數據科學方法與實踐 ——基於 Python 技術實現

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數據科學方法與實踐 ——基於 Python 技術實現

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馬上購買

內容描述

本書系統介紹數據科學的核心概念、基本方法和關鍵技術，內容涵蓋數據科學的導向目標，涉及科學計算、數據處理和分析、數據可視化等關鍵知識環節。本書基於 Python 技術框架實現，內容註重理論和實踐的有機融合，剋服單調、晦澀的知識累積之苦，以問題為導向，學以致用，提供了大量的案例代碼和樣本數據集，可以為學習者平添幾分學習的樂趣。本書既適用於高等院校“數據科學與大數據技術”專業人才的基礎培養，也適用於信息處理相關專業人才的能力提升，能夠為數據科學從業者和相關學科的科研工作者提供必要的技術支撐。

目錄大綱

目 錄
第 1 章 數據科學概述 ··················································································1
1.1 什麽是數據科學？ ············································································1
1.2 大數據技術·····················································································6
1.3 數據未來·····················································································.20
第 2 章 Python 基礎··················································································.23
2.1 編程環境與規範············································································.23
2.2 數據類型、數據載體及運算 ····························································.25
2.3 序列結構·····················································································.30
2.4 程序流程控制···············································································.38
2.5 函數···························································································.40
2.6 字符串························································································.45
2.7 文件操作·····················································································.50
2.8 面向對象程序設計·········································································.51
第 3 章 科學計算—— Numpy······································································.57
3.1 計算基礎·····················································································.58
3.1.1 什麽是科學計算？ ·······························································.58
3.1.2 Numpy 基礎········································································.62
3.2 數組的創建與訪問·········································································.65
3.2.1 創建數組 ···········································································.66
3.2.2 數組的訪問 ········································································.72
3.3 數組的基本操作············································································.74
3.4 數組的基本運算············································································.85
3.5 矩陣基礎及運算············································································112
3.6 Numpy 的簡單應用········································································127
第 4 章 數據處理和分析—— Pandas ·····························································135
4.1 數據結構·····················································································136
4.1.1 常用數據結構 ·····································································136
4.1.2 數據類型 ···········································································137
4.1.3 數據類型的簡單使用 ····························································138
4.1.4 系列的基本使用 ··································································140.VI·
4.1.5 數據幀的基本使用 ·······························································145
4.2 數據加載與文件格式······································································154
4.2.1 Pandas 的 I/O 功能································································155
4.2.2 數據讀寫與文件格式 ····························································158
4.3 數據清洗與預處理·········································································184
4.3.1 檢測與處理缺失值 ·······························································185
4.3.2 檢測和處理重復值 ·······························································192
4.3.3 檢測和處理異常值 ·······························································197
4.3.4 數據轉換 ···········································································201
4.3.5 數據匹配 ···········································································213
4.3.6 數據標準化 ········································································218
4.4 數據處理與分析············································································221
4.4.1 層次化索引 ········································································222
4.4.2 數據連接與合並 ··································································234
4.4.3 數據聚合與分組運算 ····························································245
4.5 時間序列分析···············································································261
4.5.1 時間序列基礎 ·····································································261
4.5.2 時間戳（ Timestamp） ···························································265
4.5.3 時區（ Timezone） ·······························································274
4.5.4 時期（ Period） ····································································277
4.5.5 時間差（ Timedelta） ····························································283
4.5.6 時間序列重構 ·····································································285
4.6 Pandas 高級應用 ···········································································302
4.6.1 分類數據 ···········································································302
4.6.2 鏈式編程技術 ·····································································310
第 5 章 數據可視化—— Matplotlib·······························································315
5.1 繪圖基礎·····················································································315
5.2 二維圖形可視化············································································328
5.3 三維圖形可視化············································································354
5.4 使用動畫·····················································································365
5.4.1 使用 Animation 模塊創建動畫 ·················································365
5.4.2 使用 OpenGL 創建動畫 ·························································373
5.5 復雜網絡結構可視化······································································379
5.5.1 網絡可視化基礎 ··································································380
5.5.2 網絡圖的生成 ·····································································385
5.5.3 網絡圖的繪制 ·····································································387