Metadata-Version: 2.1
Name: dtale
Version: 1.7.12
Summary: Web Client for Visualizing Pandas Objects
Home-page: https://github.com/man-group/dtale
Author: MAN Alpha Technology
Author-email: ManAlphaTech@man.com
License: LGPL
Description: |image0|
        
        `Live Demo <http://andrewschonfeld.pythonanywhere.com>`__
        
        --------------
        
        |CircleCI| |PyPI| |ReadTheDocs| |codecov| |Downloads|
        
        What is it?
        -----------
        
        D-Tale is the combination of a Flask back-end and a React front-end to
        bring you an easy way to view & analyze Pandas data structures. It
        integrates seamlessly with ipython notebooks & python/ipython terminals.
        Currently this tool supports such Pandas objects as DataFrame, Series,
        MultiIndex, DatetimeIndex & RangeIndex.
        
        Origins
        -------
        
        D-Tale was the product of a SAS to Python conversion. What was
        originally a perl script wrapper on top of SAS’s ``insight`` function is
        now a lightweight web client on top of Pandas data structures.
        
        In The News
        -----------
        
        -  `Man Institute <https://www.man.com/maninstitute/d-tale>`__ (warning:
           contains deprecated functionality)
        -  `Python
           Bytes <https://pythonbytes.fm/episodes/show/169/jupyter-notebooks-natively-on-your-ipad>`__
        -  `Pip Install Python YouTube
           Channel <https://m.youtube.com/watch?v=0RihZNdQc7k&feature=youtu.be>`__
        
        Contents
        --------
        
        -  `Getting Started <#getting-started>`__
        
           -  `Python Terminal <#python-terminal>`__
           -  `Jupyter Notebook <#jupyter-notebook>`__
           -  `Google Colab & Kaggle <#google-colab--kaggle>`__
           -  `R with Reticulate <#r-with-reticulate>`__
           -  `Command-line <#command-line>`__
        
        -  `UI <#ui>`__
        
           -  `Dimensions/Main Menu <#dimensionsmain-menu>`__
           -  `Selecting/Deselecting Columns <#selectingdeselecting-columns>`__
           -  `Main Menu Functions <#main-menu-functions>`__
        
              -  `Describe <#describe>`__, `Filter <#filter>`__,
                 `Charts <#charts>`__, `Correlations <#correlations>`__, `Heat
                 Map <#heat-map>`__, `Instances <#instances>`__, `Code
                 Exports <#code-exports>`__, `About <#about>`__,
                 `Resize <#resize>`__, `Shutdown <#shutdown>`__
        
           -  `Column Menu Functions <#column-menu-functions>`__
        
              -  `Moving Columns <#moving-columns>`__, `Hiding
                 Columns <#hiding-columns>`__, `Building
                 Columns <#building-columns>`__, `Lock <#lock>`__,
                 `Unlock <#unlock>`__, `Sorting <#sorting>`__,
                 `Formats <#formats>`__, `Column Analysis <#column-analysis>`__
        
           -  `Menu Functions within a Jupyter
              Notebook <#menu-functions-within-a-jupyter-notebook>`__
        
        -  `For Developers <#for-developers>`__
        
           -  `Cloning <#cloning>`__
           -  `Running Tests <#running-tests>`__
           -  `Linting <#linting>`__
           -  `Formatting JS <#formatting-js>`__
           -  `Docker Development <#docker-development>`__
        
        -  `Startup Behavior <#startup-behavior>`__
        -  `Documentation <#documentation>`__
        -  `Requirements <#requirements>`__
        -  `Acknowledgements <#acknowledgements>`__
        -  `License <#license>`__
        
        Getting Started
        ---------------
        
        ======== ========
        PyCharm  jupyter
        ======== ========
        |image6| |image7|
        ======== ========
        
        Installing the egg
        
        .. code:: bash
        
           # install dtale egg (important to use the "--upgrade" every time you install so it will grab the latest version)
           $ pip install --upgrade dtale
        
        Now you will have the ability to use D-Tale from the command-line or
        within a python-enabled terminal
        
        Python Terminal
        ~~~~~~~~~~~~~~~
        
        This comes courtesy of PyCharm |image8| Feel free to invoke ``python``
        or ``ipython`` directly and use the commands in the screenshot above and
        it should work
        
        Issues With Windows Firewall
        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
        
        If you run into issues with viewing D-Tale in your browser on Windows
        please try making Python public under “Allowed Apps” in your Firewall
        configuration. Here is a nice article: `How to Allow Apps to Communicate
        Through the Windows
        Firewall <https://www.howtogeek.com/howto/uncategorized/how-to-create-exceptions-in-windows-vista-firewall/>`__
        
        Additional functions available programatically
        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
        
        .. code:: python
        
           import dtale
           import pandas as pd
        
           df = pd.DataFrame([dict(a=1,b=2,c=3)])
        
           # Assigning a reference to a running D-Tale process
           d = dtale.show(df)
        
           # Accessing data associated with D-Tale process
           tmp = d.data.copy()
           tmp['d'] = 4
        
           # Altering data associated with D-Tale process
           # FYI: this will clear any front-end settings you have at the time for this process (filter, sorts, formatting)
           d.data = tmp
        
           # Shutting down D-Tale process
           d.kill()
        
           # using Python's `webbrowser` package it will try and open your server's default browser to this process
           d.open_browser()
        
           # There is also some helpful metadata about the process
           d._data_id  # the process's data identifier
           d._url  # the url to access the process
        
           d2 = dtale.get_instance(d._data_id)  # returns a new reference to the instance running at that data_id
        
           dtale.instances()  # prints a list of all ids & urls of running D-Tale sessions
        
        Duplicate data check
        ^^^^^^^^^^^^^^^^^^^^
        
        To help guard against users loading the same data to D-Tale multiple
        times and thus eating up precious memory, we have a loose check for
        duplicate input data. The check runs the following: \* Are row & column
        count the same as a previously loaded piece of data? \* Are the names
        and order of columns the same as a previously loaded piece of data?
        
        If both these conditions are true then you will be presented with an
        error and a link to the previously loaded data. Here is an example of
        how the interaction looks: |image9|
        
        Jupyter Notebook
        ~~~~~~~~~~~~~~~~
        
        Within any jupyter (ipython) notebook executing a cell like this will
        display a small instance of D-Tale in the output cell. Here are some
        examples:
        
        ============== ========== =========
        ``dtale.show`` assignment instance
        ============== ========== =========
        |image10|      |image11|  |image12|
        ============== ========== =========
        
        If you are running ipython<=5.0 then you also have the ability to adjust
        the size of your output cell for the most recent instance displayed:
        
        |image13|
        
        One thing of note is that a lot of the modal popups you see in the
        standard browser version will now open separate browser windows for
        spacial convienence:
        
        ============ ============ ========= =============== =========
        Column Menus Correlations Describe  Column Analysis Instances
        ============ ============ ========= =============== =========
        |image14|    |image15|    |image16| |image17|       |image18|
        ============ ============ ========= =============== =========
        
        Google Colab & Kaggle
        ~~~~~~~~~~~~~~~~~~~~~
        
        These are hosted notebook sites and thanks to the work of
        `flask_ngrok <https://github.com/gstaff/flask-ngrok>`__ users can run
        D-Tale within their notebooks.
        
        **DISCLAIMER:** It is import that you set ``USE_NGROK`` to true when
        using D-Tale within these two services. Here is an example:
        
        ::
        
           import pandas as pd
        
           import dtale
           import dtale.app as dtale_app
        
           dtale_app.USE_NGROK = True
        
           dtale.show(pd.DataFrame([1,2,3]))
        
        Here are some video tutorials of each:
        
        +-----------------------+-----------------------+-----------------------+
        | Service               | Tutorial              | Addtl Notes           |
        +=======================+=======================+=======================+
        | Google Colab          | |image19|             |                       |
        +-----------------------+-----------------------+-----------------------+
        | Kaggle                | |image20|             | make sure you switch  |
        |                       |                       | the “Internet” toggle |
        |                       |                       | to “On” under         |
        |                       |                       | settings of your      |
        |                       |                       | notebook so you can   |
        |                       |                       | install the egg from  |
        |                       |                       | pip                   |
        +-----------------------+-----------------------+-----------------------+
        
        R with Reticulate
        ~~~~~~~~~~~~~~~~~
        
        I was able to get D-Tale running in R using reticulate. Here is an
        example:
        
        ::
        
           library('reticulate')
           dtale <- import('dtale')
           df <- read.csv('https://vincentarelbundock.github.io/Rdatasets/csv/boot/acme.csv')
           dtale$show(df, subprocess=FALSE, open_browser=TRUE)
        
        Now the problem with doing this is that D-Tale is not running as a
        subprocess so it will block your R console and you’ll lose out the
        following functions: - manipulating the state of your data from your R
        console - adding more data to D-Tale
        
        ``open_browser=TRUE`` isn’t required and won’t work if you don’t have a
        default browser installed on your machine. If you don’t use that
        parameter simply copy & paste the URL that gets printed to your console
        in the browser of your choice.
        
        I’m going to do some more digging on why R doesn’t seem to like using
        python subprocesses (not sure if it something with how reticulate
        manages the state of python) and post any findings to this thread.
        
        Here’s some helpful links for getting setup:
        
        reticulate
        
        installing python packages
        
        Command-line
        ~~~~~~~~~~~~
        
        Base CLI options (run ``dtale --help`` to see all options available)
        
        +-----------------------------+----------------------------------------+
        | Prop                        | Description                            |
        +=============================+========================================+
        | ``--host``                  | the name of the host you would like to |
        |                             | use (most likely not needed since      |
        |                             | ``socket.gethostname()`` should figure |
        |                             | this out)                              |
        +-----------------------------+----------------------------------------+
        | ``--port``                  | the port you would like to assign to   |
        |                             | your D-Tale instance                   |
        +-----------------------------+----------------------------------------+
        | ``--name``                  | an optional name you can assign to     |
        |                             | your D-Tale instance (this will be     |
        |                             | displayed in the ``<title>`` &         |
        |                             | Instances popup)                       |
        +-----------------------------+----------------------------------------+
        | ``--debug``                 | turn on Flask’s “debug” mode for your  |
        |                             | D-Tale instance                        |
        +-----------------------------+----------------------------------------+
        | ``--no-reaper``             | flag to turn off auto-reaping          |
        |                             | subprocess (kill D-Tale instances      |
        |                             | after an hour of inactivity), good for |
        |                             | long-running displays                  |
        +-----------------------------+----------------------------------------+
        | ``--open-browser``          | flag to automatically open up your     |
        |                             | server’s default browser to your       |
        |                             | D-Tale instance                        |
        +-----------------------------+----------------------------------------+
        | ``--force``                 | flag to force D-Tale to try an kill    |
        |                             | any pre-existing process at the port   |
        |                             | you’ve specified so it can use it      |
        +-----------------------------+----------------------------------------+
        
        Loading data from `arctic\ (high performance datastore for pandas
        dataframes) <https://github.com/man-group/arctic>`__
        
        .. code:: bash
        
           dtale --arctic-host mongodb://localhost:27027 --arctic-library jdoe.my_lib --arctic-node my_node --arctic-start 20130101 --arctic-end 20161231
        
        Loading data from **CSV**
        
        .. code:: bash
        
           dtale --csv-path /home/jdoe/my_csv.csv --csv-parse_dates date
        
        Loading data from **JSON**
        
        .. code:: bash
        
           dtale --json-path /home/jdoe/my_json.json --json-parse_dates date
        
        or
        
        .. code:: bash
        
           dtale --json-path http://json-endpoint --json-parse_dates date
        
        Loading data from a **Custom** loader - Using the DTALE_CLI_LOADERS
        environment variable, specify a path to a location containing some
        python modules - Any python module containing the global variables
        LOADER_KEY & LOADER_PROPS will be picked up as a custom loader -
        LOADER_KEY: the key that will be associated with your loader. By default
        you are given **arctic** & **csv** (if you use one of these are your key
        it will override these) - LOADER_PROPS: the individual props available
        to be specified. - For example, with arctic we have host, library, node,
        start & end. - If you leave this property as an empty list your loader
        will be treated as a flag. For example, instead of using all the arctic
        properties we would simply specify ``--arctic`` (this wouldn’t work well
        in arctic’s case since it depends on all those properties) - You will
        also need to specify a function with the following signature
        ``def find_loader(kwargs)`` which returns a function that returns a
        dataframe or ``None`` - Here is an example of a custom loader:
        
        .. code:: python
        
           from dtale.cli.clickutils import get_loader_options
        
           '''
             IMPORTANT!!! This global variable is required for building any customized CLI loader.
             When find loaders on startup it will search for any modules containing the global variable LOADER_KEY.
           '''
           LOADER_KEY = 'testdata'
           LOADER_PROPS = ['rows', 'columns']
        
        
           def test_data(rows, columns):
               import pandas as pd
               import numpy as np
               import random
               from past.utils import old_div
               from pandas.tseries.offsets import Day
               from dtale.utils import dict_merge
               import string
        
               now = pd.Timestamp(pd.Timestamp('now').date())
               dates = pd.date_range(now - Day(364), now)
               num_of_securities = max(old_div(rows, len(dates)), 1)  # always have at least one security
               securities = [
                   dict(security_id=100000 + sec_id, int_val=random.randint(1, 100000000000),
                        str_val=random.choice(string.ascii_letters) * 5)
                   for sec_id in range(num_of_securities)
               ]
               data = pd.concat([
                   pd.DataFrame([dict_merge(dict(date=date), sd) for sd in securities])
                   for date in dates
               ], ignore_index=True)[['date', 'security_id', 'int_val', 'str_val']]
        
               col_names = ['Col{}'.format(c) for c in range(columns)]
               return pd.concat([data, pd.DataFrame(np.random.randn(len(data), columns), columns=col_names)], axis=1)
        
        
           # IMPORTANT!!! This function is required for building any customized CLI loader.
           def find_loader(kwargs):
               test_data_opts = get_loader_options(LOADER_KEY, kwargs)
               if len([f for f in test_data_opts.values() if f]):
                   def _testdata_loader():
                       return test_data(int(test_data_opts.get('rows', 1000500)), int(test_data_opts.get('columns', 96)))
        
                   return _testdata_loader
               return None
        
        In this example we simplying building a dataframe with some dummy data
        based on dimensions specified on the command-line: - ``--testdata-rows``
        - ``--testdata-columns``
        
        Here’s how you would use this loader:
        
        .. code:: bash
        
           DTALE_CLI_LOADERS=./path_to_loaders bash -c 'dtale --testdata-rows 10 --testdata-columns 5'
        
        UI
        --
        
        Once you have kicked off your D-Tale session please copy & paste the
        link on the last line of output in your browser |image21|
        
        Dimensions/Main Menu
        ~~~~~~~~~~~~~~~~~~~~
        
        The information in the upper right-hand corner gives grid dimensions
        |image22| - lower-left => row count - upper-right => column count -
        clicking the triangle displays the menu of standard functions (click
        outside menu to close it) |image23|
        
        Main Menu Functions
        ~~~~~~~~~~~~~~~~~~~
        
        Describe
        ^^^^^^^^
        
        View all the columns & their data types as well as individual details of
        each column
        
        |image24|
        
        +-----------------------+-----------------------+-----------------------+
        | Data Type             | Display               | Notes                 |
        +=======================+=======================+=======================+
        | date                  | |image25|             |                       |
        +-----------------------+-----------------------+-----------------------+
        | string                | |image26|             | If you have less than |
        |                       |                       | or equal to 100       |
        |                       |                       | unique values they    |
        |                       |                       | will be displayed at  |
        |                       |                       | the bottom of your    |
        |                       |                       | popup                 |
        +-----------------------+-----------------------+-----------------------+
        | int                   | |image27|             | Anything with         |
        |                       |                       | standard numeric      |
        |                       |                       | classifications (min, |
        |                       |                       | max, 25%, 50%, 75%)   |
        |                       |                       | will have a nice      |
        |                       |                       | boxplot with the mean |
        |                       |                       | (if it exists)        |
        |                       |                       | displayed as an       |
        |                       |                       | outlier if you look   |
        |                       |                       | closely.              |
        +-----------------------+-----------------------+-----------------------+
        | float                 | |image28|             |                       |
        +-----------------------+-----------------------+-----------------------+
        
        Filter
        ^^^^^^
        
        Apply a simple pandas ``query`` to your data (link to pandas
        documentation included in popup)
        
        Context Variables are user-defined values passed in via the
        ``context_variables`` argument to dtale.show(); they can be referenced
        in filters by prefixing the variable name with ‘@’.
        
        For example, here is how you can use context variables in a pandas
        query:
        
        .. code:: python
        
           import pandas as pd
        
           df = pd.DataFrame([
             dict(name='Joe', age=7),
             dict(name='Bob', age=23),
             dict(name='Ann', age=45),
             dict(name='Cat', age=88),
           ])
           two_oldest_ages = df['age'].nlargest(2)
           df.query('age in @two_oldest_ages')
        
        And here is how you would pass that context variable to D-Tale:
        ``dtale.show(df, context_variables=dict(two_oldest_ages=two_oldest_ages))``
        
        ========= =========
        Editing   Result
        ========= =========
        |image29| |image30|
        ========= =========
        
        FYI: For python 3 users, there is now support for filtering on column
        names with special characters in them (EX: ‘a.b’) :metal:
        
        Charts
        ^^^^^^
        
        Build custom charts based off your data(powered by
        `plotly/dash <https://github.com/plotly/dash>`__).
        
        -  The Charts will open in a tab because of the fact there is so much
           functionality offered there you’ll probably want to be able to
           reference the main grid data in the original tab
        -  To build a chart you must pick a value for X & Y inputs which
           effectively drive what data is along the X & Y axes
        
           -  If you are working with a 3-Dimensional chart (heatmap, 3D
              Scatter, Surface) you’ll need to enter a value for the Z axis as
              well
        
        -  Once you have entered all the required axes a chart will be built
        -  If your data along the x-axis (or combination of x & y in the case of
           3D charts) has duplicates you have three options:
        
           -  Specify a group, which will create series for each group
           -  Specify an aggregation, you can choose from one of the following:
              Count, First, Last, Mean, Median, Minimum, Maximum, Standard
              Deviation, Variance, Mean Absolute Deviation, Product of All
              Items, Sum, Rolling
        
              -  Specifying a “Rolling” aggregation will also require a Window &
                 a Computation (Correlation, Count, Covariance, Kurtosis,
                 Maximum, Mean, Median, Minimum, Skew, Standard Deviation, Sum
                 or Variance)
              -  For heatmaps you will also have access to the “Correlation”
                 aggregation since viewing correlation matrices in heatmaps is
                 very useful. This aggregation is not supported elsewhere
        
           -  Specify both a group & an aggregation
        
        -  You now have the ability to toggle between different chart types:
           line, bar, pie, wordcloud, heatmap, 3D scatter & surface
        -  If you have specified a group then you have the ability between
           showing all series in one chart and breaking each series out into its
           own chart “Chart per Group”
        
        Here are some examples:
        
        ========== ========= ===============
        Chart Type Chart     Chart per Group
        ========== ========= ===============
        line       |image31| |image32|
        bar        |image33| |image34|
        stacked    |image35| |image36|
        pie        |image37| 
        wordcloud  |image38| |image39|
        heatmap    |image40| 
        3D scatter |image41| 
        surface    |image42| 
        ========== ========= ===============
        
        Selecting multiple columns for the Y-Axis will produce similar results
        to grouping in the sense that the chart will contain multiple series,
        but the difference is that for each column there will be a different
        Y-Axis associated with it in case the values contained within each
        column are on different scales.
        
        ============ ===================
        Multi Y-Axis Editing Axis Ranges
        ============ ===================
        |image43|    |image44|
        ============ ===================
        
        With a bar chart that only has a single Y-Axis you have the ability to
        sort the bars based on the values for the Y-Axis
        
        ========= =========
        Pre-sort  Post-sort
        ========= =========
        |image45| |image46|
        ========= =========
        
        This is a very powerful feature with many more features that could be
        offered (linked subplots, different statistical aggregations, etc…) so
        please submit issues :)
        
        **Disclaimer: Long Running Chart Requests**
        
        If you choose to build a chart that requires a lot of computational
        resources then it will take some time to run. Based on the way Flask &
        plotly/dash interact this will block you from performing any other
        request until it completes. There are two courses of action in this
        situation:
        
        1) Restart your jupyter notebook kernel or python console
        2) Open a new D-Tale session on a different port than the current
           session. You can do that with the following command:
           ``dtale.show(df, port=[any open port], force=True)``
        
        If you miss the legacy (non-plotly/dash) charts, not to worry! They are
        still available from the link in the upper-right corner, but on for a
        limited time… Here is the documentation for those: `Legacy
        Charts <https://github.com/man-group/dtale/blob/master/docs/LEGACY_CHARTS.md>`__
        
        Correlations
        ^^^^^^^^^^^^
        
        Shows a pearson correlation matrix of all numeric columns against all
        other numeric columns - By default, it will show a grid of pearson
        correlations (filtering available by using drop-down see 2nd table of
        screenshots) - If you have a date-type column, you can click an
        individual cell and see a timeseries of pearson correlations for that
        column combination - Currently if you have multiple date-type columns
        you will have the ability to toggle between them by way of a drop-down -
        Furthermore, you can click on individual points in the timeseries to
        view the scatter plot of the points going into that correlation
        
        ========= ========== =========
        Matrix    Timeseries Scatter
        ========= ========== =========
        |image47| |image48|  |image49|
        ========= ========== =========
        
        ============= ============= ====================
        Col1 Filtered Col2 Filtered Col1 & Col2 Filtered
        ============= ============= ====================
        |image50|     |image51|     |image52|
        ============= ============= ====================
        
        When the data being viewed in D-Tale has date or timestamp columns but
        for each date/timestamp vlaue there is only one row of data the behavior
        of the Correlations popup is a little different - Instead of a
        timeseries correlation chart the user is given a rolling correlation
        chart which can have the window (default: 10) altered - The scatter
        chart will be created when a user clicks on a point in the rollign
        correlation chart. The data displayed in the scatter will be for the
        ranges of dates involved in the rolling correlation for that date.
        
        ========= ============
        Data      Correlations
        ========= ============
        |image53| |image54|
        ========= ============
        
        Heat Map
        ^^^^^^^^
        
        This will hide any non-float columns (with the exception of the index on
        the right) and apply a color to the background of each cell - Each float
        is renormalized to be a value between 0 and 1.0 - Each renormalized
        value is passed to a color scale of red(0) - yellow(0.5) - green(1.0)
        |image55|
        
        Turn off Heat Map by clicking menu option again |image56|
        
        Code Exports
        ^^^^^^^^^^^^
        
        *Code Exports* are small snippets of code representing the current state
        of the grid you’re viewing including things like: - columns built -
        filtering - sorting
        
        Other code exports available are: - Column Analysis - Correlations
        (grid, timeseries chart & scatter chart) - Describe - Charts built using
        the Chart Builder
        
        Instances
        ^^^^^^^^^
        
        This will give you information about other D-Tale instances are running
        under your current Python process.
        
        For example, if you ran the following script:
        
        .. code:: python
        
           import pandas as pd
           import dtale
        
           dtale.show(pd.DataFrame([dict(foo=1, bar=2, biz=3, baz=4, snoopy_D_O_double_gizzle=5)]))
           dtale.show(pd.DataFrame([
               dict(a=1, b=2, c=3, d=4),
               dict(a=2, b=3, c=4, d=5),
               dict(a=3, b=4, c=5, d=6),
               dict(a=4, b=5, c=6, d=7)
           ]))
           dtale.show(pd.DataFrame([range(6), range(6), range(6), range(6), range(6), range(6)]), name="foo")
        
        This will make the **Instances** button available in all 3 of these
        D-Tale instances. Clicking that button while in the first instance
        invoked above will give you this popup:
        
        |image57|
        
        The grid above contains the following information: - Process: timestamp
        when the process was started along with the name (if specified in
        ``dtale.show()``) - Rows: number of rows - Columns: number of columns -
        Column Names: comma-separated string of column names (only first 30
        characters, hover for full listing) - Preview: this button is available
        any of the non-current instances. Clicking this will bring up left-most
        5X5 grid information for that instance - The row highlighted in green
        signifys the current D-Tale instance - Any other row can be clicked to
        switch to that D-Tale instance
        
        Here is an example of clicking the “Preview” button:
        
        |image58|
        
        About
        ^^^^^
        
        This will give you information about what version of D-Tale you’re
        running as well as if its out of date to whats on PyPi.
        
        ========== ===========
        Up To Date Out Of Date
        ========== ===========
        |image59|  |image60|
        ========== ===========
        
        Resize
        ^^^^^^
        
        Mostly a fail-safe in the event that your columns are no longer lining
        up. Click this and should fix that
        
        Shutdown
        ^^^^^^^^
        
        Pretty self-explanatory, kills your D-Tale session (there is also an
        auto-kill process that will kill your D-Tale after an hour of
        inactivity)
        
        Column Menu Functions
        ~~~~~~~~~~~~~~~~~~~~~
        
        |image61|
        
        Moving Columns
        ^^^^^^^^^^^^^^
        
        |image62|
        
        All column movements are saved on the server so refreshing your browser
        won’t lose them :ok_hand:
        
        Hiding Columns
        ^^^^^^^^^^^^^^
        
        |image63|
        
        All column movements are saved on the server so refreshing your browser
        won’t lose them :ok_hand:
        
        Building Columns
        ^^^^^^^^^^^^^^^^
        
        |image64|
        
        This video shows you how to build the following: - Numeric:
        adding/subtracting two columns or columns with static values - Bins:
        bucketing values using pandas cut & qcut as well as assigning custom
        labels - Dates: retrieving date properties (hour, weekday, month…) as
        well as conversions (month end)
        
        Lock
        ^^^^
        
        Adds your column to “locked” columns - “locked” means that if you scroll
        horizontally these columns will stay pinned to the right-hand side -
        this is handy when you want to keep track of which date or security_id
        you’re looking at - by default, any index columns on the data passed to
        D-Tale will be locked
        
        Unlock
        ^^^^^^
        
        Removed column from “locked” columns
        
        Sorting
        ^^^^^^^
        
        Applies/removes sorting (Ascending/Descending/Clear) to the column
        selected
        
        *Important*: as you add sorts they sort added will be added to the end
        of the multi-sort. For example:
        
        ========================= ================
        Action                    Sort
        ========================= ================
        click “a”                 
        sort asc                  a (asc)
        click “b”                 a (asc)
        sort desc                 a (asc), b(desc)
        click “a”                 a (asc), b(desc)
        sort None                 b(desc)
        sort desc                 b(desc), a(desc)
        click “X” on sort display 
        ========================= ================
        
        Formats
        ^^^^^^^
        
        Apply simple formats to numeric values in your grid
        
        ======= ========= =========
        Type    Editing   Result
        ======= ========= =========
        Numeric |image65| |image66|
        Date    |image67| |image68|
        String  |image69| |image70|
        ======= ========= =========
        
        Here’s a grid of all the formats available with -123456.789 as input:
        
        ============= ==============
        Format        Output
        ============= ==============
        Precision (6) -123456.789000
        Thousands Sep -123,456.789
        Abbreviate    -123k
        Exponent      -1e+5
        BPS           -1234567890BPS
        Red Negatives -123457
        ============= ==============
        
        Column Analysis
        ^^^^^^^^^^^^^^^
        
        Based on the data type of a column different charts will be shown.
        
        ========= =======================
        Data Type Chart
        ========= =======================
        Integer   Histogram, Value Counts
        Float     Value Counts
        Date      Value Counts
        String    Value Counts
        ========= =======================
        
        *Histograms* can be displayed in any number of bins (default: 20),
        simply type a new integer value in the bins input
        
        |image71|
        
        *Value Counts* are a bar chart containing the counts of each unique
        value in a column.
        
        Menu Functions Depending on Browser Dimensions
        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
        
        Depending on the dimensions of your browser window the following buttons
        will not open modals, but rather separate browser windows: Correlations,
        Describe & Instances (see images from `Jupyter
        Notebook <#jupyter-notebook>`__, also Charts will always open in a
        separate browser window)
        
        For Developers
        --------------
        
        Cloning
        ~~~~~~~
        
        Clone the code (git clone ssh://git@github.com:manahl/dtale.git), then
        start the backend server:
        
        .. code:: bash
        
           $ git clone ssh://git@github.com:manahl/dtale.git
           # install the dependencies
           $ python setup.py develop
           # start the server
           $ python dtale --csv-path /home/jdoe/my_csv.csv --csv-parse_dates date
        
        You can also run dtale from PyDev directly.
        
        You will also want to import javascript dependencies and build the
        source:
        
        .. code:: bash
        
           $ npm install
           # 1) a persistent server that serves the latest JS:
           $ npm run watch
           # 2) or one-off build:
           $ npm run build
        
        Running tests
        ~~~~~~~~~~~~~
        
        The usual npm test command works:
        
        ::
        
           $ npm test
        
        You can run individual test files:
        
        ::
        
           $ TEST=static/__tests__/dtale/DataViewer-base-test.jsx npm run test-file
        
        Linting
        ~~~~~~~
        
        You can lint all the JS and CSS to confirm there’s nothing obviously
        wrong with it:
        
        .. code:: bash
        
           $ npm run lint -s
        
        You can also lint individual JS files:
        
        .. code:: bash
        
           $ npm run lint-js-file -s -- static/dtale/DataViewer.jsx
        
        Formatting JS
        ~~~~~~~~~~~~~
        
        You can auto-format code as follows:
        
        .. code:: bash
        
           $ npm run format
        
        Docker Development
        ~~~~~~~~~~~~~~~~~~
        
        You can build python 27-3 & run D-Tale as follows:
        
        .. code:: bash
        
           $ yarn run build
           $ docker-compose build dtale_2_7
           $ docker run -it --network host dtale_2_7:latest
           $ python
           >>> import pandas as pd
           >>> df = pd.DataFrame([dict(a=1,b=2,c=3)])
           >>> import dtale
           >>> dtale.show(df)
        
        Then view your D-Tale instance in your browser using the link that gets
        printed
        
        You can build python 36-1 & run D-Tale as follows:
        
        .. code:: bash
        
           $ yarn run build
           $ docker-compose build dtale_3_6
           $ docker run -it --network host dtale_3_6:latest
           $ python
           >>> import pandas as pd
           >>> df = pd.DataFrame([dict(a=1,b=2,c=3)])
           >>> import dtale
           >>> dtale.show(df)
        
        Then view your D-Tale instance in your browser using the link that gets
        printed
        
        Startup Behavior
        ----------------
        
        Here’s a little background on how the ``dtale.show()`` function works: -
        by default it will look for ports between 40000 & 49000, but you can
        change that range by specifying the environment variables DTALE_MIN_PORT
        & DTALE_MAX_PORT - think of sessions as python consoles or jupyter
        notebooks
        
        1) Session 1 executes ``dtale.show(df)`` our state is:
        
        ======= ===== =============== ===================================
        Session Port  Active Data IDs URL(s)
        ======= ===== =============== ===================================
        1       40000 1               http://localhost:40000/dtale/main/1
        ======= ===== =============== ===================================
        
        2) Session 1 executes ``dtale.show(df)`` our state is:
        
        ======= ===== =============== =======================================
        Session Port  Active Data IDs URL(s)
        ======= ===== =============== =======================================
        1       40000 1,2             http://localhost:40000/dtale/main/[1,2]
        ======= ===== =============== =======================================
        
        2) Session 2 executes ``dtale.show(df)`` our state is:
        
        ======= ===== =============== =======================================
        Session Port  Active Data IDs URL(s)
        ======= ===== =============== =======================================
        1       40000 1,2             http://localhost:40000/dtale/main/[1,2]
        2       40001 1               http://localhost:40001/dtale/main/1
        ======= ===== =============== =======================================
        
        3) Session 1 executes ``dtale.show(df, port=40001, force=True)`` our
           state is:
        
        ======= ===== =============== =========================================
        Session Port  Active Data IDs URL(s)
        ======= ===== =============== =========================================
        1       40001 1,2,3           http://localhost:40001/dtale/main/[1,2,3]
        ======= ===== =============== =========================================
        
        4) Session 3 executes ``dtale.show(df)`` our state is:
        
        ======= ===== =============== =========================================
        Session Port  Active Data IDs URL(s)
        ======= ===== =============== =========================================
        1       40001 1,2,3           http://localhost:40001/dtale/main/[1,2,3]
        3       40000 1               http://localhost:40000/dtale/main/1
        ======= ===== =============== =========================================
        
        5) Session 2 executes ``dtale.show(df)`` our state is:
        
        ======= ===== =============== =========================================
        Session Port  Active Data IDs URL(s)
        ======= ===== =============== =========================================
        1       40001 1,2,3           http://localhost:40001/dtale/main/[1,2,3]
        3       40000 1               http://localhost:40000/dtale/main/1
        2       40002 1               http://localhost:40002/dtale/main/1
        ======= ===== =============== =========================================
        
        6) Session 4 executes ``dtale.show(df, port=8080)`` our state is:
        
        ======= ===== =============== =========================================
        Session Port  Active Data IDs URL(s)
        ======= ===== =============== =========================================
        1       40001 1,2,3           http://localhost:40001/dtale/main/[1,2,3]
        3       40000 1               http://localhost:40000/dtale/main/1
        2       40002 1               http://localhost:40002/dtale/main/1
        4       8080  1               http://localhost:8080/dtale/main/1
        ======= ===== =============== =========================================
        
        7) Session 1 executes ``dtale.get_instance(1).kill()`` our state is:
        
        ======= ===== =============== =======================================
        Session Port  Active Data IDs URL(s)
        ======= ===== =============== =======================================
        1       40001 2,3             http://localhost:40001/dtale/main/[2,3]
        3       40000 1               http://localhost:40000/dtale/main/1
        2       40002 1               http://localhost:40002/dtale/main/1
        4       8080  1               http://localhost:8080/dtale/main/1
        ======= ===== =============== =======================================
        
        7) Session 5 sets DTALE_MIN_RANGE to 30000 and DTALE_MAX_RANGE 39000 and
           executes ``dtale.show(df)`` our state is:
        
        ======= ===== ================= =======================================
        Session Port  Active Data ID(s) URL(s)
        ======= ===== ================= =======================================
        1       40001 2,3               http://localhost:40001/dtale/main/[2,3]
        3       40000 1                 http://localhost:40000/dtale/main/1
        2       40002 1                 http://localhost:40002/dtale/main/1
        4       8080  1                 http://localhost:8080/dtale/main/1
        5       30000 1                 http://localhost:30000/dtale/main/1
        ======= ===== ================= =======================================
        
        Documentation
        -------------
        
        Have a look at the `detailed
        documentation <https://dtale.readthedocs.io>`__.
        
        Requirements
        ------------
        
        D-Tale works with:
        
        -  Back-end
        
           -  arctic [extra]
           -  dash
           -  dash_daq
           -  Flask
           -  Flask-Compress
           -  Pandas
           -  scipy
           -  six
        
        -  Front-end
        
           -  react-virtualized
           -  chart.js
        
        Acknowledgements
        ----------------
        
        D-Tale has been under active development at `Man
        Numeric <http://www.numeric.com/>`__ since 2019.
        
        Original concept and implementation: `Andrew
        Schonfeld <https://github.com/aschonfeld>`__
        
        Contributors:
        
        -  `Phillip Dupuis <https://github.com/phillipdupuis>`__
        -  `Dominik Christ <https://github.com/DominikMChrist>`__
        -  `Chris Boddy <https://github.com/cboddy>`__
        -  `Jason Holden <https://github.com/jasonkholden>`__
        -  `Tom Taylor <https://github.com/TomTaylorLondon>`__
        -  `Fernando Saravia Rajal <https://github.com/fersarr>`__
        -  `Wilfred Hughes <https://github.com/Wilfred>`__
        -  Mike Kelly
        -  `Vincent Riemer <https://github.com/vincentriemer>`__
        -  `Youssef Habchi <http://youssef-habchi.com/>`__ - title font
        -  … and many others …
        
        Contributions welcome!
        
        License
        -------
        
        D-Tale is licensed under the GNU LGPL v2.1. A copy of which is included
        in `LICENSE <LICENSE>`__
        
        .. |image0| image:: https://raw.githubusercontent.com/aschonfeld/dtale-media/master/images/Title.png
           :target: https://github.com/man-group/dtale
        .. |CircleCI| image:: https://circleci.com/gh/man-group/dtale.svg?style=shield&circle-token=4b67588a87157cc03b484fb96be438f70b5cd151
           :target: https://circleci.com/gh/man-group/dtale
        .. |PyPI| image:: https://img.shields.io/pypi/pyversions/dtale.svg
           :target: https://pypi.python.org/pypi/dtale/
        .. |ReadTheDocs| image:: https://readthedocs.org/projects/dtale/badge
           :target: https://dtale.readthedocs.io
        .. |codecov| image:: https://codecov.io/gh/man-group/dtale/branch/master/graph/badge.svg
           :target: https://codecov.io/gh/man-group/dtale
        .. |Downloads| image:: https://pepy.tech/badge/dtale
           :target: https://pepy.tech/project/dtale
        .. |image6| image:: https://raw.githubusercontent.com/aschonfeld/dtale-media/master/gifs/dtale_demo_mini.gif
        .. |image7| image:: https://raw.githubusercontent.com/aschonfeld/dtale-media/master/gifs/dtale_ipython.gif
        .. |image8| image:: https://raw.githubusercontent.com/aschonfeld/dtale-media/master/images/Python_Terminal.png
        .. |image9| image:: https://raw.githubusercontent.com/aschonfeld/dtale-media/master/images/Duplicate_data.png
        .. |image10| image:: https://raw.githubusercontent.com/aschonfeld/dtale-media/master/images/ipython1.png
        .. |image11| image:: https://raw.githubusercontent.com/aschonfeld/dtale-media/master/images/ipython2.png
        .. |image12| image:: https://raw.githubusercontent.com/aschonfeld/dtale-media/master/images/ipython3.png
        .. |image13| image:: https://raw.githubusercontent.com/aschonfeld/dtale-media/master/images/ipython_adjust.png
        .. |image14| image:: https://raw.githubusercontent.com/aschonfeld/dtale-media/master/images/Column_menu.png
        .. |image15| image:: https://raw.githubusercontent.com/aschonfeld/dtale-media/master/images/correlations_popup.png
        .. |image16| image:: https://raw.githubusercontent.com/aschonfeld/dtale-media/master/images/describe_popup.png
        .. |image17| image:: https://raw.githubusercontent.com/aschonfeld/dtale-media/master/images/histogram_popup.png
        .. |image18| image:: https://raw.githubusercontent.com/aschonfeld/dtale-media/master/images/instances_popup.png
        .. |image19| image:: http://img.youtube.com/vi/pOYl2M1clIw/0.jpg
           :target: http://www.youtube.com/watch?v=pOYl2M1clIw
        .. |image20| image:: http://img.youtube.com/vi/8Il-2HHs2Mg/0.jpg
           :target: http://www.youtube.com/watch?v=8Il-2HHs2Mg
        .. |image21| image:: https://raw.githubusercontent.com/aschonfeld/dtale-media/master/images/Browser1.png
        .. |image22| image:: https://raw.githubusercontent.com/aschonfeld/dtale-media/master/images/Info_cell.png
        .. |image23| image:: https://raw.githubusercontent.com/aschonfeld/dtale-media/master/images/Info_menu_small.png
        .. |image24| image:: https://raw.githubusercontent.com/aschonfeld/dtale-media/master/images/Describe.png
        .. |image25| image:: https://raw.githubusercontent.com/aschonfeld/dtale-media/master/images/Describe_date.png
        .. |image26| image:: https://raw.githubusercontent.com/aschonfeld/dtale-media/master/images/Describe_string.png
        .. |image27| image:: https://raw.githubusercontent.com/aschonfeld/dtale-media/master/images/Describe_int.png
        .. |image28| image:: https://raw.githubusercontent.com/aschonfeld/dtale-media/master/images/Describe_float.png
        .. |image29| image:: https://raw.githubusercontent.com/aschonfeld/dtale-media/master/images/Filter_apply.png
        .. |image30| image:: https://raw.githubusercontent.com/aschonfeld/dtale-media/master/images/Post_filter.png
        .. |image31| image:: https://raw.githubusercontent.com/aschonfeld/dtale-media/master/images/charts/line.png
        .. |image32| image:: https://raw.githubusercontent.com/aschonfeld/dtale-media/master/images/charts/line_pg.png
        .. |image33| image:: https://raw.githubusercontent.com/aschonfeld/dtale-media/master/images/charts/bar.png
        .. |image34| image:: https://raw.githubusercontent.com/aschonfeld/dtale-media/master/images/charts/bar_pg.png
        .. |image35| image:: https://raw.githubusercontent.com/aschonfeld/dtale-media/master/images/charts/stacked.png
        .. |image36| image:: https://raw.githubusercontent.com/aschonfeld/dtale-media/master/images/charts/stacked_pg.png
        .. |image37| image:: https://raw.githubusercontent.com/aschonfeld/dtale-media/master/images/charts/pie.png
        .. |image38| image:: https://raw.githubusercontent.com/aschonfeld/dtale-media/master/images/charts/wordcloud.png
        .. |image39| image:: https://raw.githubusercontent.com/aschonfeld/dtale-media/master/images/charts/wordcloud_pg.png
        .. |image40| image:: https://raw.githubusercontent.com/aschonfeld/dtale-media/master/images/charts/heatmap.png
        .. |image41| image:: https://raw.githubusercontent.com/aschonfeld/dtale-media/master/images/charts/3d_scatter.png
        .. |image42| image:: https://raw.githubusercontent.com/aschonfeld/dtale-media/master/images/charts/surface.png
        .. |image43| image:: https://raw.githubusercontent.com/aschonfeld/dtale-media/master/images/charts/multi_col.png
        .. |image44| image:: https://raw.githubusercontent.com/aschonfeld/dtale-media/master/images/charts/editing_axis.png
        .. |image45| image:: https://raw.githubusercontent.com/aschonfeld/dtale-media/master/images/charts/bar_presort.png
        .. |image46| image:: https://raw.githubusercontent.com/aschonfeld/dtale-media/master/images/charts/bar_postsort.png
        .. |image47| image:: https://raw.githubusercontent.com/aschonfeld/dtale-media/master/images/Correlations.png
        .. |image48| image:: https://raw.githubusercontent.com/aschonfeld/dtale-media/master/images/Correlations_ts.png
        .. |image49| image:: https://raw.githubusercontent.com/aschonfeld/dtale-media/master/images/Correlations_scatter.png
        .. |image50| image:: https://raw.githubusercontent.com/aschonfeld/dtale-media/master/images/Correlations_col1.png
        .. |image51| image:: https://raw.githubusercontent.com/aschonfeld/dtale-media/master/images/Correlations_col2.png
        .. |image52| image:: https://raw.githubusercontent.com/aschonfeld/dtale-media/master/images/Correlations_both.png
        .. |image53| image:: https://raw.githubusercontent.com/aschonfeld/dtale-media/master/images/rolling_corr_data.png
        .. |image54| image:: https://raw.githubusercontent.com/aschonfeld/dtale-media/master/images/rolling_corr.png
        .. |image55| image:: https://raw.githubusercontent.com/aschonfeld/dtale-media/master/images/Heatmap.png
        .. |image56| image:: https://raw.githubusercontent.com/aschonfeld/dtale-media/master/images/Heatmap_toggle.png
        .. |image57| image:: https://raw.githubusercontent.com/aschonfeld/dtale-media/master/images/Instances.png
        .. |image58| image:: https://raw.githubusercontent.com/aschonfeld/dtale-media/master/images/Instances_preview.png
        .. |image59| image:: https://raw.githubusercontent.com/aschonfeld/dtale-media/master/images/About-up-to-date.png
        .. |image60| image:: https://raw.githubusercontent.com/aschonfeld/dtale-media/master/images/About-out-of-date.png
        .. |image61| image:: https://raw.githubusercontent.com/aschonfeld/dtale-media/master/images/Col_menu.png
        .. |image62| image:: http://img.youtube.com/vi/We4TH477rRs/0.jpg
           :target: http://www.youtube.com/watch?v=We4TH477rRs
        .. |image63| image:: http://img.youtube.com/vi/ryZT2Lk_YaA/0.jpg
           :target: http://www.youtube.com/watch?v=ryZT2Lk_YaA
        .. |image64| image:: http://img.youtube.com/vi/G6wNS9-lG04/0.jpg
           :target: http://www.youtube.com/watch?v=G6wNS9-lG04
        .. |image65| image:: https://raw.githubusercontent.com/aschonfeld/dtale-media/master/images/Formatting_apply.png
        .. |image66| image:: https://raw.githubusercontent.com/aschonfeld/dtale-media/master/images/Post_formatting.png
        .. |image67| image:: https://raw.githubusercontent.com/aschonfeld/dtale-media/master/images/Formatting_date_apply.png
        .. |image68| image:: https://raw.githubusercontent.com/aschonfeld/dtale-media/master/images/Post_date_formatting.png
        .. |image69| image:: https://raw.githubusercontent.com/aschonfeld/dtale-media/master/images/Formatting_string_apply.png
        .. |image70| image:: https://raw.githubusercontent.com/aschonfeld/dtale-media/master/images/Post_string_formatting.png
        .. |image71| image:: https://raw.githubusercontent.com/aschonfeld/dtale-media/master/images/Histogram.png
        
        Changelog
        ---------
        
        1.7.12 (2020-3-1)
        ~~~~~~~~~~~~~~~~~
        
        -  added syntax highlighting to code exports with
           react-syntax-highlighting
        -  added arctic integration test
        -  updated Histogram popup to “Column Analysis” which allows for the
           following
        
           -  Histograms -> integers and floats
           -  Value Counts -> integers, strings & dates
        
        .. _section-1:
        
        1.7.11 (2020-2-27)
        ~~~~~~~~~~~~~~~~~~
        
        -  hotfix for dash custom.js file missing from production webpack build
           script
        
        .. _section-2:
        
        1.7.10 (2020-2-27)
        ~~~~~~~~~~~~~~~~~~
        
        -  `#75 <https://github.com/man-group/dtale/issues/75>`__, added code
           snippet functionality to the following:
        
           -  main grid, histogram, correlations, column building & charts
        
        -  exposed CLI loaders through the following functions dtale.show_csv,
           dtale.show_json, dtale.show_arctic
        
           -  build in such a way that it is easy for custom loaders to be
              exposed as well
        
        -  `#82 <https://github.com/man-group/dtale/issues/82>`__, pinned
           ``future`` package to be >= 0.14.0
        
        .. _section-3:
        
        1.7.9 (2020-2-24)
        ~~~~~~~~~~~~~~~~~
        
        -  support for google colab
        -  bugfixes: `#71 <https://github.com/man-group/dtale/issues/71>`__,
           `#72 <https://github.com/man-group/dtale/issues/72>`__,
           `#73 <https://github.com/man-group/dtale/issues/73>`__
        
        .. _section-4:
        
        1.7.8 (2020-2-22)
        ~~~~~~~~~~~~~~~~~
        
        -  `#77 <https://github.com/man-group/dtale/issues/77>`__, removal of
           multiprocessed timeouts
        
        .. _section-5:
        
        1.7.7 (2020-2-22)
        ~~~~~~~~~~~~~~~~~
        
        -  centralized global state
        
        .. _section-6:
        
        1.7.6 (2020-2-21)
        ~~~~~~~~~~~~~~~~~
        
        -  allowing the usage of context variables within filters
        -  `#64 <https://github.com/man-group/dtale/issues/64>`__, handling for
           loading duplicate data to dtale.show
        -  updated dtale.instances() to print urls rather than show all
           instances
        -  removal of Dash “Export to png” function
        -  passing data grid queries to chart page as default
        -  added sys.exit() to the thread that manages the reaper
        
        .. _section-7:
        
        1.7.5 (2020-2-20)
        ~~~~~~~~~~~~~~~~~
        
        -  hotfix for KeyError loading metadata for columns with min/max
           information
        
        .. _section-8:
        
        1.7.4 (2020-2-20)
        ~~~~~~~~~~~~~~~~~
        
        -  `#63 <https://github.com/man-group/dtale/issues/63>`__: filtering
           columns with special characters in name
        -  added json_loader CLI options
        -  updated moving/locking of columns to be persisted to back-end as well
           as front-end
        -  added the ability to show/hide columns
        -  `#61 <https://github.com/man-group/dtale/issues/61>`__: added column
           builder popup
        
        .. _section-9:
        
        1.7.3 (2020-2-13)
        ~~~~~~~~~~~~~~~~~
        
        -  added the ability to move columns left or right as well as to the
           front
        -  added formatting capabilities for strings & dates
        -  persist formatting settings to popup on reopening
        -  bugfix for width-calculation on formatting change
        
        .. _section-10:
        
        1.7.2 (2020-2-12)
        ~~~~~~~~~~~~~~~~~
        
        -  60 timeout handling around chart requests
        -  pre-loaded charts through URL search strings
        -  pandas query examples in Filter popup
        
        .. _section-11:
        
        1.7.1 (2020-2-7)
        ~~~~~~~~~~~~~~~~
        
        -  added pie, 3D scatter & surface charts
        -  updated popups to be displayed when the browser dimensions are too
           small to host a modal
        -  removed Swagger due to its lack up support for updated dependencies
        
        .. _section-12:
        
        1.7.0 (2020-1-28)
        ~~~~~~~~~~~~~~~~~
        
        -  redesign of charts popup to use plotly/dash
        -  `#55 <https://github.com/man-group/dtale/issues/55>`__: raise
           exception when data contains duplicate column names
        -  heatmap integration
        -  combination of "_main.jsx" files into one for spacial optimization
        -  `#15 <https://github.com/man-group/dtale/issues/15>`__: made arctic
           an “extra” dependency
        
        .. _section-13:
        
        1.6.10 (2020-1-12)
        ~~~~~~~~~~~~~~~~~~
        
        -  better front-end handling of dates for charting as to avoid timezone
           issues
        -  the ability to switch between sorting any axis in bar charts
        
        .. _section-14:
        
        1.6.9 (2020-1-9)
        ~~~~~~~~~~~~~~~~
        
        -  bugfix for timezone issue around passing date filters to server for
           scatter charts in correlations popup
        
        .. _section-15:
        
        1.6.8 (2020-1-9)
        ~~~~~~~~~~~~~~~~
        
        -  additional information about how to use Correlations popup
        -  handling of all-nan data in charts popup
        -  styling issues on popups (especially Histogram)
        -  removed auto-filtering on correlation popup
        -  scatter point color change
        -  added chart icon to cell that has been selected in correlation popup
        -  responsiveness to scatter charts
        -  handling of links to ‘main’,‘iframe’ & ‘popup’ missing data_id
        -  handling of ‘inf’ values when getting min/max & describe data
        -  added header to window popups (correlations, charts, …) and a link
           back to the grid
        -  added egg building to cirleci script
        -  correlation timeseries chart hover line
        
        .. _section-16:
        
        1.6.7 (2020-1-3)
        ~~~~~~~~~~~~~~~~
        
        -  `#50 <https://github.com/man-group/dtale/issues/50>`__: updates to
           rolling correlation functionality
        
        .. _section-17:
        
        1.6.6 (2020-1-2)
        ~~~~~~~~~~~~~~~~
        
        -  `#47 <https://github.com/man-group/dtale/issues/47>`__: selection of
           multiple columns for y-axis
        -  updated histogram bin selection to be an input box for full
           customization
        -  better display of timestamps in axis ticks for charts
        -  sorting of bar charts by y-axis
        -  `#48 <https://github.com/man-group/dtale/issues/48>`__: scatter
           charts in chart builder
        -  “nunique” added to list of aggregations
        -  turned on “threaded=True” for app.run to avoid hanging popups
        -  `#45 <https://github.com/man-group/dtale/issues/45>`__: rolling
           computations as aggregations
        -  Y-Axis editor
        
        .. _section-18:
        
        1.6.5 (2019-12-29)
        ~~~~~~~~~~~~~~~~~~
        
        -  test whether filters entered will return no data and block the user
           from apply those
        -  allow for group values of type int or float to be displayed in charts
           popup
        -  timeseries correlation values which return ‘nan’ will be replaced by
           zero for chart purposes
        -  update ‘distribution’ to ‘series’ on charts so that missing dates
           will not show up as ticks
        -  added “fork on github” flag for demo version & links to github/docs
           on “About” popup
        -  limited lz4 to <= 2.2.1 in python 27-3 since latest version is no
           longer supported
        
        .. _section-19:
        
        1.6.4 (2019-12-26)
        ~~~~~~~~~~~~~~~~~~
        
        -  testing of hostname returned by ``socket.gethostname``, use
           ‘localhost’ if it fails
        -  removal of flask dev server banner when running in production
           environments
        -  better handling of long strings in wordclouds
        -  `#43 <https://github.com/man-group/dtale/issues/43>`__: only show
           timeseries correlations if datetime columns exist with multiple
           values per date
        
        .. _section-20:
        
        1.6.3 (2019-12-23)
        ~~~~~~~~~~~~~~~~~~
        
        -  updated versions of packages in yarn.lock due to issue with chart.js
           box & whisker plots
        
        .. _section-21:
        
        1.6.2 (2019-12-23)
        ~~~~~~~~~~~~~~~~~~
        
        -  `#40 <https://github.com/man-group/dtale/issues/40>`__: loading
           initial chart as non-line in chart builder
        -  `#41 <https://github.com/man-group/dtale/issues/41>`__: double
           clicking cells in correlation grid for scatter will cause chart not
           to display
        -  “Open Popup” button for ipython iframes
        -  column width resizing on sorting
        -  additional int/float descriptors (sum, median, mode, var, sem, skew,
           kurt)
        -  wordcloud chart type
        
        .. _section-22:
        
        1.6.1 (2019-12-19)
        ~~~~~~~~~~~~~~~~~~
        
        -  bugfix for url display when running from command-line
        
        .. _section-23:
        
        1.6.0 (2019-12-19)
        ~~~~~~~~~~~~~~~~~~
        
        -  charts integration
        
           -  the ability to look at data in line, bar, stacked bar & pie charts
           -  the ability to group & aggregate data within the charts
        
        -  direct ipython iframes to correlations & charts pages with
           pre-selected inputs
        -  the ability to access instances from code by data id
           ``dtale.get_instance(data_id)``
        -  view all active data instances ``dtale.instances()``
        
        .. _section-24:
        
        1.5.1 (2019-12-12)
        ~~~~~~~~~~~~~~~~~~
        
        -  conversion of new flask instance for each ``dtale.show`` call to
           serving all data associated with one parent process under the same
           flask instance unless otherwise specified by the user (the ``force``
           parameter)
        
        .. _section-25:
        
        1.5.0 (2019-12-02)
        ~~~~~~~~~~~~~~~~~~
        
        -  ipython integration
        
           -  ipython output cell adjustment
           -  column-wise menu support
           -  browser window popups for: Correlations, Coverage, Describe,
              Histogram & Instances
        
        .. _section-26:
        
        1.4.1 (2019-11-20)
        ~~~~~~~~~~~~~~~~~~
        
        -  `#32 <https://github.com/man-group/dtale/issues/32>`__: unpin
           jsonschema by moving flasgger to ``extras_require``
        
        .. _section-27:
        
        1.4.0 (2019-11-19)
        ~~~~~~~~~~~~~~~~~~
        
        -  Correlations Pearson Matrix filters
        -  “name” display in title tab
        -  “Heat Map” toggle
        -  dropped unused “Flask-Caching” requirement
        
        .. _section-28:
        
        1.3.7 (2019-11-12)
        ~~~~~~~~~~~~~~~~~~
        
        -  Bug fixes for:
        
           -  `#28 <https://github.com/man-group/dtale/issues/28>`__:
              “Instances” menu option will now be displayed by default
           -  `#29 <https://github.com/man-group/dtale/issues/29>`__: add hints
              to how users can navigate the correlations popup
           -  add “unicode” as a string classification for column width
              calculation
        
        .. _section-29:
        
        1.3.6 (2019-11-08)
        ~~~~~~~~~~~~~~~~~~
        
        -  Bug fixes for:
        
           -  choose between ``pandas.corr`` & ``numpy.corrcoef`` depending on
              presence of NaNs
           -  hide timeseries correlations when date columns only contain one
              day
        
        .. _section-30:
        
        1.3.5 (2019-11-07)
        ~~~~~~~~~~~~~~~~~~
        
        -  Bug fixes for:
        
           -  duplicate loading of histogram data
           -  string serialization failing when mixing ``future.str`` & ``str``
              in scatter function
        
        .. _section-31:
        
        1.3.4 (2019-11-07)
        ~~~~~~~~~~~~~~~~~~
        
        -  updated correlation calculation to use ``numpy.corrcoef`` for
           performance purposes
        -  github rebranding from manahl -> man-group
        
        .. _section-32:
        
        1.3.3 (2019-11-05)
        ~~~~~~~~~~~~~~~~~~
        
        -  hotfix for failing test under certain versions of ``future`` package
        
        .. _section-33:
        
        1.3.2 (2019-11-05)
        ~~~~~~~~~~~~~~~~~~
        
        -  Bug fixes for:
        
           -  display of histogram column information
           -  reload of hidden “processes” input when loading instances data
           -  correlations json failures on string conversion
        
        .. _section-34:
        
        1.3.1 (2019-10-29)
        ~~~~~~~~~~~~~~~~~~
        
        -  fix for incompatible str types when directly altering state of data
           in running D-Tale instance
        
        .. _section-35:
        
        1.3.0 (2019-10-29)
        ~~~~~~~~~~~~~~~~~~
        
        -  ``webbrowser`` integration (the ability to automatically open a
           webbrowser upon calling ``dtale.show()``)
        -  flag for hiding the “Shutdown” button for long-running demos
        -  “Instances” navigator popup for viewing all activate D-Tale instances
           for the current python process
        
        .. _section-36:
        
        1.2.0 (2019-10-24)
        ~~~~~~~~~~~~~~~~~~
        
        -  `#20 <https://github.com/man-group/dtale/issues/13>`__: fix for data
           being overriden with each new instance
        -  `#21 <https://github.com/man-group/dtale/issues/13>`__: fix for
           displaying timestamps if they exist
        -  calling ``show()`` now returns an object which can alter the state of
           a process
        
           -  accessing/altering state through the ``data`` property
           -  shutting down a process using the ``kill()`` function
        
        .. _section-37:
        
        1.1.1 (2019-10-23)
        ~~~~~~~~~~~~~~~~~~
        
        -  `#13 <https://github.com/man-group/dtale/issues/13>`__: fix for
           auto-detection of column widths for strings and floats
        
        .. _section-38:
        
        1.1.0 (2019-10-08)
        ~~~~~~~~~~~~~~~~~~
        
        -  IE support
        -  **Describe** & **About** popups
        -  Custom CLI support
        
        .. _section-39:
        
        1.0.0 (2019-09-06)
        ~~~~~~~~~~~~~~~~~~
        
        -  Initial public release
        
Keywords: numeric,pandas,visualization,flask
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: License :: OSI Approved :: GNU Library or Lesser General Public License (LGPL)
Classifier: Operating System :: OS Independent
Classifier: Intended Audience :: Science/Research
Classifier: Programming Language :: Python
Classifier: Topic :: Scientific/Engineering
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3.6
Provides-Extra: arctic
Provides-Extra: r
