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ushillsushills
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updated Byte21 to use comprehensions
1 parent 15a1060 commit 922f754

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Lines changed: 161 additions & 63 deletions

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‎days/07-09-data-structures/code/Byte21.ipynb‎

Lines changed: 161 additions & 63 deletions
Original file line numberDiff line numberDiff line change
@@ -9,7 +9,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 31,
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"execution_count": 4,
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"metadata": {},
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"outputs": [],
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"source": [
@@ -24,7 +24,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 32,
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"execution_count": 5,
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"metadata": {},
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"outputs": [],
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"source": [
@@ -33,31 +33,59 @@
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" pass"
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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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"**Standard solution**"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"440 ns ± 29.6 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n"
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]
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}
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],
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"source": [
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"def get_all_jeeps():\n",
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" jeeps = cars['Jeep']\n",
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" models = ', '.join(jeeps)\n",
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" return models\n",
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"\n",
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"%timeit get_all_jeeps()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 33,
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"execution_count": 7,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"'Grand Cherokee, Cherokee, Trailhawk, Trackhawk'"
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]
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},
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"execution_count": 33,
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"metadata": {},
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"output_type": "execute_result"
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"394 ns ± 13.3 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n"
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]
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}
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],
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"source": [
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"jeeps = cars['Jeep']\n",
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"models = ', '.join(jeeps)\n",
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"print(models)"
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"def get_all_jeeps():\n",
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" return ', '.join(cars['Jeep'])\n",
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"\n",
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"# print(get_all_jeeps())\n",
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"%timeit get_all_jeeps()\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 34,
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"execution_count": 8,
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"metadata": {},
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"outputs": [],
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"source": [
@@ -66,33 +94,71 @@
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" pass"
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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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"**Standard loop**"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 74,
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"execution_count": 9,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"['Fairlane', 'Barina', '350Z', 'Accord', 'Cherokee']"
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]
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},
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"execution_count": 74,
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"metadata": {},
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"output_type": "execute_result"
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"1.14 µs ± 24 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n",
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"['Falcon', 'Commodore', 'Maxima', 'Civic', 'Grand Cherokee']\n"
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]
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}
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],
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"source": [
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"firstmodels = []\n",
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"for maker in cars:\n",
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" models = cars[maker]\n",
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" firstmodels.append(models[0])\n",
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"firstmodels"
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"def get_first_model_each_manufacturer():\n",
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" firstmodels = []\n",
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" for maker in cars:\n",
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" models = cars[maker]\n",
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" firstmodels.append(models[0])\n",
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" return firstmodels\n",
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"\n",
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"%timeit get_first_model_each_manufacturer()\n",
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"print(get_first_model_each_manufacturer())"
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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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"**Solution using list comprehenson**"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"829 ns ± 67.3 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n",
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"['Falcon', 'Commodore', 'Maxima', 'Civic', 'Grand Cherokee']\n"
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]
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}
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],
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"source": [
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"def get_first_model_each_manufacturer():\n",
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" return [models[0] for models in cars.values()]\n",
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"\n",
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"%timeit get_first_model_each_manufacturer()\n",
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"print(get_first_model_each_manufacturer())"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 75,
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"execution_count": 11,
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"metadata": {},
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"outputs": [],
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"source": [
@@ -103,56 +169,91 @@
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" pass"
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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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"**Standard Solution**"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 76,
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"execution_count": 15,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"8.89 µs ± 496 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n",
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"['Trailblazer', 'Trailhawk']\n"
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]
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}
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],
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"source": [
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"grep='trail'\n",
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"search_result = []\n",
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"search_string = grep\n",
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"for models in cars.values():\n",
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" for model in models:\n",
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" if search_string.lower() in model.lower():\n",
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" search_result.append(model)\n",
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" search_result.sort()\n",
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"print(search_result)\n",
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" "
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"def get_all_matching_models(grep='trail'):\n",
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" search_result = []\n",
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" search_string = grep\n",
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" for models in cars.values():\n",
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" for model in models:\n",
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" if search_string.lower() in model.lower():\n",
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" search_result.append(model)\n",
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" search_result.sort()\n",
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" return search_result\n",
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"\n",
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"%timeit get_all_matching_models()\n",
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"print(get_all_matching_models())"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 77,
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"cell_type": "markdown",
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"metadata": {},
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"outputs": [],
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"source": [
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"def sort_car_models():\n",
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" \"\"\"sort the car models (values) and return the resulting cars dict\"\"\"\n",
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" pass"
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"**Using List Comprehension**"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 69,
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"execution_count": 13,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"{'Ford': ['Fairlane', 'Falcon', 'Festiva', 'Focus'], 'Holden': ['Barina', 'Captiva', 'Commodore', 'Trailblazer'], 'Nissan': ['350Z', 'Maxima', 'Navara', 'Pulsar'], 'Honda': ['Accord', 'Civic', 'Jazz', 'Odyssey'], 'Jeep': ['Cherokee', 'Grand Cherokee', 'Trackhawk', 'Trailhawk']}\n"
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"5.57 µs ± 567 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n",
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"['Trailblazer', 'Trailhawk']\n"
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]
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}
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],
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"source": [
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"def get_all_matching_models(grep='trail'):\n",
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" grep = grep.lower()\n",
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" models = sum(cars.values(), []) # flatten list of lists\n",
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" matching_models = [model for model in models\n",
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" if grep in model.lower()]\n",
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" return sorted(matching_models)\n",
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"\n",
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"%timeit get_all_matching_models()\n",
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"print(get_all_matching_models())"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"def sort_car_models():\n",
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" \"\"\"sort the car models (values) and return the resulting cars dict\"\"\"\n",
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" pass"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"new_cars = {}\n",
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"for maker in cars:\n",
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},
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{
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"cell_type": "code",
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"execution_count": 95,
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"execution_count": null,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"['Accord', 'Commodore', 'Falcon']"
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]
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},
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"execution_count": 95,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"outputs": [],
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"source": [
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"def get_all_matching_models(grep='trail'):\n",
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" search_result = []\n",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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"source": [
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"def get_all_matching_models(grep='trail'):\n",
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" search_result = []\n",
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" for models in cars.values():\n",
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" search_result = [model for model in models if (grep.lower() == model.lower())]\n",
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" return search_result\n",
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"\n",
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"get_all_matching_models('CO')"
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]
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},
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{
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"cell_type": "code",

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