Skip to main content
Join the official 2020 Python Developers SurveyStart the survey!

Web Client for Visualizing Pandas Objects

Project description


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.


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.

Where To get It

The source code is currently hosted on GitHub at:

Binary installers for the latest released version are available at the Python package index and on conda using conda-forge.

# conda
conda install dtale -c conda-forge
# 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

Additional functions available programmatically

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 =

# Accessing data associated with D-Tale process
tmp =
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) = tmp

# Shutting down D-Tale process

# using Python's `webbrowser` package it will try and open your server's default browser to this process

# 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: 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:


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

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 as dtale_app

dtale_app.JUPYTER_SERVER_PROXY = True[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 as dtale_app


d =[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 as dtale_app[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 as dtale_app

dtale_app.USE_COLAB = True[1,2,3]))

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


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 as dtale_app

dtale_app.USE_NGROK = True[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:


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:

dtale <- import('dtale')
df <- read.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:


installing python packages

Startup with No Data

It is now possible to run D-Tale with no data loaded up front. So simply call 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.


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!


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) (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


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:

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')

Embedding Within Your Own Flask App

So one of the nice features of D-Tale is that is it a Flask application. And because that is the case it makes it easy to embed within your own Flask application. Here’s some sample code for a Flask app that embeds D-Tale inside it:

from flask import redirect

from import build_app
from dtale.views import startup

if __name__ == '__main__':
    app = build_app(reaper_on=False)

    def create_df():
        df = pd.DataFrame(dict(a=[1, 2, 3], b=[4, 5, 6]))
        instance = startup(data=df, ignore_duplicate=True)
        return redirect(f"/dtale/main/{instance._data_id}", code=302)

    def hello_world():
        return 'Hi there, load data using <a href="/create-df">create-df</a>'"", port=8080)

Here’s some details on what is going on here: 1. Instantiate the D-Tale application build_app(reaper_on=False) and take care to make sure the reaper is turned off (that way the app isn’t killed after 60 minutes of inactivity) 2. Add our own Flask routes, in this case we have /create-df & / * So take a second to look at /create-df and how it calls startup(). This stores data in the global state of D-Tale so it can be viewed * We are not setting a data_id so this will automatically create one for us and assign that dataframe to it. * If you want to just have one continuous piece of data that you can use this code: python   cleanup("1")   startup(data_id="1",data=df) This will cleanup any data associated with the the data_id “1” and then assign the new data “df” in its place

  1. Start the application

Here is a link to the source code for a little more complex example of embedding D-Tale (the frontend leaves a lot to be disired though :rofl:).


Hope this leads to lots of new ideas of how D-Tale can be used!


Once you have kicked off your D-Tale session please copy & paste the link on the last line of output in your browser image26

Dimensions/Main Menu

The information in the upper right-hand corner gives grid dimensions image27 - lower-left => row count - upper-right => column count - clicking the triangle displays the menu of standard functions (click outside menu to close it)


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:


Copy Cells Into Clipboard

Select Copy Paste
image33 image34 image35

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


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


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


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:, 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

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 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


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
image79 image80 image81
Col1 Filtered Col2 Filtered Col1 & Col2 Filtered
image82 image83 image84

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
image85 image86

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) image87

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


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.


Highlight Outliers

Highlight any cells for numeric columns which surpass the upper or lower bounds of a custom outlier computation. * 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.


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
image91 image92

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


Type Code Export
Main Grid image94
Histogram image95
Describe image96
Correlation Grid image97
Correlation Timeseries image98
Correlation Scatter image99
Charts image100

Export CSV

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


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:


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


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:

import pandas as pd
import dtale[dict(foo=1, bar=2, biz=3, baz=4, snoopy_D_O_double_gizzle=5)]))[
    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)
]))[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:


The grid above contains the following information: - Process: timestamp when the process was started along with the name (if specified in - 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:



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
image105 image106


Mostly a fail-safe in the event that your columns are no longer lining up. Click this and should fix that


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




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 image109 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 image110 dates Specify a range of dates to filter on based on start & end inputs
Numeric image111 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


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

Hiding Columns


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


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


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



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 specific values in a column with raw values, output from another column or an aggregation on your column image115
Spaces Only strings Replace string values consisting only of spaces with raw values image116
Contains Char/Substring strings Replace string values containing a specific character or substring image117
Scikit-Learn Imputer numeric Replace missing values with the output of using different Scikit-Learn imputers like iterative, knn & simple image118

Here’s a quick demo: image119


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


Removed column from “locked” columns


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  


Apply simple formats to numeric values in your grid

Type Editing Result
Numeric image120 image121
Date image122 image123
String image124 image125

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 image126
Value Counts Int, String, Bool, Date, Category image127
Category Float image128

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:


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).


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.

For Developers


Clone the code (git clone ssh://, then start the backend server:

$ git clone ssh://
# install the dependencies
$ python 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


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

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

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) 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

Startup Behavior

Here’s a little background on how the 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 our state is:
Session Port Active Data IDs URL(s)
1 40000 1 http://localhost:40000/dtale/main/1
  1. Session 1 executes our state is:
Session Port Active Data IDs URL(s)
1 40000 1,2 http://localhost:40000/dtale/main/[1,2]
  1. Session 2 executes 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
  1. Session 1 executes, 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]
  1. Session 3 executes 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
  1. Session 2 executes 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
  1. Session 4 executes, 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
  1. 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
  1. Session 5 sets DTALE_MIN_RANGE to 30000 and DTALE_MAX_RANGE 39000 and executes 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


Have a look at the detailed documentation.


  • 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


D-Tale has been under active development at Man Numeric since 2019.

Original concept and implementation: Andrew Schonfeld


Contributions welcome!


D-Tale is licensed under the GNU LGPL v2.1. A copy of which is included in LICENSE


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

1.19.1 (2020-10-24)

  • Load CSV/TSV/JSON from the web as well as some sample datasets
  • #310: handling for nan in ordinal & label encoders

1.19.0 (2020-10-23)

  • #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: encoders for categorical columns

1.18.2 (2020-10-17)

  • #305: Show Duplicates column(s) label
  • #304: Link to Custom Filter from Column Menu
  • #301: Normalized Similarity
  • #289: Show Duplicates query bug & scrollable duplicate groups

1.18.1 (2020-10-16)

  • #299: Value counts not matching top unique values in Column Analysis
  • #298: Standardize column builder bug
  • #297: Similarity bugs
  • #290: show top 5 values with highest frequency in describe details
  • #288: Add Duplicates popup to Column Menu

1.18.0 (2020-10-15)

  • #282: additional exception handling for overriding routes
  • #271: standardized column builder
  • #282: better support for using D-Tale within another Flask application
  • #270: filter outliers from column menu
  • allow users to start D-Tale without loading data
  • #264: similarity column builder
  • #286: column description lag after type conversion

1.17.0 (2020-10-10)

  • #269: column based range highlighting
  • #268: better button labels for inplace vs. new column
  • #283: triple-quote formatting around queries in code exports
  • #266: string concatenation string builder
  • #267: Column discretion UI
  • Fix: Pandas throws error when converting numeric column names to string.

1.16.0 (2020-10-4)

  • #263: word value counts
  • #262: duplicate values
  • #274: URL linkouts

1.15.2 (2020-9-4)

  • hotfix to move HIDE_SHUTDOWN & GITHUB_FORK to dtale module

1.15.1 (2020-9-3)

  • hotfix to expose HIDE_SHUTDOWN & GITHUB_FORK from dtale.global_state

1.15.0 (2020-9-3)

  • #248: Added ‘minPeriods’ & the rolling means (multi-value per date) dateframes for Correlations popup
  • #254: Options to hide shutdown and turn off cell editing
  • #244: Treemap charts

1.14.1 (2020-8-20)

  • #252: Describe shows proper values, but repeats ‘Total Rows:’ heading instead of proper headings

1.14.0 (2020-8-19)

  • #243: column menu descriptions
  • #247: code export compilation error fixes
  • #242: type conversion column menu option & auto-column names on “Build Column”
  • #235: nan formatting

1.13.0 (2020-8-13)

  • #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: update to line animations so that you can lock axes and highlight last point
  • #233: add candlestick charts
  • #241: total counts vs. count (non-nan) in describe
  • #240: force convert to float
  • #239: converting mixed columns
  • #237: updated “Pivot” reshaper to always using pivot_table
  • #236: “inplace” & “drop_index” parameters for memory optimization and parquet loader
  • #229: added histogram sample chart to bins column builder

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

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

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

1.10.0 (2020-7-21)

  • #223: 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: Fill missing values
  • #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: Changing data type

1.8.7 (2020-4-8)

  • #137: Outliers in the description window
  • #141: Currency Symbole for column Format
  • #156: heat map
  • #160: Option to filter out outliers from charts
  • #161: Syntax error in 1.8.6
  • #162: chart map animation errors with aggrigations
  • #163: Fix scale for animation mode in the chart

1.8.6 [hotfix] (2020-4-5)

  • updates to to include images

1.8.5 [hotfix] (2020-4-5)

  • fixed bug with column calculation for map inputs
  • #149: Icon for Map charts

1.8.4 [hotfix] (2020-4-5)

  • update to to include missing static topojson files
  • #145: Choropleth Map

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
  • #71: Filter & Formats popups no longer have smooth transition from top of screen
  • #72: Column Menu cutoff on last column of wide dataframes
  • #73: Describe popup does full refresh when clicking rows in dtype grid

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
  • 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 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 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
  • 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

Project details

Release history Release notifications | RSS feed

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for dtale, version 1.19.2
Filename, size File type Python version Upload date Hashes
Filename, size dtale-1.19.2-py2.7.egg (7.8 MB) File type Egg Python version 2.7 Upload date Hashes View
Filename, size dtale-1.19.2-py2.py3-none-any.whl (7.7 MB) File type Wheel Python version py2.py3 Upload date Hashes View
Filename, size dtale-1.19.2-py3.6.egg (7.8 MB) File type Egg Python version 3.6 Upload date Hashes View
Filename, size dtale-1.19.2-py3.7.egg (7.8 MB) File type Egg Python version 3.7 Upload date Hashes View
Filename, size dtale-1.19.2-py3.8.egg (7.8 MB) File type Egg Python version 3.8 Upload date Hashes View
Filename, size dtale-1.19.2.tar.gz (7.6 MB) File type Source Python version None Upload date Hashes View

Supported by

Pingdom Pingdom Monitoring Google Google Object Storage and Download Analytics Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN DigiCert DigiCert EV certificate StatusPage StatusPage Status page