Why use Python for data analysis?
For data analyst practitioners, it is often necessary to engage in: database operations, report writing, data visualization, and data mining. These tasks can be operated without writing codes. Using Excel for data visualization and using some people-friendly platform tools such as SPSS for data mining, although the use of platform tools is highly manipulative, there will inevitably be repetitive mechanical labor, thereby reducing Your own work efficiency, but if you can write code in Python, you have a higher degree of freedom in operation and greater potential for development.
How to use Python for data analysis?
This book "Data Analysis Using Python" is recommended here, which is suitable for beginners who are new to Python data analysis. Using the interactive shell of IPython as your primary development environment, it tells about starting to use high-performance tools from the data analysis tools of the pandas library.
Let's show it directly:
Chapter 1 Preparations
1.1 Contents of this book
1.2 Why use Python for data analysis
1.3 Important Python libraries
1.4 Installation and Setup
…
Chapter 2 Basics of Python Syntax, IPython and Jupyter Notebooks
2.1 Python interpreter
2.2 Basics of IPython
2.3 Basics of Python syntax
Chapter 3 Python Data Structures, Functions, and Files
3.1 Data Structures and Sequences
3.2 Functions
3.3 Files and Operating System
3.4 Conclusion
Chapter 4 NumPy Basics: Array and Vector Computing
4.1 NumPy's ndarray: a multidimensional array object
4.2 Universal Functions: Fast Element-wise Array Functions
4.3 Using arrays for data processing
4.4 File input and output for arrays
…
Chapter 5 Getting Started with Pandas
5.1 Introduction to data structure of pandas
5.2 Basic functions
5.3 Summarizing and computing descriptive statistics
5.4 Summary
Chapter 6 Data Loading, Storage, and File Formats
6.1 Reading and writing data in text format
6.2 Binary data format
6.3 Interaction with Web APIs
6.4 Database Interaction
6.5 Summary
Chapter 7 Data Cleaning and Preparation
7.1 Handling missing data
7.2 Data Conversion
7.3 String manipulation
7.4 Summary
Chapter 8 Data Wrangling: Aggregating, Merging, and Reshaping
8.1 Hierarchical Index
8.2 Merging datasets
8.3 Reshaping and axial rotation
8.4 Summary
Chapter 9 Plotting and Visualization
9.1 Getting started with the matplotlib API
9.2 Plotting with pandas and seaborn
9.3 Other Python visualization tools
9.4 Summary
Chapter 10 Data Aggregation and Grouping Operations
10.1 GroupBy mechanism
10.2 Data Aggregation
10.3 apply: general "split-apply-merge"
10.4 Pivot tables and crosstabs
10.5 Summary
Chapter 11 Time Series
11.1 Date and time data types and tools
11.2 Time Series Basics
11.3 Range, frequency and movement of dates
11.4 Time Zone Handling
…
Chapter 12 Advanced Applications of Pandas
12.1 Categorical data
12.2 Advanced Application of GroupBy
12.3 Chain programming technology
12.4 Summary
Chapter 13 Introduction to the Python Modeling Library
13.1 Interface between pandas and model code
13.2 Creating Model Descriptions with Patsy
13.3 Introduction to statsmodels
13.4 Introduction to scikit-learn
13.5 Continue learning
Chapter 14 Data Analysis Cases
14.1 USA.gov data from Bitly
14.2 MovieLens 1M Dataset
14.3 National Baby Names 1880-2010
14.4 USDA Food Database
14.5 2012 Federal Election Commission Database
14.6 Summary
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