[DAY 5]開発ログ:Pandas 2&Matplotlib&Sklern

1.学習内容

2.詳細

Pandas2 import pandas as pd from pandas import Series, DataFrame births = pd.read_csv('https://raw.githubusercontent.com/jakevdp/data-CDCbirths/master/births.csv') births birth['10年]=birth['year']//10*10#//で割って残りの部分を捨てます births births.pivot_table('births', index='decade', columns = 'gender', aggfunc='sum') import matplotlib.pyplot as plt births.pivot_table('births', index='year', columns = 'gender', aggfunc='sum').plot()

Matplotlib import matplotlib.pyplot as plt import numpy as np x = np.linspace(0, 10, 100) plt.plot(x, np.sin(x)) plt.plot(x, np.cos(x)) plt.plot(x, np.sin(x)) plt.plot(x, np.cos(x)) fig = plt.figure() plt.plot(x, np.sin(x), '-') plt.plot(x, np.cos(x), '--') 画像ファイルを保存("my figure.png")# from IPython.display import Image#保存した画像を読み込みます Image('my_figure.png') fig.canvas.get_supported_filetypes() plt.figure() plt.サブブロック(2,1,1)#上図 plt.plot(x, np.sin(x)) plt.サブブロック(2,1,2)#下図 plt.plot(x, np.cos(x)) plt.plot(x, np.sin(x-0), color='blue') plt.plot(x,np.sin(x-1),color="g")#緑 plt.plot(x,np.sin(x-2),color="0.75")#25%程度の色が生きています plt.plot(x,np.sin(x-3),color="#FFDD 44")#RGB(光の三原色) plt.plot(x,np.sin(x-4),color=(1.0,0.2,0.3)#可変式 plt.plot(x,np.sin(x-5)、color="chartreeuse")#ライトグリーン(直接色名を作成) plt.plot(x, x+0, linestyle='solid') plt.plot(x, x+1, linestyle='dashed') plt.plot(x, x+2, linestyle='dashdot') plt.plot(x, x+3, linestyle='dotted') plt.plot(x, x+4, linestyle='-') plt.plot(x, x+5, linestyle='--') plt.plot(x, x+6, linestyle='-.') plt.plot(x, x+7, linestyle=':') plt.plot(x, x+0, '-g') plt.plot(x, x+1, '--c') plt.plot(x, x+2, '-.k') plt.plot(x, x+3, ':r') plt.plot(x, np.sin(x)) plt.xlim(10, 0) plt.ylim(1.2, -1.2) plt.plot(x, np.sin(x)) plt.axis([1, 11, -1.5, 1.5]) plt.plot(x, np.sin(x)) plt.axis('tight') plt.plot(x, np.sin(x)) plt.axis('equal') plt.plot(x, np.sin(x)) plt.title('A Sine Curve') plt.xlabel('x') plt.ylabel('sin(x)') plt.plot(x, np.sin(x), '-g', label='sin(x)') plt.plot(x, np.cos(x), ':b', label='cos(x)') plt.axis('equal') plt.legend() x = np.linspace(0, 10, 30) plt.plot(x, np.sin(x), 'o', color='k') rng = np.random.RandomState(0) for marker in ['o', ',', ',', 'x', '+', 'v', '^', '<', '>', 's', 'd' ]: plt.plot(rng.rand(5), rng.rand(5), marker, label='marker={0}'.format(marker) ) plt.legend() y = np.sin(x) plt.plot(x, y , '-ok') plt.plot(x, y, '-p', color='gray', markersize=15, linewidth=4, markerfacecolor='white', markeredgecolor='gray', markeredgewidth='2' ) plt.scatter(x,y) rng = np.random.RandomState(0) x = rng.randn(100) y = rng.randn(100) color = rng.rand(100) sizes = 1000 * rng.rand(100) plt.scatter(x, y, c=color, s=sizes, alpha=0.3, cmap='viridis') plt.colorbar() from sklearn.datasets import load_iris iris = load_iris() features = iris.data.T plt.scatter(features[0], features[1], alpha=0.2, s=features[3]*100, cmap='viridis', c=iris.target) plt.xlabel(iris.feature_names[0]) plt.ylabel(iris.feature_names[1]) #errorbar(エラーの誤差、範囲) x = np.linspace(0, 10 ,50) dy=0.8#誤差範囲 y = np.sin(x) + dy * np.random.randn(50) plt.errorbar(x, y, yerr=dy, fmt='.k') plt.style.use('seaborn-whitegrid') plt.errorbar(x, y, yerr=dy, fmt='o', color='black', ecolor='lightgray', elinewidth=3)

Sklearn import sklearn from sklearn.datasets import load_iris iris_dataset = load_iris() print(f'iris_dataset key:{iris_dataset.keys()}') #print(iris_dataset['data']) #print(iris_dataset['data'].shape) #print(iris_dataset['feature_names']) #print(iris_dataset['target']) #print(iris_dataset['target_names']) print(iris_dataset['DESCR']) from sklearn.model_selection import train_test_split train_input, test_input, train_label, test_label = train_test_split(iris_dataset['data'],iris_dataset['target'], test_size=0.25, random_state=42) print(train_input.shape) print(test_input.shape) print(train_label.shape) print(test_label.shape) from sklearn.neighbors import KNeighborsClassifier knn = KNeighborsClassifier(n_neighbors=3) knn.fit(train_input, train_label) predict_label = knn.predict(test_input) print(predict_label) print(test_label) import numpy as np print(f'test accuracy: {np.mean(predict_label == test_label)}')

3.今日の感想

<ol>
	<li>Pandas를 잘하면, 통계에 대한 정리강점을 가질듯</li>
	<li>Matplotlib을 잘하면 통계에 대한 보고강점을 가질듯</li>
    <li>SKlearn의 cheatsheet를 보고 알고리즘 배울게 엄청 많다는 것을 느꼈음</li>
    <li>지금 AI이론은 단기간에 배우고있는데, 
    이걸 지금 다 마스터하겠다라는 생각보다는
    배운 것들을 꾸준히 기록하고 이걸 나중에 
    볼 생각을 해야할 것으로 보인다.</li>
</ol>