Entire books or other large collections of notebooks on a topic, Statistics, Machine Learning and Data Science, Scientific computing and data analysis with the SciPy Stack, Machine Learning, Statistics and Probability, Miscellaneous topics about doing various things, Accessing an IBM quantum computer via notebooks, Lectures on Scientific Computing with Python, Growing Neat Software Architecture from Jupyter Notebooks, Introduction to Programming (using Python), CS1001.py - Extended Introduction to Computer Science, Understanding evolutionary strategies and covariance matrix adaptation, Advanced Evolutionary Computation: Theory and Practice, Jupyter notebook activities for Part IA of the computing course (Michaelmas Term) in the Engineering Tripos at University of Cambridge, Introduction to Python for Computational Science and Engineering, Predicting PewDiePie's daily subscribers using Linear Regression, Top Python Libraries Used In Data Science, Web scraping using Python with BeautifulSoup and Requests libraries, An introductory notebook on uncertainty quantification and sensitivity analysis, Workshop On Uncertainty Quantification And Sensitivity Analysis For You can also turn your NB into a slideshow. Github repository | Main notebook. Raw cells − The text written in them is displayed as it is. The power of using Jupyter Notebooks should also be evident, and we covered plenty of leads to get you started exploring more advanced features in your own projects. 5. St. Louis County Segregation Analysis , analysis for the article The Ferguson Area Is Even More Segregated Than You Probably Guessed by Jeremy Singer-Vine. Brown et al.. Full notebook, ArXiv link and project repository. Motivating Examples — Jupyter Notebook 6.1.4 documentation The Markdown parser included in the Jupyter Notebook is MathJax-aware. Signal: Filtering, STFT, and Laplace Transform Filtering signal with a butterworth low-pass filter and plotting the STFT of it with a Hanning window and then plotting the Laplace transform. These cells are basically used to add the text that we do not wish to be converted by the automatic conversion mechanism of jupyter notebook. Top Python Libraries Used In Data Science, this notebook explain the important library used in data science, by Tanu Nanda Prabhu, author and editor at Towards data science.
A complete collection of notebooks accompanying Matthew Russell and Mikhail Klassen's book by O'Reilly. Although it is possible to use many different programming languages in Jupyter Notebooks, this article will focus on Python, as it is the most common use case. Having grown to render hundreds of thousands of notebooks every week by 2015, NBViewer is the most popular notebook renderer on the web. IRkernel, an R kernel for Jupyter, allows you to write and execute R code in a Jupyter notebook. We can understand these data structures as the higher dimensional data structure is the container of lower dimensional data structure.

Paper is derived from a notebook converted to LaTeX with nbconvert. Cardiovascular Modeling, Python Data Science Handbook Supplemental Materials, Data Cleaning using Python with Pandas Library, Social Network Analysis: From Graph Theory to Applications with Python, "ISP": Introduction to Statistics with Python, Notebooks for the exercises in Andrew Ng's online ML course, Spark and TensorFlow, AM207: Monte Carlo Methods, Stochastic Optimization, Probabilistic Programming and Bayesian Methods for Hackers Using Python and PyMC, An introduction to machine learning with Python and scikit-learn, A progressive collection notebooks of the Machine Learning course by the University of Turin (with exercises), The Kaggle bulldozers competition example, course on statistics and data analysis for psychologists, Graphical Representations of Linear Models, Seaborn statistical visualization library, Representing variability in timeseries plots, 'An Introduction to Statistical Learning with Applications in R', Python Notebooks for StatLearning Exercises, four courses in foundations of data science, algorithms and databases, SciPy and OpenCV as an interactive computing environment for computer vision, collection in Data Analysis and Machine Learning, Classification on raw time series in TensorFlow with a LSTM RNN, Signal forecasting with a Sequence-to-Sequence (seq2seq) Recurrent Neural Network (RNN) model in TensorFlow, lectures on quantum mechanics and quantum optics using QuTiP, Scientific Computing in Biomechanics and Motor Control, cheminformatics and machine learning with the rdkit project, AeroPython: Aerodynamics-Hydrodynamics with Python, pyuvvis: tools for explorative spectroscopy, HyperPython: a practical introduction to the solution of hyperbolic conservation laws, An Introduction to Applied Bioinformatics, dedicated collection of IPython Notebooks, Learning Population Genetics in an RNA world, An open RNA-Seq data analysis pipeline tutorial with an example of reprocessing data from a recent Zika virus study, Lung Cancer Post-Translational Modification and Gene Expression Regulation, Materials Science in Python using pymatgen, Find graffiti close to NY subway entrances, collection of notebooks on large-scale data analysis, Logistic models of well switching in Bangladesh, Estimated likelihood of observing a large earthquake on a continental low‐angle normal fault and implications for low‐angle normal fault activity, resource-demanding numerical computations with functions in compiled languages, visualization of various geo data using fancy things like interactive maps, Department of Geosciences and Geography at University of Helsinki, Finland, Detecting Algorithmically Generated Domains, Matthew Russell and Mikhail Klassen's book, Sound Analysis with the Fourier Transform. Too many there to directly duplicate here, but they provide great learning materials on statistical modeling with Python. The “In” part of the label is simply short for “Input,” while the label number indicates when the cell was executed on the kernel — in this case the cell was executed first. Extensions are precisely what they sound like — additional features that extend Jupyter Notebooks's functionality. We can export the Notebook as an HTML, PDF, .py, .ipynb, Markdown, and reST file. Another one, called Variable Inspector, will show you the value, type, size, and shape of every variable in your notebook for easy quick reference and debugging. Brown et al. Personal IPython Weight Notebook. With Jupyter Notebook open in your browser, you may have noticed that the URL for the dashboard is something like http://localhost:8888/tree. If you’d like further inspiration for your own Notebooks, Jupyter has put together a gallery of interesting Jupyter Notebooks that you may find helpful and the Nbviewer homepage links to some really fancy examples of quality notebooks. It’s finally time to get started with that Fortune 500 data set mentioned earlier. We've already covered everything you need to get rolling in Jupyter Notebooks. The themes can be used to change not only background colour but also the style of the text.

By Matthias Bussonnier. Below are a few such: Video on how to learn Python featuring IPython as the platform of choice for learning! Applied Predictive Modeling with Python, Python implementations of the examples (originally written in R) from a famous introductory book, Applied Predictive Modeling, by Max Kuhn and Kjell Johnson. Exploratory Computing with Python, a set of 15 Notebooks that cover exploratory computing, data analysis, and visualization. Website and notebook. This repo also links to the related useful library, neurodsp, which contains notebooks of tutorials. Remember, our goal is to find out how the profits of the largest companies in the US changed historically.
No surprises here! When you “Save and Checkpoint,” both the notebook and checkpoint files are updated. For our purposes, let’s say this is acceptable and go ahead and remove these rows.

Other notebooks examine international markets: especially, gold and foreign exchange. Plotly is now integrated with Mapbox. It is basically a two-dimensional data structure which can contain heterogeneous data. Loading and visualizing fMRI data, part of the Functional connectivity with NiLearn course by Gaël Varoquaux. Clustering of smartphone sensor data for human activity detection using pandas and scipy, part of Coursera data analysis course, done in Python (repo). Size-mutable array, container of DataFrame. You can always update your selection by clicking Cookie Preferences at the bottom of the page. The Notebook has the advantage of looking the same when you're coding and publishing. If you are using Anaconda distribution, then you need not install jupyter notebook separately as it is already installed with it. Pandas .head() to .tail(), an in-depth tutorial on Pandas by Tom Augspurger. Machinalis has a public repo with material support for geospatial-data processing related blog posts. An introduction to Predictive Modeling in Python, by Olivier Grisel. Meteorol.

Learn Data Science, an entire self-directed course by Nitin Borwankar. 2-d rigid-body transformations.