Metadata-Version: 2.1
Name: dtale
Version: 1.23.0
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
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
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Requires-Dist: dash (>=1.5.0)
Requires-Dist: dash-colorscales
Requires-Dist: dash-daq
Requires-Dist: Flask (>=1.0)
Requires-Dist: Flask-Compress
Requires-Dist: future (>=0.14.0)
Requires-Dist: itsdangerous
Requires-Dist: kaleido
Requires-Dist: pandas
Requires-Dist: plotly (>=4.9.0)
Requires-Dist: requests
Requires-Dist: scikit-learn (>='0.21.0)
Requires-Dist: scipy
Requires-Dist: squarify
Requires-Dist: statsmodels
Requires-Dist: strsimpy
Requires-Dist: six
Requires-Dist: xarray
Requires-Dist: lz4 (<=2.2.1) ; python_version < "3.0"
Requires-Dist: dash-bootstrap-components (<=0.10.5) ; python_version < "3.0"
Requires-Dist: lz4 ; python_version > "3.0"
Requires-Dist: dash-bootstrap-components ; python_version > "3.0"
Requires-Dist: flask-ngrok ; python_version > "3.0"
Provides-Extra: arctic
Requires-Dist: arctic ; extra == 'arctic'
Provides-Extra: r
Requires-Dist: rpy2 (<=2.8.6) ; (python_version < "3.0") and extra == 'r'
Requires-Dist: rpy2 ; (python_version > "3.0") and extra == 'r'
Provides-Extra: redis
Requires-Dist: redislite ; extra == 'redis'
Provides-Extra: streamlit
Requires-Dist: streamlit ; extra == 'streamlit'
Provides-Extra: tests
Requires-Dist: ipython ; extra == 'tests'
Requires-Dist: mock ; extra == 'tests'
Requires-Dist: pytest ; extra == 'tests'
Requires-Dist: pytest-cov ; extra == 'tests'
Requires-Dist: pytest-server-fixtures ; extra == 'tests'

|image0|

-  `Live Demo <http://alphatechadmin.pythonanywhere.com>`__
-  `Animated US COVID-19 Deaths By
   State <http://alphatechadmin.pythonanywhere.com/dtale/charts/3?chart_type=maps&query=date+%3E+%2720200301%27&agg=raw&map_type=choropleth&loc_mode=USA-states&loc=state_code&map_val=deaths&colorscale=Reds&cpg=false&animate_by=date>`__
-  `3D Scatter
   Chart <http://alphatechadmin.pythonanywhere.com/dtale/charts/4?chart_type=3d_scatter&query=&x=date&z=Col0&agg=raw&cpg=false&y=%5B%22security_id%22%5D>`__
-  `Surface
   Chart <http://alphatechadmin.pythonanywhere.com/dtale/charts/4?chart_type=surface&query=&x=date&z=Col0&agg=raw&cpg=false&y=%5B%22security_id%22%5D>`__

--------------

|CircleCI| |PyPI Python Versions| |PyPI| |Conda| |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
-----------

-  `4 Libraries that can perform EDA in one line of python
   code <https://towardsdatascience.com/4-libraries-that-can-perform-eda-in-one-line-of-python-code-b13938a06ae>`__
-  `React Status <https://react.statuscode.com/issues/204>`__
-  `KDNuggets <https://www.kdnuggets.com/2020/08/bring-pandas-dataframes-life-d-tale.html>`__
-  `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>`__
-  `FlaskCon 2020 <https://www.youtube.com/watch?v=BNgolmUWBp4&t=33s>`__
-  `San Diego
   Python <https://www.youtube.com/watch?v=fLsGur5YqeE&t=29s>`__
-  `Medium: towards data
   science <https://towardsdatascience.com/introduction-to-d-tale-5eddd81abe3f>`__
-  `Medium: Exploratory Data Analysis – Using
   D-Tale <https://medium.com/da-tum/exploratory-data-analysis-1-4-using-d-tale-99a2c267db79>`__
-  `EOD Notes: Using python and dtale to analyze
   correlations <https://www.google.com/amp/s/eod-notes.com/2020/05/07/using-python-and-dtale-to-analyze-correlations/amp/>`__

Tutorials
---------

-  `Pip Install Python YouTube
   Channel <https://m.youtube.com/watch?v=0RihZNdQc7k&feature=youtu.be>`__
-  `machine_learning_2019 <https://www.youtube.com/watch?v=-egtEUVBy9c>`__
-  `D-Tale The Best Library To Perform Exploratory Data Analysis Using
   Single Line Of
   Code🔥🔥🔥🔥 <https://www.youtube.com/watch?v=xSXGcuiEzUc>`__

## Related Resources

-  `Adventures In Flask While Developing
   D-Tale <https://github.com/man-group/dtale/blob/master/docs/FlaskCon/FlaskAdventures.md>`__

Contents
--------

-  `Where To Get It <#where-to-get-it>`__
-  `Getting Started <#getting-started>`__

   -  `Python Terminal <#python-terminal>`__
   -  `Jupyter Notebook <#jupyter-notebook>`__
   -  `Jupyterhub w/ Jupyter Server
      Proxy <#jupyterhub-w-jupyter-server-proxy>`__
   -  `Jupyterhub w/
      Kubernetes <https://github.com/man-group/dtale/blob/master/docs/JUPYTERHUB_KUBERNETES.md>`__
   -  `Google Colab <#google-colab>`__
   -  `Kaggle <#kaggle>`__
   -  `R with Reticulate <#r-with-reticulate>`__
   -  `Startup with No Data <#startup-with-no-data>`__
   -  `Command-line <#command-line>`__
   -  `Custom Command-line Loaders <#custom-command-line-loaders>`__
   -  `Embedding Within Your Own Flask
      App <https://github.com/man-group/dtale/blob/master/docs/EMBEDDED_FLASK.md>`__
   -  `Embedding Within Your Own Django
      App <https://github.com/man-group/dtale/blob/master/docs/EMBEDDED_DJANGO.md>`__
   -  `Embedding Within
      Streamlit <https://github.com/man-group/dtale/blob/master/docs/EMBEDDED_DTALE_STREAMLIT.md>`__
   -  `Running D-Tale On Gunicorn w/
      Redis <https://github.com/man-group/dtale/blob/master/docs/GUNICORN_REDIS.md>`__
   -  `Configuration <https://github.com/man-group/dtale/blob/master/docs/CONFIGURATION.md>`__

-  `UI <#ui>`__

   -  `Dimensions/Main Menu <#dimensionsmain-menu>`__
   -  `Header <#header>`__
   -  `Editing Cells <#editing-cells>`__
   -  `Copy Cells Into Clipboard <#copy-cells-into-clipboard>`__
   -  `Main Menu Functions <#main-menu-functions>`__

      -  `XArray Operations <#xarray-operations>`__,
         `Describe <#describe>`__, `Outlier
         Detection <#outlier-detection>`__, `Custom
         Filter <#custom-filter>`__, `Building
         Columns <#building-columns>`__, `Summarize
         Data <#summarize-data>`__, `Coverage
         (Deprecated) <#coverage-deprecated>`__,
         `Correlations <#correlations>`__, `Heat Map <#heat-map>`__,
         `Highlight Dtypes <#highlight-dtypes>`__, `Highlight
         Missing <#highlight-missing>`__, `Highlight
         Outliers <#highlight-outliers>`__, `Highlight
         Range <#highlight-range>`__, `Instances <#instances>`__, `Code
         Exports <#code-exports>`__, `Export CSV <#export-csv>`__, `Load
         Data & Sample Datasets <#load-data-&-sample-datasets>`__,
         `About <#about>`__, `Resize <#resize>`__,
         `Shutdown <#shutdown>`__

   -  `Column Menu Functions <#column-menu-functions>`__

      -  `Filtering <#filtering>`__, `Moving
         Columns <#moving-columns>`__, `Hiding
         Columns <#hiding-columns>`__, `Delete <#delete>`__,
         `Rename <#rename>`__, `Replacements <#replacements>`__,
         `Lock <#lock>`__, `Unlock <#unlock>`__, `Sorting <#sorting>`__,
         `Formats <#formats>`__, `Column Analysis <#column-analysis>`__

   -  `Charts <#charts>`__
   -  `Hotkeys <#hotkeys>`__
   -  `Menu Functions Depending on Browser
      Dimensions <#menu-functions-depending-on-browser-dimensions>`__

-  `For Developers <#for-developers>`__

   -  `Cloning <#cloning>`__
   -  `Running Tests <#running-tests>`__
   -  `Linting <#linting>`__
   -  `Formatting JS <#formatting-js>`__
   -  `Docker Development <#docker-development>`__

-  `Global State/Data
   Storage <https://github.com/man-group/dtale/blob/master/docs/GLOBAL_STATE.md>`__
-  `Startup Behavior <#startup-behavior>`__
-  `Documentation <#documentation>`__
-  `Dependencies <#dependencies>`__
-  `Acknowledgements <#acknowledgements>`__
-  `License <#license>`__

Where To get It
---------------

The source code is currently hosted on GitHub at:
https://github.com/man-group/dtale

Binary installers for the latest released version are available at the
`Python package index <https://pypi.org/project/dtale>`__ and on conda
using `conda-forge <https://github.com/conda-forge/dtale-feedstock>`__.

.. code:: sh

   # conda
   conda install dtale -c conda-forge
   # if you want to also use "Export to PNG" for charts
   conda install -c plotly python-kaleido

.. code:: sh

   # or PyPI
   pip install dtale

Getting Started
---------------

======== ========
PyCharm  jupyter
======== ========
|image8| |image9|
======== ========

Python Terminal
~~~~~~~~~~~~~~~

This comes courtesy of PyCharm |image10| 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 programmatically
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

.. 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: |image11|

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
============== ========== =========
|image12|      |image13|  |image14|
============== ========== =========

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:

|image15|

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
============ ============ ========= =============== =========
|image16|    |image17|    |image18| |image19|       |image20|
============ ============ ========= =============== =========

JupyterHub w/ Jupyter Server Proxy
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

JupyterHub has an extension that allows to proxy port for user,
`JupyterHub Server
Proxy <https://github.com/jupyterhub/jupyter-server-proxy>`__

To me it seems like this extension might be the best solution to getting
D-Tale running within kubernetes. Here’s how to use it:

.. code:: python

   import pandas as pd

   import dtale
   import dtale.app as dtale_app

   dtale_app.JUPYTER_SERVER_PROXY = True

   dtale.show(pd.DataFrame([1,2,3]))

Notice the command ``dtale_app.JUPYTER_SERVER_PROXY = True`` this will
make sure that any D-Tale instance will be served with the jupyter
server proxy application root prefix:

``/user/{jupyter username}/proxy/{dtale instance port}/``

One thing to note is that if you try to look at the ``_main_url`` of
your D-Tale instance in your notebook it will not include the hostname
or port:

.. code:: python

   import pandas as pd

   import dtale
   import dtale.app as dtale_app

   dtale_app.JUPYTER_SERVER_PROXY = True

   d = dtale.show(pd.DataFrame([1,2,3]))
   d._main_url # /user/johndoe/proxy/40000/dtale/main/1

This is because it’s very hard to promgramatically figure out the
host/port that your notebook is running on. So if you want to look at
``_main_url`` please be sure to preface it with:

``http[s]://[jupyterhub host]:[jupyterhub port]``

If for some reason jupyterhub changes their API so that the application
root changes you can also override D-Tale’s application root by using
the ``app_root`` parameter to the ``show()`` function:

.. code:: python

   import pandas as pd

   import dtale
   import dtale.app as dtale_app

   dtale.show(pd.DataFrame([1,2,3]), app_root='/user/johndoe/proxy/40000/`)

Using this parameter will only apply the application root to that
specific instance so you would have to include it on every call to
``show()``.

JupyterHub w/ Kubernetes
~~~~~~~~~~~~~~~~~~~~~~~~

Please read this
`post <https://github.com/man-group/dtale/blob/master/docs/JUPYTERHUB_KUBERNETES.md>`__

Google Colab
~~~~~~~~~~~~

This is a hosted notebook site and thanks to Colab’s internal function
``google.colab.output.eval_js`` & the JS function
``google.colab.kernel.proexyPort`` users can run D-Tale within their
notebooks.

**DISCLAIMER:** It is import that you set ``USE_COLAB`` to true when
using D-Tale within this service. Here is an example:

.. code:: python

   import pandas as pd

   import dtale
   import dtale.app as dtale_app

   dtale_app.USE_COLAB = True

   dtale.show(pd.DataFrame([1,2,3]))

If this does not work for you try using ``USE_NGROK`` which is described
in the next section.

Kaggle
~~~~~~

This is yet another hosted notebook site 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 this service. Here is an example:

.. code:: python

   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          | |image21|             |                       |
+-----------------------+-----------------------+-----------------------+
| Kaggle                | |image22|             | make sure you switch  |
|                       |                       | the “Internet” toggle |
|                       |                       | to “On” under         |
|                       |                       | settings of your      |
|                       |                       | notebook so you can   |
|                       |                       | install the egg from  |
|                       |                       | pip                   |
+-----------------------+-----------------------+-----------------------+

It is important to note that using NGROK will limit you to 20
connections per mintue so if you see this error:

|image23|

Wait a little while and it should allow you to do work again. I am
actively working on finding a more sustainable solution similar to what
I did for google colab. :pray:

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

Startup with No Data
~~~~~~~~~~~~~~~~~~~~

It is now possible to run D-Tale with no data loaded up front. So simply
call ``dtale.show()`` and this will start the application for you and
when you go to view it you will be presented with a screen where you can
upload either a CSV or TSV file for data.

|image24|

Once you’ve loaded a file it will take you directly to the standard data
grid comprised of the data from the file you loaded. This might make it
easier to use this as an on demand application within a container
management system like kubernetes. You start and stop these on demand
and you’ll be presented with a new instance to load any CSV or TSV file
to!

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>`__ (this requires
either installing **arctic** or **dtale[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

Custom Command-line Loaders
~~~~~~~~~~~~~~~~~~~~~~~~~~~

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'

Accessing CLI Loaders in Notebook or Console
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

I am pleased to announce that all CLI loaders will be available within
notebooks & consoles. Here are some examples (the last working if you’ve
installed ``dtale[arctic]``): -
``dtale.show_csv(path='test.csv', parse_dates=['date'])`` -
``dtale.show_csv(path='http://csv-endpoint', index_col=0)`` -
``dtale.show_json(path='http://json-endpoint', parse_dates=['date'])`` -
``dtale.show_json(path='test.json', parse_dates=['date'])`` -
``dtale.show_arctic(host='host', library='library', node='node', start_date='20200101', end_date='20200101')``

UI
--

Once you have kicked off your D-Tale session please copy & paste the
link on the last line of output in your browser |image25|

Dimensions/Main Menu
~~~~~~~~~~~~~~~~~~~~

The information in the upper right-hand corner gives grid dimensions
|image26| - lower-left => row count - upper-right => column count -
clicking the triangle displays the menu of standard functions (click
outside menu to close it)

|image27|

Header
~~~~~~

The header gives users an idea of what operations have taken place on
your data (sorts, filters, hidden columns). These values will be
persisted across broswer instances. So if you perform one of these
operations and then send a link to one of your colleagues they will see
the same thing :)

Notice the “X” icon on the right of each display. Clicking this will
remove those operations.

When performing multiple of the same operation the description will
become too large to display so the display will truncate the description
and if users click it they will be presented with a tooltip where you
can crop individual operations. Here are some examples:

========= ========= ==============
Sorts     Filters   Hidden Columns
========= ========= ==============
|image28| |image29| |image30|
========= ========= ==============

Editing Cells
~~~~~~~~~~~~~

You may edit any cells in your grid (with the exception of the row
indexes or headers, the ladder can be edited using the
`Rename <#rename>`__ column menu function).

In order to eddit a cell simply double-click on it. This will convert it
into a text-input field and you should see a blinking cursor. It is
assumed that the value you type in will match the data type of the
column you editing. For example:

-  integers -> should be a valid positive or negative integer
-  float -> should be a valid positive or negative float
-  string -> any valid string will do
-  category -> either a pre-existing category or this will create a new
   category for (so beware!)
-  date, timestamp, timedelta -> should be valid string versions of each
-  boolean -> any string you input will be converted to lowercase and if
   it equals “true” then it will make the cell ``True``, otherwise
   ``False``

Users can make use of two protected values as well:

-  “nan” -> ``numpy.nan``
-  “inf” -> ``numpy.inf``

To save your change simply press “Enter” or to cancel your changes press
“Esc”.

If there is a conversion issue with the value you have entered it will
display a popup with the specific exception in question.

Here’s a quick demo:

|image31|

Copy Cells Into Clipboard
~~~~~~~~~~~~~~~~~~~~~~~~~

========= ========= =========
Select    Copy      Paste
========= ========= =========
|image32| |image33| |image34|
========= ========= =========

One request that I have heard time and time again while working on
D-Tale is “it would be great to be able to copy a range of cells into
excel”. Well here is how that is accomplished: 1) Shift + Click on a
cell 2) Shift + Click on another cell (this will trigger a popup) 3)
Choose whether you want to include headers in your copy by clicking the
checkbox 4) Click Yes 5) Go to your excel workbook and execute Ctrl + V
or manually choose “Paste” \* You can also paste this into a standard
text editor and what you’re left with is tab-delimited data

Main Menu Functions
~~~~~~~~~~~~~~~~~~~

XArray Operations
^^^^^^^^^^^^^^^^^

-  **Convert To XArray**: If you have are currently viewing a pandas
   dataframe in D-Tale you will be given this option to convert your
   data to an ``xarray.Dataset``. It is as simple as selecting one or
   many columns as an index and then your dataframe will be converted to
   a dataset (``df.set_index([...]).to_xarray()``) which makes toggling
   between indexes slices much easier.

|image35|

-  **XArray Dimensions**: If you are currently viewing data associated
   with an ``xarray.Dataset`` you will be given the ability to toggle
   which dimension coordinates you’re viewing by clicking this button.
   You can select values for all, some or none (all data - no filter) of
   your coordinates and the data displayed in your grid will match your
   selections. Under the hood the code being executed is as follows:
   ``ds.sel(dim1=coord1,...).to_dataframe()``

|image36|

Describe
^^^^^^^^

View all the columns & their data types as well as individual details of
each column

|image37|

+-----------------------+-----------------------+-----------------------+
| Data Type             | Display               | Notes                 |
+=======================+=======================+=======================+
| date                  | |image38|             |                       |
+-----------------------+-----------------------+-----------------------+
| string                | |image39|             | If you have less than |
|                       |                       | or equal to 100       |
|                       |                       | unique values they    |
|                       |                       | will be displayed at  |
|                       |                       | the bottom of your    |
|                       |                       | popup                 |
+-----------------------+-----------------------+-----------------------+
| int                   | |image40|             | 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                 | |image41|             |                       |
+-----------------------+-----------------------+-----------------------+

Outlier Detection
^^^^^^^^^^^^^^^^^

When viewing integer & float columns in the `“Describe”
popup <#describe>`__ you will see in the lower right-hand corner a
toggle for Uniques & Outliers.

========= =========
Outliers  Filter
========= =========
|image42| |image43|
========= =========

If you click the “Outliers” toggle this will load the top 100 outliers
in your column based on the following code snippet:

.. code:: python

   s = df[column]
   q1 = s.quantile(0.25)
   q3 = s.quantile(0.75)
   iqr = q3 - q1
   iqr_lower = q1 - 1.5 * iqr
   iqr_upper = q3 + 1.5 * iqr
   outliers = s[(s < iqr_lower) | (s > iqr_upper)]

If you click on the “Apply outlier filter” link this will add an
addtional “outlier” filter for this column which can be removed from the
`header <#header>`__ or the `custom filter <#custom-filter>`__ shown in
picture above to the right.

Custom Filter
^^^^^^^^^^^^^

Apply a custom pandas ``query`` to your data (link to pandas
documentation included in popup)

========= =========
Editing   Result
========= =========
|image44| |image45|
========= =========

You can also see any outlier or column filters you’ve applied (which
will be included in addition to your custom query) and remove them if
you’d like.

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))``

Here’s some nice documentation on the performance of `pandas
queries <https://pandas.pydata.org/pandas-docs/stable/user_guide/enhancingperf.html#pandas-eval-performance>`__

Building Columns
^^^^^^^^^^^^^^^^

|image46|

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) - Random: columns of data type (int,
float, string & date) populated with random uniformly distributed
values. - Type Conversion: switch columns from one data type to another,
fun. :smile:

Summarize Data
^^^^^^^^^^^^^^

This is very powerful functionality which allows users to create a new
data from currently loaded data. The operations currently available are:
- **Aggregation**: consolidate data by running different aggregations on
columns by a specific index - **Pivot**: this is simple wrapper around
`pandas.Dataframe.pivot <https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.pivot.html>`__
and
`pandas.pivot_table <https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.pivot_table.html>`__
- **Transpose**: transpose your data on a index (be careful dataframes
can get very wide if your index has many unique values)

============ =========
Function     Data
============ =========
No Reshaping |image47|
|image48|    |image49|
|image50|    |image51|
|image52|    |image53|
|image54|    |image55|
============ =========

|image56|

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               |image57| |image58|
bar                |image59| |image60|
stacked            |image61| |image62|
pie                |image63| 
wordcloud          |image64| |image65|
heatmap            |image66| 
3D scatter         |image67| 
surface            |image68| 
Maps (Scatter GEO) |image69| 
Maps (Choropleth)  |image70| 
================== ========= ===============

Y-Axis Toggling

Users now have the ability to toggle between 3 different behaviors for
their y-axis display: - *Default*: selecting this option will use the
default behavior that comes with plotly for your chart’s y-axis -
*Single*: this allows users to set the range of all series in your chart
to be on the same basis as well as making that basis (min/max) editable
- *Multi*: this allows users to give each series its own y-axis and
making that axis’ range editable

Here’s a quick tutorial: |image71|

And some screenshots:

========= ========= =========
Default   Single    Multi
========= ========= =========
|image72| |image73| |image74|
========= ========= =========

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
========= =========
|image75| |image76|
========= =========

**Popup Charts**

Viewing multiple charts at once and want to separate one out into its
own window or simply move one off to the side so you can work on
building another for comparison? Well now you can by clicking the
“Popup” button :smile:

**Copy Link**

Want to send what you’re looking at to someone else? Simply click the
“Copy Link” button and it will save a pre-populated chart URL into your
clipboard. As long as your D-Tale process is still running when that
link is opened you will see your original chart.

**Exporting Charts**

You can now export your dash charts (with the exception of Wordclouds)
to static HTML files which can be emailed to others or saved down to be
viewed at a later time. The best part is that all of the javascript for
plotly is embedded in these files so the nice zooming, panning, etc is
still available! :boom:

**Exporting CSV**

I’ve been asked about being able to export the data that is contained
within your chart to a CSV for further analysis in tools like Excel.
This button makes that possible.

OFFLINE CHARTS
~~~~~~~~~~~~~~

Want to run D-Tale in a jupyter notebook and build a chart that will
still be displayed even after your D-Tale process has shutdown? Now you
can! Here’s an example code snippet show how to use it:

::

   import dtale

   def test_data():
       import random
       import pandas as pd
       import numpy as np

       df = pd.DataFrame([
           dict(x=i, y=i % 2)
           for i in range(30)
       ])
       rand_data = pd.DataFrame(np.random.randn(len(df), 5), columns=['z{}'.format(j) for j in range(5)])
       return pd.concat([df, rand_data], axis=1)

   d = dtale.show(test_data())
   d.offline_chart(chart_type='bar', x='x', y='z3', agg='sum')

|image77|

**Pro Tip: If generating offline charts in jupyter notebooks and you run
out of memory please add the following to your command-line when
starting jupyter**

``--NotebookApp.iopub_data_rate_limit=1.0e10``

**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>`__

**Your Feedback is Valuable**

This is a very powerful feature with many more features that could be
offered (linked subplots, different statistical aggregations, etc…) so
please submit issues :)

Coverage (Deprecated)
^^^^^^^^^^^^^^^^^^^^^

If you have watched the video within the `Man
Institute <https://www.man.com/maninstitute/d-tale>`__ blog post you’ll
notice that there is a “Coverage” popup. This was deprecated with the
creation of the “Charts” page. You can create the same coverage chart in
that video by choosing the following options in the “Charts” page: -
Type: **Line** - X: **date** - Y: **security_id** - Aggregation:
**Count** or **Unique Count**

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
========= ========== =========
|image78| |image79|  |image80|
========= ========== =========

============= ============= ====================
Col1 Filtered Col2 Filtered Col1 & Col2 Filtered
============= ============= ====================
|image81|     |image82|     |image83|
============= ============= ====================

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
========= ============
|image84| |image85|
========= ============

Heat Map
^^^^^^^^

This will hide any non-float or non-int 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
-  You have two options for the renormalization

   -  **By Col**: each value is calculated based on the min/max of its
      column
   -  **Overall**: each value is caluclated by the overall min/max of
      all the non-hidden float/int columns in the dataset

-  Each renormalized value is passed to a color scale of red(0) -
   yellow(0.5) - green(1.0) |image86|

Turn off Heat Map by clicking menu option you previously selected one
more time

Highlight Dtypes
^^^^^^^^^^^^^^^^

This is a quick way to check and see if your data has been categorized
correctly. By clicking this menu option it will assign a specific
background color to each column of a specific data type.

======== ========= ===== ========== ==== ====== ======
category timedelta float int        date string bool
======== ========= ===== ========== ==== ====== ======
purple   orange    green light blue pink white  yellow
======== ========= ===== ========== ==== ====== ======

|image87|

Highlight Missing
^^^^^^^^^^^^^^^^^

-  Any cells which contain ``nan`` values will be highlighted in yellow.
-  Any string column cells which are empty strings or strings consisting
   only of spaces will be highlighted in orange.
-  ❗will be prepended to any column header which contains missing
   values.

|image88|

Highlight Outliers
^^^^^^^^^^^^^^^^^^

Highlight any cells for numeric columns which surpass the upper or lower
bounds of a `custom outlier computation <#outlier-detection>`__. \*
Lower bounds outliers will be on a red scale, where the darker reds will
be near the maximum value for the column. \* Upper bounds outliers will
be on a blue scale, where the darker blues will be closer to the minimum
value for the column. \* ⭐ will be prepended to any column header which
contains outliers.

|image89|

Highlight Range
^^^^^^^^^^^^^^^

Highlight any range of numeric cells based on three different criteria:
\* equals \* greater than \* less than

You can activate as many of these criteria as you’d like nad they will
be treated as an “or” expression. For example,
``(x == 0) or (x < -1) or (x > 1)``

========== =========
Selections Output
========== =========
|image90|  |image91|
========== =========

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

|image92|

====================== ===========
Type                   Code Export
====================== ===========
Main Grid              |image93|
Histogram              |image94|
Describe               |image95|
Correlation Grid       |image96|
Correlation Timeseries |image97|
Correlation Scatter    |image98|
Charts                 |image99|
====================== ===========

Export CSV
^^^^^^^^^^

Export your current data to either a CSV or TSV file:

|image100|

Load Data & Sample Datasets
^^^^^^^^^^^^^^^^^^^^^^^^^^^

So either when starting D-Tale with no pre-loaded data or after you’ve
already loaded some data you now have the ability to load data or choose
from some sample datasets directly from the GUI:

|image101|

Here’s the options at you disposal: \* Load a CSV/TSV file by dragging a
file to the dropzone in the top or select a file by clicking the
dropzone \* Load a CSV/TSV or JSON directly from the web by entering a
URL (also throw in a proxy if you are using one) \* Choose from one of
our sample datasets: \* US COVID-19 data from NY Times (updated daily)
\* Script breakdowns of popular shows Seinfeld & The Simpsons \* Movie
dataset containing release date, director, actors, box office, reviews…
\* Video games and their sales \* pandas.util.testing.makeTimeDataFrame

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:

|image102|

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:

|image103|

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
========== ===========
|image104| |image105|
========== ===========

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
~~~~~~~~~~~~~~~~~~~~~

|image106|

Filtering
^^^^^^^^^

|image107|

These interactive filters come in 3 different types: String, Numeric &
Date. Note that you will still have the ability to apply custom filters
from the “Filter” popup on the main menu, but it will get applied in
addition to any column filters.

+---------+--------------+------------------------+-------------------+
| Type    | Filter       | Data Types             | Features          |
+=========+==============+========================+===================+
| String  | |image108|   | strings & booleans     | The ability to    |
|         |              |                        | select multiple   |
|         |              |                        | values based on   |
|         |              |                        | what exists in    |
|         |              |                        | the column.       |
|         |              |                        | Notice the “Show  |
|         |              |                        | Missing Only”     |
|         |              |                        | toggle, this will |
|         |              |                        | only show up if   |
|         |              |                        | your column has   |
|         |              |                        | nan values        |
+---------+--------------+------------------------+-------------------+
| Date    | |image109|   | dates                  | Specify a range   |
|         |              |                        | of dates to       |
|         |              |                        | filter on based   |
|         |              |                        | on start & end    |
|         |              |                        | inputs            |
+---------+--------------+------------------------+-------------------+
| Numeric | |image110|   | ints & floats          | For integers the  |
|         |              |                        | “=” will be       |
|         |              |                        | similar to        |
|         |              |                        | strings where you |
|         |              |                        | can select        |
|         |              |                        | multiple values   |
|         |              |                        | based on what     |
|         |              |                        | exists in the     |
|         |              |                        | column. You also  |
|         |              |                        | have access to    |
|         |              |                        | other operands:   |
|         |              |                        | <,>,<=,>=,() -    |
|         |              |                        | “Range exclusve”, |
|         |              |                        | [] - “Range       |
|         |              |                        | inclusive”.       |
+---------+--------------+------------------------+-------------------+

Moving Columns
^^^^^^^^^^^^^^

|image111|

All column movements are saved on the server so refreshing your browser
won’t lose them :ok_hand:

Hiding Columns
^^^^^^^^^^^^^^

|image112|

All column movements are saved on the server so refreshing your browser
won’t lose them :ok_hand:

Delete
^^^^^^

As simple as it sounds, click this button to delete this column from
your dataframe.

Rename
^^^^^^

Update the name of any column in your dataframe to a name that is not
currently in use by your dataframe.

|image113|

Replacements
^^^^^^^^^^^^

This feature allows users to replace content on their column directly or
for safer purposes in a brand new column. Here are the options you have:

+------------------+--------------------+-----------------+------------+
| Type             | Data Types         | Description     | Menu       |
+==================+====================+=================+============+
| Value(s)         | all                | Replace         | |image114| |
|                  |                    | specific values |            |
|                  |                    | in a column     |            |
|                  |                    | with raw        |            |
|                  |                    | values, output  |            |
|                  |                    | from another    |            |
|                  |                    | column or an    |            |
|                  |                    | aggregation on  |            |
|                  |                    | your column     |            |
+------------------+--------------------+-----------------+------------+
| Spaces Only      | strings            | Replace string  | |image115| |
|                  |                    | values          |            |
|                  |                    | consisting only |            |
|                  |                    | of spaces with  |            |
|                  |                    | raw values      |            |
+------------------+--------------------+-----------------+------------+
| Contains         | strings            | Replace string  | |image116| |
| Char/Substring   |                    | values          |            |
|                  |                    | containing a    |            |
|                  |                    | specific        |            |
|                  |                    | character or    |            |
|                  |                    | substring       |            |
+------------------+--------------------+-----------------+------------+
| Scikit-Learn     | numeric            | Replace missing | |image117| |
| Imputer          |                    | values with the |            |
|                  |                    | output of using |            |
|                  |                    | different       |            |
|                  |                    | Scikit-Learn    |            |
|                  |                    | imputers like   |            |
|                  |                    | iterative, knn  |            |
|                  |                    | & simple        |            |
+------------------+--------------------+-----------------+------------+

Here’s a quick demo: |image118|

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 |image119| |image120|
Date    |image121| |image122|
String  |image123| |image124|
======= ========== ==========

For all data types you have the ability to change what string is ued for
display.

For numbers 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
============= ==============

For strings you can apply the follwoing formats: \* **Truncation:**
truncate long strings to a certain number of characters and replace with
an allipses “…” and see the whole value on hover. \* **Hyperlinks:** If
your column is comprised of URL strings you can make them hyperlinks
which will open a new tab

Column Analysis
^^^^^^^^^^^^^^^

Based on the data type of a column different charts will be shown.

============ ================================= ==========
Chart        Data Types                        Sample
============ ================================= ==========
Histogram    Float, Int                        |image125|
Value Counts Int, String, Bool, Date, Category |image126|
Category     Float                             |image127|
============ ================================= ==========

**Histogram** can be displayed in any number of bins (default: 20),
simply type a new integer value in the bins input

**Value Count** by default, show the top 100 values ranked by frequency.
If you would like to show the least frequent values simply make your
number negative (-10 => 10 least frequent value)

**Value Count w/ Ordinal** you can also apply an ordinal to your **Value
Count** chart by selecting a column (of type int or float) and applying
an aggregation (default: sum) to it (sum, mean, etc…) this column will
be grouped by the column you’re analyzing and the value produced by the
aggregation will be used to sort your bars and also displayed in a line.
Here’s an example:

|image128|

**Category (Category Breakdown)** when viewing float columns you can
also see them broken down by a categorical column (string, date, int,
etc…). This means that when you select a category column this will then
display the frequency of each category in a line as well as bars based
on the float column you’re analyzing grouped by that category and
computed by your aggregation (default: mean).

Hotkeys
~~~~~~~

These are key combinations you can use in place of clicking actual
buttons to save a little time:

=========== =================================================
Keymap      Action
=========== =================================================
``shift+m`` Opens main menu\*
``shift+d`` Opens “Describe” page\*
``shift+f`` Opens “Custom Filter”\*
``shift+b`` Opens “Build Column”\*
``shift+c`` Opens “Charts” page\*
``shift+x`` Opens “Code Export”\*
``esc``     Closes any open modal window & exits cell editing
=========== =================================================

``*`` Does not fire if user is actively editing a cell.

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

Global State/Data Storage
-------------------------

If D-Tale is running in an environment with multiple python processes
(ex: on a web server running
`gunicorn <https://github.com/benoitc/gunicorn>`__) it will most likely
encounter issues with inconsistent state. Developers can fix this by
configuring the system D-Tale uses for storing data. Detailed
documentation is available here: `Data Storage and managing Global
State <https://github.com/man-group/dtale/blob/master/docs/GLOBAL_STATE.md>`__

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>`__.

Dependencies
------------

-  Back-end

   -  dash
   -  dash_daq
   -  Flask
   -  Flask-Compress
   -  flask-ngrok
   -  Pandas
   -  plotly
   -  scikit-learn
   -  scipy
   -  xarray
   -  arctic [extra]
   -  redis [extra]
   -  rpy2 [extra]

-  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>`__
-  `Fernando Saravia Rajal <https://github.com/fersarr>`__
-  `Dominik Christ <https://github.com/DominikMChrist>`__
-  `Reza Moshksar <https://github.com/reza1615>`__
-  `Bertrand Nouvel <https://github.com/bnouvelbmll>`__
-  `Chris Boddy <https://github.com/cboddy>`__
-  `Jason Holden <https://github.com/jasonkholden>`__
-  `Tom Taylor <https://github.com/TomTaylorLondon>`__
-  `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 Python Versions| image:: https://img.shields.io/pypi/pyversions/dtale.svg
   :target: https://pypi.python.org/pypi/dtale/
.. |PyPI| image:: https://img.shields.io/pypi/v/dtale
   :target: https://pypi.org/project/dtale/
.. |Conda| image:: https://img.shields.io/conda/v/conda-forge/dtale
   :target: https://anaconda.org/conda-forge/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
.. |image8| image:: https://raw.githubusercontent.com/aschonfeld/dtale-media/master/gifs/dtale_demo_mini.gif
.. |image9| image:: https://raw.githubusercontent.com/aschonfeld/dtale-media/master/gifs/dtale_ipython.gif
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.. |image19| image:: https://raw.githubusercontent.com/aschonfeld/dtale-media/master/images/histogram_popup.png
.. |image20| image:: https://raw.githubusercontent.com/aschonfeld/dtale-media/master/images/instances_popup.png
.. |image21| image:: http://img.youtube.com/vi/pOYl2M1clIw/0.jpg
   :target: http://www.youtube.com/watch?v=pOYl2M1clIw
.. |image22| image:: http://img.youtube.com/vi/8Il-2HHs2Mg/0.jpg
   :target: http://www.youtube.com/watch?v=8Il-2HHs2Mg
.. |image23| image:: https://raw.githubusercontent.com/aschonfeld/dtale-media/master/images/ngrok_limit.png
.. |image24| image:: https://raw.githubusercontent.com/aschonfeld/dtale-media/master/images/no_data.png
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.. |image27| image:: https://raw.githubusercontent.com/aschonfeld/dtale-media/master/images/Info_menu_small.png
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   :target: http://www.youtube.com/watch?v=MY5w0m_4IAc
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.. |image36| image:: https://raw.githubusercontent.com/aschonfeld/dtale-media/master/images/xarray_dimensions.png
.. |image37| image:: https://raw.githubusercontent.com/aschonfeld/dtale-media/master/images/Describe.png
.. |image38| image:: https://raw.githubusercontent.com/aschonfeld/dtale-media/master/images/Describe_date.png
.. |image39| image:: https://raw.githubusercontent.com/aschonfeld/dtale-media/master/images/Describe_string.png
.. |image40| image:: https://raw.githubusercontent.com/aschonfeld/dtale-media/master/images/Describe_int.png
.. |image41| image:: https://raw.githubusercontent.com/aschonfeld/dtale-media/master/images/Describe_float.png
.. |image42| image:: https://raw.githubusercontent.com/aschonfeld/dtale-media/master/images/outliers.png
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.. |image45| image:: https://raw.githubusercontent.com/aschonfeld/dtale-media/master/images/Post_filter.png
.. |image46| image:: http://img.youtube.com/vi/G6wNS9-lG04/0.jpg
   :target: http://www.youtube.com/watch?v=G6wNS9-lG04
.. |image47| image:: https://raw.githubusercontent.com/aschonfeld/dtale-media/master/images/reshape/original_data.png
.. |image48| image:: https://raw.githubusercontent.com/aschonfeld/dtale-media/master/images/reshape/agg_col.png
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.. |image55| image:: https://raw.githubusercontent.com/aschonfeld/dtale-media/master/images/reshape/transpose_data.png
.. |image56| image:: http://img.youtube.com/vi/fYsxogXKZ2c/0.jpg
   :target: http://www.youtube.com/watch?v=fYsxogXKZ2c
.. |image57| image:: https://raw.githubusercontent.com/aschonfeld/dtale-media/master/images/charts/line.png
.. |image58| image:: https://raw.githubusercontent.com/aschonfeld/dtale-media/master/images/charts/line_pg.png
.. |image59| image:: https://raw.githubusercontent.com/aschonfeld/dtale-media/master/images/charts/bar.png
.. |image60| image:: https://raw.githubusercontent.com/aschonfeld/dtale-media/master/images/charts/bar_pg.png
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.. |image68| image:: https://raw.githubusercontent.com/aschonfeld/dtale-media/master/images/charts/surface.png
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.. |image70| image:: https://raw.githubusercontent.com/aschonfeld/dtale-media/master/images/charts/choropleth.png
.. |image71| image:: http://img.youtube.com/vi/asblF-rAACY/0.jpg
   :target: http://www.youtube.com/watch?v=asblF-rAACY
.. |image72| image:: https://raw.githubusercontent.com/aschonfeld/dtale-media/master/images/axis_toggle/default.PNG
.. |image73| image:: https://raw.githubusercontent.com/aschonfeld/dtale-media/master/images/axis_toggle/single.PNG
.. |image74| image:: https://raw.githubusercontent.com/aschonfeld/dtale-media/master/images/axis_toggle/multi.PNG
.. |image75| image:: https://raw.githubusercontent.com/aschonfeld/dtale-media/master/images/charts/bar_presort.png
.. |image76| image:: https://raw.githubusercontent.com/aschonfeld/dtale-media/master/images/charts/bar_postsort.png
.. |image77| image:: http://img.youtube.com/vi/DseSmc3fZvc/0.jpg
   :target: http://www.youtube.com/watch?v=DseSmc3fZvc
.. |image78| image:: https://raw.githubusercontent.com/aschonfeld/dtale-media/master/images/Correlations.png
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.. |image92| image:: http://img.youtube.com/vi/6CkKgpv3d6I/0.jpg
   :target: http://www.youtube.com/watch?v=6CkKgpv3d6I
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.. |image99| image:: https://raw.githubusercontent.com/aschonfeld/dtale-media/master/images/code_export/charts.png
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.. |image102| image:: https://raw.githubusercontent.com/aschonfeld/dtale-media/master/images/Instances.png
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.. |image106| image:: https://raw.githubusercontent.com/aschonfeld/dtale-media/master/images/Col_menu.png
.. |image107| image:: http://img.youtube.com/vi/8zo5ZiI1Yzo/0.jpg
   :target: http://www.youtube.com/watch?v=8zo5ZiI1Yzo
.. |image108| image:: https://raw.githubusercontent.com/aschonfeld/dtale-media/master/images/filters/string.PNG
.. |image109| image:: https://raw.githubusercontent.com/aschonfeld/dtale-media/master/images/filters/dates.PNG
.. |image110| image:: https://raw.githubusercontent.com/aschonfeld/dtale-media/master/images/filters/numeric.PNG
.. |image111| image:: http://img.youtube.com/vi/We4TH477rRs/0.jpg
   :target: http://www.youtube.com/watch?v=We4TH477rRs
.. |image112| image:: http://img.youtube.com/vi/ryZT2Lk_YaA/0.jpg
   :target: http://www.youtube.com/watch?v=ryZT2Lk_YaA
.. |image113| image:: https://raw.githubusercontent.com/aschonfeld/dtale-media/master/images/rename.png
.. |image114| image:: https://raw.githubusercontent.com/aschonfeld/dtale-media/master/images/replacements_value.png
.. |image115| image:: https://raw.githubusercontent.com/aschonfeld/dtale-media/master/images/replacements_spaces.png
.. |image116| image:: https://raw.githubusercontent.com/aschonfeld/dtale-media/master/images/replacements_strings.png
.. |image117| image:: https://raw.githubusercontent.com/aschonfeld/dtale-media/master/images/replacements_sklearn.png
.. |image118| image:: http://img.youtube.com/vi/GiNFRtcpIt8/0.jpg
   :target: http://www.youtube.com/watch?v=GiNFRtcpIt8
.. |image119| image:: https://raw.githubusercontent.com/aschonfeld/dtale-media/master/images/Formatting_apply.png
.. |image120| image:: https://raw.githubusercontent.com/aschonfeld/dtale-media/master/images/Post_formatting.png
.. |image121| image:: https://raw.githubusercontent.com/aschonfeld/dtale-media/master/images/Formatting_date_apply.png
.. |image122| image:: https://raw.githubusercontent.com/aschonfeld/dtale-media/master/images/Post_date_formatting.png
.. |image123| image:: https://raw.githubusercontent.com/aschonfeld/dtale-media/master/images/Formatting_string_apply.png
.. |image124| image:: https://raw.githubusercontent.com/aschonfeld/dtale-media/master/images/Post_string_formatting.png
.. |image125| image:: https://raw.githubusercontent.com/aschonfeld/dtale-media/master/images/analysis/histogram.PNG
.. |image126| image:: https://raw.githubusercontent.com/aschonfeld/dtale-media/master/images/analysis/value_counts.PNG
.. |image127| image:: https://raw.githubusercontent.com/aschonfeld/dtale-media/master/images/analysis/category.PNG
.. |image128| image:: https://raw.githubusercontent.com/aschonfeld/dtale-media/master/images/analysis/value_counts_ordinal.PNG

Changelog
---------

1.23.0 (2020-11-21)
~~~~~~~~~~~~~~~~~~~

-  Added better handling for ``open_browser``
-  `#319 <https://github.com/man-group/dtale/issues/319>`__: fix for
   loading xarray dimensions
-  Added support for embedding D-Tale within Streamlit

.. _section-1:

1.22.1 (2020-11-15)
~~~~~~~~~~~~~~~~~~~

-  additional updates to how int/float hex conversions work

.. _section-2:

1.22.0 (2020-11-14)
~~~~~~~~~~~~~~~~~~~

-  `#317 <https://github.com/man-group/dtale/issues/317>`__: convert
   int/float to hexadecimal
-  Copy Columns/Rows to clipboard using Shift+Click & Ctrl+Click
-  Added “items” function to global state mechanisms
-  `#296 <https://github.com/man-group/dtale/issues/296>`__: heatmap
   doesn’t respect current filters

.. _section-3:

1.21.1 (2020-11-8)
~~~~~~~~~~~~~~~~~~

-  Additional fixes for #313 & #302

   -  Handling for partial ``.ini`` files
   -  Handling for dictionary inputs w/ non-iterable values

.. _section-4:

1.21.0 (2020-11-6)
~~~~~~~~~~~~~~~~~~

-  `#313 <https://github.com/man-group/dtale/issues/313>`__: support for
   numpy.array, lists & dictionaries
-  `#302 <https://github.com/man-group/dtale/issues/302>`__:
   configuration file for default options
-  Removal of legacy charting code & updating flask route to plotly dash
   charts from ``/charts`` to ``/dtale/charts``
-  Update to how routes are overriden so it will work with gunicorn
-  Documentation

   -  running within gunicorn
   -  embedding in another Flask or Django app
   -  configuration settings

.. _section-5:

1.20.0 (2020-11-1)
~~~~~~~~~~~~~~~~~~

-  `#311 <https://github.com/man-group/dtale/issues/311>`__: png chart
   exports and fix for trandlines in exports
-  Added the option to switch grid to “Dark Mode”

.. _section-6:

1.19.2 (2020-10-25)
~~~~~~~~~~~~~~~~~~~

-  Documentation updates & better formatting of sample dataset buttons
-  bugfixes for loading empty dtale in a notebook and chart display in
   embedded app demo

.. _section-7:

1.19.1 (2020-10-24)
~~~~~~~~~~~~~~~~~~~

-  Load CSV/TSV/JSON from the web as well as some sample datasets
-  `#310 <https://github.com/man-group/dtale/issues/310>`__: handling
   for nan in ordinal & label encoders

.. _section-8:

1.19.0 (2020-10-23)
~~~~~~~~~~~~~~~~~~~

-  `#123 <https://github.com/man-group/dtale/issues/123>`__: selecting a
   range of cells to paste into excel
-  Documentation on embedding D-Tale in another Flask app
-  bugfix for altering single y-axis ranges when x-axis is non-numeric
-  `#272 <https://github.com/man-group/dtale/issues/272>`__: encoders
   for categorical columns

.. _section-9:

1.18.2 (2020-10-17)
~~~~~~~~~~~~~~~~~~~

-  `#305 <https://github.com/man-group/dtale/issues/305>`__: Show
   Duplicates column(s) label
-  `#304 <https://github.com/man-group/dtale/issues/304>`__: Link to
   Custom Filter from Column Menu
-  `#301 <https://github.com/man-group/dtale/issues/301>`__: Normalized
   Similarity
-  `#289 <https://github.com/man-group/dtale/issues/289>`__: Show
   Duplicates query bug & scrollable duplicate groups

.. _section-10:

1.18.1 (2020-10-16)
~~~~~~~~~~~~~~~~~~~

-  `#299 <https://github.com/man-group/dtale/issues/299>`__: Value
   counts not matching top unique values in Column Analysis
-  `#298 <https://github.com/man-group/dtale/issues/298>`__: Standardize
   column builder bug
-  `#297 <https://github.com/man-group/dtale/issues/297>`__: Similarity
   bugs
-  `#290 <https://github.com/man-group/dtale/issues/290>`__: show top 5
   values with highest frequency in describe details
-  `#288 <https://github.com/man-group/dtale/issues/288>`__: Add
   Duplicates popup to Column Menu

.. _section-11:

1.18.0 (2020-10-15)
~~~~~~~~~~~~~~~~~~~

-  `#282 <https://github.com/man-group/dtale/issues/282>`__: additional
   exception handling for overriding routes
-  `#271 <https://github.com/man-group/dtale/issues/271>`__:
   standardized column builder
-  `#282 <https://github.com/man-group/dtale/issues/282>`__: better
   support for using D-Tale within another Flask application
-  `#270 <https://github.com/man-group/dtale/issues/270>`__: filter
   outliers from column menu
-  allow users to start D-Tale without loading data
-  `#264 <https://github.com/man-group/dtale/issues/264>`__: similarity
   column builder
-  `#286 <https://github.com/man-group/dtale/issues/286>`__: column
   description lag after type conversion

.. _section-12:

1.17.0 (2020-10-10)
~~~~~~~~~~~~~~~~~~~

-  `#269 <https://github.com/man-group/dtale/issues/269>`__: column
   based range highlighting
-  `#268 <https://github.com/man-group/dtale/issues/268>`__: better
   button labels for inplace vs. new column
-  `#283 <https://github.com/man-group/dtale/issues/283>`__:
   triple-quote formatting around queries in code exports
-  `#266 <https://github.com/man-group/dtale/issues/266>`__: string
   concatenation string builder
-  `#267 <https://github.com/man-group/dtale/issues/267>`__: Column
   discretion UI
-  Fix: Pandas throws error when converting numeric column names to
   string.

.. _section-13:

1.16.0 (2020-10-4)
~~~~~~~~~~~~~~~~~~

-  `#263 <https://github.com/man-group/dtale/issues/263>`__: word value
   counts
-  `#262 <https://github.com/man-group/dtale/issues/262>`__: duplicate
   values
-  `#274 <https://github.com/man-group/dtale/issues/274>`__: URL
   linkouts

.. _section-14:

1.15.2 (2020-9-4)
~~~~~~~~~~~~~~~~~

-  hotfix to move HIDE_SHUTDOWN & GITHUB_FORK to ``dtale`` module

.. _section-15:

1.15.1 (2020-9-3)
~~~~~~~~~~~~~~~~~

-  hotfix to expose HIDE_SHUTDOWN & GITHUB_FORK from
   ``dtale.global_state``

.. _section-16:

1.15.0 (2020-9-3)
~~~~~~~~~~~~~~~~~

-  `#248 <https://github.com/man-group/dtale/issues/248>`__: Added
   ‘minPeriods’ & the rolling means (multi-value per date) dateframes
   for Correlations popup
-  `#254 <https://github.com/man-group/dtale/issues/254>`__: Options to
   hide shutdown and turn off cell editing
-  `#244 <https://github.com/man-group/dtale/issues/244>`__: Treemap
   charts

.. _section-17:

1.14.1 (2020-8-20)
~~~~~~~~~~~~~~~~~~

-  `#252 <https://github.com/man-group/dtale/issues/252>`__: Describe
   shows proper values, but repeats ‘Total Rows:’ heading instead of
   proper headings

.. _section-18:

1.14.0 (2020-8-19)
~~~~~~~~~~~~~~~~~~

-  `#243 <https://github.com/man-group/dtale/issues/243>`__: column menu
   descriptions
-  `#247 <https://github.com/man-group/dtale/issues/247>`__: code export
   compilation error fixes
-  `#242 <https://github.com/man-group/dtale/issues/242>`__: type
   conversion column menu option & auto-column names on “Build Column”
-  `#235 <https://github.com/man-group/dtale/issues/235>`__: nan
   formatting

.. _section-19:

1.13.0 (2020-8-13)
~~~~~~~~~~~~~~~~~~

-  `#231 <https://github.com/man-group/dtale/issues/231>`__: “Lock Zoom”
   button on 3D Scatter & Surface charts for locking camera on
   animations
-  global & instance-level flag to turn off cell editing
-  added the ability to upload CSVs
-  upgraded prismjs
-  `#234 <https://github.com/man-group/dtale/issues/234>`__: update to
   line animations so that you can lock axes and highlight last point
-  `#233 <https://github.com/man-group/dtale/issues/233>`__: add
   candlestick charts
-  `#241 <https://github.com/man-group/dtale/issues/241>`__: total
   counts vs. count (non-nan) in describe
-  `#240 <https://github.com/man-group/dtale/issues/240>`__: force
   convert to float
-  `#239 <https://github.com/man-group/dtale/issues/239>`__: converting
   mixed columns
-  `#237 <https://github.com/man-group/dtale/issues/237>`__: updated
   “Pivot” reshaper to always using pivot_table
-  `#236 <https://github.com/man-group/dtale/issues/236>`__: “inplace” &
   “drop_index” parameters for memory optimization and parquet loader
-  `#229 <https://github.com/man-group/dtale/issues/229>`__: added
   histogram sample chart to bins column builder

.. _section-20:

1.12.1 (2020-8-5)
~~~~~~~~~~~~~~~~~

-  better axis display on heatmaps
-  handling for column filter data on “mixed” type columns
-  “title” parameter added for offline charts
-  heatmap drilldowns on animations
-  bugfix for refreshing custom geojson charts

.. _section-21:

1.12.0 (2020-8-1)
~~~~~~~~~~~~~~~~~

-  added better notification for when users view Category breakdowns in
   “Column Analysis” & “Describe”
-  fixed code snippets in “Numeric” column builder when no operation is
   selected
-  fixed code exports for transform, winsorixe & z-score normalize
   column builders
-  added colorscale option to 3D Scatter charts
-  added “Animate By” to Heatmaps
-  initial chart drilldown functionality (histogram, bar)
-  fixed bug with code exports on transform, winsorize & z-score
   normalize column builders
-  updated labeling & tooltips on histogram charts
-  npm package upgrades

.. _section-22:

1.11.0 (2020-7-23)
~~~~~~~~~~~~~~~~~~

-  updated column filters so that columns with more than 500 unique
   values are loaded asynchronously as with AsyncSelect
-  added code export to Variance report
-  added z-score normalize column builder

.. _section-23:

1.10.0 (2020-7-21)
~~~~~~~~~~~~~~~~~~

-  `#223 <https://github.com/man-group/dtale/issues/223>`__:
   six.collections.abc import errors in Google Colab
-  `#135 <https://github.com/man-group/dtale/issues/135>`__: added
   plotly chart construction to code exports
-  `#192 <https://github.com/man-group/dtale/issues/192>`__: Variance
   Report & flag toggle
-  npm package upgrades
-  added “winsorize” column builder

.. _section-24:

1.9.2 (2020-7-12)
~~~~~~~~~~~~~~~~~

-  `#127 <https://github.com/man-group/dtale/issues/127>`__: add “Column
   Analysis” to “Describe”
-  `#85 <https://github.com/man-group/dtale/issues/85>`__: hotkeys

.. _section-25:

1.9.1 (2020-7-3)
~~~~~~~~~~~~~~~~

-  `#213 <https://github.com/man-group/dtale/issues/213>`__: Support for
   koalas dataframes
-  `#214 <https://github.com/man-group/dtale/issues/214>`__: fix for
   USE_COLAB (colab proxy endpoint injection)

.. _section-26:

1.9.0 (2020-7-3)
~~~~~~~~~~~~~~~~

-  added the ability to build columns using transform
-  added USE_COLAB for accessing D-Tale within google colab using their
   proxy
-  `#211 <https://github.com/man-group/dtale/issues/211>`__: Code export
   doesnt work on google colab

.. _section-27:

1.8.19 (2020-6-28)
~~~~~~~~~~~~~~~~~~

-  backwards compatibility of ‘colorscale’ URL parameters in charts
-  dropping of NaN locations/groups in choropleth maps

.. _section-28:

1.8.18 (2020-6-28)
~~~~~~~~~~~~~~~~~~

-  `#150 <https://github.com/man-group/dtale/issues/150>`__: replace
   colorscale dropdown with component from dash-colorscales
-  added the ability to choose been ols & lowess trendlines in scatter
   charts
-  `#83 <https://github.com/man-group/dtale/issues/83>`__: allow for
   names to be used in url strings for data_id

.. _section-29:

1.8.17 (2020-6-18)
~~~~~~~~~~~~~~~~~~

-  `#151 <https://github.com/man-group/dtale/issues/151>`__: allow users
   to load custom topojson into choropleth maps

.. _section-30:

1.8.16 (2020-6-7)
~~~~~~~~~~~~~~~~~

-  `#200 <https://github.com/man-group/dtale/issues/200>`__: support for
   xarray

.. _section-31:

1.8.15 (2020-5-31)
~~~~~~~~~~~~~~~~~~

-  `#202 <https://github.com/man-group/dtale/issues/202>`__: maximum
   recursion errors when using Pyzo IDE

.. _section-32:

1.8.14 (2020-5-31)
~~~~~~~~~~~~~~~~~~

-  `#168 <https://github.com/man-group/dtale/issues/168>`__: updated
   default colorscale for heatmaps to be Jet
-  `#152 <https://github.com/man-group/dtale/issues/152>`__: added
   scattermapbox as a valid map type
-  `#136 <https://github.com/man-group/dtale/issues/136>`__: Fill
   missing values
-  `#86 <https://github.com/man-group/dtale/issues/86>`__: column
   replacements for values
-  `#87 <https://github.com/man-group/dtale/issues/87>`__: highlight
   ranges of numeric cells

.. _section-33:

1.8.13 (2020-5-20)
~~~~~~~~~~~~~~~~~~

-  `#193 <https://github.com/man-group/dtale/issues/193>`__: Support for
   JupyterHub Proxy

.. _section-34:

1.8.12 (2020-5-15)
~~~~~~~~~~~~~~~~~~

-  `#196 <https://github.com/man-group/dtale/issues/196>`__: dataframes
   that have datatime indexes without a name
-  Added the ability to apply formats to all columns of same dtype

.. _section-35:

1.8.11 (2020-5-3)
~~~~~~~~~~~~~~~~~

-  `#196 <https://github.com/man-group/dtale/issues/191>`__: improving
   outlier filter suggestions
-  `#190 <https://github.com/man-group/dtale/issues/190>`__: hide
   “Animate” inputs when “Percentage Sum” or “Percentage Count”
   aggregations are used
-  `#189 <https://github.com/man-group/dtale/issues/189>`__: hide
   “Barsort” when grouping is being applied
-  `#187 <https://github.com/man-group/dtale/issues/187>`__: missing &
   outlier tooltip descriptions on column headers
-  `#186 <https://github.com/man-group/dtale/issues/186>`__: close
   “Describe” tab after clicking “Update Grid”
-  `#122 <https://github.com/man-group/dtale/issues/122>`__: editable
   cells
-  npm package upgrades
-  circleci build script refactoring

.. _section-36:

1.8.10 (2020-4-26)
~~~~~~~~~~~~~~~~~~

-  `#184 <https://github.com/man-group/dtale/issues/184>`__: “nan” not
   showing up for numeric columns
-  `#181 <https://github.com/man-group/dtale/issues/181>`__: percentage
   sum/count charts
-  `#179 <https://github.com/man-group/dtale/issues/179>`__:
   confirmation for column deletion
-  `#176 <https://github.com/man-group/dtale/issues/176>`__: highlight
   background of outliers/missing values
-  `#175 <https://github.com/man-group/dtale/issues/175>`__: column
   renaming
-  `#174 <https://github.com/man-group/dtale/issues/174>`__: moved
   “Describe” popup to new browser tab
-  `#173 <https://github.com/man-group/dtale/issues/173>`__: wider
   column input box for GroupBy in “Summarize Data” popup
-  `#172 <https://github.com/man-group/dtale/issues/172>`__: allowing
   groups to be specified in 3D scatter
-  `#170 <https://github.com/man-group/dtale/issues/170>`__: filter
   “Value” dropdown for maps to only int or float columns
-  `#164 <https://github.com/man-group/dtale/issues/164>`__: show
   information about missing data in “Describe” popup

.. _section-37:

1.8.9 (2020-4-18)
~~~~~~~~~~~~~~~~~

-  updated correlations & “Open Popup” to create new tabs instead
-  test fixes for dash 1.11.0
-  added python 3.7 & 3.8 support

.. _section-38:

1.8.8 (2020-4-9)
~~~~~~~~~~~~~~~~

-  `#144 <https://github.com/man-group/dtale/issues/144>`__: Changing
   data type

.. _section-39:

1.8.7 (2020-4-8)
~~~~~~~~~~~~~~~~

-  `#137 <https://github.com/man-group/dtale/issues/137>`__: Outliers in
   the description window
-  `#141 <https://github.com/man-group/dtale/issues/141>`__: Currency
   Symbole for column Format
-  `#156 <https://github.com/man-group/dtale/issues/156>`__: heat map
-  `#160 <https://github.com/man-group/dtale/issues/160>`__: Option to
   filter out outliers from charts
-  `#161 <https://github.com/man-group/dtale/issues/161>`__: Syntax
   error in 1.8.6
-  `#162 <https://github.com/man-group/dtale/issues/162>`__: chart map
   animation errors with aggrigations
-  `#163 <https://github.com/man-group/dtale/issues/163>`__: Fix scale
   for animation mode in the chart

1.8.6 [hotfix] (2020-4-5)
~~~~~~~~~~~~~~~~~~~~~~~~~

-  updates to setup.py to include images

.. _hotfix-2020-4-5-1:

1.8.5 [hotfix] (2020-4-5)
~~~~~~~~~~~~~~~~~~~~~~~~~

-  fixed bug with column calculation for map inputs
-  `#149 <https://github.com/man-group/dtale/issues/149>`__: Icon for
   Map charts

.. _hotfix-2020-4-5-2:

1.8.4 [hotfix] (2020-4-5)
~~~~~~~~~~~~~~~~~~~~~~~~~

-  update to setup.py to include missing static topojson files
-  `#145 <https://github.com/man-group/dtale/issues/145>`__: Choropleth
   Map

.. _section-40:

1.8.3 (2020-4-4)
~~~~~~~~~~~~~~~~

-  `#143 <https://github.com/man-group/dtale/issues/143>`__: scattergeo
   map chart UI changes
-  updated offline chart generation of maps to work without loading
   topojson from the web
-  fix to allow correlations timeseries to handle when date columns jump
   between rolling & non-rolling
-  added slider to animation and added animation to maps
-  fixes for IE 11 compatibility issues
-  labeling changes for “Reshape” popup
-  added grouping to maps

.. _section-41:

1.8.2 (2020-4-1)
~~~~~~~~~~~~~~~~

-  `#129 <https://github.com/man-group/dtale/issues/129>`__: show dtype
   when hovering over header in “Highlight Dtypes” mode and description
   tooltips added to main menu
-  made “No Aggregation” the default aggregation in charts
-  bugfix for line charts with more than 15000 points
-  updated “Value Counts” & “Category Breakdown” to return top on
   initial load
-  `#118 <https://github.com/man-group/dtale/issues/118>`__: added
   scattergeo & choropleth maps
-  `#121 <https://github.com/man-group/dtale/issues/121>`__: added “not
   equal” toggle to filters
-  `#132 <https://github.com/man-group/dtale/issues/132>`__: updated
   resize button to “Refresh Widths”
-  added “Animate” toggle to scatter, line & bar charts
-  `#131 <https://github.com/man-group/dtale/issues/131>`__: changes to
   “Reshape Data” window
-  `#130 <https://github.com/man-group/dtale/issues/130>`__: updates to
   pivot reshaper
-  `#128 <https://github.com/man-group/dtale/issues/128>`__: additional
   hover display of code snippets for column creation
-  `#112 <https://github.com/man-group/dtale/issues/112>`__: updated
   “Group” selection to give users the ability to select group values

.. _section-42:

1.8.1 (2020-3-29)
~~~~~~~~~~~~~~~~~

-  `#92 <https://github.com/man-group/dtale/issues/92>`__: column
   builders for random data
-  `#84 <https://github.com/man-group/dtale/issues/84>`__: highlight
   columns based on dtype
-  `#111 <https://github.com/man-group/dtale/issues/111>`__: fix for
   syntax error in charts code export
-  `#113 <https://github.com/man-group/dtale/issues/113>`__: updates to
   “Value Counts” chart in “Column Analysis” for number of values and
   ordinal entry
-  `#114 <https://github.com/man-group/dtale/issues/114>`__: export data
   to CSV/TSV
-  `#116 <https://github.com/man-group/dtale/issues/116>`__: upodated
   styling for github fork link so “Code Export” is partially clickable
-  `#119 <https://github.com/man-group/dtale/issues/119>`__: fixed bug
   with queries not being passed to functions
-  `#120 <https://github.com/man-group/dtale/issues/120>`__: fix to
   allow duplicate x-axis entries in bar charts
-  added “category breakdown” in column analysis popup for float columns
-  fixed bug where previous “show missing only” selection was not being
   recognized

.. _section-43:

1.8.0 (2020-3-22)
~~~~~~~~~~~~~~~~~

-  `#102 <https://github.com/man-group/dtale/issues/102>`__: interactive
   column filtering for string, date, int, float & bool
-  better handling for y-axis management in charts. Now able to toggle
   between default, single & multi axis
-  increased maximum groups to 30 in charts and updated error messaging
   when it surpasses that for easier filter creation
-  bugfix for date string width calculation
-  updated sort/filter/hidden header so that you can now click values
   which will trigger a tooltip for removing individual values
-  updated Filter popup to be opened as separate window when needed

.. _section-44:

1.7.15 (2020-3-9)
~~~~~~~~~~~~~~~~~

-  `#105 <https://github.com/man-group/dtale/issues/105>`__: better
   error handling for when JS files are missing
-  `#103 <https://github.com/man-group/dtale/issues/103>`__: pinned
   Flask to be >= 1.0.0
-  Updated file exporting to no longer use ``flask.send_file`` since
   that doesn’t play nice with WSGI

.. _section-45:

1.7.14 (2020-3-7)
~~~~~~~~~~~~~~~~~

-  Hotfix for “Reshape” popup when forwarding browser to new data
   instances

.. _section-46:

1.7.13 (2020-3-7)
~~~~~~~~~~~~~~~~~

-  New data storage mechanisms available: Redis, Shelve
-  `#100 <https://github.com/man-group/dtale/issues/100>`__: turned off
   data limits on charts by using WebGL
-  `#99 <https://github.com/man-group/dtale/issues/99>`__: graceful
   handling of issue calculating min/max information for Describe popup
-  `#91 <https://github.com/man-group/dtale/issues/91>`__: reshaping of
   data through usage of aggregations, pivots or transposes
-  Export chart to HTML
-  Export chart dat to CSV
-  Offline chart display for use within notebooks
-  Removal of data from the Instances popup
-  Updated styling of charts to fit full window dimensions

.. _section-47:

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-48:

1.7.11 (2020-2-27)
~~~~~~~~~~~~~~~~~~

-  hotfix for dash custom.js file missing from production webpack build
   script

.. _section-49:

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-50:

1.7.9 (2020-2-24)
~~~~~~~~~~~~~~~~~

-  support for google colab
-  `#71 <https://github.com/man-group/dtale/issues/71>`__: Filter &
   Formats popups no longer have smooth transition from top of screen
-  `#72 <https://github.com/man-group/dtale/issues/72>`__: Column Menu
   cutoff on last column of wide dataframes
-  `#73 <https://github.com/man-group/dtale/issues/73>`__: Describe
   popup does full refresh when clicking rows in dtype grid

.. _section-51:

1.7.8 (2020-2-22)
~~~~~~~~~~~~~~~~~

-  `#77 <https://github.com/man-group/dtale/issues/77>`__: removal of
   multiprocessed timeouts

.. _section-52:

1.7.7 (2020-2-22)
~~~~~~~~~~~~~~~~~

-  centralized global state

.. _section-53:

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-54:

1.7.5 (2020-2-20)
~~~~~~~~~~~~~~~~~

-  hotfix for KeyError loading metadata for columns with min/max
   information

.. _section-55:

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-56:

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-57:

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-58:

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-59:

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-60:

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-61:

1.6.9 (2020-1-9)
~~~~~~~~~~~~~~~~

-  bugfix for timezone issue around passing date filters to server for
   scatter charts in correlations popup

.. _section-62:

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-63:

1.6.7 (2020-1-3)
~~~~~~~~~~~~~~~~

-  `#50 <https://github.com/man-group/dtale/issues/50>`__: updates to
   rolling correlation functionality

.. _section-64:

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-65:

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-66:

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-67:

1.6.3 (2019-12-23)
~~~~~~~~~~~~~~~~~~

-  updated versions of packages in yarn.lock due to issue with chart.js
   box & whisker plots

.. _section-68:

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-69:

1.6.1 (2019-12-19)
~~~~~~~~~~~~~~~~~~

-  bugfix for url display when running from command-line

.. _section-70:

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-71:

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-72:

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-73:

1.4.1 (2019-11-20)
~~~~~~~~~~~~~~~~~~

-  `#32 <https://github.com/man-group/dtale/issues/32>`__: unpin
   jsonschema by moving flasgger to ``extras_require``

.. _section-74:

1.4.0 (2019-11-19)
~~~~~~~~~~~~~~~~~~

-  Correlations Pearson Matrix filters
-  “name” display in title tab
-  “Heat Map” toggle
-  dropped unused “Flask-Caching” requirement

.. _section-75:

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-76:

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-77:

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-78:

1.3.4 (2019-11-07)
~~~~~~~~~~~~~~~~~~

-  updated correlation calculation to use ``numpy.corrcoef`` for
   performance purposes
-  github rebranding from manahl -> man-group

.. _section-79:

1.3.3 (2019-11-05)
~~~~~~~~~~~~~~~~~~

-  hotfix for failing test under certain versions of ``future`` package

.. _section-80:

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-81:

1.3.1 (2019-10-29)
~~~~~~~~~~~~~~~~~~

-  fix for incompatible str types when directly altering state of data
   in running D-Tale instance

.. _section-82:

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-83:

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-84:

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-85:

1.1.0 (2019-10-08)
~~~~~~~~~~~~~~~~~~

-  IE support
-  **Describe** & **About** popups
-  Custom CLI support

.. _section-86:

1.0.0 (2019-09-06)
~~~~~~~~~~~~~~~~~~

-  Initial public release


