GitHub - ipython-books/ipython-books.github.io: IPython books website, by Cyrille Rossant

Name		Name	Last commit message	Last commit date
Latest commit History 9 Commits
101-analyzing-the-frequency-components-of-a-signal-with-a-fast-fourier-transform		101-analyzing-the-frequency-components-of-a-signal-with-a-fast-fourier-transform
102-applying-a-linear-filter-to-a-digital-signal		102-applying-a-linear-filter-to-a-digital-signal
103-computing-the-autocorrelation-of-a-time-series		103-computing-the-autocorrelation-of-a-time-series
11-introducing-ipython-and-the-jupyter-notebook		11-introducing-ipython-and-the-jupyter-notebook
111-manipulating-the-exposure-of-an-image		111-manipulating-the-exposure-of-an-image
112-applying-filters-on-an-image		112-applying-filters-on-an-image
113-segmenting-an-image		113-segmenting-an-image
114-finding-points-of-interest-in-an-image		114-finding-points-of-interest-in-an-image
115-detecting-faces-in-an-image-with-opencv		115-detecting-faces-in-an-image-with-opencv
116-applying-digital-filters-to-speech-sounds		116-applying-digital-filters-to-speech-sounds
117-creating-a-sound-synthesizer-in-the-notebook		117-creating-a-sound-synthesizer-in-the-notebook
12-getting-started-with-exploratory-data-analysis-in-the-jupyter-notebook		12-getting-started-with-exploratory-data-analysis-in-the-jupyter-notebook
121-plotting-the-bifurcation-diagram-of-a-chaotic-dynamical-system		121-plotting-the-bifurcation-diagram-of-a-chaotic-dynamical-system
122-simulating-an-elementary-cellular-automaton		122-simulating-an-elementary-cellular-automaton
123-simulating-an-ordinary-differential-equation-with-scipy		123-simulating-an-ordinary-differential-equation-with-scipy
124-simulating-a-partial-differential-equation-reaction-diffusion-systems-and-turing-patterns		124-simulating-a-partial-differential-equation-reaction-diffusion-systems-and-turing-patterns
13-introducing-the-multidimensional-array-in-numpy-for-fast-array-computations		13-introducing-the-multidimensional-array-in-numpy-for-fast-array-computations
131-simulating-a-discrete-time-markov-chain		131-simulating-a-discrete-time-markov-chain
132-simulating-a-poisson-process		132-simulating-a-poisson-process
133-simulating-a-brownian-motion		133-simulating-a-brownian-motion
134-simulating-a-stochastic-differential-equation		134-simulating-a-stochastic-differential-equation
14-creating-an-ipython-extension-with-custom-magic-commands		14-creating-an-ipython-extension-with-custom-magic-commands
141-manipulating-and-visualizing-graphs-with-networkx		141-manipulating-and-visualizing-graphs-with-networkx
142-drawing-flight-routes-with-networkx		142-drawing-flight-routes-with-networkx
143-resolving-dependencies-in-a-directed-acyclic-graph-with-a-topological-sort		143-resolving-dependencies-in-a-directed-acyclic-graph-with-a-topological-sort
144-computing-connected-components-in-an-image		144-computing-connected-components-in-an-image
145-computing-the-voronoi-diagram-of-a-set-of-points		145-computing-the-voronoi-diagram-of-a-set-of-points
146-manipulating-geospatial-data-with-cartopy		146-manipulating-geospatial-data-with-cartopy
147-creating-a-route-planner-for-a-road-network		147-creating-a-route-planner-for-a-road-network
15-mastering-ipythons-configuration-system		15-mastering-ipythons-configuration-system
151-diving-into-symbolic-computing-with-sympy		151-diving-into-symbolic-computing-with-sympy
152-solving-equations-and-inequalities		152-solving-equations-and-inequalities
153-analyzing-real-valued-functions		153-analyzing-real-valued-functions
154-computing-exact-probabilities-and-manipulating-random-variables		154-computing-exact-probabilities-and-manipulating-random-variables
155-a-bit-of-number-theory-with-sympy		155-a-bit-of-number-theory-with-sympy
156-finding-a-boolean-propositional-formula-from-a-truth-table		156-finding-a-boolean-propositional-formula-from-a-truth-table
157-analyzing-a-nonlinear-differential-system-lotka-volterra-predator-prey-equations		157-analyzing-a-nonlinear-differential-system-lotka-volterra-predator-prey-equations
158-getting-started-with-sage		158-getting-started-with-sage
16-creating-a-simple-kernel-for-jupyter		16-creating-a-simple-kernel-for-jupyter
21-learning-the-basics-of-the-unix-shell		21-learning-the-basics-of-the-unix-shell
22-using-the-latest-features-of-python-3		22-using-the-latest-features-of-python-3
23-learning-the-basics-of-the-distributed-version-control-system-git		23-learning-the-basics-of-the-distributed-version-control-system-git
24-a-typical-workflow-with-git-branching		24-a-typical-workflow-with-git-branching
25-efficient-interactive-computing-workflows-with-ipython		25-efficient-interactive-computing-workflows-with-ipython
26-ten-tips-for-conducting-reproducible-interactive-computing-experiments		26-ten-tips-for-conducting-reproducible-interactive-computing-experiments
27-writing-high-quality-python-code		27-writing-high-quality-python-code
28-writing-unit-tests-with-pytest		28-writing-unit-tests-with-pytest
29-debugging-code-with-ipython		29-debugging-code-with-ipython
31-teaching-programming-in-the-notebook-with-ipython-blocks		31-teaching-programming-in-the-notebook-with-ipython-blocks
32-converting-a-jupyter-notebook-to-other-formats-with-nbconvert		32-converting-a-jupyter-notebook-to-other-formats-with-nbconvert
33-mastering-widgets-in-the-jupyter-notebook		33-mastering-widgets-in-the-jupyter-notebook
34-creating-custom-jupyter-notebook-widgets-in-python-html-and-javascript		34-creating-custom-jupyter-notebook-widgets-in-python-html-and-javascript
35-configuring-the-jupyter-notebook		35-configuring-the-jupyter-notebook
36-introducing-jupyterlab		36-introducing-jupyterlab
41-evaluating-the-time-taken-by-a-command-in-ipython		41-evaluating-the-time-taken-by-a-command-in-ipython
42-profiling-your-code-easily-with-cprofile-and-ipython		42-profiling-your-code-easily-with-cprofile-and-ipython
43-profiling-your-code-line-by-line-with-line_profiler		43-profiling-your-code-line-by-line-with-line_profiler
44-profiling-the-memory-usage-of-your-code-with-memory_profiler		44-profiling-the-memory-usage-of-your-code-with-memory_profiler
45-understanding-the-internals-of-numpy-to-avoid-unnecessary-array-copying		45-understanding-the-internals-of-numpy-to-avoid-unnecessary-array-copying
46-using-stride-tricks-with-numpy		46-using-stride-tricks-with-numpy
47-implementing-an-efficient-rolling-average-algorithm-with-stride-tricks		47-implementing-an-efficient-rolling-average-algorithm-with-stride-tricks
48-processing-large-numpy-arrays-with-memory-mapping		48-processing-large-numpy-arrays-with-memory-mapping
49-manipulating-large-arrays-with-hdf5		49-manipulating-large-arrays-with-hdf5
51-knowing-python-to-write-faster-code		51-knowing-python-to-write-faster-code
510-interacting-with-asynchronous-parallel-tasks-in-ipython		510-interacting-with-asynchronous-parallel-tasks-in-ipython
511-performing-out-of-core-computations-on-large-arrays-with-dask		511-performing-out-of-core-computations-on-large-arrays-with-dask
512-trying-the-julia-programming-language-in-the-jupyter-notebook		512-trying-the-julia-programming-language-in-the-jupyter-notebook
52-accelerating-pure-python-code-with-numba-and-just-in-time-compilation		52-accelerating-pure-python-code-with-numba-and-just-in-time-compilation
53-accelerating-array-computations-with-numexpr		53-accelerating-array-computations-with-numexpr
54-wrapping-a-c-library-in-python-with-ctypes		54-wrapping-a-c-library-in-python-with-ctypes
55-accelerating-python-code-with-cython		55-accelerating-python-code-with-cython
56-optimizing-cython-code-by-writing-less-python-and-more-c		56-optimizing-cython-code-by-writing-less-python-and-more-c
57-releasing-the-gil-to-take-advantage-of-multi-core-processors-with-cython-and-openmp		57-releasing-the-gil-to-take-advantage-of-multi-core-processors-with-cython-and-openmp
58-writing-massively-parallel-code-for-nvidia-graphics-cards-gpus-with-cuda		58-writing-massively-parallel-code-for-nvidia-graphics-cards-gpus-with-cuda
59-distributing-python-code-across-multiple-cores-with-ipython		59-distributing-python-code-across-multiple-cores-with-ipython
61-using-matplotlib-styles		61-using-matplotlib-styles
62-creating-statistical-plots-easily-with-seaborn		62-creating-statistical-plots-easily-with-seaborn
63-creating-interactive-web-visualizations-with-bokeh-and-holoviews		63-creating-interactive-web-visualizations-with-bokeh-and-holoviews
64-visualizing-a-networkx-graph-in-the-notebook-with-d3js		64-visualizing-a-networkx-graph-in-the-notebook-with-d3js
65-discovering-interactive-visualization-libraries-in-the-notebook		65-discovering-interactive-visualization-libraries-in-the-notebook
66-creating-plots-with-altair-and-the-vega-lite-specification		66-creating-plots-with-altair-and-the-vega-lite-specification
71-exploring-a-dataset-with-pandas-and-matplotlib		71-exploring-a-dataset-with-pandas-and-matplotlib
72-getting-started-with-statistical-hypothesis-testing-a-simple-z-test		72-getting-started-with-statistical-hypothesis-testing-a-simple-z-test
73-getting-started-with-bayesian-methods		73-getting-started-with-bayesian-methods
74-estimating-the-correlation-between-two-variables-with-a-contingency-table-and-a-chi-squared-test		74-estimating-the-correlation-between-two-variables-with-a-contingency-table-and-a-chi-squared-test
75-fitting-a-probability-distribution-to-data-with-the-maximum-likelihood-method		75-fitting-a-probability-distribution-to-data-with-the-maximum-likelihood-method
76-estimating-a-probability-distribution-nonparametrically-with-a-kernel-density-estimation		76-estimating-a-probability-distribution-nonparametrically-with-a-kernel-density-estimation
77-fitting-a-bayesian-model-by-sampling-from-a-posterior-distribution-with-a-markov-chain-monte-carlo-method		77-fitting-a-bayesian-model-by-sampling-from-a-posterior-distribution-with-a-markov-chain-monte-carlo-method
78-analyzing-data-with-the-r-programming-language-in-the-jupyter-notebook		78-analyzing-data-with-the-r-programming-language-in-the-jupyter-notebook
81-getting-started-with-scikit-learn		81-getting-started-with-scikit-learn
82-predicting-who-will-survive-on-the-titanic-with-logistic-regression		82-predicting-who-will-survive-on-the-titanic-with-logistic-regression
83-learning-to-recognize-handwritten-digits-with-a-k-nearest-neighbors-classifier		83-learning-to-recognize-handwritten-digits-with-a-k-nearest-neighbors-classifier
84-learning-from-text-naive-bayes-for-natural-language-processing		84-learning-from-text-naive-bayes-for-natural-language-processing
85-using-support-vector-machines-for-classification-tasks		85-using-support-vector-machines-for-classification-tasks
86-using-a-random-forest-to-select-important-features-for-regression		86-using-a-random-forest-to-select-important-features-for-regression
87-reducing-the-dimensionality-of-a-dataset-with-a-principal-component-analysis		87-reducing-the-dimensionality-of-a-dataset-with-a-principal-component-analysis
88-detecting-hidden-structures-in-a-dataset-with-clustering		88-detecting-hidden-structures-in-a-dataset-with-clustering
91-finding-the-root-of-a-mathematical-function		91-finding-the-root-of-a-mathematical-function
92-minimizing-a-mathematical-function		92-minimizing-a-mathematical-function
93-fitting-a-function-to-data-with-nonlinear-least-squares		93-fitting-a-function-to-data-with-nonlinear-least-squares

ipython-books/ipython-books.github.io

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