Master Python Data Analysis: From Reading Files to Visualization

导读： Python is the dominant language in data science, used by scientists, engineers, and analysts. Two main audiences need to learn it: finance or statistics professionals handling large data sets, and busy developers who want an efficient way to master Python's data‑technology stack.

Python data‑analysis workflow consists of four parts: reading/writing, processing/computation, analysis/modeling, and visualization, each relying on specific Python libraries.

01 Python Data Analysis Process and Learning Path

The learning path covers reading and writing data, processing with NumPy and pandas, modeling with statsmodels and scikit‑learn, and visualizing with Matplotlib.

02 Reading and Writing Data with Python

Python can read and write various data formats with just a few lines of code, such as importing Excel files using pandas.

03 Processing and Computing Data

NumPy provides vectorized scientific computation, while pandas handles tabular data manipulation.

04 Analysis and Modeling

Statsmodels enables statistical modeling and testing, while scikit‑learn offers a wide range of machine‑learning algorithms.

05 Data Visualization

Matplotlib is the most widely used library for creating static, animated, and interactive visualizations in Python.

06 Why Choose This Book

The second edition of "Python for Data Analysis" is authored by Wes McKinney, the creator of pandas. Updated in 2017, it reflects the latest developments in the Python data‑science ecosystem and has been well received worldwide.

Translator Xu Jingyi, a data analyst at Industrial and Commercial Bank of China, ensures high‑quality translation based on extensive professional experience.