PythonDataScienceHandbook | Python Data Science Handbook : full text in Jupyter Notebooks | Machine Learning library

 by   jakevdp Jupyter Notebook Version: Current License: MIT

kandi X-RAY | PythonDataScienceHandbook Summary

kandi X-RAY | PythonDataScienceHandbook Summary

PythonDataScienceHandbook is a Jupyter Notebook library typically used in Artificial Intelligence, Machine Learning, Numpy, Jupyter, Pandas applications. PythonDataScienceHandbook has no bugs, it has no vulnerabilities, it has a Permissive License and it has medium support. You can download it from GitHub.

The book was written and tested with Python 3.5, though other Python versions (including Python 2.7) should work in nearly all cases. The book introduces the core libraries essential for working with data in Python: particularly IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and related packages. Familiarity with Python as a language is assumed; if you need a quick introduction to the language itself, see the free companion project, A Whirlwind Tour of Python: it's a fast-paced introduction to the Python language aimed at researchers and scientists. See Index.ipynb for an index of the notebooks available to accompany the text.

            kandi-support Support

              PythonDataScienceHandbook has a medium active ecosystem.
              It has 38656 star(s) with 16962 fork(s). There are 1770 watchers for this library.
              It had no major release in the last 6 months.
              There are 104 open issues and 75 have been closed. On average issues are closed in 55 days. There are 90 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of PythonDataScienceHandbook is current.

            kandi-Quality Quality

              PythonDataScienceHandbook has 0 bugs and 0 code smells.

            kandi-Security Security

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

            kandi-License License

              PythonDataScienceHandbook is licensed under the MIT License. This license is Permissive.
              Permissive licenses have the least restrictions, and you can use them in most projects.

            kandi-Reuse Reuse

              PythonDataScienceHandbook releases are not available. You will need to build from source code and install.
              Installation instructions are not available. Examples and code snippets are available.
              It has 1401 lines of code, 27 functions and 24 files.
              It has high code complexity. Code complexity directly impacts maintainability of the code.

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            PythonDataScienceHandbook Examples and Code Snippets

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            Community Discussions


            Grouping by Multi-Indices of both Row and Column
            Asked 2022-Jan-30 at 14:34

            I have created a table using Pandas following material from here.

            The table created makes use of Multi-Indices for both columns and rows.

            I am trying to compute the descriptive statistics for each year and subject, meaning, displaying for instance the mean of 2013 for Bob, the mean for 2013 for Guido, and the mean for 2013 for Sue, for all subjects, and for all years. The means for Bob would consider the means for HR and Temp. Note: The types are the same as a coincidence, as this is not the case for the table implemented. Other subjects not included in the screenshot have varying types.

            The closest I have managed to come to the solution is through the following code df.groupby(level = 0, axis = 0).describe() This grouped the data by the year, however, did not group by subject also.



            Answered 2022-Jan-30 at 14:34

            Providing links to external websites is also discouraged as they may change/disappear at any time without SO control

            Having said that, the link provides most of the tools you need to answer your questions. More specifically, a combination of stack and mean should give you what you specifically asked about:



            heatmap simple customized binary legend
            Asked 2020-Sep-03 at 16:31

            I have this dataframe with True and False values with a heatmap plot:



            Answered 2020-Sep-03 at 16:31


            how to create a tidy x-axis of datetime indexes of a data for my plot
            Asked 2020-Aug-05 at 13:28

            I'm plotting a dataframe which its index is of type datetime (like 2018-05-29 08:20:00).

            I slice the data based on last hour and last day and last week and last month and then I plot them.

            The data is collected every one minuet. So, the index of each row differs only one minute.

            When I plot the data for last hour, the x axis is plotted like:

            Or, for the last month it is like:

            which is clean and readable. But, when I plot the last day data the x-axis index is like:

            Why it is overlapped? how to fix it?

            the codes to plot these time frames are the same, just the given dataframe is changed:



            Answered 2020-Aug-05 at 13:28

            from the first answer to How to plot day and month which is also an answer from question owner I found the solution:



            Is it possible to make a contour plot using ALTAIR 4.1 in python?
            Asked 2020-Jun-30 at 22:35

            I was wondering if there is a way to draw contour plots in altair.

            Matplotlib has api for contour plots as demonstrated here

            There isn't a dedicated api for contour in Altair documentation but i guess may be we could use the grammar to create one ?



            Answered 2020-Jun-30 at 22:35

            Altair does not support contour plots, because they are not supported in Vega-Lite. The issue that tracks contour support in Vega-Lite can be found here:



            How Pandas is doing groupby for below scenario
            Asked 2020-Mar-28 at 07:47

            I am facing problem while trying to understand the below code snippet of group by.I am trying to understand how is calculation is happening for df.groupby(L).sum().

            This is a code snippet that i got from the urlenter link description here. Thanks for any help.



            Answered 2020-Mar-28 at 07:47

            Rows are grouping by values of list, because length of list is same like number of rows in DataFrame, it means:



            I'm learning from Python Data Science Handbook and got different graph with same code. What's wrong?
            Asked 2020-Feb-25 at 22:46

            Here is the exact code from the book and output:



            Answered 2020-Feb-25 at 22:46

            Concerning the mirroring, consider that the projection involves an arbitrary choice of basis vectors. I'm in no way a machine-learning expert, so I cannot go into detail about the exact working of the algorithm. But if you run the same code several times, you may get all possible orientations, e.g. for 25 runs:



            Pandas time series-specific operations in Altair
            Asked 2020-Feb-22 at 20:39

            Is it possible to perform groupby operations for datetime object in Altair using transform_aggregate function? I am trying to replicate some of the time series plot from "Example: Visualizing Seattle Bicycle Counts" example of Jake VDP's book -

            Does transform_aggregate allow time-series specific operations like resample?



            Answered 2020-Feb-22 at 20:39

            Altair has built-in time groupings using the TimeUnit transform, which can be used either via an explicit transform, or via encoding shorthands.

            Here is an example of reproducing one of the charts from that section of the book – note that the Vega-Lite renderer becomes slow when data grows to tens of thousands of entries, so I use altair_data_server to serve the data and limit the chart to the first year:



            Run a Jupyter notebook directly online (without downloading it locally)
            Asked 2020-Feb-11 at 16:48

            When finding an interesting Python Jupyter Notebook, such as 02.00-Introduction-to-NumPy.ipynb, I usally have to:

            • download it locally
            • open a shell in the same folder (tip: use SHIFT+RIGHT CLICK+ Open command window here to save 30 second browsing in the different folders) and do jupyter notebook
            • select the right .ipynb file, and finally run the code

            Isn't there an easier way to do this?

            What is the natural way to open a .ipynb notebook which is online, and run the code, without having to manually download the .ipynb?

            Note: the notebook is visible here: but we can't run the code



            Answered 2020-Feb-11 at 16:48

            @jakevdp builds in a nice way to do that, see here. In short, on each page he has an Open in Google Colab button:

            @GoogleColab can open any @ProjectJupyter notebook directly from @github!
            To run the notebook, just replace " " with " " in the notebook URL, and it will be loaded into Colab.

            Example: 02.00-Introduction-to-NumPy.ipynb becomes:

            By default, code will run on Colab's distant server, but it's also possible to run it locally, by clicking on top right's Connect to local runtime...:

            I personally prefer the MyBinder project as a route. It will open temporary, active sessions with the contents of any Github repo, Github Gists, Gitlab repo, Zenodo archive, Dataverse repo, Datashare archive, Figshare archive, and others. Many repositories already include the necessary configuration files and even put a launch binder button them. Some don't but you can go to the form at MyBinder project and generate a session. That form will also generate a URL that you can use to target the public MyBinder system to open a session alter For example, this person posted the link to open a session for all of Jakes notebooks, you just got to the URL to tell MyBinder to start a session. Then from the index page that comes up you can click on the link you listed above and run it. Jake included configuration files that MyBinder also recognizes. Note, for some repositories or archives you'll point MyBinder at, it won't have the necessary configuration files and so you can run %pip install or !conda install in the current session and continue on running code. Limitations include that you have to be concerned with not sharing anything you wouldn't mind be public, limited resources, and FTP is not allowed to avoid abuse.

            Some others to get you started:



            Can we plot image data in Altair?
            Asked 2020-Feb-02 at 01:08

            I am trying to plot image data in altair, specifically trying to replicate face recognition example in this link from Jake VDP's book -

            Any one had luck plotting image data in altair?



            Answered 2020-Feb-02 at 01:08

            Altair features an image mark that can be used if you want to plot images that are available at a URL; for example:



            Interpretation of method plt.fill_between()? Discussion
            Asked 2020-Jan-20 at 23:26

            A question for discussion:

            The matplotlib documentation says that the method plt.fill_between is used to "fill the area between two horizontal curves".

            What exactly is meant by "horizontal"? Intuitively, I would say "two parallel curves". Like in this example

            The curves are not horizontal, but parallel.



            Answered 2020-Jan-19 at 17:07

            "Two horizontal curves" is a set of data where you have two arrays y1 and y2 defined on a single support x.
            Equally, "two vertical curves" would be a set of data where you have a single y support for two x arrays.


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