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numpy test
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‎.ipynb_checkpoints/timeit_tests-checkpoint.ipynb‎

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"metadata": {
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@@ -66,7 +66,8 @@
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" - [Adding elements to a dictionary](#adding_dict_elements)\n",
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"- [Comprehensions vs. for-loops](#comprehensions)\n",
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"- [Copying files by searching directory trees](#find_copy)\n",
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"- [Returning column vectors slicing through a numpy array](#row_vectors)"
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"- [Returning column vectors slicing through a numpy array](#row_vectors)\n",
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"- [Speed of numpy functions vs Python built-ins and std. lib.](#numpy)"
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]
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},
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{
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],
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"prompt_number": 91
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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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"<a name='numpy'></a>\n",
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"<br>\n",
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"<br>\n"
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]
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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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"# Speed of numpy functions vs Python built-ins and std. lib."
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]
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},
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{
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"cell_type": "code",
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"collapsed": false,
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"input": [
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"import numpy as np\n",
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"import timeit\n",
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"\n",
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"samples = list(range(1000000))\n",
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"\n",
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"%timeit(sum(samples))\n",
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"%timeit(np.sum(samples))"
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],
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"language": "python",
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"metadata": {},
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"outputs": [
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{
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"output_type": "stream",
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"stream": "stdout",
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"text": [
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"100 loops, best of 3: 18.3 ms per loop\n",
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"10 loops, best of 3: 136 ms per loop"
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]
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},
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{
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"output_type": "stream",
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"stream": "stdout",
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]
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}
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],
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"prompt_number": 6
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},
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{
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"cell_type": "code",
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"collapsed": false,
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"input": [
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"%timeit(list(range(1000000)))\n",
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"%timeit(np.arange(1000000))\n",
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"\n",
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"# note that in Python range() is implemented as xrange()\n",
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"# with lazy evaluation (generator)"
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],
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"language": "python",
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"metadata": {},
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"outputs": [
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{
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"output_type": "stream",
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"stream": "stdout",
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"text": [
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"10 loops, best of 3: 82.6 ms per loop\n",
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"100 loops, best of 3: 5.35 ms per loop"
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]
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},
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{
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"output_type": "stream",
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"stream": "stdout",
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"text": [
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"\n"
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]
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}
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],
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"prompt_number": 11
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},
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{
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"cell_type": "code",
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"collapsed": false,
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"input": [
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"import statistics\n",
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"\n",
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"%timeit(statistics.mean(samples))\n",
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"%timeit(np.mean(samples))"
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],
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"language": "python",
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"metadata": {},
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"outputs": [
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{
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"output_type": "stream",
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"stream": "stdout",
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"text": [
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"1 loops, best of 3: 1.14 s per loop\n",
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"10 loops, best of 3: 141 ms per loop"
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]
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},
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{
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"output_type": "stream",
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"stream": "stdout",
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"text": [
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‎benchmarks/timeit_tests.ipynb‎

Lines changed: 113 additions & 2 deletions
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{
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"name": "",
4-
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},
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"nbformat": 3,
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"nbformat_minor": 0,
@@ -66,7 +66,8 @@
6666
" - [Adding elements to a dictionary](#adding_dict_elements)\n",
6767
"- [Comprehensions vs. for-loops](#comprehensions)\n",
6868
"- [Copying files by searching directory trees](#find_copy)\n",
69-
"- [Returning column vectors slicing through a numpy array](#row_vectors)"
69+
"- [Returning column vectors slicing through a numpy array](#row_vectors)\n",
70+
"- [Speed of numpy functions vs Python built-ins and std. lib.](#numpy)"
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]
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},
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{
@@ -1691,6 +1692,116 @@
16911692
],
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"prompt_number": 91
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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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"<a name='numpy'></a>\n",
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"<br>\n",
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"<br>\n"
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]
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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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"# Speed of numpy functions vs Python built-ins and std. lib."
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]
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},
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{
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"cell_type": "code",
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"collapsed": false,
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"input": [
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"import numpy as np\n",
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"import timeit\n",
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"\n",
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"samples = list(range(1000000))\n",
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"\n",
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"%timeit(sum(samples))\n",
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"%timeit(np.sum(samples))"
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],
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"language": "python",
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"metadata": {},
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"outputs": [
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{
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"output_type": "stream",
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"stream": "stdout",
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"text": [
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"100 loops, best of 3: 18.3 ms per loop\n",
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"10 loops, best of 3: 136 ms per loop"
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]
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},
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{
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"output_type": "stream",
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"stream": "stdout",
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"text": [
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"\n"
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}
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],
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"prompt_number": 6
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},
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{
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"cell_type": "code",
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"collapsed": false,
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"input": [
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"%timeit(list(range(1000000)))\n",
1749+
"%timeit(np.arange(1000000))\n",
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"\n",
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"# note that in Python range() is implemented as xrange()\n",
1752+
"# with lazy evaluation (generator)"
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],
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"language": "python",
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"metadata": {},
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"outputs": [
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{
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"output_type": "stream",
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"stream": "stdout",
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"text": [
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"10 loops, best of 3: 82.6 ms per loop\n",
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"100 loops, best of 3: 5.35 ms per loop"
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]
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},
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{
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"output_type": "stream",
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"stream": "stdout",
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}
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"prompt_number": 11
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},
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{
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"cell_type": "code",
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"collapsed": false,
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"input": [
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"import statistics\n",
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"\n",
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"%timeit(statistics.mean(samples))\n",
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"%timeit(np.mean(samples))"
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],
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"language": "python",
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"metadata": {},
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"outputs": [
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{
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"output_type": "stream",
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"stream": "stdout",
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"text": [
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"1 loops, best of 3: 1.14 s per loop\n",
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"10 loops, best of 3: 141 ms per loop"
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]
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},
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{
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"output_type": "stream",
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"stream": "stdout",
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"text": [
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"\n"
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]
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}
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],
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"prompt_number": 14
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},
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