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Python for Data Science 1.0.0
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Python for Data Science 1.0.0
  • Introduction
  • Workspace
    • IPython
      • Start the IPython shell
      • IPython examples
      • IPython magic
      • Shell commands in IPython
      • Unix shell
        • Navigate through files and directories
        • Create, update and delete files and directories
        • Pipes and filters
        • grep and find
        • Shell variables
      • Show objects with display
      • foo.ipynb
      • Import notebooks
      • IPython extensions
      • Debugging
    • Jupyter
    • NumPy
      • Introduction to NumPy
      • ndarray – an N-dimensional array object
      • dtype
      • Arithmetic
      • Indexing and slicing
      • Transpose arrays and swap axes
      • Universal functions (ufunc)
      • Array-oriented programming – vectorisation
      • Conditional logic as array operations – where
      • Mathematical and statistical methods
      • Methods for Boolean arrays
      • Sort
      • unique and other set logic
      • File input and output with arrays
    • pandas
      • Introduction to the data structures of pandas
      • Converting Python data structures into pandas
      • Indexing
      • Date and Time
      • Select and filter data
      • Add, change and delete data
      • Manipulation of strings
      • Arithmetic
      • Descriptive statistics
      • Sorting and ranking
      • Subdividing and categorising data
      • Combining and merging data sets
      • Group operations
      • Aggregation
      • Apply
      • Pivot tables and crosstabs
      • Convert dtype
  • Read, persist and provide data
    • Open data
    • pandas IO tools
    • Serialisation formats
      • Data serialisation
      • CSV
        • CSV example
      • JSON
        • JSON example
      • Excel
      • XML/HTML
        • XML/HTML examples
        • BeautifulSoup
      • YAML
        • Example
      • TOML
        • Example
      • Pickle
        • Pickle examples
      • Protocol Buffers (Protobuf)
      • Other Formats
    • Intake
      • Install Intake
      • Intake for data scientists
      • Intake-GUI: Exploring data in a graphical user interface
      • Intake for data engineers
    • Requests
      • Requests installation and sample application
      • Create module
    • Overview
    • Geodata
    • PostgreSQL
      • Foreign Data Wrappers (FDW)
      • Procedural programming languages
      • DB-API 2.0
      • Psycopg
      • Object-relational mapping
      • SQLAlchemy
      • Alembic
      • ipython-sql
      • PostGIS
        • Install PostGIS
        • Optimising PostgreSQL for GIS database objects
        • Loading geospatial data
      • Database security
      • PostgreSQL performance
      • pgMonitor
      • pganalyze
    • NoSQL databases
      • Key-value database systems
      • Column-oriented database systems
      • Document-oriented database systems
      • Graph database systems
      • Object database systems
      • XML database systems
    • Application Programming Interface (API)
      • FastAPI
        • Installation
        • Example
        • Extensions
      • gRPC
        • gRPC-Example
        • Test gRPC
    • Glossary
  • Data cleansing and validation
    • Managing missing data with pandas
    • Detecting and filtering outliers
    • String comparisons
    • Deduplicating data
    • pandas DataFrame Validation with Bulwark
    • Hypothesis: Property-based testing
    • TDDA: Test-Driven Data Analysis
    • Data validation with Voluptuous (schema definitions)
    • Normalisation and Preprocessing
    • Assigning satellite data to geo-locations
  • Visualise data
  • Performance
    • iPython Profiler
    • scalene
    • perflint
    • Parallelise pandas
    • Dask
    • Introduction to multithreading, multiprocessing and async
    • Threading example
    • Multi-processing example
    • Threading and forking combined
    • asyncio example
  • Create a product
    • Manage code with Git
      • Workspaces
      • Git installation and configuration
      • Working with Git
      • Git log
      • Git tags
      • Git branches
      • Undo changes
      • Git rebase
      • Git best practices
      • Git workflows
        • Git Flow
        • Feature branch workflows
        • Deployment and release branches
        • Trunk Based Development
        • Monorepos and large repositories
        • Splitting repos
        • CI-friendly Git Repos
      • Advanced Git
        • Git cherry-pick
        • Find regressions with git bisect
        • Git hooks
          • pre-commit framework
          • pre-commit scripts
          • Other pre-commit hooks
          • pre-commit in CI pipelines
          • Skip hooks
          • Template for Git repositories
        • Jupyter Notebooks with Git
        • Git for binary files
        • Visual Studio Code
          • GitLab VS Code Extension
        • GitLab
          • Roles, groups and permissions
          • Merge requests
          • GitLab CI/CD
          • Migrating GitHub Actions
          • GitLab Package Registry
        • git-big-picture
        • etckeeper
      • Git glossary
    • Manage data with DVC
      • Create a project
      • Pipelines
      • Parameterisation
      • Trial metrics
      • View pipelines
      • Reproduce
      • Vim and IDE integration
      • FastDS
    • Reproduce environments
      • Spack
        • Spack installation
        • Combinatorial builds
        • Benefits of the build automation
        • Use case 1: managing combinatorial installations
        • Use case 2: Python and other interpreted languages
        • Future features
        • Use spack
        • Environments, spack.yaml and spack.lock
        • Spack mirrors
      • Pipenv
        • Installation
        • Usage
        • Deterministic builds
        • Workflows
        • Environment variables
        • Pipenv and Spack
    • Creating programme libraries and packages
    • Document
    • Licensing
    • Citing
      • Cite data
      • Cite software
      • Software journals
    • Testing
    • Logging
      • Logging examples
    • Check and improve code quality and complexity
      • flake8
      • check-manifest
      • Mypy
      • Pytype
      • Wily
      • PyRe
      • Pysa
      • Black
      • isort
      • prettier
      • Code-Smells and Anti-Patterns
      • Rope
    • Security
  • Create web applications
  • Index
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Workspace#

Setting up the workspace includes installing and configuring IPython and Jupyter with nbextensions and ipywidgets, and NumPy.

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IPython
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Introduction
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