Numeric Python
Python Data Analysis with NumPy, Pandas, and Matplotlib
- ISBN: 978-1-56990-495-4
- Buchangaben: 1. Edition, 05/2026
450 Pages, Flexibler Einband, komplett in Farbe
- 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
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
- 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
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
Der Diplom-Informatiker Bernd Klein genießt internationales Ansehen als Python-Dozent. Bisher hat er über 500 Python-Kurse in Firmen, Forschungsinstituten und Lehraufträgen von Universitäten in Deutschland, Frankreich, der Schweiz, Österreich, den Niederlanden, Luxemburg, Rumänien und Kanada durchgeführt. Er ist Gründer und Inhaber des Schulungsanbieters Bodenseo. Große Aufmerksamkeit finden seine Python-Webseiten www.python-kurs.eu und www.python-course.eu mit jährlich über 6 Millionen Besuchenden.
The computer scientist Bernd Klein enjoys an international reputation as a Python lecturer. To date, he has conducted over 500 Python courses in companies, research institutes and teaching positions at universities in Germany, France, Switzerland, Austria, the Netherlands, Luxembourg, Romania and Canada. He is the founder and owner of the training provider Bodenseo. His Python websites www.python-kurs.eu and www.python-course.eu attract a lot of attention with over 6 million visitors every year.
Der Diplom-Informatiker Bernd Klein genießt internationales Ansehen als Python-Dozent. Bisher hat er über 500 Python-Kurse in Firmen, Forschungsinstituten und Lehraufträgen von Universitäten in Deutschland, Frankreich, der Schweiz, Österreich, den Niederlanden, Luxemburg, Rumänien und Kanada durchgeführt. Er ist Gründer und Inhaber des Schulungsanbieters Bodenseo. Große Aufmerksamkeit finden seine Python-Webseiten www.python-kurs.eu und www.python-course.eu mit jährlich über 6 Millionen Besuchenden.
The computer scientist Bernd Klein enjoys an international reputation as a Python lecturer. To date, he has conducted over 500 Python courses in companies, research institutes and teaching positions at universities in Germany, France, Switzerland, Austria, the Netherlands, Luxembourg, Romania and Canada. He is the founder and owner of the training provider Bodenseo. His Python websites www.python-kurs.eu and www.python-course.eu attract a lot of attention with over 6 million visitors every year.
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Carl Hanser Verlag GmbH & Co KG
Vilshofener Str. 10
81679 München
E-Mail: info@hanser.de
Sicherheitshinweis entsprechend Art.9 Abs. 7 Satz 2 GPSR entbehrlich
