # [ 0 2 4 6 8 10 12 14 16 18 20 22] print(arr / 2) # [0. 0.5 1. 1.5 2. 2.5 3. 3.5 4. 4.5 5. 5.5] print(arr ** 2) # [ 0 1 4 9 16 25 36 49 64 81 100 121] print(arr % 2) # [0 1 0 1 0 1 0 1 0 1 0 1] print(arr // 2) # [0 0 1 1 2 2 3 3 4 4 5 5] ``` ---- ### Matrix Multiplication ```python= # array dot operation arr1 = np.arange(6).reshape((2, 3)) arr2 = np.arange(6).reshape((3, 2)) print(arr1) # [[0 1 2] # [3 4 5]] print(arr2) # [[0 1] # [2 3] # [4 5]] print(np.dot(arr1, arr2)) # [[10 13] # 0*0+1*2+2*4, 0*1+1*3+2*5 # [28 40]] # 3*0+4*2+5*4, 3*1+4*3+5*5 ``` ---- ### Questions? --- ## Matplotlib ---- ### Matplotlib 可以幹嘛？ - 數據與資料視覺化 - 畫圖功能齊全 ---- ### 安裝 matplotlib ``` pip install matplotlib --user python3 -m pip install matplotlib --user ``` ### import matplotlib ```python= import matplotlib.pyplot as plt ``` --- ## 折線圖 Line Chart ---- ### 只有 y 值的折線圖 ```python= # plot by 1d ndarray or list y = [2, 3, 1, 4] plt.plot(y) # only plot by y value plt.show() # 顯示整張圖 ``` <img src="https://hackmd.io/_uploads/r1_wxZu42.png" width="40%" style="background-color:white;"/> ---- ### 有 x 與 y 值的折線圖 ```python= # plot by x and y x = np.arange(0, 5, 0.5) # [0, 0.5, 1, 1.5, 2, 2.5, 3, 3.5, 4, 4.5] y = np.sin(x) # 每個 x 對應的 sin 值 [0., 0.47942554, 0.84147098, 0.99749499, 0.90929743, 0.59847214, 0.14112001, -0.35078323, -0.7568025, -0.97753012] plt.plot(x, y) plt.show() ``` <img src="https://hackmd.io/_uploads/HyJB-bdNn.png" width="40%" style="background-color:white;"/> ---- ### 修改線的格式 ```python= # plot by x and y x = np.arange(0, 5, 0.5) # [0. , 0.5, 1. , 1.5, 2. , 2.5, 3. , 3.5, 4. , 4.5] y = np.sin(x) # 每個 x 對應的 sin 值 # marker: 代表點的樣式, linestyle: 代表線的樣式, color: 代表線的顏色 plt.plot(x, y, marker='o', linestyle='--', color='g') plt.show() ``` <img src="https://hackmd.io/_uploads/BJPhb-_E2.png" width="50%" style="background-color:white;"/> ---- ### 多條線段 ```python= # plot two lines x = np.arange(0, 5, 0.5) # [0. , 0.5, 1. , 1.5, 2. , 2.5, 3. , 3.5, 4. , 4.5] y1 = np.sin(x) # 每個 x 對應的 sin 值 y2 = np.cos(x) # 每個 x 對應的 cos 值 plt.plot(x, y1, marker='o', linestyle='--', color='g', label='sin') plt.plot(x, y2, marker='x', linestyle='-.', color='r', label='cos') plt.xlabel("x label") # x 軸的 label plt.ylabel("y label") # y 軸的 label plt.legend(title="Legend title") # legend 中文為圖例，顯示每條線的 label，title: legend 的 title plt.title("This is title") # 圖表的 title plt.show() ``` 圖附於下一頁 ---- <img src="https://hackmd.io/_uploads/Sks7zZd43.png" width="80%" style="background-color:white;"/> ---- ### Questions? --- ## 其他圖 ---- ### 分布圖 ```python= # scatter plot x = np.arange(0, 5, 0.5) # [0. , 0.5, 1. , 1.5, 2. , 2.5, 3. , 3.5, 4. , 4.5] y = np.sin(x) # 每個 x 對應的 sin 值 plt.scatter(x, y) plt.show() ``` <img src="https://hackmd.io/_uploads/ry79MW_43.png" width="45%" style="background-color:white;"/> ---- ### 長條圖 ```python= # bar plot x = np.array(["A", "B", "C", "D"]) # x 軸的 label y = np.array([3, 8, 1, 10]) # 每個 x 對應的值 plt.bar(x,y) plt.show() ``` <img src="https://hackmd.io/_uploads/HJz6zWuEn.png" width="45%" style="background-color:white;"/> ---- ### 直方圖 ```python= # histogram x = np.random.normal(170, 10, 250) # 平均值 170，標準差 10，共 250 個數字，符合常態分佈 print(x.shape) # (250,) plt.hist(x) plt.show() ``` <img src="https://hackmd.io/_uploads/rktZ7bdN2.png" width="40%" style="background-color:white;"/> ---- ### 圓餅圖 ```python= # pie chart mylabels = ["Apples", "Bananas", "Cherries", "Watermelons"] # 每個 label y = [35, 25, 25, 15] # 每個 label 的值 myexplode = [0.2, 0, 0, 0] # 每個部分離開中心的距離 plt.pie(y, labels=mylabels, explode=myexplode, shadow=True) # shadow: 是否要陰影 plt.show() ``` <img src="https://hackmd.io/_uploads/By4gN-uVh.png" width="40%" style="background-color:white;"/> ---- ### 顯示多張子圖 ```python= # subplot 2 rows and 3 columns x = np.arange(0, 5, 0.5) # [0. , 0.5, 1. , 1.5, 2. , 2.5, 3. , 3.5, 4. , 4.5] # adjust figure size plt.figure(figsize=(10, 7)) # 定義 figure 大小 (width, height) plt.subplot(2, 3, 1) # 2 rows, 3 columns, 1st plot plt.plot(x, x) plt.title("Subplot 1") plt.subplot(2, 3, 2) # 2 rows, 3 columns, 2nd plot plt.plot(x, x**2) plt.title("Subplot 2") plt.subplot(2, 3, 3) # 2 rows, 3 columns, 3rd plot plt.plot(x, x**3) plt.title("Subplot 3") plt.subplot(2, 3, 4) # 2 rows, 3 columns, 4th plot plt.plot(x, x**4) plt.title("Subplot 4") plt.subplot(2, 3, 5) # 2 rows, 3 columns, 5th plot plt.plot(x, x**5) plt.title("Subplot 5") plt.subplot(2, 3, 6) # 2 rows, 3 columns, 6th plot plt.plot(x, x**6) plt.title("Subplot 6") plt.suptitle("This is suptitle") # 整個圖表的 title plt.show() ``` 圖附於下一頁 ---- <img src="https://hackmd.io/_uploads/HyJUV-_Nn.png" width="80%" style="background-color:white;"/> ---- ### Questions? --- # Thanks！

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