Thinking-in-Python

 by   KeshawnVan Python Version: Current License: No License

X-RayKey FeaturesCode SnippetsCommunity Discussions(1)VulnerabilitiesInstallSupport

kandi X-RAY | Thinking-in-Python Summary

kandi X-RAY | Thinking-in-Python Summary

Thinking-in-Python is a Python library. Thinking-in-Python has no bugs, it has no vulnerabilities and it has low support. However Thinking-in-Python build file is not available. You can download it from GitHub.

Thinking-in-Python
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            kandi-support Support

              Thinking-in-Python has a low active ecosystem.
              It has 4 star(s) with 0 fork(s). There are no watchers for this library.
              OutlinedDot
              It had no major release in the last 6 months.
              Thinking-in-Python has no issues reported. There are no pull requests.
              It has a neutral sentiment in the developer community.
              The latest version of Thinking-in-Python is current.

            kandi-Quality Quality

              Thinking-in-Python has 0 bugs and 0 code smells.

            kandi-Security Security

              Thinking-in-Python has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              Thinking-in-Python code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              Thinking-in-Python does not have a standard license declared.
              Check the repository for any license declaration and review the terms closely.
              OutlinedDot
              Without a license, all rights are reserved, and you cannot use the library in your applications.

            kandi-Reuse Reuse

              Thinking-in-Python releases are not available. You will need to build from source code and install.
              Thinking-in-Python has no build file. You will be need to create the build yourself to build the component from source.
              It has 1005 lines of code, 91 functions and 58 files.
              It has medium code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed Thinking-in-Python and discovered the below as its top functions. This is intended to give you an instant insight into Thinking-in-Python implemented functionality, and help decide if they suit your requirements.
            • Monitor the database
            • Find the number of failure messages
            • Handles send message
            • Send a failure message
            • Send a message
            • Get access token
            • Generator for primes
            • Returns a function that returns a function that is not the not - divisible
            • Measure the execution of a function
            • Get current time
            • Return a private greeting
            • Print the score
            • Run a single episode
            • Fast function
            • Low - level function
            • Get access token
            • Returns the device s sex
            • Sends an email
            • Calculates the sum of a number of arguments
            • Calculates the sum of the arguments
            • Generate Fibonacci Fibonacci
            • Create a new counter function
            • Test the user
            • Finds the minimum and maximum item from a list
            • Create a counter function
            • Get a character from a color
            • Send a message
            Get all kandi verified functions for this library.

            Thinking-in-Python Key Features

            No Key Features are available at this moment for Thinking-in-Python.

            Thinking-in-Python Examples and Code Snippets

            No Code Snippets are available at this moment for Thinking-in-Python.

            Community Discussions

            Trending Discussions on Thinking-in-Python

            Why to initialize an array in Numpy

            QUESTION

            Why to initialize an array in Numpy
            Asked 2020-Aug-16 at 16:20

            I'm doing a DataCamp course on statistical thinking in Python. At one point in the course, the instructor advises initializing an empty array before filling it with random floats, e.g.

            ...

            ANSWER

            Answered 2020-Aug-16 at 16:16

            If all the code they provided is just like above, your way of initializing is better.

            Their code might lead to something else later on

            Source https://stackoverflow.com/questions/63439095

            Community Discussions, Code Snippets contain sources that include Stack Exchange Network

            Vulnerabilities

            No vulnerabilities reported

            Install Thinking-in-Python

            You can download it from GitHub.
            You can use Thinking-in-Python like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

            Support

            For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .
            Find more information at:

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          • CLI

            gh repo clone KeshawnVan/Thinking-in-Python

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            git@github.com:KeshawnVan/Thinking-in-Python.git

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