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Learning NumPy Array : supercharge your scientific Python computations by understanding how to use the NumPy library effectively / / Ivan Idris ; Duraid Fatouhi, cover image
| Learning NumPy Array : supercharge your scientific Python computations by understanding how to use the NumPy library effectively / / Ivan Idris ; Duraid Fatouhi, cover image |
| Autore | Idris Ivan |
| Edizione | [1st edition] |
| Pubbl/distr/stampa | Birmingham, England : , : Packt Publishing, , 2014 |
| Descrizione fisica | 1 online resource (164 p.) |
| Disciplina | 005.133 |
| Collana | Community experience distilled |
| Soggetto topico |
Python (Computer program language)
Numerical analysis - Data processing |
| Soggetto genere / forma | Electronic books. |
| ISBN | 1-78398-391-4 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover; Copyright; Credits; About the Author; About the Reviewers; www.PacktPub.com; Table of Contents; Preface; Chapter 1: Getting Started with NumPy; Python; Installing NumPy, Matplotlib, SciPy, and IPython on Windows; Installing NumPy, Matplotlib, SciPy, and IPython on Linux; Installing NumPy, Matplotlib, and SciPy on Mac OS X; Building from source; NumPy arrays; Adding arrays; Online resources and help; Summary; Chapter 2: NumPy Basics; The NumPy array object; The advantages of using NumPy arrays; Creating a multidimensional array; Selecting array elements; NumPy numerical types
Data type objectsCharacter codes; dtype constructors; dtype attributes; Creating a record data type; One-dimensional slicing and indexing; Manipulating array shapes; Stacking arrays; Splitting arrays; Array attributes; Converting arrays; Creating views and copies; Fancy indexing; Indexing with a list of locations; Indexing arrays with Booleans; Stride tricks for Sudoku; Broadcasting arrays; Summary; Chapter 3: Basic Data Analysis with NumPy; Introducing the dataset; Determining the daily temperature range; Looking for evidence of global warming; Comparing solar radiation versus temperature Analyzing wind directionAnalyzing wind speed; Analyzing precipitation and sunshine duration; Analyzing monthly precipitation in De Bilt; Analyzing atmospheric pressure in De Bilt; Analyzing atmospheric humidity in De Bilt; Summary; Chapter 4: Simple Predictive Analytics with NumPy; Examining autocorrelation of average temperature with pandas; Describing data with pandas DataFrames; Correlating weather and stocks with pandas; Predicting temperature; Autoregressive model with lag 1; Autoregressive model with lag 2; Analysing intra-year daily average temperatures Introducing the day-of-the-year temperature modelModeling temperature with the SciPy leastsq function; Day-of-year temperature take two; Moving-average temperature model with lag 1; The Autoregressive Moving Average temperature model; The time-dependent temperature mean adjusted autoregressive model; Outliers analysis of average De Bilt temperature; Using more robust statistics; Summary; Chapter 5: Signal Processing Techniques; Introducing the Sunspot data; Sifting continued; Moving averages; Smoothing functions; Forecasting with an ARMA model; Filtering a signal; Designing the filter Demonstrating cointegrationSummary; Chapter 6: Profiling, Debugging, and Testing; Assert functions; The assert_almost_equal function; Approximately equal arrays; The assert_array_almost_equal function; Profiling a program with IPython; Debugging with IPython; Performing Unit tests; Nose tests decorators; Summary; Chapter 7: The Scientific Python Ecosystem; Numerical integration; Interpolation; Using Cython with NumPy; Clustering stocks with scikit-learn; Detecting corners; Comparing NumPy to Blaze; Summary; Index |
| Record Nr. | UNINA-9910458552603321 |
Idris Ivan
|
||
| Birmingham, England : , : Packt Publishing, , 2014 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Learning NumPy Array : supercharge your scientific Python computations by understanding how to use the NumPy library effectively / / Ivan Idris ; Duraid Fatouhi, cover image
| Learning NumPy Array : supercharge your scientific Python computations by understanding how to use the NumPy library effectively / / Ivan Idris ; Duraid Fatouhi, cover image |
| Autore | Idris Ivan |
| Edizione | [1st edition] |
| Pubbl/distr/stampa | Birmingham, England : , : Packt Publishing, , 2014 |
| Descrizione fisica | 1 online resource (164 p.) |
| Disciplina | 005.133 |
| Collana | Community experience distilled |
| Soggetto topico |
Python (Computer program language)
Numerical analysis - Data processing |
| ISBN | 1-78398-391-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover; Copyright; Credits; About the Author; About the Reviewers; www.PacktPub.com; Table of Contents; Preface; Chapter 1: Getting Started with NumPy; Python; Installing NumPy, Matplotlib, SciPy, and IPython on Windows; Installing NumPy, Matplotlib, SciPy, and IPython on Linux; Installing NumPy, Matplotlib, and SciPy on Mac OS X; Building from source; NumPy arrays; Adding arrays; Online resources and help; Summary; Chapter 2: NumPy Basics; The NumPy array object; The advantages of using NumPy arrays; Creating a multidimensional array; Selecting array elements; NumPy numerical types
Data type objectsCharacter codes; dtype constructors; dtype attributes; Creating a record data type; One-dimensional slicing and indexing; Manipulating array shapes; Stacking arrays; Splitting arrays; Array attributes; Converting arrays; Creating views and copies; Fancy indexing; Indexing with a list of locations; Indexing arrays with Booleans; Stride tricks for Sudoku; Broadcasting arrays; Summary; Chapter 3: Basic Data Analysis with NumPy; Introducing the dataset; Determining the daily temperature range; Looking for evidence of global warming; Comparing solar radiation versus temperature Analyzing wind directionAnalyzing wind speed; Analyzing precipitation and sunshine duration; Analyzing monthly precipitation in De Bilt; Analyzing atmospheric pressure in De Bilt; Analyzing atmospheric humidity in De Bilt; Summary; Chapter 4: Simple Predictive Analytics with NumPy; Examining autocorrelation of average temperature with pandas; Describing data with pandas DataFrames; Correlating weather and stocks with pandas; Predicting temperature; Autoregressive model with lag 1; Autoregressive model with lag 2; Analysing intra-year daily average temperatures Introducing the day-of-the-year temperature modelModeling temperature with the SciPy leastsq function; Day-of-year temperature take two; Moving-average temperature model with lag 1; The Autoregressive Moving Average temperature model; The time-dependent temperature mean adjusted autoregressive model; Outliers analysis of average De Bilt temperature; Using more robust statistics; Summary; Chapter 5: Signal Processing Techniques; Introducing the Sunspot data; Sifting continued; Moving averages; Smoothing functions; Forecasting with an ARMA model; Filtering a signal; Designing the filter Demonstrating cointegrationSummary; Chapter 6: Profiling, Debugging, and Testing; Assert functions; The assert_almost_equal function; Approximately equal arrays; The assert_array_almost_equal function; Profiling a program with IPython; Debugging with IPython; Performing Unit tests; Nose tests decorators; Summary; Chapter 7: The Scientific Python Ecosystem; Numerical integration; Interpolation; Using Cython with NumPy; Clustering stocks with scikit-learn; Detecting corners; Comparing NumPy to Blaze; Summary; Index |
| Record Nr. | UNINA-9910791027703321 |
|
Idris Ivan
|
||
| Birmingham, England : , : Packt Publishing, , 2014 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Learning NumPy Array : supercharge your scientific Python computations by understanding how to use the NumPy library effectively / / Ivan Idris ; Duraid Fatouhi, cover image
| Learning NumPy Array : supercharge your scientific Python computations by understanding how to use the NumPy library effectively / / Ivan Idris ; Duraid Fatouhi, cover image |
| Autore | Idris Ivan |
| Edizione | [1st edition] |
| Pubbl/distr/stampa | Birmingham, England : , : Packt Publishing, , 2014 |
| Descrizione fisica | 1 online resource (164 p.) |
| Disciplina | 005.133 |
| Collana | Community experience distilled |
| Soggetto topico |
Python (Computer program language)
Numerical analysis - Data processing |
| ISBN | 1-78398-391-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover; Copyright; Credits; About the Author; About the Reviewers; www.PacktPub.com; Table of Contents; Preface; Chapter 1: Getting Started with NumPy; Python; Installing NumPy, Matplotlib, SciPy, and IPython on Windows; Installing NumPy, Matplotlib, SciPy, and IPython on Linux; Installing NumPy, Matplotlib, and SciPy on Mac OS X; Building from source; NumPy arrays; Adding arrays; Online resources and help; Summary; Chapter 2: NumPy Basics; The NumPy array object; The advantages of using NumPy arrays; Creating a multidimensional array; Selecting array elements; NumPy numerical types
Data type objectsCharacter codes; dtype constructors; dtype attributes; Creating a record data type; One-dimensional slicing and indexing; Manipulating array shapes; Stacking arrays; Splitting arrays; Array attributes; Converting arrays; Creating views and copies; Fancy indexing; Indexing with a list of locations; Indexing arrays with Booleans; Stride tricks for Sudoku; Broadcasting arrays; Summary; Chapter 3: Basic Data Analysis with NumPy; Introducing the dataset; Determining the daily temperature range; Looking for evidence of global warming; Comparing solar radiation versus temperature Analyzing wind directionAnalyzing wind speed; Analyzing precipitation and sunshine duration; Analyzing monthly precipitation in De Bilt; Analyzing atmospheric pressure in De Bilt; Analyzing atmospheric humidity in De Bilt; Summary; Chapter 4: Simple Predictive Analytics with NumPy; Examining autocorrelation of average temperature with pandas; Describing data with pandas DataFrames; Correlating weather and stocks with pandas; Predicting temperature; Autoregressive model with lag 1; Autoregressive model with lag 2; Analysing intra-year daily average temperatures Introducing the day-of-the-year temperature modelModeling temperature with the SciPy leastsq function; Day-of-year temperature take two; Moving-average temperature model with lag 1; The Autoregressive Moving Average temperature model; The time-dependent temperature mean adjusted autoregressive model; Outliers analysis of average De Bilt temperature; Using more robust statistics; Summary; Chapter 5: Signal Processing Techniques; Introducing the Sunspot data; Sifting continued; Moving averages; Smoothing functions; Forecasting with an ARMA model; Filtering a signal; Designing the filter Demonstrating cointegrationSummary; Chapter 6: Profiling, Debugging, and Testing; Assert functions; The assert_almost_equal function; Approximately equal arrays; The assert_array_almost_equal function; Profiling a program with IPython; Debugging with IPython; Performing Unit tests; Nose tests decorators; Summary; Chapter 7: The Scientific Python Ecosystem; Numerical integration; Interpolation; Using Cython with NumPy; Clustering stocks with scikit-learn; Detecting corners; Comparing NumPy to Blaze; Summary; Index |
| Record Nr. | UNINA-9910810554503321 |
|
Idris Ivan
|
||
| Birmingham, England : , : Packt Publishing, , 2014 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
NumPy beginner's guide : build efficient, high-speed programs using the high-performance NumPy mathematical library / / Ivan Idris
| NumPy beginner's guide : build efficient, high-speed programs using the high-performance NumPy mathematical library / / Ivan Idris |
| Autore | Idris Ivan |
| Edizione | [3rd ed.] |
| Pubbl/distr/stampa | Birmingham, England : , : Packt Publishing, , 2015 |
| Descrizione fisica | 1 online resource (348 p.) |
| Disciplina |
005.13
005.133 |
| Collana | Learn by doing : less theory, more results |
| Soggetto topico |
Mathematics - Data processing
Python (Computer program language) Mathematical analysis |
| ISBN | 1-78528-883-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover; Copyright; Credits; About the Author; About the Reviewers; www.PacktPub.com; Table of Contents; Preface; Chapter 1: NumPy Quick Start; Python; Time for action - installing Python on different operating systems; The Python help system; Time for action - using the Python help system; Basic arithmetic and variable assignment; Time for action - using Python as a calculator; Time for action - assigning values to variables; The print() function; Time for action - printing with the print() function; Code comments; Time for action - commenting code; The if statement
Time for action - deciding with the if statementThe for loop; Time for action - repeating instructions with loops; Python functions; Time for action - defining functions; Python modules; Time for action - importing modules; NumPy on Windows; Time for action - installing NumPy, matplotlib, SciPy, and IPython on Windows; NumPy on Linux; Time for action - installing NumPy, matplotlib, SciPy, and IPython on Linux; NumPy on Mac OS X; Time for action - installing NumPy, SciPy, matplotlib, and IPython with MacPorts or Fink; Building from source; Arrays; Time for action - adding vectors IPython - an interactive shellOnline resources and help; Summary; Chapter 2: Beginning with NumPy Fundamentals; NumPy array object; Time for action - creating a multidimensional array; Selecting elements; NumPy numerical types; Data type objects; Character codes; The dtype constructors; The dtype attributes; Time for action - creating a record data type; One-dimensional slicing and indexing; Time for action - slicing and indexing multidimensional arrays; Time for action - manipulating array shapes; Time for action - stacking arrays; Time for action - splitting arrays Time for action - converting arraysSummary; Chapter 3: Getting Familiar with Commonly Used Functions; File I/O; Time for action - reading and writing files; Comma Separated Values files; Time for action - loading from CSV files; Volume Weighted Average Price; Time for action - calculating volume weighted average price; The mean() function; Time-weighted average price; Value range; Time for action - finding highest and lowest values; Statistics; Time for action - doing simple statistics; Stock returns; Time for action - analyzing stock returns; Dates; Time for action - dealing with dates Time for action - using the datetime64 data typeWeekly summary; Time for action - summarizing data; Average True Range; Time for action - calculating the average true range; Simple Moving Average; Time for action - computing the simple moving average; Exponential Moving Average; Time for action - calculating the exponential moving average; Bollinger Bands; Time for action - enveloping with Bollinger bands; Linear model; Time for action - predicting price with a linear model; Trend lines; Time for action - drawing trend lines; Methods of ndarray Time for action - clipping and compressing arrays |
| Record Nr. | UNINA-9910797389303321 |
|
Idris Ivan
|
||
| Birmingham, England : , : Packt Publishing, , 2015 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
NumPy beginner's guide : build efficient, high-speed programs using the high-performance NumPy mathematical library / / Ivan Idris
| NumPy beginner's guide : build efficient, high-speed programs using the high-performance NumPy mathematical library / / Ivan Idris |
| Autore | Idris Ivan |
| Edizione | [3rd ed.] |
| Pubbl/distr/stampa | Birmingham, England : , : Packt Publishing, , 2015 |
| Descrizione fisica | 1 online resource (348 p.) |
| Disciplina |
005.13
005.133 |
| Collana | Learn by doing : less theory, more results |
| Soggetto topico |
Mathematics - Data processing
Python (Computer program language) Mathematical analysis |
| ISBN | 1-78528-883-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover; Copyright; Credits; About the Author; About the Reviewers; www.PacktPub.com; Table of Contents; Preface; Chapter 1: NumPy Quick Start; Python; Time for action - installing Python on different operating systems; The Python help system; Time for action - using the Python help system; Basic arithmetic and variable assignment; Time for action - using Python as a calculator; Time for action - assigning values to variables; The print() function; Time for action - printing with the print() function; Code comments; Time for action - commenting code; The if statement
Time for action - deciding with the if statementThe for loop; Time for action - repeating instructions with loops; Python functions; Time for action - defining functions; Python modules; Time for action - importing modules; NumPy on Windows; Time for action - installing NumPy, matplotlib, SciPy, and IPython on Windows; NumPy on Linux; Time for action - installing NumPy, matplotlib, SciPy, and IPython on Linux; NumPy on Mac OS X; Time for action - installing NumPy, SciPy, matplotlib, and IPython with MacPorts or Fink; Building from source; Arrays; Time for action - adding vectors IPython - an interactive shellOnline resources and help; Summary; Chapter 2: Beginning with NumPy Fundamentals; NumPy array object; Time for action - creating a multidimensional array; Selecting elements; NumPy numerical types; Data type objects; Character codes; The dtype constructors; The dtype attributes; Time for action - creating a record data type; One-dimensional slicing and indexing; Time for action - slicing and indexing multidimensional arrays; Time for action - manipulating array shapes; Time for action - stacking arrays; Time for action - splitting arrays Time for action - converting arraysSummary; Chapter 3: Getting Familiar with Commonly Used Functions; File I/O; Time for action - reading and writing files; Comma Separated Values files; Time for action - loading from CSV files; Volume Weighted Average Price; Time for action - calculating volume weighted average price; The mean() function; Time-weighted average price; Value range; Time for action - finding highest and lowest values; Statistics; Time for action - doing simple statistics; Stock returns; Time for action - analyzing stock returns; Dates; Time for action - dealing with dates Time for action - using the datetime64 data typeWeekly summary; Time for action - summarizing data; Average True Range; Time for action - calculating the average true range; Simple Moving Average; Time for action - computing the simple moving average; Exponential Moving Average; Time for action - calculating the exponential moving average; Bollinger Bands; Time for action - enveloping with Bollinger bands; Linear model; Time for action - predicting price with a linear model; Trend lines; Time for action - drawing trend lines; Methods of ndarray Time for action - clipping and compressing arrays |
| Record Nr. | UNINA-9910812719603321 |
|
Idris Ivan
|
||
| Birmingham, England : , : Packt Publishing, , 2015 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
NumPy beginner's guide [[electronic resource] ] : an action-packed guide for the easy-to-use, high performance, Python based free open source NumPy mathematical library using real-world examples / / Ivan Idris
| NumPy beginner's guide [[electronic resource] ] : an action-packed guide for the easy-to-use, high performance, Python based free open source NumPy mathematical library using real-world examples / / Ivan Idris |
| Autore | Idris Ivan |
| Edizione | [2nd ed.] |
| Pubbl/distr/stampa | Birmingham, England, : Packt Publishing, c2013 |
| Descrizione fisica | 1 online resource (310 p.) |
| Disciplina | 005.13 |
| Soggetto topico |
Programming languages (Electronic computers)
Python (Computer program language) |
| Soggetto genere / forma | Electronic books. |
| ISBN | 1-78216-609-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | NumPy quick start -- Beginning with NumPy fundementals -- Get in terms with commonly used functions -- Convenience functions for your convenience -- Working with matrices and ufuncs -- Move further with NumPy modules -- Peeking into special rountines -- Assure quality with testing -- Plotting with Matplotlib -- When NumPy is not enough - SciPy and beyond -- Playing with Pygame. |
| Record Nr. | UNINA-9910462960703321 |
|
Idris Ivan
|
||
| Birmingham, England, : Packt Publishing, c2013 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
NumPy beginner's guide [[electronic resource] ] : an action-packed guide for the easy-to-use, high performance, Python based free open source NumPy mathematical library using real-world examples / / Ivan Idris
| NumPy beginner's guide [[electronic resource] ] : an action-packed guide for the easy-to-use, high performance, Python based free open source NumPy mathematical library using real-world examples / / Ivan Idris |
| Autore | Idris Ivan |
| Edizione | [2nd ed.] |
| Pubbl/distr/stampa | Birmingham, England, : Packt Publishing, c2013 |
| Descrizione fisica | 1 online resource (310 p.) |
| Disciplina | 005.13 |
| Soggetto topico |
Programming languages (Electronic computers)
Python (Computer program language) |
| ISBN | 1-78216-609-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | NumPy quick start -- Beginning with NumPy fundementals -- Get in terms with commonly used functions -- Convenience functions for your convenience -- Working with matrices and ufuncs -- Move further with NumPy modules -- Peeking into special rountines -- Assure quality with testing -- Plotting with Matplotlib -- When NumPy is not enough - SciPy and beyond -- Playing with Pygame. |
| Record Nr. | UNINA-9910786974803321 |
|
Idris Ivan
|
||
| Birmingham, England, : Packt Publishing, c2013 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
NumPy beginner's guide : an action-packed guide for the easy-to-use, high performance, Python based free open source NumPy mathematical library using real-world examples / / Ivan Idris
| NumPy beginner's guide : an action-packed guide for the easy-to-use, high performance, Python based free open source NumPy mathematical library using real-world examples / / Ivan Idris |
| Autore | Idris Ivan |
| Edizione | [2nd ed.] |
| Pubbl/distr/stampa | Birmingham, England, : Packt Publishing, c2013 |
| Descrizione fisica | 1 online resource (310 p.) |
| Disciplina | 005.13 |
| Soggetto topico |
Programming languages (Electronic computers)
Python (Computer program language) |
| ISBN |
9781782166092
1782166092 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | NumPy quick start -- Beginning with NumPy fundementals -- Get in terms with commonly used functions -- Convenience functions for your convenience -- Working with matrices and ufuncs -- Move further with NumPy modules -- Peeking into special rountines -- Assure quality with testing -- Plotting with Matplotlib -- When NumPy is not enough - SciPy and beyond -- Playing with Pygame. |
| Record Nr. | UNINA-9910975109503321 |
|
Idris Ivan
|
||
| Birmingham, England, : Packt Publishing, c2013 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
NumPy cookbook : over 90 fascinating recipes to learn and perform mathematical, scientific, and engineering Python computations with NumPy / / Ivan Idris
| NumPy cookbook : over 90 fascinating recipes to learn and perform mathematical, scientific, and engineering Python computations with NumPy / / Ivan Idris |
| Autore | Idris Ivan |
| Edizione | [Second edition.] |
| Pubbl/distr/stampa | Birmingham, [England] : , : Packt Publishing, , 2015 |
| Descrizione fisica | 1 online resource (258 p.) |
| Disciplina | 519.4 |
| Collana | Community Experience Distilled |
| Soggetto topico |
Numerical analysis - Data processing
Object-oriented programming (Computer science) |
| Soggetto genere / forma | Electronic books. |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover; Copyright; Credits; About the Author; About the Reviewers; www.PacktPub.com; Table of Contents; Preface; Chapter 1: Winding Along with IPython; Introduction; Installing IPython; Using IPython as a shell; Reading manual pages; Installing matplotlib; Running an IPython notebook; Exporting an IPython notebook; Importing a web notebook; Configuring a notebook server; Exploring the SymPy profile; Chapter 2: Advanced Indexing and Array Concepts; Introduction; Installing SciPy; Installing PIL; Resizing images; Creating views and copies; Flipping Lena; Fancy indexing
Indexing with a list of locationsIndexing with Booleans; Stride tricks for Sudoku; Broadcasting arrays; Chapter 3: Getting to Grips with Commonly Used Functions; Introduction; Summing Fibonacci numbers; Finding prime factors; Finding palindromic numbers; The steady state vector; Discovering a power law; Trading periodically on dips; Simulating trading at random; Sieving integers with the Sieve of Eratosthenes; Chapter 4: Connecting NumPy with the Rest of the World; Introduction; Using the buffer protocol; Using the array interface; Exchanging data with MATLAB and Octave; Installing RPy2 Interfacing with RInstalling JPype; Sending a NumPy array to JPype; Installing Google App Engine; Deploying the NumPy code on the Google Cloud; Running the NumPy code in a PythonAnywhere web console; Chapter 5: Audio and Image Processing; Introduction; Loading images into memory maps; Combining images; Blurring images; Repeating audio fragments; Generating sounds; Designing an audio filter; Edge detection with the Sobel filter; Chapter 6: Special Arrays and Universal Functions; Introduction; Creating a universal function; Finding Pythagorean triples Performing string operations with chararrayCreating a masked array; Ignoring negative and extreme values; Creating a scores table with a recarray function; Chapter 7: Profiling and Debugging; Introduction; Profiling with timeit; Profiling with IPython; Installing line_profiler; Profiling code with line_profiler; Profiling code with the cProfile extension; Debugging with IPython; Debugging with PuDB; Chapter 8: Quality Assurance; Introduction; Installing Pyflakes; Performing static analysis with Pyflakes; Analyzing code with Pylint; Performing static analysis with Pychecker Testing code with docstringsWriting unit tests; Testing code with mocks; Testing the BDD way; Chapter 9: Speeding Up Code with Cython; Introduction; Installing Cython; Building a Hello World program; Using Cython with NumPy; Calling C functions; Profiling the Cython code; Approximating factorials with Cython; Chapter 10: Fun with Scikits; Introduction; Installing scikit-learn; Loading an example dataset; Clustering Dow Jones stocks with scikits-learn; Installing statsmodels; Performing a normality test with statsmodels; Installing scikit-image; Detecting corners; Detecting edges Installing pandas |
| Record Nr. | UNINA-9910463835903321 |
|
Idris Ivan
|
||
| Birmingham, [England] : , : Packt Publishing, , 2015 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
NumPy cookbook [[electronic resource] /] / Ivan Idris
| NumPy cookbook [[electronic resource] /] / Ivan Idris |
| Autore | Idris Ivan |
| Pubbl/distr/stampa | Birmingham, [Eng.], : Packt Publishing, 2012 |
| Descrizione fisica | 1 online resource (226 p.) |
| Disciplina | 006.76 |
| Soggetto topico |
Python (Computer program language)
Numerical analysis - Data processing |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-283-73904-6
1-84951-893-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover; Copyright; Credits; About the Author; About the Reviewers; www.PacktPub.com; Table of Contents; Preface; Chapter 1:Winding Along with IPython; Introduction; Installing IPython; Using IPython as a shell; Reading manual pages; Installing Matplotlib; Running a web notebook; Exporting a web notebook; Importing a web notebook; Configuring a notebook server; Exploring the SymPy profile; Chapter 2:Advanced Indexing and Array Concepts; Introduction; Installing SciPy; Installing PIL; Resizing images; Creating views and copies; Flipping Lena; Fancy indexing; Indexing with a list of locations
Indexing with booleansStride tricks for Sudoku; Broadcasting arrays; Chapter 3:Get to Grips with Commonly Used Functions; Introduction; Summing Fibonacci numbers; Finding prime factors; Finding palindromic numbers; The steady state vector determination; Discovering a power law; Trading periodically on dips; Simulating trading at random; Sieving integers with the Sieve of Erasthothenes; Chapter 4:Connecting NumPy with the Rest of the World; Introduction; Using the buffer protocol; Using the array interface; Exchanging data with MATLAB and Octave; Installing RPy2; Interfacing with R Installing JPypeSending a NumPy array to JPype; Installing Google App Engine; Deploying NumPy code in the Google cloud; Running NumPy code in a Python Anywhere web console; Setting up PiCloud; Chapter 5:Audio and Image Processing; Introduction; Loading images into memory map; Combining images; Blurring images; Repeating audio fragments; Generating sounds; Designing an audio filter; Edge detection with the Sobel filter; Chapter 6:Special Arrays and Universal Functions; Introduction; Creating a universal function; Finding Pythagorean triples; Performing string operations with chararray Creating a masked arrayIgnoring negative and extreme values; Creating a scores table with recarray; Chapter 7:and Debugging; Introduction; Profiling with timeit; Profiling with IPython; Installing line_profiler; Profiling code with line_profiler; Profiling code with the cProfile extension; Debugging with IPython; Debugging with pudb; Chapter 8:Quality Assurance; Introduction; Installing Pyflakes; Performing static analysis with Pyflakes; Analyzing code with Pylint; Performing static analysis with Pychecker; Testing code with docstrings; Writing unit tests; Testing code with mocks Testing the BDD wayChapter 9:Speed Up Code with Cython; Introduction; Installing Cython; Building a Hello World program; Using Cython with NumPy; Calling C functions; Profiling Cython code; Approximating factorials with Cython; Chapter 10:Fun with Scikits; Introduction; Installing scikits-learn; Loading an example dataset; Clustering Dow Jones stocks with scikits-learn; Installing scikits-statsmodels; Performing a normality test with scikits-statsmodels; Installing scikits-image; Detecting corners; Detecting edges; Installing Pandas; Estimating stock returns correlation with Pandas Loading data as pandas objects from statsmodels |
| Record Nr. | UNINA-9910462513203321 |
|
Idris Ivan
|
||
| Birmingham, [Eng.], : Packt Publishing, 2012 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
