- Basics of solving numerical problems with Python
- Processing large amounts of data with NumPy, e.g. in machine learning
- Data visualization with Matplotlib
- Ideal for people in science, engineering and data analysis
- Ideal for switching from Matlab to Python
- Introduction based on many examples and practical cases as well as sample solutions
- Your exclusive advantage: E-Book inside when purchasing the printed book
This book teaches the Python basics for solving numerical problems in the areas of data science and machine learning.
The first part is about NumPy as the basis for numerical programming with Python. Arrays are covered in detail as the central data type for everything, numerical operations, broadcasting and Ufuncs. A separate chapter is devoted to statistics and probability, as well as Boolean masking and file handling.
Data visualization with Matplotlib is the focus of the second part. First of all, it's about the terminology of Matplotlib. Line charts, bar charts, histograms and contour plots are covered.
The third part is about Pandas with its Series and DataFrames. Dealing with a wide variety of file formats such as Excel, CSV and JSON as well as incomplete data and NaN is also covered. The possibilities of data visualization directly with Pandas are shown.
The fourth part offers example applications of the material learned, such as a budget book and a practical income surplus calculation. There is also an introduction to image processing techniques here.
Almost each of the 32 chapters contains additional exercises to test and deepen what you have learned; the associated solutions are summarized in the fifth part.
FROM THE CONTENT //
NumPy:
? Numerical operations on multidimensional arrays
? Broadcasting and Ufuncs
Matplotlib:
? Discrete and continuous graphs
? Bar and column charts, histograms, contour plots
Pandas:
? Series and DataFrames
? Work with Excel, CSV and JSON files
? Incomplete data (NaN)
? Data visualization
Practical examples:
? Image processing
? Budget book and income surplus statement
- Numerical computing with NumPy arrays, dtypes, vectorized operations
- Data analysis using Pandas DataFrames, grouping, pivoting, and time series
- Scientific visualization with Matplotlib plots, layouts, and contour graphics
- Real-world data work: files, missing data, binning, and indexing
- Applied Python: image processing, probability, and practical projects
This book teaches the Python fundamentals required to solve numerical problems in data science and machine learning.
The first part focuses on NumPy as the foundation of numerical programming, covering arrays as the core data type, numerical operations, broadcasting, and universal functions, as well as statistics, probability, Boolean masking, and file handling.
The second part is devoted to data visualization with Matplotlib, ranging from core concepts to line, bar, histogram, and contour plots. The third part introduces Pandas, including Series and DataFrames, importing and exporting Excel, CSV, and JSON files, handling missing data, and visualization directly within Pandas.
The fourth part presents practical applications, including a household budget project, an incomeexpenditure analysis, and an introduction to image processing.
The book concludes with a fifth part containing solutions to the numerous exercises that accompany almost every one of the 33 chapters.
WHAT'S INSIDE //
Numerical operations on multidimensional arrays/Broadcasting and universal functions (ufuncs)/Discrete & continuous plots/Bar charts, histograms, and contour plots/Series and DataFrames/Working with Excel, CSV, and JSON files/Handling missing data (NaN)/Data visualization techniques/Image processing funda mentals/Budget tracking and incomeexpenditure analysis
