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taroyabuki · Mar 13, 2024 · ed12464 · ed12464
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diff --git a/README.md b/README.md
@@ -61,7 +61,7 @@ Wolfram|Alpha，Python，R，Mathematicaをフル活用して，大学教養レ
 環境（言語）|サンプルコード|備考
 --|--|--
 [Wolfram\|Alpha](https://www.wolframalpha.com/)|[クエリと結果](code/wolframalpha)|無料
-Google Colab (Python)|[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/taroyabuki/comath/blob/master/code/python.ipynb)|無料（要登録）
+Google Colab (Python)|[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/taroyabuki/comath/blob/master/code/python/python.ipynb)|無料（要登録）
 Google Colab (R)|[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/taroyabuki/comath/blob/master/code/r/r.ipynb)|無料（要登録）
 [Wolfram Cloud](https://www.wolframcloud.com)|動的生成[^1]|無料（要登録）
 Mathematica|[mathematica.nb](code/mathematica/mathematica.nb) あるいは動的生成[^2]|[ラズベリーパイ版](https://www.wolfram.com/raspberry-pi/)は無料

diff --git a/code/python/python.Rmd b/code/python/python.Rmd
@@ -10,7 +10,7 @@ output:
 [矢吹太朗『コンピュータでとく数学』（オーム社,\ 2024）](https://github.com/taroyabuki/comath)
 
 ```{r setup, include=FALSE}
-system("pip install matplotlib pandas seaborn see scikit-learn statsmodels sympy")
+system("pip install matplotlib IPython pandas seaborn see scikit-learn statsmodels sympy")
 ```
 
 ```{python}
@@ -19,9 +19,8 @@ from IPython.display import display
 
 # 1 実行環境
 
-
 ```{python}
-!python -m pip install see
+#!python -m pip install see
 ```
 
 ```{python}
@@ -66,7 +65,6 @@ see(model)
 
 # 2 数と変数
 
-
 ```{python}
 2 * (-3)
 ```
@@ -269,7 +267,6 @@ refine(sqrt((x - 1)**2), Q.nonnegative(x - 1))
 
 # 3 データ構造
 
-
 ```{python}
 v = [2, 3, 5]; len(v)
 ```
@@ -404,7 +401,6 @@ english, math = m.T; english, math
 
 # 4 可視化と方程式
 
-
 ```{python}
 var('x')
 plot(x**2 + 2 * x - 4, (x, -5, 3));
@@ -483,10 +479,8 @@ solve(Lt(x**2 + 2 * x - 4, 0), x)
 
 # 5 論理式
 
-
 # 6 1次元のデータ
 
-
 ```{python}
 a = pd.Series([36, 43, 53, 55, 56, 56, 57, 60, 61, 73])
 b = pd.Series([34, 39, 39, 49, 50, 52, 52, 55, 83, 97])
@@ -550,7 +544,6 @@ a.std(ddof=1) * z + a.mean()
 
 # 7 2次元のデータ
 
-
 ```{python}
 x = pd.Series([35, 45, 55, 65, 75])
 y = pd.Series([114, 124, 143, 158, 166])
@@ -618,7 +611,6 @@ sns.regplot(x=data.x, y=data.y, ci=None);
 
 # 8 確率変数と確率分布
 
-
 ```{python}
 X = DiscreteUniform('X', range(1, 7)) # X = Die('X', 6)でもよい．
 density(X)(2)
@@ -999,7 +991,6 @@ quad(lambda x: (x - rv.mean())**2 * rv.pdf(x), -1, 1)
 
 # 9 多次元の確率分布
 
-
 ```{python}
 X1 = DiscreteUniform('X1', range(1, 7))
 X2 = DiscreteUniform('X2', range(1, 7))
@@ -1120,7 +1111,6 @@ plt.contourf(Y1, Y2, z, cmap='viridis'); plt.xlabel('Y1'); plt.ylabel('Y2');
 
 # 10 推測統計
 
-
 ```{python}
 mu = 2; sigma = 3; rv = stats.norm(mu, sigma)
 data1 = [rv.rvs(5).mean() for _ in range(10000)]
@@ -1235,7 +1225,6 @@ stats.f.ppf((alpha / 2, 1 - alpha / 2), m - 1, n - 1)
 
 # 11 線形回帰分析
 
-
 ```{python}
 data = pd.DataFrame({
     'x1': [1, 1, 2, 3], 'x2': [2, 3, 5, 7], 'y': [3, 6, 3, 6]})
@@ -1364,7 +1353,6 @@ sns.regplot(x=data.x1, y=data.y, ci=95);
 
 # 12 関数の極限と連続性
 
-
 ```{python}
 f = lambda x: 2 * x - 3; var('x')
 limit(f(x), x, 1)
@@ -1393,7 +1381,6 @@ limit(abs(x) / x, x, 0, dir='+'), limit(abs(x) / x, x, 0, dir='-')
 
 # 13 \kintou{3zw
 
-
 ```{python}
 f = lambda x: x**3; var('h'); a = 1
 limit((f(a + h) - f(a)) / h, h, 0)
@@ -1452,7 +1439,6 @@ series(f(x), x0=sol[0], n=3)
 
 # 14 \kintou{3zw
 
-
 ```{python}
 var('x')
 integrate(-x**2 + 4 * x + 1, (x, 1, 4))
@@ -1524,7 +1510,6 @@ var('x'); integrate(exp(-x**2), (x, -oo, oo))
 
 # 15 多変数関数の微分積分
 
-
 ```{python}
 f = lambda x, y: x**2 * y / (x**4 + y**2); var('x y r theta')
 (limit(limit(f(x, y), x, 0), y, 0),              # ①
@@ -1601,7 +1586,6 @@ simplify(J.det())
 
 # 16 ベクトル
 
-
 ```{python}
 a = Matrix([2, 3, 5]); len(a)
 ```
@@ -1673,7 +1657,6 @@ np.arccos(a @ b / (linalg.norm(a) * linalg.norm(b)))
 
 # 17 \kintou{3zw
 
-
 ```{python}
 A = Matrix([[1, 2, 0], [0, 3, 4]]); A
 ```
@@ -1837,7 +1820,6 @@ np.linalg.matrix_rank(A)
 
 # 18 ベクトル空間
 
-
 ```{python}
 a1 = Matrix([3, 1]); a2 = Matrix([2, 2]); var('c1 c2')
 solve(c1 * a1 + c2 * a2, (c1, c2))
@@ -1948,7 +1930,6 @@ Q, np.allclose(Q.T @ Q, np.eye(3))
 
 # 19 固有値と固有ベクトル
 
-
 ```{python}
 A = Matrix([[5, 6, 3], [0, 9, 2], [0, 6, 8]])
 eigs = A.eigenvects()
@@ -2104,7 +2085,6 @@ print(np.cumsum(s2) / sum(s2))      # 累積寄与率（後述）
 
 # 20 特異値分解と擬似逆行列
 
-
 ```{python}
 A = Matrix([[1, 0], [1, 1], [0, 1]])
 Ur, Sr, Vr = A.singular_value_decomposition()