dtale 1.10.0

Python Terminal

This comes courtesy of PyCharm Feel free to invoke python or ipython directly and use the commands in the screenshot above and it should work

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

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:

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:

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:

JupyterHub w/ Jupyter Server Proxy

JupyterHub has an extension that allows to proxy port for user, JupyterHub 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:

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:

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:

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

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:

import pandas as pd

import dtale
import dtale.app as dtale_app

dtale_app.USE_COLAB = True

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

IFfthis 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 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:

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:

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

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

Command-line

Base CLI options (run dtale --help to see all options available)

Loading data from arctic(high performance datastore for pandas dataframes) (this requires either installing arctic or dtale[arctic])

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

dtale --csv-path /home/jdoe/my_csv.csv --csv-parse_dates date

Loading data from JSON

dtale --json-path /home/jdoe/my_json.json --json-parse_dates date

or

dtale --json-path http://json-endpoint --json-parse_dates date

Loading data from a Custom loader - Using the DTALE_CLI_LOADERS environment variable, specify a path to a location containing some python modules - Any python module containing the global variables LOADER_KEY & LOADER_PROPS will be picked up as a custom loader - LOADER_KEY: the key that will be associated with your loader. By default you are given arctic & csv (if you use one of these are your key it will override these) - LOADER_PROPS: the individual props available to be specified. - For example, with arctic we have host, library, node, start & end. - If you leave this property as an empty list your loader will be treated as a flag. For example, instead of using all the arctic properties we would simply specify --arctic (this wouldn’t work well in arctic’s case since it depends on all those properties) - You will also need to specify a function with the following signature def find_loader(kwargs) which returns a function that returns a dataframe or None - Here is an example of a custom loader:

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:

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')

Dimensions/Main Menu

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

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:

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 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:

Main Menu Functions

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)]

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')

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')

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")

Column Menu Functions

Hotkeys

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

* 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, also Charts will always open in a separate browser window)

Cloning

Clone the code (git clone ssh://git@github.com:manahl/dtale.git), then start the backend server:

$ 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:

$ 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:

$ npm run lint -s

You can also lint individual JS files:

$ npm run lint-js-file -s -- static/dtale/DataViewer.jsx

Formatting JS

You can auto-format code as follows:

$ npm run format

Docker Development

You can build python 27-3 & run D-Tale as follows:

$ 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:

$ 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

1.10.0 (2020-7-21)

• #223: six.collections.abc import errors in Google Colab

• #135: added plotly chart construction to code exports

• #192: Variance Report & flag toggle

• npm package upgrades

• added “winsorize” column builder

1.9.2 (2020-7-12)

• #127: add “Column Analysis” to “Describe”

• #85: hotkeys

1.9.1 (2020-7-3)

• #213: Support for koalas dataframes

• #214: fix for USE_COLAB (colab proxy endpoint injection)

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: Code export doesnt work on google colab

1.8.19 (2020-6-28)

• backwards compatibility of ‘colorscale’ URL parameters in charts

• dropping of NaN locations/groups in choropleth maps

1.8.18 (2020-6-28)

• #150: replace colorscale dropdown with component from dash-colorscales

• added the ability to choose been ols & lowess trendlines in scatter charts

• #83: allow for names to be used in url strings for data_id

1.8.17 (2020-6-18)

• #151: allow users to load custom topojson into choropleth maps

1.8.16 (2020-6-7)

• #200: support for xarray

1.8.15 (2020-5-31)

• #202: maximum recursion errors when using Pyzo IDE

1.8.14 (2020-5-31)

• #168: updated default colorscale for heatmaps to be Jet

• #152: added scattermapbox as a valid map type

• #136 & #86: column replacements for values

• #87: highlight ranges of numeric cells

1.8.13 (2020-5-20)

• #193: Support for JupyterHub Proxy

1.8.12 (2020-5-15)

• #196: dataframes that have datatime indexes without a name

• Added the ability to apply formats to all columns of same dtype

1.8.11 (2020-5-3)

• #196: improving outlier filter suggestions

• #190: hide “Animate” inputs when “Percentage Sum” or “Percentage Count” aggregations are used

• #189: hide “Barsort” when grouping is being applied

• #187: missing & outlier tooltip descriptions on column headers

• #186: close “Describe” tab after clicking “Update Grid”

• #122: editable cells

• npm package upgrades

• circleci build script refactoring

1.8.10 (2020-4-26)

• #184: “nan” not showing up for numeric columns

• #181: percentage sum/count charts

• #179: confirmation for column deletion

• #176: highlight background of outliers/missing values

• #175: column renaming

• #174: moved “Describe” popup to new browser tab

• #173: wider column input box for GroupBy in “Summarize Data” popup

• #172: allowing groups to be specified in 3D scatter

• #170: filter “Value” dropdown for maps to only int or float columns

• #164: show information about missing data in “Describe” popup

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

1.8.8 (2020-4-9)

• #144

1.8.7 (2020-4-8)

• #137

• #141

• #156

• #160

• #161

• #162

• #163

1.8.6 [hotfix] (2020-4-5)

• updates to setup.py to include images

1.8.5 [hotfix] (2020-4-5)

• fixed bug with column calculation for map inputs

• #149

1.8.4 [hotfix] (2020-4-5)

• update to setup.py to include missing static topojson files

• #145

1.8.3 (2020-4-4)

• #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

1.8.2 (2020-4-1)

• #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, added scattergeo & choropleth maps

• #121, added “not equal” toggle to filters

• #132, updated resize button to “Refresh Widths”

• added “Animate” toggle to scatter, line & bar charts

• #131, changes to “Reshape Data” window

• #130, updates to pivot reshaper

• #128, additional hover display of code snippets for column creation

• #112, updated “Group” selection to give users the ability to select group values

1.8.1 (2020-3-29)

• #92, column builders for random data

• #84, highlight columns based on dtype

• #111, fix for syntax error in charts code export

• #113, updates to “Value Counts” chart in “Column Analysis” for number of values and ordinal entry

• #114, export data to CSV/TSV

• #116, upodated styling for github fork link so “Code Export” is partially clickable

• #119, fixed bug with queries not being passed to functions

• #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

1.8.0 (2020-3-22)

• #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

1.7.15 (2020-3-9)

• #105, better error handling for when JS files are missing

• #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

1.7.14 (2020-3-7)

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

1.7.13 (2020-3-7)

• New data storage mechanisms available: Redis, Shelve

• #100, turned off data limits on charts by using WebGL

• #99, graceful handling of issue calculating min/max information for Describe popup

• #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

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

1.7.11 (2020-2-27)

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

1.7.10 (2020-2-27)

• #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, pinned future package to be >= 0.14.0

1.7.9 (2020-2-24)

• support for google colab

• bugfixes: #71, #72, #73

1.7.8 (2020-2-22)

• #77, removal of multiprocessed timeouts

1.7.7 (2020-2-22)

• centralized global state

1.7.6 (2020-2-21)

• allowing the usage of context variables within filters

• #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

1.7.5 (2020-2-20)

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

1.7.4 (2020-2-20)

• #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: added column builder popup

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

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

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

1.7.0 (2020-1-28)

• redesign of charts popup to use plotly/dash

• #55: raise exception when data contains duplicate column names

• heatmap integration

• combination of “_main.jsx” files into one for spacial optimization

• #15: made arctic an “extra” dependency

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

1.6.9 (2020-1-9)

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

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

1.6.7 (2020-1-3)

• #50: updates to rolling correlation functionality

1.6.6 (2020-1-2)

• #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: scatter charts in chart builder

• “nunique” added to list of aggregations

• turned on “threaded=True” for app.run to avoid hanging popups

• #45: rolling computations as aggregations

• Y-Axis editor

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

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: only show timeseries correlations if datetime columns exist with multiple values per date

1.6.3 (2019-12-23)

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

1.6.2 (2019-12-23)

• #40: loading initial chart as non-line in chart builder

• #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

1.6.1 (2019-12-19)

• bugfix for url display when running from command-line

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()

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)

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

1.4.1 (2019-11-20)

• #32: unpin jsonschema by moving flasgger to extras_require

1.4.0 (2019-11-19)

• Correlations Pearson Matrix filters

• “name” display in title tab

• “Heat Map” toggle

• dropped unused “Flask-Caching” requirement

1.3.7 (2019-11-12)

• Bug fixes for:

  • #28: “Instances” menu option will now be displayed by default

  • #29: add hints to how users can navigate the correlations popup

  • add “unicode” as a string classification for column width calculation

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

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

1.3.4 (2019-11-07)

• updated correlation calculation to use numpy.corrcoef for performance purposes

• github rebranding from manahl -> man-group

1.3.3 (2019-11-05)

• hotfix for failing test under certain versions of future package

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

1.3.1 (2019-10-29)

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

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

1.2.0 (2019-10-24)

• #20: fix for data being overriden with each new instance

• #21: 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

1.1.1 (2019-10-23)

• #13: fix for auto-detection of column widths for strings and floats

1.1.0 (2019-10-08)

• IE support

• Describe & About popups

• Custom CLI support

1.0.0 (2019-09-06)

• Initial public release