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-16
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‎.python-version

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python-fluent

‎Part_3-Fuctions_as_Objects/Chap_6-Design_Patterns_With_First_Class_Funcions/ex_6_4__example_function_strategy.ipynb

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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"execution_count": 5,
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"metadata": {},
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"outputs": [
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{
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"<Order total: 42.00 due: 39.90>"
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]
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},
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"execution_count": 1,
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"execution_count": 5,
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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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{
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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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"<Order total: 42.00 due: 39.90>"
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]
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},
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"execution_count": 2,
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"execution_count": 6,
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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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{
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"cell_type": "code",
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"execution_count": 3,
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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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"<Order total: 30.00 due: 28.50>"
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]
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},
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"execution_count": 3,
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"execution_count": 7,
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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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{
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"cell_type": "code",
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"execution_count": 4,
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"execution_count": 8,
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"metadata": {},
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"outputs": [
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{
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"ename": "NameError",
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"evalue": "name 'large_order_promo' is not defined",
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"output_type": "error",
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"traceback": [
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
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"\u001b[0;32m<ipython-input-4-80e58dad44d7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mitem_code\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m ]\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mOrder\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mjoe\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlong_order\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlarge_order_promo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
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"\u001b[0;31mNameError\u001b[0m: name 'large_order_promo' is not defined"
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]
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"data": {
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"text/plain": [
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"<Order total: 10.00 due: 9.30>"
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]
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},
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"execution_count": 8,
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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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"source": [
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"Order(joe, long_order, large_order_promo)"
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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": 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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"<Order total: 42.00 due: 42.00>"
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]
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},
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"execution_count": 9,
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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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"source": [
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"Order(joe, cart, large_order_promo)"
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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,

‎Part_3-Fuctions_as_Objects/Chap_6-Design_Patterns_With_First_Class_Funcions/ex_6_5,6__best_promo.ipynb

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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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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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"<Order total: 10.00 due: 9.30>"
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]
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},
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"execution_count": 1,
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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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"source": [
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"from ex_6_3__functional_strategy import (\n",
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" Customer,\n",
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" LineItem,\n",
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" Order,\n",
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" fidelity_promo,\n",
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" bulk_item_promo,\n",
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" large_order_promo,\n",
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")\n",
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"\n",
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"joe = Customer('John Doe', 0)\n",
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"ann = Customer('Ann Smith', 1100)\n",
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"\n",
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"promos = [fidelity_promo, bulk_item_promo, large_order_promo]\n",
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"\n",
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"def best_promo(order):\n",
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" \"\"\"Select best discount available\n",
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" \"\"\"\n",
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" return max(promo(order) for promo in promos)\n",
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"\n",
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"long_order = [\n",
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" LineItem(str(item_code), 1, 1.0)\n",
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" for item_code in range(10)\n",
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"]\n",
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"\n",
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"banana_cart = [\n",
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" LineItem('banana', 30, .5),\n",
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" LineItem('apple', 10, 1.5),\n",
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"]\n",
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"\n",
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"cart = [\n",
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" LineItem('banana', 4, .5),\n",
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" LineItem('apple', 10, 1.5),\n",
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" LineItem('watermellon', 5, 5.0),\n",
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"]\n",
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"\n",
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"Order(joe, long_order, best_promo)"
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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": 2,
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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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"<Order total: 30.00 due: 28.50>"
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]
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},
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"execution_count": 2,
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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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"source": [
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"Order(joe, banana_cart, best_promo)"
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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": 3,
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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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"<Order total: 42.00 due: 39.90>"
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]
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},
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"execution_count": 3,
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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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"source": [
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"Order(ann, cart, best_promo)"
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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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}
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],
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"metadata": {
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"interpreter": {
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"hash": "91801c9ce6957ebd0e9246650a12ff0ec9065db97224ddc8ea854cc44b68487c"
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},
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"kernelspec": {
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"display_name": "Python 3.8.10 64-bit ('python-fluent': pyenv)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.10"
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},
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"orig_nbformat": 4
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}

‎Part_3-Fuctions_as_Objects/Chap_6-Design_Patterns_With_First_Class_Funcions/ex_6_7__promos_with_globals.ipynb

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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 3,
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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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"[<function ex_6_3__functional_strategy.fidelity_promo(order)>,\n",
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" <function ex_6_3__functional_strategy.bulk_item_promo(order)>,\n",
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" <function ex_6_3__functional_strategy.large_order_promo(order)>]"
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]
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},
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"execution_count": 3,
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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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"source": [
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"promos = [globals()[name] for name in globals()\n",
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" if name.endswith('_promo')\n",
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" and name != 'best_promo']\n",
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"\n",
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"def best_promo(order):\n",
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" \"\"\"Select best discount available\n",
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" \"\"\"\n",
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" return max(promo(order) for promo in promos)\n",
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"\n",
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"promos"
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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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}
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],
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"metadata": {
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"interpreter": {
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"hash": "91801c9ce6957ebd0e9246650a12ff0ec9065db97224ddc8ea854cc44b68487c"
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},
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"kernelspec": {
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"display_name": "Python 3.8.10 64-bit ('python-fluent': pyenv)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.10"
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},
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"orig_nbformat": 4
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}

‎Part_3-Fuctions_as_Objects/Chap_6-Design_Patterns_With_First_Class_Funcions/ex_6_8__promos_with_introspection.ipynb

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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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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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"[<function ex_6_3__functional_strategy.bulk_item_promo(order)>,\n",
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" <function ex_6_3__functional_strategy.fidelity_promo(order)>,\n",
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" <function ex_6_3__functional_strategy.large_order_promo(order)>,\n",
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" <function collections.namedtuple(typename, field_names, *, rename=False, defaults=None, module=None)>]"
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]
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},
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"execution_count": 1,
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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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"source": [
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"import inspect\n",
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"import ex_6_3__functional_strategy\n",
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"\n",
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"promos = [\n",
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" func for name, func in\n",
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" inspect.getmembers(ex_6_3__functional_strategy, inspect.isfunction)\n",
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"]\n",
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"\n",
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"promos"
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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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}
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],
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"metadata": {
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"interpreter": {
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"hash": "91801c9ce6957ebd0e9246650a12ff0ec9065db97224ddc8ea854cc44b68487c"
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},
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"kernelspec": {
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"display_name": "Python 3.8.10 64-bit ('python-fluent': pyenv)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.10"
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},
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"orig_nbformat": 4
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}

‎Part_3-Fuctions_as_Objects/Chap_6-Design_Patterns_With_First_Class_Funcions/ex_6_9__MacroCommand.py

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class MacroCommand:
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"""A command that executes a list of commands"""
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def __init__(self, commands):
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self.commands = list(commands)
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def __call__(self):
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for command in self.commands:
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command()

‎Part_3-Fuctions_as_Objects/Chap_6-Design_Patterns_With_First_Class_Funcions/promotions.py

Whitespace-only changes.

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