data-science-ipython-notebooks | Data science Python notebooks : Deep learning | Machine Learning library

by LqNoob Python Version: Current License: Non-SPDX

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kandi X-RAY | data-science-ipython-notebooks Summary

data-science-ipython-notebooks is a Python library typically used in Institutions, Learning, Education, Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow, Numpy, Pandas, Hadoop applications. data-science-ipython-notebooks has no bugs, it has no vulnerabilities and it has low support. However data-science-ipython-notebooks build file is not available and it has a Non-SPDX License. You can download it from GitHub.

Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.

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data-science-ipython-notebooks has a low active ecosystem.

It has 7 star(s) with 4 fork(s). There are 2 watchers for this library.

It had no major release in the last 6 months.

data-science-ipython-notebooks has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of data-science-ipython-notebooks is current.

Quality

data-science-ipython-notebooks has no bugs reported.

Security

data-science-ipython-notebooks has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

data-science-ipython-notebooks has a Non-SPDX License.

Non-SPDX licenses can be open source with a non SPDX compliant license, or non open source licenses, and you need to review them closely before use.

Reuse

data-science-ipython-notebooks releases are not available. You will need to build from source code and install.

data-science-ipython-notebooks has no build file. You will be need to create the build yourself to build the component from source.

Installation instructions are not available. Examples and code snippets are available.

Top functions reviewed by kandi - BETA

kandi has reviewed data-science-ipython-notebooks and discovered the below as its top functions. This is intended to give you an instant insight into data-science-ipython-notebooks implemented functionality, and help decide if they suit your requirements.

Plots a polynomial regression
Fit the model
Compute the decision surface
Called when an event has changed
Contour plot
Override options
Summarize the evidence
Calculates Cohen Effect Size
Aggregate log entries
Parses a date time zone and returns a datetime object
Prints the extremes
Compute a conditional conditional distribution
Makes a NormalPdf
Least squares
Make the difference between two histograms
Logs probabilities
Updates the set
Make a PMF
Make the gist
Reads and returns data sets
Plot the separator
Plot a linear regression
Plot a 2D mesh
Perform a single step
Plot the K nearest neighbors
Compute rows of ys_seq

Get all kandi verified functions for this library.

data-science-ipython-notebooks Key Features

No Key Features are available at this moment for data-science-ipython-notebooks.

data-science-ipython-notebooks Examples and Code Snippets

No Code Snippets are available at this moment for data-science-ipython-notebooks.

Community Discussions

Trending Discussions on data-science-ipython-notebooks

Convert Firefox bookmarks JSON file to markdown

Having trouble with multiple figures on pyplot

QUESTION

Convert Firefox bookmarks JSON file to markdown

Asked 2020-Dec-08 at 16:10

Background

I want to show part of my bookmarks on my Hugo website. The bookmarks from Firefox can be saved in JSON format, this is the source. The result should represent the nested structure somehow, in a format of a nested list, treeview or accordion. The source files of contents on the website are written in markdown. I want to generate a markdown file from the JSON input.

As I searched for possible solutions:

treeview or accordion: HTML, CSS and Javascript needed. I could not nest accordions with the tag. Also, seems like overkill at the moment.
unordered list: can be done with bare markdown.

I chose to generate an unordered nested list from JSON. I would like to do this with R.

Input/output

Input sample: https://gist.github.com/hermanp/c01365b8f4931ea7ff9d1aee1cbbc391

Preferred output (indentation with two spaces):

...

ANSWER

Answered 2020-Dec-08 at 16:10

After I watched a few videos on recursion and saw a few code examples, I tried, manually stepped through the code and somehow managed to do it with recursion. This solution is independent on the nestedness of the bookmarks, therefore a generalized solution for everyone.

Note: all the bookmarks were in the Bookmarks Toolbar in Firefox. This is highlighted in the generate_md function. You can tackle with it there. If I improve the answer later, I will make it more general.

Source https://stackoverflow.com/questions/65111588

QUESTION

Having trouble with multiple figures on pyplot

Asked 2017-May-01 at 09:32

I am currently going through the Kaggle Titanic Machine Learning thing and using http://nbviewer.jupyter.org/github/donnemartin/data-science-ipython-notebooks/blob/master/kaggle/titanic.ipynb to figure it out as I am a relative beginner to Python. I thought I understood what the first few steps were doing and I am trying to recreate an earlier step by making a figure with multiple plots on it. I can't seem to get the plots to actually show up.

Here is my code:

...

ANSWER

Answered 2017-May-01 at 09:32

In order to plot the pandas plot to apreviously created subplot, you may use the ax argument of the pandas plotting function.

Source https://stackoverflow.com/questions/43712153

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

Vulnerabilities

No vulnerabilities reported

Install data-science-ipython-notebooks

You can download it from GitHub.
You can use data-science-ipython-notebooks 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.