"Python Data Science Handbook," a total of five chapters, each devoted to one or two Python data science focus toolkit. Firstly IPython and Jupyter start, they provide a computing environment data scientists need; Chapter 2 explains provide ndarray object NumPy, which can efficiently store and manipulate large arrays using the Python; Chapter 3 relates generally provided DataFrame object Pandas , which can efficiently store and operate with a tag / columnar data using the Python; protagonist Chapter 4 Matplotlib, it offers many data visualization of the Python; Chapter 5 Scikit-Learn mainly, the library of the most important machine learning algorithms provide efficient clean version of the Python implementation.
This book framework:
Chapter 2: NumPy Introduction
2.1 understand the data type in Python
2.2 NumPy array base
2.3 NumPy array calculation: General Functions
2.4 the polymerization: min, max and other values
2.5 Calculation of the array: Broadcast
2.6 comparison, mask, and Boolean operations
2.7 fancy index
Sort 2.8 Array
2.9 Structured data: NumPy array of structured
3 Pandas data processing
3.1 Pandas objects Introduction
3.2 Data Value and Choice
3.3 Pandas numerical calculation method
3.4 deal with missing values
3.5 Level Index
The combined dataset 3.6: ConCat and Append method
The combined dataset 3.7: merging connected
3.8 Cumulative packet
3.9 PivotTable
3.10 string operations vectorization
3.11 time series
3.12 High performance Pandas: eval () to query ()
4 Matplotlib data visualization
4.1 simple line graph
4.2 simple scatter plot
4.3 Visualization exception handling
4.4 density contour plots of FIG.
4.5 Histogram
4.6 Configuration Legend
4.7 Configuring color bar
More than 480 sub-graph
4.9 Text and Notes
4.10 painted three-dimensional map
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