Hands-On Data Analysis with Pandas : A Python data science handbook for data collection, wrangling, analysis, and visualization, 2/e (Paperback)
Molin, Stefanie
- 出版商: Packt Publishing
- 出版日期: 2021-04-29
- 售價: $1,570
- 貴賓價: 9.5 折 $1,492
- 語言: 英文
- 頁數: 788
- 裝訂: Quality Paper - also called trade paper
- ISBN: 1800563450
- ISBN-13: 9781800563452
-
相關分類:
Python、程式語言、Data Science
-
相關翻譯:
Pandas數據分析 (簡中版)
立即出貨 (庫存=1)
買這商品的人也買了...
-
$1,850$1,758 -
$1,224Visual Insights: A Practical Guide to Making Sense of Data (Paperback)
-
$280$218 -
$1,950$1,853 -
$556ANSYS FLUENT 16.0 超級學習手冊
-
$1,617Deep Learning (Hardcover)
-
$390$308 -
$490$417 -
$580$452 -
$450$356 -
$500$390 -
$880$695 -
$1,560$1,529 -
$680$666 -
$1,000$850 -
$3,980$3,781 -
$1,660$1,627 -
$530$519 -
$1,700$1,615 -
$1,620$1,539 -
$780$764 -
$650$507 -
$2,233$2,115 -
$2,520Python for Data Analysis: Data Wrangling with pandas, NumPy, and Jupyter, 3/e (Paperback)
-
$620$465
相關主題
商品描述
Get to grips with pandas by working with real datasets and master data discovery, data manipulation, data preparation, and handling data for analytical tasks
Key Features- Perform efficient data analysis and manipulation tasks using pandas 1.x
- Apply pandas to different real-world domains with the help of step-by-step examples
- Make the most of pandas as an effective data exploration tool
Extracting valuable business insights is no longer a 'nice-to-have', but an essential skill for anyone who handles data in their enterprise. Hands-On Data Analysis with Pandas is here to help beginners and those who are migrating their skills into data science get up to speed in no time.
This book will show you how to analyze your data, get started with machine learning, and work effectively with the Python libraries often used for data science, such as pandas, NumPy, matplotlib, seaborn, and scikit-learn.
Using real-world datasets, you will learn how to use the pandas library to perform data wrangling to reshape, clean, and aggregate your data. Then, you will learn how to conduct exploratory data analysis by calculating summary statistics and visualizing the data to find patterns. In the concluding chapters, you will explore some applications of anomaly detection, regression, clustering, and classification using scikit-learn to make predictions based on past data.
This updated edition will equip you with the skills you need to use pandas 1.x to efficiently perform various data manipulation tasks, reliably reproduce analyses, and visualize your data for effective decision making - valuable knowledge that can be applied across multiple domains.
What you will learn- Understand how data analysts and scientists gather and analyze data
- Perform data analysis and data wrangling using Python
- Combine, group, and aggregate data from multiple sources
- Create data visualizations with pandas, matplotlib, and seaborn
- Apply machine learning algorithms to identify patterns and make predictions
- Use Python data science libraries to analyze real-world datasets
- Solve common data representation and analysis problems using pandas
- Build Python scripts, modules, and packages for reusable analysis code
This book is for data science beginners, data analysts, and Python developers who want to explore each stage of data analysis and scientific computing using a wide range of datasets. Data scientists looking to implement pandas in their machine learning workflow will also find plenty of valuable know-how as they progress.
You'll find it easier to follow along with this book if you have a working knowledge of the Python programming language, but a Python crash-course tutorial is provided in the code bundle for anyone who needs a refresher.
Table of Contents- Introduction to Data Analysis
- Working with Pandas DataFrames
- Data Wrangling with Pandas
- Aggregating Pandas DataFrames
- Visualizing Data with Pandas and Matplotlib
- Plotting with Seaborn and Customization Techniques
- Financial Analysis - Bitcoin and the Stock Market
- Rule-Based Anomaly Detection
- Getting Started with Machine Learning in Python
- Making Better Predictions - Optimizing Models
- Machine Learning Anomaly Detection
- The Road Ahead
商品描述(中文翻譯)
透過使用真實數據集,掌握pandas並精通數據探索、數據操作、數據準備和處理數據以進行分析任務的技巧。
主要特點:
- 使用pandas 1.x執行高效的數據分析和操作任務
- 通過逐步示例將pandas應用於不同的現實世界領域
- 充分利用pandas作為有效的數據探索工具
書籍描述:
在企業中,提取有價值的業務洞察力不再是“好事”,而是任何處理數據的人必備的技能。《Hands-On Data Analysis with Pandas》將幫助初學者和那些正在將自己的技能轉移到數據科學領域的人們迅速上手。
本書將向您展示如何分析數據,開始機器學習,並有效地使用Python庫進行數據科學,如pandas、NumPy、matplotlib、seaborn和scikit-learn。
通過使用真實世界的數據集,您將學習如何使用pandas庫進行數據整理,重塑、清理和聚合數據。然後,您將學習如何通過計算摘要統計數據和可視化數據來進行探索性數據分析,以尋找模式。在結尾章節中,您將使用scikit-learn探索異常檢測、回歸、聚類和分類等應用,以基於過去數據進行預測。
本更新版將使您掌握使用pandas 1.x高效執行各種數據操作任務、可靠地重現分析並對數據進行可視化以進行有效決策的技能,這些寶貴的知識可以應用於多個領域。
您將學到:
- 瞭解數據分析師和科學家如何收集和分析數據
- 使用Python進行數據分析和數據整理
- 從多個來源結合、分組和聚合數據
- 使用pandas、matplotlib和seaborn創建數據可視化
- 應用機器學習算法識別模式並進行預測
- 使用Python數據科學庫分析真實世界數據集
- 使用pandas解決常見的數據表示和分析問題
- 構建可重用的分析代碼的Python腳本、模塊和包
本書適合對數據科學初學者、數據分析師和Python開發人員,他們希望探索數據分析和科學計算的每個階段,並使用各種數據集。在機器學習工作流程中實施pandas的數據科學家也會在進展中找到許多有價值的知識。
如果您對Python編程語言有一定的了解,閱讀本書將更加容易,但對於需要複習的人,程式碼包中提供了Python快速入門教程。
目錄:
1. 數據分析簡介
2. 使用Pandas DataFrames
3. 使用Pandas進行數據整理
4. 聚合Pandas DataFrames
5. 使用Pandas和Matplotlib進行數據可視化
6. 使用Seaborn和自定義技巧進行繪圖
7. 金融分析-比特幣和股市
8. 基於規則的異常檢測
9. Python機器學習入門
10. 做出更好的預測-優化模型
11. 機器學習異常檢測
12. 未來之路