Web Client for Visualizing Pandas Objects
Project description
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 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
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 |
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'Python Terminal
This comes courtesy of PyCharm 
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._port # the process's port
d._url # the url to access the processUI
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
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 |
|---|---|
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 (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
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
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://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
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:
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
1.2.0 (2019-10-24)
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
1.3.1 (2019-10-29)
fix for incompatible str types when directly altering state of data in running D-Tale instance
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
1.3.3 (2019-11-05)
hotfix for failing test under certain versions of future package
1.3.4 (2019-11-07)
updated correlation calculation to use numpy.corrcoef for performance purposes
github rebranding from manahl -> man-group
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
1.3.6 (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
1.3.7 (2019-11-12)
1.4.0 (2019-11-19)
Correlations Pearson Matrix filters
“name” display in title tab
“Heat Map” toggle
dropped unused “Flask-Caching” requirement
1.4.1 (2019-11-20)
#32: unpin jsonschema by moving flasgger to extras_require
Release history Release notifications | RSS feed
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