If your main goal in using SciPy is to do data exploration and analysis or scientific computations, Jupyter provides an ideal interactive environment. Using Jupyter, we can integrate computations, graphs, formatted text, and even more sophisticated media. Essentially, anything that can be inserted in a web page can be handled by Jupyter.
Running SciPy in Jupyter
by V Kishore Ayyadevara, Ruben Oliva Ramos, L. Felipe Martins
SciPy Recipes
- Preface
- Getting to Know the Tools
- Introduction
- Installing Anaconda on Windows
- Installing Anaconda on macOS
- Installing Anaconda on Linux
- Checking the Anaconda installation
- Installing SciPy from a binary distribution on Windows
- Installing SciPy from a binary distribution on macOS
- Installing SciPy from source on Linux
- Installing optional packages with conda
- Installing packages with pip
- Setting up a virtual environment with conda
- Creating a virtual environment for development with conda
- Creating a conda environment with a different version of a package
- Using conda environments to run different versions of Python
- Creating virtual environments with venv
- Running SciPy in a script
- Running SciPy in Jupyter
- Running SciPy in Spyder
- Running SciPy in PyCharm
- Getting Started with NumPy
- Introduction
- Creating NumPy arrays
- How to do it…
- Creating an array from a list
- Specifying the data type for elements in an array
- Creating an empty array with a given shape
- Creating arrays of zeros and ones with a single value
- Creating arrays with equally spaced values
- Creating an array by repeating elements
- Creating an array by tiling another array
- Creating an array with the same shape as another array
- Using object arrays to store heterogeneous data
- See also
- How to do it…
- Querying and changing the shape of an array
- Storing and retrieving NumPy arrays
- Indexing
- Operations on arrays
- Using masked arrays to represent invalid data
- Using object arrays to store heterogeneous data
- Defining, symbolically, a function operating on arrays
- Using Matplotlib to Create Graphs
- Introduction
- Creating two-dimensional plots of functions and data
- Generating multiple plots in a single figure
- Setting line styles and markers
- Using different backends to display graphs
- Saving plots to disk
- Annotating graphs
- Generating histograms and box plots
- Creating three-dimensional plots
- Generating interactive displays in the Jupyter Notebook
- Object-oriented graph creation using Artist objects
- Creating a map with cartopy
- Data Wrangling with pandas
- Creating Series objects
- Creating DataFrame objects
- Inserting and deleting columns to a DataFrame
- Inserting and deleting rows to a DataFrame
- Selecting items by row indexes and column labels
- Selecting items by integer location
- Selecting items using mixed indexing
- Accessing, selecting, and modifying data
- Selecting rows using Boolean selection
- Reading and storing data in different formats
- Data displays employing different kinds of visual representation
- How to apply numerical functions and operations to Series and DataFrame objects
- Computing statistical functions on Series and DataFrame objects
- How to sort data in Series and DataFrame objects
- Performing merging, joins, concatenation, and grouping
- Matrices and Linear Algebra
- Introduction
- Matrix operations and functions on two-dimensional arrays
- Solving linear systems using matrices
- Calculating the null space of a matrix
- Calculating the LU decompositions of a matrix
- Calculating the QR decomposition of a matrix
- Calculating the eigenvalue and eigenvector of a matrix
- Diagonalizing a matrix
- Calculating the Jordan form of a matrix
- Calculating the singular value decomposition of a matrix
- Creating a sparse matrix
- Computations on top of a sparse matrix
- Solving Equations and Optimization
- Introduction
- Non-linear equations and systems
- System of equations and how to solve it
- Choosing the solver used to find the solution of equations
- Solving constrained non-linear optimization problems in several variables
- Solving one-dimensional optimization problems
- Solving multidimensional non-linear equations using the Newton-Krylov method
- Solving multidimensional non-linear equations using the Anderson method
- Finding the best linear fit for a set of data
- Doing non-linear regression for a set of data
- Regression
- Constants and Special Functions
- Calculus, Interpolation, and Differential Equations
- Introduction
- Integration
- Computing integrals using a Gaussian quadrature
- Computing integrals with weighting functions
- Computing multiple integrals
- Interpolation
- Computing a polynomial interpolation for a set of data points
- Univariate interpolation
- Finding a cubic spline that interpolates a set of data
- Defining a B-spline for a given set of control points
- Differentiation
- Solving a one-dimensional ordinary differential equation
- Solving a system of ordinary differential equations
- Solving differential equations and systems with parameters
- Using ode and the objected-oriented interface to solve differential equations
- Statistics and Probability
- Introduction
- Computing the probability mass function of a discrete random variable
- Computing the probability density function of a continuous random variable
- Computing the cumulative distribution function for a random variable
- Computing the values of inverse probabilities associated with a random variable
- Computing the average, standard deviation, and higher moments of a distribution
- Computing probabilities associated with the multivariate Gaussian distribution
- Computing the summary statistics of a dataset
- Advanced Computations with SciPy
- Discrete Fourier transforms
- Computing the discrete Fourier transform (DFT) of a data series using the FFT algorithm
- Computing the inverse DFT of a data series
- Computing signal construction
- Getting started with filters
- Computing the DFT for two-dimensional data
- How to find the DFT of the derivative of a function
- Computing the convolution of two functions
- Mathematical imaging
- Computing pairwise distances from a dataset, using different distance metrics
- How to identify neighborhoods and nearest neighbors for a dataset and a given metric
- Nearest neighbors regression
Running SciPy in Jupyter
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