Python Data Science Handbook
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Jake VanderPlas

Python Data Science Handbook

Essential Tools for Working with Data

Page count: 548
Average rating: 5 based on 1 votes
Language: English

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Words used in the book
Pandas
Numpy
Matplotlib
Scikit-learn
Jupyter
IPython
Seaborn
Dataframe
Series
Visualization
Algorithm
Machine Learning
Model
Feature
Regression
Classification
Clustering
Anaconda
Conda
SciPy
Scikit-image
TensorFlow
Keras
Aggregation
Transformation
Imputation
Normalization
Preprocessing
29

Description

For many researchers, Python is a first-class tool mainly because 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 familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python.

With this handbook, you’ll learn how to use:

  • IPython and Jupyter: provide computational environments for data scientists using 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 the most important and established machine learning algorithms
Enjoy reading! If not, change the book, there are thousands ...

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