diff --git a/Day66-70/66.数据分析概述.md b/Day66-70/66.数据分析概述.md index 63749da..f665e09 100644 --- a/Day66-70/66.数据分析概述.md +++ b/Day66-70/66.数据分析概述.md @@ -195,12 +195,10 @@ Notebook是基于网页的用于交互计算的应用程序，可以用于代码 $ - **分位数**：将一个随机变量的概率分布范围分为几个具有相同概率的连续区间，比如最常见的中位数（二分位数，median），就是将数据集划分为数量相等的上下两个部分。除此之外，常见的分位数还有四分位数（quartile）、百分位数（percentile）等。 - + - 中位数： - $ - {Q}_{\frac {1}{2}}(x)={\begin{cases}x'_{\frac{n+1}{2}},&n是奇数\\{\frac {1}{2}}(x'_{\frac{n}{2}}+x'_{{\frac{n}{2}}+1}),&n是偶数\end{cases}} - $ - + ${Q}_{\frac {1}{2}}(x)={\begin{cases}x'_{\frac{n+1}{2}},&{n是奇数}\\{\frac {1}{2}}(x'_{\frac{n}{2}}+x'_{{\frac{n}{2}}+1}),&{n是偶数}\end{cases}}$ + - 四分位数： **第一四分位数**（$Q_1$），又称**较小四分位数**或**下四分位数**，等于该样本中所有数值由小到大排列后第25%的数字。 diff --git a/Day66-70/67.NumPy的应用.md b/Day66-70/67.NumPy的应用.md index 50a029b..4905e2d 100644 --- a/Day66-70/67.NumPy的应用.md +++ b/Day66-70/67.NumPy的应用.md @@ -7,12 +7,24 @@ Numpy最为核心的数据类型是`ndarray`，使用`ndarray`可以处理一维 ### 创建数组对象 1. 一维数组 + - 方法一：使用array函数，通过list创建数组对象 + - 方法二：使用arange函数，指定取值范围创建数组对象 + - 方法三：使用linspace函数，用指定范围均匀间隔的数字创建数组对象 + - 方法四：使用numpy.random模块的函数生成随机数创建数组对象 2. 二维数组 -3. 多维数组 + - 方法一：使用array函数，通过嵌套的list创建数组对象 + - 方法二：使用zeros、ones、full函数指定数组的形状创建数组对象 + - 方法三：使用eye函数创建单位矩阵 + - 方法四：通过reshape将一维数组变成二维数组 + - 方法五：通过numpy.random模块的函数生成随机数创建数组对象 +3. 多维数组：跟上面的情况相似，可以通过下面的例子进行了解。 ### 数组对象基本属性 - +1. size属性：元素个数 +2. shape属性：数组的形状 +3. dtype属性：元素的数据类型 +4. ndim属性：数组的维度 ### 数组对象常用方法 diff --git a/Day66-70/69.数据可视化.md b/Day66-70/69.数据可视化.md index fb1891d..0f701a5 100644 --- a/Day66-70/69.数据可视化.md +++ b/Day66-70/69.数据可视化.md @@ -24,6 +24,12 @@ from matplotlib import pyplot as plt %matplotlib inline ``` +通过下面的魔法指令，可以生成矢量图（SVG）。 + +```Python +%config InlineBackend.figure_format='svg' +``` + #### 绘图的流程 1. 创建画布 diff --git a/Day66-70/70.数据分析项目实战.md b/Day66-70/70.数据分析项目实战.md index 09158a7..c7de35c 100644 --- a/Day66-70/70.数据分析项目实战.md +++ b/Day66-70/70.数据分析项目实战.md @@ -1,2 +1,14 @@ ## 数据分析项目实战 +### 2020年北京积分落户分析 + + + +### 某招聘网站招聘数据分析 + + + +### 某电商网站订单数据分析 + + + 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