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‎week13-Interactive_Data_Analysis_And_Speedups/Performance_Optimization.ipynb‎

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"7.578306792995136 seconds\n"
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"7.389281970004959 seconds\n"
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"1.6924289310045424 seconds\n"
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"1.6421769209991908 seconds\n"
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"Took 36.493831396102905s\n"
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"Took 75.16112017631531s\n"
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"Took 28.848686695098877s\n"
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"text": [
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"CPU times: user 942 µs, sys: 0 ns, total: 942 µs\n",
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"Wall time: 811 µs\n"
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"CPU times: user 556 µs, sys: 262 µs, total: 818 µs\n",
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"Wall time: 706 µs\n"
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" [ 9999., 10000., 10001., ..., 10096., 10097., 10098.]])"
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"text": [
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"CPU times: user 71 µs, sys: 4 µs, total: 75 µs\n",
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"Wall time: 77.7 µs\n"
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"CPU times: user 163 µs, sys: 77 µs, total: 240 µs\n",
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"Wall time: 260 µs\n"
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" [ 9999., 10000., 10001., ..., 10096., 10097., 10098.]])"
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"1 1 2 3 5 8 13 21 34 55 89 144 233 377 610 987 1597 2584 4181 6765 10946 17711 28657 46368 75025 121393 196418 317811 514229 832040 1346269 2178309 3524578 5702887 9227465 14930352 24157817 39088169 63245986 102334155 165580141 267914296 433494437 701408733 1134903170 1836311903 2971215073 4807526976 7778742049 \n",
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"CPU times: user 15 ms, sys: 4.2 ms, total: 19.2 ms\n",
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"Wall time: 14.5 ms\n"
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"CPU times: user 6.67 ms, sys: 5.61 ms, total: 12.3 ms\n",
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"Wall time: 7.16 ms\n"
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"1 1 2 3 5 8 13 21 34 55 89 144 233 377 610 987 1597 2584 4181 6765 10946 17711 28657 46368 75025 121393 196418 317811 514229 832040 1346269 2178309 3524578 5702887 9227465 14930352 24157817 39088169 63245986 102334155 165580141 267914296 433494437 701408733 1134903170 1836311903 2971215073 4807526976 7778742049 \n",
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"CPU times: user 2.89 ms, sys: 162 µs, total: 3.05 ms\n",
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"Wall time: 2.05 ms\n"
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"CPU times: user 5.26 ms, sys: 0 ns, total: 5.26 ms\n",
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"Wall time: 3.03 ms\n"
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‎week13-Interactive_Data_Analysis_And_Speedups/cython/hello_world/helloworld.cpython-37m-x86_64-linux-gnu.so‎

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‎week13-Interactive_Data_Analysis_And_Speedups/lecture.ipynb‎

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"4 ford torino USA 3449 1970-01-01 "
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"![Voyager 2](voyager2.png)"
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]
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"You can get the Jupyterlab-Extension for Voyager [here](https://github.com/altair-viz/jupyterlab_voyager). This extension can automatically generate Altair-Code from the plots you make there, however unfortunately the export-function of the extension is currently broken (as of June 2019). \n",
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"To use the extension, right-click a `json` or `csv`-file in the view on the left and open it with Voyager."
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]
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},
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‎week13-Interactive_Data_Analysis_And_Speedups/numba/basics.ipynb‎

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‎week13-Interactive_Data_Analysis_And_Speedups/voyager2_todo.txt‎

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---
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-upon selection of a dataset, focus view is empty, related view shows univariate summaries for all fields
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usage scenario:
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-loading dataset -> familiarize with dataset -> univariate summaries (most cars have even number of cylinders, multiple times the same model name, most of USA, all between 1970-1982, acceleration normally distributed, ..)
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-then go to potential associations. -> Drag two quantitative field wildcards to "any" shelves --> each pair of quanitative fields to x and y axes to make gallery of scatterplots. We see a roughly quadratic relationship between horsepower and miles per gallon, we're interested in that, let's bookmark it with a note, then specify button to make it focus

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