Python大數(shù)據(jù)分析與應用實戰(zhàn)

第 1 章 Python 語法基礎 ··························· 1
1.1 安裝 Anaconda ····································· 1
1.1.1 代碼提示 ······························· 4
1.1.2 變量瀏覽 ······························· 5
1.1.3 安裝第三方庫 ························· 5
1.2 語法基礎 ············································ 6
1.2.1 字符串、列表、元組、字典和
集合 ····································· 6
1.2.2 條件判斷、循環(huán)和函數(shù) ··········· 13
1.2.3 異常 ··································· 17
1.2.4 特殊函數(shù) ····························· 20
1.3 Python 基礎庫應用入門 ························ 22
1.3.1 NumPy 庫應用入門 ················ 23
1.3.2 Pandas 庫應用入門 ················· 29
1.3.3 Matplotlib 庫應用入門 ············· 40
1.4 本章小結 ·········································· 45
第 2 章 天氣數(shù)據(jù)的獲取與建模分析 ·········· 52
2.1 準備工作 ·········································· 52
2.2 利用抓取方法獲取天氣數(shù)據(jù) ·················· 54
2.2.1 網(wǎng)頁解析 ····························· 54
2.2.2 抓取一個靜態(tài)頁面中的天氣
數(shù)據(jù) ··································· 57
2.2.3 抓取歷史天氣數(shù)據(jù) ················· 60
2.3 天氣數(shù)據(jù)可視化 ································· 63
2.3.1 查看數(shù)據(jù)基本信息 ················· 63
2.3.2 變換數(shù)據(jù)格式 ······················· 64
2.3.3 氣溫走勢的折線圖 ················· 66
2.3.4 歷年氣溫對比圖 ···················· 67
2.3.5 天氣情況的柱狀圖 ················· 69
2.3.6 使用 Tableau 制作天氣情況的
氣泡云圖 ····························· 70
2.3.7 風向占比的餅圖 ···················· 73
2.3.8 使用 windrose 庫繪制風玫瑰圖 ·· 74
2.4 機器學習在天氣預報中的應用 ··············· 76
2.4.1 線性回歸的基本概念 ·············· 76
2.4.2 使用一元線性回歸預測氣溫 ····· 77
2.4.3 使用多元線性回歸預測氣溫 ····· 85
2.5 本章小結 ·········································· 91
第 3 章 養(yǎng)成游戲中人物的數(shù)據(jù)搭建 ·········· 92
3.1 準備工作 ·········································· 92
3.2 利用 Pyecharts 庫進行數(shù)據(jù)基本情況分析 ··· 93
3.2.1 感染人數(shù)分布圖 ···················· 94
3.2.2 病情分布圖 ·························· 96
3.2.3 病癥情況堆疊圖 ···················· 97
3.2.4 繪制出院、死亡情況折線圖 ····· 98
3.2.5 病情熱力圖 ························· 100
3.2.6 病情分布象形圖 ··················· 101
3.2.7 人口流動示意圖 ··················· 103
| Python 大數(shù)據(jù)分析與應用實戰(zhàn) |
VI
3.3 感染病例分析 ··································· 105
3.3.1 基本信息統(tǒng)計 ······················ 106
3.3.2 使用直方圖展示感染周期 ······· 108
3.3.3 使用詞云圖展示死亡病例情況 ··· 111
3.4 疫情趨勢預測 ··································· 114
3.4.1 利用邏輯方程預測感染人數(shù) ···· 115
3.4.2 利用 SIR 模型進行疫情預測 ···· 120
3.4.3 Logistic 模型和 SIR 模型的
對比 ·································· 128
3.5 本章小結 ········································· 131
第 4 章 航空數(shù)據(jù)分析 ···························· 132
4.1 準備工作 ········································· 132
4.2 基本情況統(tǒng)計分析 ····························· 135
4.2.1 查看數(shù)據(jù)的基本信息 ············· 135
4.2.2 航空公司、機型分布 ············· 137
4.2.3 展示各個城市航班數(shù)量的 3D
地圖 ·································· 139
4.2.4 從首都機場出發(fā)的?；鶊D ······· 142
4.2.5 通過關系圖展示航線 ············· 145
4.3 利用 Floyd 算法計算最短飛行時間 ········· 148
4.3.1 Floyd 算法簡介 ···················· 148
4.3.2 Floyd 算法的流程 ················· 150
4.3.3 算法程序實現(xiàn) ······················ 150
4.3.4 結果分析 ···························· 154
4.4 本章小結 ········································· 158
第 5 章 市民服務熱線文本數(shù)據(jù)分析 ········· 160
5.1 準備工作 ········································· 160
5.2 基本情況分析 ··································· 162
5.2.1 數(shù)據(jù)分布基本信息 ················ 162
5.2.2 每日平均工單量分析 ············· 165
5.2.3 來電時間分析 ······················ 166
5.2.4 工單類型分析 ······················ 167
5.3 利用詞云圖展示工單內容 ···················· 171
5.3.1 工單分詞 ···························· 171
5.3.2 去除停用詞 ························· 172
5.3.3 詞頻統(tǒng)計 ···························· 173
5.3.4 市民反映問題詞云圖 ············· 175
5.3.5 保存數(shù)據(jù) ···························· 176
5.4 基于樸素貝葉斯的工單自動分類轉辦 ····· 177
5.4.1 需求概述 ···························· 177
5.4.2 樸素貝葉斯模型的基本概念 ···· 177
5.4.3 樸素貝葉斯文本分類算法的
流程 ·································· 181
5.4.4 程序實現(xiàn) ···························· 182
5.5 基于 K-Means 算法和 PCA 方法降維的
熱點問題挖掘 ··································· 189
5.5.1 應用場景 ···························· 189
5.5.2 K-Means 算法和 PCA 方法的
基本原理 ···························· 189
5.5.3 熱點問題挖掘算法的流程 ······· 193
5.5.4 程序實現(xiàn) ···························· 194
5.6 本章小結 ········································· 205
第 6 章 決策樹信貸風險控制 ·················· 206
6.1 準備工作 ········································· 206
6.2 數(shù)據(jù)集基本情況分析 ·························· 209
6.2.1 查看數(shù)據(jù)大小和缺失情況 ······· 209
6.2.2 繪制直方圖查看數(shù)據(jù)的分布
情況 ·································· 211
6.2.3 繪制直方圖的 3 種方法 ·········· 212
| 目錄 |
VII
6.2.4 通過箱型圖查看異常值的情況 ···· 213
6.2.5 異常值和缺失值的處理 ·········· 217
6.2.6 使用小提琴圖展示預處理后的
數(shù)據(jù) ·································· 218
6.3 利用決策樹進行信貸數(shù)據(jù)建模 ·············· 219
6.3.1 決策樹原理簡介 ··················· 219
6.3.2 決策樹信貸建模流程 ············· 225
6.3.3 利用 scikit-learn 庫實現(xiàn)決策樹
風險控制算法 ······················ 226
6.3.4 模型優(yōu)化 ···························· 231
6.4 本章小結 ········································· 233
第 7 章 利用深度學習進行垃圾圖片分類 ···· 234
7.1 準備工作 ········································· 234
7.2 深度學習的基本原理 ·························· 237
7.2.1 CNN 的基本原理 ·················· 237
7.2.2 Keras 庫簡介 ······················· 240
7.3 利用 Keras 庫實現(xiàn)基于 CNN 的垃圾
圖片分類 ········································ 241
7.3.1 算法流程 ···························· 241
7.3.2 數(shù)據(jù)預處理 ························· 241
7.3.3 CNN 模型實現(xiàn) ····················· 247
7.4 優(yōu)化 CNN 模型 ································· 252
7.4.1 選擇優(yōu)化器 ························· 252
7.4.2 選擇損失函數(shù) ······················ 254
7.4.3 調整模型 ···························· 256
7.4.4 圖片增強 ···························· 259
7.4.5 改變學習率 ························· 263
7.5 模型應用 ········································· 265
7.6 本章小結 ········································· 268
第 8 章 協(xié)同過濾和矩陣分解推薦算法
分析 ········································· 269
8.1 準備工作 ········································· 269
8.2 基于協(xié)同過濾算法的短視頻完播情況
分析 ··············································· 271
8.2.1 基于用戶的協(xié)同過濾算法的
原理 ·································· 271
8.2.2 算法流程 ···························· 274
8.2.3 程序實現(xiàn) ···························· 275
8.3 基于矩陣分解算法的短視頻完播情況
預測 ·············································· 283
8.3.1 算法原理 ···························· 283
8.3.2 利用 Surprise 庫實現(xiàn) SVD
算法 ·································· 286
8.4 幾種方法在測試集中的表現(xiàn) ················· 289
8.5 本章小結 ········································· 291
第 9 章 《紅樓夢》文本數(shù)據(jù)分析 ············ 292
9.1 準備工作 ········································· 292
9.1.1 編程環(huán)境 ···························· 292
9.1.2 數(shù)據(jù)情況簡介 ······················ 293
9.2 分詞 ··············································· 294
9.2.1 讀取數(shù)據(jù) ···························· 295
9.2.2 數(shù)據(jù)預處理 ························· 298
9.2.3 分詞及去除停用詞 ················ 306
9.2.4 制作詞云圖 ························· 307
9.3 文本聚類分析 ··································· 316
9.3.1 構建分詞 TF-IDF 矩陣 ··········· 317
9.3.2 K-Means 聚類 ······················ 318
9.3.3 MDS 降維 ··························· 320
9.3.4 PCA 降維 ··························· 321
| Python 大數(shù)據(jù)分析與應用實戰(zhàn) |
VIII
9.3.5 HC 聚類 ····························· 323
9.3.6 t -SNE 高維數(shù)據(jù)可視化 ·········· 325
9.4 LDA 主題模型 ·································· 326
9.5 人物社交網(wǎng)絡分析 ····························· 332
9.6 本章小結 ········································· 338
附錄 A 抓取數(shù)據(jù)請求頭查詢 ··················· 339
附錄 B GraphViz 庫的安裝方法 ·············· 341
附錄 C 在 Windows 10 中安裝 TensorFlow
的方法 ······································ 343
參考文獻 ··············································· 346
致謝 ····················································· 34