10 minutes to cuxfilter¶
This is a short introduction to the cuxfilter.py library, mostly going over the basic usage and features provided as a quick summary.
What is cuxfilter?¶
cuxfilter is inspired from the Crossfilter library, which is a fast, browser-based filtering mechanism across multiple dimensions and offers features do groupby operations on top of the dimensions. One of the major limitations of using Crossfilter is that it keeps data in-memory on a client-side browser, making it inefficient for processing large datasets.
cuxfilter solves the issues by leveraging the power of the rapids.ai stack, mainly cudf. The data is maintained in a gpu as a GPU DataFrame and operations like groupby aggregations, sorting and querying are done on the gpu itself, only returning the result as the output to the charts.
cuxfilter acts as a connector library, which provides the connections between different visualization libraries and a GPU dataframe without much hassle. This also allows the user to use charts from different libraries in a single dashboard, while also providing the interaction.
cuxfilter uses data-tiles on the front-end, which are precomputed aggregations, for all possible interactions for a single chart, for updating all remaining charts in a dashboard. data-tiles are just smartly computed groupbys, and generally take around 250ms per chart for a 100M row dataset(do the rest of the math!!!!, ps: its fast!). Once it’s downloaded, interactions are seamless, and well, realtime.
The modules¶
cuxfilter has following usable modules
cuxfilter.DataFrame
cuxfilter.DashBoard
cuxfilter.charts
cuxfilter.layouts
cuxfilter.themes
cuxfilter.assets
Architecure¶
The current version of cuxfilter leverages jupyter notebook and bokeh server to reduce architecture and installation complexity.
Usage¶
1. Import the required modules¶
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import cuxfilter
from cuxfilter import DataFrame, themes, layouts
from cuxfilter.assets.custom_tiles import get_provider, Vendors
Download required datasets¶
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#update data_dir if you have downloaded datasets elsewhere
DATA_DIR = './'
! curl https://data.rapids.ai/viz-data/auto_accidents.arrow.gz --create-dirs -o $DATA_DIR/auto_accidents.arrow.gz
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from cuxfilter.sampledata import datasets_check
datasets_check('auto_accidents', base_dir=DATA_DIR)
2. Read some data¶
cuxfilter can read arrow files off disk, or an inmemory cudf dataframe
[6]:
#create cuxfilter DataFrame
cux_df = DataFrame.from_arrow(DATA_DIR + './auto_accidents.arrow')
cux_df.data['ST_CASE'] = cux_df.data['ST_CASE'].astype('float64')
cux_df.data.head()
[6]:
| STATE | ST_CASE | VEH_NO | PER_NO | COUNTY | CITY | DAY | MONTH | YEAR | DAY_WEEK | ... | ROUTE | RELJCT2 | AGE | LAG_HRS | ALC_RES | UNITS_SOLD | LATITUDE | LONGITUD | dropoff_x | dropoff_y | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| __index_level_0__ | |||||||||||||||||||||
| 0 | 1 | 10001.0 | 1 | 1 | 831 | 968 | 19 | 2 | 2017 | 1 | ... | 1 | 1 | 42 | 0 | 0.96 | 186161.0 | 33.335661 | -87.007094 | -9.685585e+06 | 3.939943e+06 |
| 1 | 1 | 10002.0 | 1 | 1 | 1009 | 5923 | 14 | 2 | 2017 | 3 | ... | 1 | 1 | 43 | 0 | 0.00 | 150219.0 | 34.661528 | -86.786853 | -9.661068e+06 | 4.117979e+06 |
| 2 | 1 | 10003.0 | 1 | 1 | 1120 | 8314 | 31 | 1 | 2017 | 3 | ... | 1 | 1 | 63 | 999 | 0.00 | 0.0 | 32.366519 | -86.145281 | -9.589649e+06 | 3.811519e+06 |
| 3 | 1 | 10003.0 | 2 | 1 | 1120 | 8314 | 31 | 1 | 2017 | 3 | ... | 1 | 1 | 47 | 0 | 0.00 | 207479.0 | 32.366519 | -86.145281 | -9.589649e+06 | 3.811519e+06 |
| 4 | 1 | 10003.0 | 3 | 1 | 1120 | 8314 | 31 | 1 | 2017 | 3 | ... | 1 | 1 | 64 | 999 | 0.96 | 0.0 | 32.366519 | -86.145281 | -9.589649e+06 | 3.811519e+06 |
5 rows × 63 columns
3. Create some charts¶
see charts section to see available chart options
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demo_red_blue_palette = [ "#3182bd", "#6baed6", "#7b8ed8", "#e26798", "#ff0068" , "#323232" ]
chart1 = cuxfilter.charts.scatter(x='dropoff_x', y='dropoff_y', aggregate_col='DAY_WEEK', aggregate_fn='mean',
color_palette=demo_red_blue_palette, tile_provider='CartoLight',
pixel_shade_type='linear')
chart2 = cuxfilter.charts.bar('YEAR')
#creating a label map for days of week strings
label_map = {
1: 'Sunday',
2: 'Monday',
3: 'Tuesday',
4: 'Wednesday',
5: 'Thursday',
6: 'Friday',
7: 'Saturday',
9: 'Unknown'
}
chart3 = cuxfilter.charts.multi_select('DAY_WEEK', label_map=label_map)
chart4 = cuxfilter.charts.number(x="AGE", aggregate_fn="mean", title="Mean age", widget=True)
chart5 = cuxfilter.charts.number(expression="SIDE_DRIV_STARS + FRNT_DRIV_STARS", aggregate_fn="mean", title="Vehicle(Mean front+side safety rating)", widget=True)
charts_list = [chart1, chart2, chart3, chart4, chart5]
4. Create a dashboard object¶
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d = cux_df.dashboard(charts_list, title='Custom dashboard', layout=layouts.feature_and_base, theme=themes.light, data_size_widget=True)
5. View the dashboard¶
[41]:
# preview, uncomment below line to see the dashboard preview in a notebook cell
# await d.preview()
# for using the interactive web-app version, use d.app() for in notebook, and d.show() for using it in a separate window as a web-app
# Bokeh and Datashader based charts also have a `save` tool on the side toolbar, which can download and save the individual chart when interacting with the dashboard.
6. Run the dashboard¶
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"""
1. d.show('current_notebook_url:current_notebook_port') remote dashboard
2. d.app() inline within the notebook cell
Incase you need to stop the server:
- d.stop()
"""
# uncomment the line below to start the dashboard in the notebook
# d.app()
6. After you do some interactions, you can take a snapshot of the current state and save it as a dataframe!¶
[19]:
current_state_df = d.export()
no querying done, returning original dataframe
[20]:
current_state_df
[20]:
| STATE | ST_CASE | VEH_NO | PER_NO | COUNTY | CITY | DAY | MONTH | YEAR | DAY_WEEK | ... | ROUTE | RELJCT2 | AGE | LAG_HRS | ALC_RES | UNITS_SOLD | LATITUDE | LONGITUD | dropoff_x | dropoff_y | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| __index_level_0__ | |||||||||||||||||||||
| 0 | 1 | 10001.0 | 1 | 1 | 831 | 968 | 19 | 2 | 2017 | 1 | ... | 1 | 1 | 42 | 0 | 0.96 | 186161.0 | 33.335661 | -87.007094 | -9.685585e+06 | 3.939943e+06 |
| 1 | 1 | 10002.0 | 1 | 1 | 1009 | 5923 | 14 | 2 | 2017 | 3 | ... | 1 | 1 | 43 | 0 | 0.00 | 150219.0 | 34.661528 | -86.786853 | -9.661068e+06 | 4.117979e+06 |
| 2 | 1 | 10003.0 | 1 | 1 | 1120 | 8314 | 31 | 1 | 2017 | 3 | ... | 1 | 1 | 63 | 999 | 0.00 | 0.0 | 32.366519 | -86.145281 | -9.589649e+06 | 3.811519e+06 |
| 3 | 1 | 10003.0 | 2 | 1 | 1120 | 8314 | 31 | 1 | 2017 | 3 | ... | 1 | 1 | 47 | 0 | 0.00 | 207479.0 | 32.366519 | -86.145281 | -9.589649e+06 | 3.811519e+06 |
| 4 | 1 | 10003.0 | 3 | 1 | 1120 | 8314 | 31 | 1 | 2017 | 3 | ... | 1 | 1 | 64 | 999 | 0.96 | 0.0 | 32.366519 | -86.145281 | -9.589649e+06 | 3.811519e+06 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 3891287 | 56 | 560154.0 | 1 | 2 | 1609 | 0 | 29 | 12 | 2001 | 7 | ... | 1 | 1 | 29 | 0 | 0.96 | 0.0 | 41.717372 | -107.776083 | -1.199758e+07 | 5.118737e+06 |
| 3891288 | 56 | 560154.0 | 1 | 3 | 1609 | 0 | 29 | 12 | 2001 | 7 | ... | 1 | 1 | 10 | 999 | 0.96 | 0.0 | 41.717372 | -107.776083 | -1.199758e+07 | 5.118737e+06 |
| 3891289 | 56 | 560154.0 | 1 | 4 | 1609 | 0 | 29 | 12 | 2001 | 7 | ... | 1 | 1 | 9 | 999 | 0.96 | 0.0 | 41.717372 | -107.776083 | -1.199758e+07 | 5.118737e+06 |
| 3891290 | 56 | 560154.0 | 1 | 5 | 1609 | 0 | 29 | 12 | 2001 | 7 | ... | 1 | 1 | 7 | 999 | 0.96 | 0.0 | 41.717372 | -107.776083 | -1.199758e+07 | 5.118737e+06 |
| 3891291 | 56 | 560154.0 | 1 | 6 | 1609 | 0 | 29 | 12 | 2001 | 7 | ... | 1 | 1 | 4 | 999 | 0.96 | 0.0 | 41.717372 | -107.776083 | -1.199758e+07 | 5.118737e+06 |
1296221 rows × 63 columns
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