dtale 1.1.1

Web Client for Visualizing Pandas Objects

Live Demo

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Getting Started

Setup/Activate your environment and install the egg

Python 3

# create a virtualenv, if you haven't already created one
$ python3 -m venv ~/pyenvs/dtale
$ source ~/pyenvs/dtale/bin/activate

# install dtale egg (important to use the "--upgrade" every time you install so it will grab the latest version)
$ pip install --upgrade dtale

Python 2

# create a virtualenv, if you haven't already created one
$ python -m virtualenv ~/pyenvs/dtale
$ source ~/pyenvs/dtale/bin/activate

# install dtale egg (important to use the "--upgrade" every time you install so it will grab the latest version)
$ pip install --upgrade dtale

Now you will have to ability to use D-Tale from the command-line or within a python-enabled terminal

Command-line

Loading data from 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 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 = old_div(rows, len(dates))
    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'

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

UI

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

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

Selecting/Deselecting Columns - to select a column, simply click on the column header (to deselect, click the column header again) - You’ll notice that the columns you’ve selected will display in the top of your browser

Menu functions w/ no columns selected

Menu

• Describe: view all the columns & their data types as well as individual details of each column

Data Type	Display	Notes
date
string		If you have less than or equal to 100 unique values they will be displayed at the bottom of your popup
int		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

• Filter: apply a simple pandas query to your data (link to pandas documentation included in popup)

Editing	Result

• Coverage: check for coverage gaps on column(s) by way of other column(s) as group(s)

• Select column(s) in “Group(s)” & “Col(s)”

  • date-type columns you can also specify a frequency of D, W, M, Q, Y

  • Select multiple values in “Cols(s)” and/or “Groups(s)” by holdings the SHIFT key as you click

• Click “Load”

• The output will be the counts of non-nan records in “Col(s)” grouped by your selections in “Group(s)”

Daily	Daily Regional

• Correlations: shows a pearson correlation matrix of all numeric columns against all other numeric columns

• By deafult, it will show a grid of pearson correlations

• 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

• 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

• 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)

Menu functions w/ one column is selected

Menu Single-Column

• Move To Front: moves your column to the front of the “unlocked” columns

• 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 (Ascending/Descending/Clear): applies/removes sorting 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
select “a”
sort asc	a (asc)
deselect “a”	a (asc)
select “b”	a (asc)
sort desc	a (asc), b(desc)
select “a”	a (asc), b(desc)
clear sort	b(desc)
sort desc	b(desc), a(desc)
select “b”	b(desc), a(desc)
clear sort
sort asc	a (asc), b(asc)

• Formats: apply simple formats to numeric values in your grid

|Editing|Result| |——–|:——:| ||| - 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

• Histogram: display histograms in bins of 5, 10, 20 or 50 for any numeric column

For Developers

Getting Started

Clone the code (git clone ssh://[e-mail]:manahl/dtale.git), then start the backend server:

$ git clone ssh://[e-mail]: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

Documentation

Have a look at the detailed documentation.

Requirements

D-Tale works with:

• Back-end

  • arctic

  • Flask

  • Flask-Caching

  • Flask-Compress

  • flasgger

  • Pandas

  • scipy

  • six

• Front-end

  • react-virtualized

  • chart.js

Acknowledgements

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

Original concept and implementation: Andrew Schonfeld

Contributors:

• Wilfred Hughes

• Dominik Christ

• Chris Boddy

• Jason Holden

• Youssef Habchi - 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

Changelog

1.0.0 (2019-09-06)

• Initial public release

1.1.0 (2019-10-08)

• IE support

• Describe & About popups

• Custom CLI support

1.1.1 (2019-10-23)

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