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guipsamoraguipsamora
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fixed chipotle exercise
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‎01_Getting_&_Knowing_Your_Data/Chipotle/Exercise_with_Solutions.ipynb‎

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@@ -19,13 +19,14 @@
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"execution_count": 25,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"import pandas as pd"
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"import pandas as pd\n",
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"import numpy as np"
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]
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},
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{
@@ -44,7 +45,7 @@
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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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"execution_count": 3,
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"metadata": {
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"collapsed": false
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},
@@ -64,7 +65,7 @@
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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": 4,
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"metadata": {
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"collapsed": false,
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"scrolled": false
@@ -196,7 +197,7 @@
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"9 [Fresh Tomato Salsa, [Rice, Black Beans, Pinto... $9.25 "
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]
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},
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"execution_count": 32,
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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}
@@ -215,7 +216,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 111,
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"execution_count": 5,
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"metadata": {
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"collapsed": false
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},
@@ -242,7 +243,7 @@
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"4622"
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]
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},
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"execution_count": 111,
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"execution_count": 5,
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"metadata": {},
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"output_type": "execute_result"
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}
@@ -265,7 +266,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 109,
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"execution_count": 6,
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"metadata": {
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"collapsed": false
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},
@@ -276,7 +277,7 @@
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"5"
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]
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},
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"execution_count": 109,
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"execution_count": 6,
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"metadata": {},
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"output_type": "execute_result"
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}
@@ -294,7 +295,7 @@
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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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"execution_count": 7,
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"metadata": {
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"collapsed": false
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},
@@ -307,7 +308,7 @@
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" dtype='object')"
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]
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},
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"execution_count": 9,
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"execution_count": 7,
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"metadata": {},
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"output_type": "execute_result"
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}
@@ -325,7 +326,7 @@
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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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"execution_count": 8,
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"metadata": {
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"collapsed": false
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},
@@ -336,7 +337,7 @@
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"RangeIndex(start=0, stop=4622, step=1)"
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]
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},
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"execution_count": 10,
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"execution_count": 8,
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"metadata": {},
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"output_type": "execute_result"
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}
@@ -354,27 +355,53 @@
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},
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{
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"cell_type": "code",
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"execution_count": 139,
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"execution_count": 41,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>order_id</th>\n",
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" <th>quantity</th>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>item_name</th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>Chicken Bowl</th>\n",
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" <td>713926</td>\n",
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" <td>761</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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"Chicken Bowl 761\n",
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"*Don't forget to include the quantity in your calculations!"
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" order_id quantity\n",
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"item_name \n",
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"Chicken Bowl 713926 761"
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]
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},
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"execution_count": 139,
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"execution_count": 41,
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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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"c = chipo.groupby('item_name')",
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"c = c.sum()",
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"c = c.sort_values(['quantity'], ascending=False)",
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"c = chipo.groupby('item_name')\n",
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"c = c.sum()\n",
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"c = c.sort_values(['quantity'], ascending=False)\n",
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"c.head(1)"
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]
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},
@@ -387,28 +414,55 @@
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},
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{
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"cell_type": "code",
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"execution_count": 93,
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"execution_count": 21,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>order_id</th>\n",
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" <th>quantity</th>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>item_name</th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>Chicken Bowl</th>\n",
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" <td>713926</td>\n",
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" <td>761</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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"761"
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" order_id quantity\n",
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"item_name \n",
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"Chicken Bowl 713926 761"
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]
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},
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"execution_count": 93,
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"execution_count": 21,
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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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"c = chipo.groupby('item_name')",
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"c = c.sum()",
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"c = c.sort_values(['quantity'], ascending=False)",
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"c = chipo.groupby('item_name')\n",
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"c = c.sum()\n",
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"c = c.sort_values(['quantity'], ascending=False)\n",
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"c.head(1)"
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]
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]
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},
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{
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"cell_type": "markdown",
@@ -419,32 +473,54 @@
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"execution_count": 23,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>order_id</th>\n",
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" <th>quantity</th>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>choice_description</th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>[Diet Coke]</th>\n",
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" <td>123455</td>\n",
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" <td>159</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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"[Diet Coke] 159\n",
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"[Coke] 143\n",
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"[Sprite] 89\n",
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"[Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream, Lettuce]] 49\n",
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"[Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream]] 42\n",
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"Name: choice_description, dtype: int64"
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" order_id quantity\n",
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"choice_description \n",
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"[Diet Coke] 123455 159"
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]
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},
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"execution_count": 12,
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"execution_count": 23,
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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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"c = chipo.groupby('choice_description').sum()",
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"c = c.sort_values(['quantity'], ascending=False)",
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"c.head(1)",
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"Diet Coke 159"
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"c = chipo.groupby('choice_description').sum()\n",
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"c = c.sort_values(['quantity'], ascending=False)\n",
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"c.head(1)\n",
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"# Diet Coke 159"
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]
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},
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{
@@ -456,7 +532,7 @@
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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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"execution_count": 42,
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"metadata": {
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"collapsed": false
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},
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"4972"
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]
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},
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"execution_count": 9,
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"execution_count": 42,
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"metadata": {},
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"output_type": "execute_result"
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}
@@ -486,7 +562,7 @@
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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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"execution_count": 43,
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"metadata": {
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"collapsed": true
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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": 122,
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"execution_count": 47,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"34500.16000000046"
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]
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},
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"execution_count": 130,
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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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"Revenue was: $39237.02\n"
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]
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}
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],
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"source": [
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"chipo.item_price.sum()"
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"revenue = (chipo['quantity']* chipo['item_price']).sum()\n",
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"\n",
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"print('Revenue was: $' + str(np.round(revenue,2)))"
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]
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},
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{
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}
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],
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"metadata": {
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"anaconda-cloud": {},
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"kernelspec": {
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"display_name": "Python 2",
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"display_name": "Python [default]",
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"language": "python",
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"name": "python2"
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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": 2
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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": "ipython2",
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"version": "2.7.11"
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}
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},
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"nbformat": 4,

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