Description
For many researchers, Python is a firstclass tool mainly as a result of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all—IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools.
Working scientists and data crunchers accustomed to reading and writing Python code will find this comprehensive desk reference ideal for tackling daily issues: manipulating, transforming, and cleaning data; visualizing various kinds of data; and the use of data to build statistical or machine learning models. Rather simply, this is the will have to-have reference for scientific computing in Python.
With this handbook, you’ll learn to use:
- IPython and Jupyter: provide computational environments for data scientists the use of Python
- NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python
- Pandas: features the DataFrame for efficient storage and manipulation of labeled/columnar data in Python
- Matplotlib: includes capabilities for a flexible range of data visualizations in Python
- Scikit-Learn: for efficient and clean Python implementations of crucial and established machine learning algorithms