/ Data Visualization

Flask/React client for visualizing pandas data structures

Flask/React client for visualizing pandas data structures

dtale

D-Tale is the combination of a Flask back-end and a React front-end to bring you an easy way to view & analyze Pandas data structures. It integrates seamlessly with ipython notebooks & python/ipython terminals. Currently this tool supports such Pandas objects as DataFrame, Series, MultiIndex, DatetimeIndex & RangeIndex.

Origins

D-Tale was the product of a SAS to Python conversion. What was originally a perl script wrapper on top of SAS's insight function is now a lightweight web client on top of Pandas data structures.

Getting Started

PyCharm jupyter

Installing the egg

# 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

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

Additional functions available programatically

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()  # returns a dictionary of all instances available, this would be { 1: ... }

Jupyter Notebook

Within any jupyter (ipython) notebook executing a cell like this will display a small instance of D-Tale in the output cell. Here are some examples:

dtale.show assignment instance

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 Histogram Instances

Command-line

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

Prop Description
--host the name of the host you would like to use (most likely not needed since socket.gethostname() should figure this out)
--port the port you would like to assign to your D-Tale instance
--name an optional name you can assign to your D-Tale instance (this will be displayed in the <title> & Instances popup)
--debug turn on Flask's "debug" mode for your D-Tale instance
--no-reaper flag to turn off auto-reaping subprocess (kill D-Tale instances after an hour of inactivity), good for long-running displays
--open-browser flag to automatically open up your server's default browser to your D-Tale instance
--force flag to force D-Tale to try an kill any pre-existing process at the port you've specified so it can use it

Loading data from arctic(high performance datastore for pandas dataframes)

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 = 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'

UI

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

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)

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

Charts

Build custom charts based off your data(powered by plotly/dash).

  • The Charts will open in a tab because of the fact there is so much functionality offered there you'll probably want to be able to reference the main grid data in the original tab
  • To build a chart you must pick a value for X & Y inputs which effectively drive what data is along the X & Y axes
    • If you are working with a 3-Dimensional chart (heatmap, 3D Scatter, Surface) you'll need to enter a value for the Z axis as well
  • Once you have entered all the required axes a chart will be built
  • If your data along the x-axis (or combination of x & y in the case of 3D charts) has duplicates you have three options:
    • Specify a group, which will create series for each group
    • Specify an aggregation, you can choose from one of the following: Count, First, Last, Mean, Median, Minimum, MAximum, Standard Deviation, Variance, Mean Absolute Deviation, Product of All Items, Sum, Rolling
      • Specifying a "Rolling" aggregation will also require a Window & a Computation (Correlation, Coiunt, Covariance, Kurtosis, Maximum, Mean, Median, Minimum, Skew, Standard Deviation, Sum or Variance)
      • For heatmaps you will also have access to the "Correlation" aggregation since viewing correlation matrices in heatmaps is very useful. This aggregation is not supported elsewhere
    • Specify both a group & an aggregation
  • You now have the ability to toggle between different chart types: line, bar, pie, wordcloud, heatmap, 3D scatter & surface
  • If you have specified a group then you have the ability between showing all series in one chart and breaking each series out into its own chart "Chart per Group"

Here are some examples:

Chart Type Chart Chart per Group
line
bar
stacked
pie
wordcloud
heatmap
3D scatter
surface

Selecting multiple columns for the Y-Axis will produce similar results to grouping in the sense that the chart will contain multiple series, but the difference is that for each column there will be a different Y-Axis associated with it in case the values contained within each column are on different scales.

Multi Y-Axis Editing Axis Ranges

With a bar chart that only has a single Y-Axis you have the ability to sort the bars based on the values for the Y-Axis

Pre-sort Post-sort

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

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

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 (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
Col1 Filtered Col2 Filtered Col1 & Col2 Filtered

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

Heat Map

This will hide any non-float columns (with the exception of the index on the right) and apply a color to the background of each cell

  • Each float is renormalized to be a value between 0 and 1.0
  • Each renormalized value is passed to a color scale of red(0) - yellow(0.5) - green(1.0)

Turn off Heat Map by clicking menu option again

Instances

This will give you information about other D-Tale instances are running under your current Python process.

For example, if you ran the following script:

import pandas as pd
import dtale

dtale.show(pd.DataFrame([dict(foo=1, bar=2, biz=3, baz=4, snoopy_D_O_double_gizzle=5)]))
dtale.show(pd.DataFrame([
    dict(a=1, b=2, c=3, d=4),
    dict(a=2, b=3, c=4, d=5),
    dict(a=3, b=4, c=5, d=6),
    dict(a=4, b=5, c=6, d=7)
]))
dtale.show(pd.DataFrame([range(6), range(6), range(6), range(6), range(6), range(6)]), name="foo")

This will make the Instances button available in all 3 of these D-Tale instances. Clicking that button while in the first instance invoked above will give you this popup:

The grid above contains the following information:

  • Process: timestamp when the process was started along with the name (if specified in dtale.show())
  • Rows: number of rows
  • Columns: number of columns
  • Column Names: comma-separated string of column names (only first 30 characters, hover for full listing)
  • Preview: this button is available any of the non-current instances. Clicking this will bring up left-most 5X5 grid information for that instance
  • The row highlighted in green signifys the current D-Tale instance
  • Any other row can be clicked to switch to that D-Tale instance

Here is an example of clicking the "Preview" button:

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)

Column Menu Functions

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

Applies/removes sorting (Ascending/Descending/Clear) to the column selected

Important: as you add sorts they sort added will be added to the end of the multi-sort. For example:

Action Sort
click "a"
sort asc a (asc)
click "b" a (asc)
sort desc a (asc), b(desc)
click "a" a (asc), b(desc)
sort None b(desc)
sort desc b(desc), a(desc)
click "X" on sort display

Formats

Apply simple formats to numeric values in your grid

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 any number of bins (default: 20), simply type a new integer value in the bins input

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)

For Developers

Cloning

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

$ git clone ssh://[email protected]: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

Startup Behavior

Here's a little background on how the dtale.show() function works:

  • by default it will look for ports between 40000 & 49000, but you can change that range by specifying the environment variables DTALE_MIN_PORT & DTALE_MAX_PORT
  • think of sessions as python consoles or jupyter notebooks
  1. Session 1 executes dtale.show(df) our state is:
Session Port Active Data IDs URL(s)
1 40000 1 http://localhost:40000/dtale/main/1
  1. Session 1 executes dtale.show(df) our state is:
Session Port Active Data IDs URL(s)
1 40000 1,2 http://localhost:40000/dtale/main/[1,2]
  1. Session 2 executes dtale.show(df) our state is:
Session Port Active Data IDs URL(s)
1 40000 1,2 http://localhost:40000/dtale/main/[1,2]
2 40001 1 http://localhost:40001/dtale/main/1
  1. Session 1 executes dtale.show(df, port=40001, force=True) our state is:
Session Port Active Data IDs URL(s)
1 40001 1,2,3 http://localhost:40001/dtale/main/[1,2,3]
  1. Session 3 executes dtale.show(df) our state is:
Session Port Active Data IDs URL(s)
1 40001 1,2,3 http://localhost:40001/dtale/main/[1,2,3]
3 40000 1 http://localhost:40000/dtale/main/1
  1. Session 2 executes dtale.show(df) our state is:
Session Port Active Data IDs URL(s)
1 40001 1,2,3 http://localhost:40001/dtale/main/[1,2,3]
3 40000 1 http://localhost:40000/dtale/main/1
2 40002 1 http://localhost:40002/dtale/main/1
  1. Session 4 executes dtale.show(df, port=8080) our state is:
Session Port Active Data IDs URL(s)
1 40001 1,2,3 http://localhost:40001/dtale/main/[1,2,3]
3 40000 1 http://localhost:40000/dtale/main/1
2 40002 1 http://localhost:40002/dtale/main/1
4 8080 1 http://localhost:8080/dtale/main/1
  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 dtale.show(df) our state is:
Session Port Active Data ID(s) URL(s)
1 40001 2,3 http://localhost:40001/dtale/main/[2,3]
3 40000 1 http://localhost:40000/dtale/main/1
2 40002 1 http://localhost:40002/dtale/main/1
4 8080 1 http://localhost:8080/dtale/main/1
5 30000 1 http://localhost:30000/dtale/main/1

Documentation

Have a look at the detailed documentation.

Requirements

D-Tale works with:

  • Back-end
    • arctic
    • Flask
    • Flask-Caching
    • Flask-Compress
    • flasgger
    • Pandas
    • scipy
    • six
  • Front-end
    • react-virtualized
    • chart.js

GitHub