Python数据分析与机器学习

前言 \n
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第1章Python与数据分析 \n
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11概述 \n
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111引例 \n
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112数据分析与数据挖掘 \n
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12Python简介 \n
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121Python特点 \n
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122Python应用场合 \n
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13数据分析流程 \n
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131明确目标 \n
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132获取数据 \n
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133清洗数据 \n
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134特征工程 \n
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135构建模型 \n
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136模型评估 \n
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14数据分析库 \n
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141NumPy \n
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142Matplotlib \n
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143Pandas \n
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144Seaborn \n
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145Scipy \n
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146Sklearn \n
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15Python解释器 \n
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151Ubuntu下安装Python \n
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152Windows下安装Python \n
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16Python编辑器 \n
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161IDLE \n
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162VScode \n
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163PyCharm \n
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164Anaconda \n
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165Jupyter \n
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17习题 \n
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第2章NumPy——数据分析基础 \n
工具 \n
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21安装NumPy \n
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22ndarray对象 \n
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221认识ndarray对象 \n
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222ndarray对象属性 \n
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23创建ndarray对象 \n
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231zeros \n
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232ones \n
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233diag \n
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234arange \n
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235linspace \n
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236logspace \n
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24数组变换 \n
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241维度变换 \n
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242数组拼接 \n
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243数组分割 \n
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244数组复制 \n
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25索引和切片 \n
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26线性代数 \n
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261矩阵运算 \n
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262矩阵转置 \n
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263特征根和特征向量 \n
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27统计量 \n
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271平均值 \n
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272最值 \n
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273中位数 \n
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274极差 \n
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275方差 \n
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276协方差 \n
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277皮尔森相关系数 \n
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28习题 \n
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第3章Matplotlib——数据可视化 \n
工具 \n
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31安装Matplotlib \n
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32绘图步骤 \n
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321创建画布 \n
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322绘图函数 \n
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323绘图属性 \n
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33子图基本操作 \n
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331pltsubplot \n
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332figureadd_subplot \n
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333pltsubplots \n
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34绘图 \n
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341折线图 \n
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342气泡图 \n
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343饼图 \n
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344直方图 \n
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345条形图 \n
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35概率分布 \n
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351泊松分布 \n
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352正态分布 \n
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353均匀分布 \n
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354二项分布 \n
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36习题 \n
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第4章Pandas——数据处理工具 \n
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41认识Pandas \n
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42Series \n
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421创建Series \n
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422 Series属性 \n
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423访问Series数据 \n
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43操作Series \n
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431更新Series \n
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432插入Series \n
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433删除Series \n
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44DataFrame \n
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441创建DataFrame \n
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442DataFrame属性 \n
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443选取行列数据 \n
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45操作DataFrame \n
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451更新DataFrame \n
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452插入DataFrame \n
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453删除DataFrame \n
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46Index \n
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461创建Index \n
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462常用属性 \n
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463常用方法 \n
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464重建Index \n
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47可视化 \n
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471线形图 \n
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472条形图 \n
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473饼状图 \n
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474直方图与密度图 \n
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48数据转换 \n
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481数据值替换 \n
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482数据映射 \n
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483数据值合并 \n
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484数据值补充 \n
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485数据离散化 \n
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49数据分组与聚合 \n
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491数据分组 \n
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492数据聚合 \n
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410读取外部数据 \n
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4101操作Excel \n
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4102操作文本文件 \n
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4103操作数据库 \n
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411习题 \n
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第5章Scipy——数据统计工具 \n
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51认识Scipy \n
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52稀疏矩阵 \n
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53线性代数 \n
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531矩阵运算 \n
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532线性方程组求解 \n
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54数据优化 \n
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541非线性方程组求解 \n
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542函数最值 \n
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543最小二乘法 \n
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55数据分布 \n
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551泊松分布 \n
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552正态分布 \n
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553均匀分布 \n
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554二项分布 \n
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555指数分布 \n
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56统计量 \n
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561众数 \n
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562皮尔森相关系数 \n
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57图像处理 \n
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571旋转图像 \n
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572图像滤波 \n
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573边缘检测 \n
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58习题 \n
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第6章Seaborn——数据可视化 \n
工具 \n
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61认识Seaborn \n
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611绘图特色 \n
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612图表分类 \n
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613数据集 \n
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62绘图设置 \n
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621绘图元素 \n
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622主题 \n
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623调色板 \n
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63绘图 \n
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631直方图 \n
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632核密度图 \n
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633小提琴图 \n
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634分类散点图 \n
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635条形图 \n
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636热力图 \n
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637点图 \n
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64习题 \n
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第7章Sklearn——机器学习工具 \n
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71Sklearn简介 \n
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72安装Sklearn \n
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73数据集 \n
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731小数据集 \n
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732大数据集 \n
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733生成数据集 \n
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74机器学习流程 \n
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741数据清洗 \n
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742划分数据集 \n
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743特征工程 \n
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744机器算法 \n
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745模型评估 \n
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75习题 \n
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第8章数据处理 \n
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81认识数据处理 \n
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82数据清洗 \n
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821处理缺失值 \n
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822处理异常值 \n
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823处理重复值 \n
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83特征处理 \n
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831规范化 \n
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832标准化 \n
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833鲁棒化 \n
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84数据分析可视库 \n
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841missingno库 \n
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842词云 \n
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85案例——学生信息清洗 \n
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86习题 \n
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第9章特征工程 \n
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91认识特征工程 \n
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92独热编码 \n
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93特征提取 \n
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931DictVectorizer \n
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932CountVectorizer \n
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933TfidfVectorizer \n
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94中文分词 \n
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941Jieba分词库 \n
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942停用词表 \n
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95案例——中文特征提取 \n
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96习题 \n
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第10章评价指标 \n
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