diff --git a/1_numpy_matplotlib_scipy_sympy/1-numpy_tutorial.ipynb b/1_numpy_matplotlib_scipy_sympy/1-numpy_tutorial.ipynb index d3771df..4dd02c0 100644 --- a/1_numpy_matplotlib_scipy_sympy/1-numpy_tutorial.ipynb +++ b/1_numpy_matplotlib_scipy_sympy/1-numpy_tutorial.ipynb @@ -33,16 +33,16 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## 简介" + "## 1. 简介" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "这个`numpy`包(模块)用在几乎所有使用Python的数值计算中。他是一个为Python提供高性能向量,矩阵和高维数据结构的模块。它是用C和Fortran语言实现的,因此当计算被向量化(用向量和矩阵表示)时,性能非常的好。\n", + "这个`numpy`包(模块)用在几乎所有使用Python的数值计算中。他是一个为Python提供高性能向量,矩阵和高维数据结构的模块。它是用C和Fortran语言实现的,因此当计算向量化数据(用向量和矩阵表示)时,性能非常的好。\n", "\n", - "为了使用`numpy`模块,你先要向下面的例子一样导入这个模块:" + "为了使用`numpy`模块,你先要像下面的例子一样导入这个模块:" ] }, { @@ -60,14 +60,15 @@ "metadata": {}, "source": [ "在`numpy`模块中,用于向量,矩阵和高维数据集的术语是*数组*。\n", - "\n" + "\n", + "**建议大家使用第二种导入方法** `import numpy as np`\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## 创建`numpy`数组" + "## 2. 创建`numpy`数组" ] }, { @@ -85,7 +86,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### 从列表中" + "### 2.1 从列表中" ] }, { @@ -399,7 +400,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### 使用数组生成函数" + "### 2.2 使用数组生成函数" ] }, { @@ -758,14 +759,14 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## 文件 I/O" + "## 3. 文件 I/O" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### 逗号分隔值 (CSV)" + "### 3.1 逗号分隔值 (CSV)" ] }, { @@ -945,7 +946,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Numpy 的本地文件格式" + "### 3.2 numpy 的本地文件格式" ] }, { @@ -1000,7 +1001,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## 更多Numpy数组的性质" + "## 4. 更多Numpy数组的性质" ] }, { @@ -1066,14 +1067,14 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## 操作数组" + "## 5. 操作数组" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### 索引" + "### 5.1 索引" ] }, { @@ -1299,7 +1300,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### 切片索引" + "### 5.2 切片索引" ] }, { @@ -1627,7 +1628,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### 花式索引" + "### 5.3 花式索引" ] }, { @@ -1852,14 +1853,14 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## 用于从数组中提取数据和创建数组的函数" + "## 6. 用于从数组中提取数据和创建数组的函数" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### where" + "### 6.1 where" ] }, { @@ -1915,7 +1916,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### diag" + "### 6.2 diag" ] }, { @@ -1969,7 +1970,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### take" + "### 6.3 take" ] }, { @@ -2072,7 +2073,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### choose" + "### 6.4 choose" ] }, { @@ -2109,7 +2110,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## 线性代数" + "## 7. 线性代数" ] }, { @@ -2123,7 +2124,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Scalar-array 操作" + "### 7.1 Scalar-array 操作" ] }, { @@ -2214,7 +2215,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### 数组间的元素操作" + "### 7.2 数组间的元素操作" ] }, { @@ -2319,7 +2320,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### 矩阵代数" + "### 7.4 矩阵代数" ] }, { @@ -2602,7 +2603,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### 数组/矩阵转换" + "### 7.5 数组/矩阵转换" ] }, { @@ -2812,7 +2813,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### 矩阵计算" + "### 7.6 矩阵计算" ] }, { @@ -2915,7 +2916,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### 数据处理" + "### 7.7 数据处理" ] }, { @@ -3226,7 +3227,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### 数组子集的计算" + "### 7.8 数组子集的计算" ] }, { @@ -3363,7 +3364,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### 高维数据的计算" + "### 7.9 高维数据的计算" ] }, { @@ -3472,7 +3473,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## 阵列的重塑、调整大小和堆叠" + "## 8. 阵列的重塑、调整大小和堆叠" ] }, { @@ -3749,7 +3750,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## 添加新的维度:newaxis" + "## 9. 添加新的维度:newaxis" ] }, { @@ -3890,7 +3891,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## 叠加和重复数组" + "## 10. 叠加和重复数组" ] }, { @@ -3904,7 +3905,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### tile and repeat" + "### 10.1 tile and repeat" ] }, { @@ -4020,7 +4021,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### concatenate" + "### 10.2 concatenate" ] }, { @@ -4079,7 +4080,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### hstack and vstack" + "### 10.3 hstack and vstack" ] }, { @@ -4129,7 +4130,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## 复制和“深度复制”" + "## 11. 复制和“深度复制”" ] }, { @@ -4282,7 +4283,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## 遍历数组元素" + "## 12. 遍历数组元素" ] }, { @@ -4408,7 +4409,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## 向量化功能" + "## 13. 向量化功能" ] }, { @@ -4589,7 +4590,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## 在条件中使用数组" + "## 14. 在条件中使用数组" ] }, { @@ -4685,7 +4686,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## 类型转换" + "## 15. 类型转换" ] }, { @@ -4881,7 +4882,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.8" + "version": "3.6.9" } }, "nbformat": 4,