Web Client for Visualizing Pandas Objects
Project description
What is it?
D-Tale was born out a conversion from SAS to Python. 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. 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. Currently this tool supports such Pandas objects as DataFrame, Series, MultiIndex, DatetimeIndex & RangeIndex. It integrates seamlessly with ipython notebooks & python/ipython terminals.
Contents
Getting Started
PyCharm |
jupyter |
|---|---|
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 dtalePython 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 dtaleNow 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 
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 alot 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|Charts|Instances|
|:——:|:——:|:——:|:——:|:——:|:——:|
|
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
dtale --arctic-host mongodb://localhost:27027 --arctic-library jdoe.my_lib --arctic-node my_node --arctic-start 20130101 --arctic-end 20161231Loading data from CSV
dtale --csv-path /home/jdoe/my_csv.csv --csv-parse_dates dateLoading 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 NoneIn 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
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
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.
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 your data along the x-axis 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
specify both a group & an aggregation
Click the “Load” button which will load the data and display the default cahrt type “line”
You now have the ability to toggle between different chart types: line, bar, stacked bar, pie & wordcloud
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 with the following inputs: X: date, Y: Col0, Group: security_id, Aggregation: Mean, Query: security_id in (100000, 100001) and date >= '20181220' and date <= '20181231'
Chart Type |
Chart |
Chart per Group |
|---|---|---|
line |
||
bar |
||
stacked |
||
pie |
||
wordcloud |
This is a very powerful feature with many more features that could be offered (word cloud, different statistical aggregations, etc…) so please submit issues :)
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 |
|---|---|---|
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
Iframe-mode/Full-mode
This is only available if you are not viewing D-Tale from an jupyter notebook output cell. This will toggle between the two types of functionality: - Full-mode: column selection, column-specific options in in the main menu & all tools are displayed in modal windows - Iframe-mode: no column selection, column-specific menus on head click & some tools will now open separate browser windows (Correlations, Describe, Histogram & Instances)
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/ column(s) selected
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 |
|---|---|
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
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 dateYou 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 buildRunning 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 -sYou can also lint individual JS files:
$ npm run lint-js-file -s -- static/dtale/DataViewer.jsxFormatting JS
You can auto-format code as follows:
$ npm run formatDocker 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
Session 1 executes dtale.show(df) our state is:
Session |
Port |
Active Data IDs |
URL(s) |
|---|---|---|---|
1 |
40000 |
1 |
Session 1 executes dtale.show(df) our state is:
Session |
Port |
Active Data IDs |
URL(s) |
|---|---|---|---|
1 |
40000 |
1,2 |
Session 2 executes dtale.show(df) our state is:
Session |
Port |
Active Data IDs |
URL(s) |
|---|---|---|---|
1 |
40000 |
1,2 |
|
2 |
40001 |
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 |
Session 3 executes dtale.show(df) our state is:
Session |
Port |
Active Data IDs |
URL(s) |
|---|---|---|---|
1 |
40001 |
1,2,3 |
|
3 |
40000 |
1 |
Session 2 executes dtale.show(df) our state is:
Session |
Port |
Active Data IDs |
URL(s) |
|---|---|---|---|
1 |
40001 |
1,2,3 |
|
3 |
40000 |
1 |
|
2 |
40002 |
1 |
Session 4 executes dtale.show(df, port=8080) our state is:
Session |
Port |
Active Data IDs |
URL(s) |
|---|---|---|---|
1 |
40001 |
1,2,3 |
|
3 |
40000 |
1 |
|
2 |
40002 |
1 |
|
4 |
8080 |
1 |
Session 1 executes dtale.get_instance(1).kill() our state is:
Session |
Port |
Active Data IDs |
URL(s) |
|---|---|---|---|
1 |
40001 |
2,3 |
|
3 |
40000 |
1 |
|
2 |
40002 |
1 |
|
4 |
8080 |
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 |
|
3 |
40000 |
1 |
|
2 |
40002 |
1 |
|
4 |
8080 |
1 |
|
5 |
30000 |
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
Acknowledgements
D-Tale has been under active development at Man Numeric since 2019.
Original concept and implementation: Andrew Schonfeld
Contributors:
Mike Kelly
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
1.6.5 (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)
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)
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
Release history Release notifications | RSS feed
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