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proj_6-1_ramen.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/fenago/ad9f6e8fa9ab1dd57bf08fdf8267d105/proj_6-1_ramen.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "EGsuWm_qknUt"
},
"source": [
"# Project 6-1: Clean the ramen data"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "InULc2-sknU1"
},
"outputs": [],
"source": [
"# https://www.kaggle.com/residentmario/ramen-ratings\n",
"import pandas as pd\n",
"import seaborn as sns"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "KxoDNiXhknU4"
},
"source": [
"## Tasks"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "mh4TUx0xknU4",
"outputId": "b2c1b438-bb42-42c2-dcc1-c0dcd5ecbc62"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Brand</th>\n",
" <th>Variety</th>\n",
" <th>Style</th>\n",
" <th>Country</th>\n",
" <th>Stars</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>New Touch</td>\n",
" <td>T's Restaurant Tantanmen</td>\n",
" <td>Cup</td>\n",
" <td>Japan</td>\n",
" <td>3.75</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Just Way</td>\n",
" <td>Noodles Spicy Hot Sesame Spicy Hot Sesame Guan...</td>\n",
" <td>Pack</td>\n",
" <td>Taiwan</td>\n",
" <td>1.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Nissin</td>\n",
" <td>Cup Noodles Chicken Vegetable</td>\n",
" <td>Cup</td>\n",
" <td>USA</td>\n",
" <td>2.25</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Wei Lih</td>\n",
" <td>GGE Ramen Snack Tomato Flavor</td>\n",
" <td>Pack</td>\n",
" <td>Taiwan</td>\n",
" <td>2.75</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Ching's Secret</td>\n",
" <td>Singapore Curry</td>\n",
" <td>Pack</td>\n",
" <td>India</td>\n",
" <td>3.75</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2572</th>\n",
" <td>Vifon</td>\n",
" <td>Hu Tiu Nam Vang [\"Phnom Penh\" style] Asian Sty...</td>\n",
" <td>Bowl</td>\n",
" <td>Vietnam</td>\n",
" <td>3.50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2573</th>\n",
" <td>Wai Wai</td>\n",
" <td>Oriental Style Instant Noodles</td>\n",
" <td>Pack</td>\n",
" <td>Thailand</td>\n",
" <td>1.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2574</th>\n",
" <td>Wai Wai</td>\n",
" <td>Tom Yum Shrimp</td>\n",
" <td>Pack</td>\n",
" <td>Thailand</td>\n",
" <td>2.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2575</th>\n",
" <td>Wai Wai</td>\n",
" <td>Tom Yum Chili Flavor</td>\n",
" <td>Pack</td>\n",
" <td>Thailand</td>\n",
" <td>2.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2576</th>\n",
" <td>Westbrae</td>\n",
" <td>Miso Ramen</td>\n",
" <td>Pack</td>\n",
" <td>USA</td>\n",
" <td>0.50</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>2577 rows × 5 columns</p>\n",
"</div>"
],
"text/plain": [
" Brand Variety Style \\\n",
"0 New Touch T's Restaurant Tantanmen Cup \n",
"1 Just Way Noodles Spicy Hot Sesame Spicy Hot Sesame Guan... Pack \n",
"2 Nissin Cup Noodles Chicken Vegetable Cup \n",
"3 Wei Lih GGE Ramen Snack Tomato Flavor Pack \n",
"4 Ching's Secret Singapore Curry Pack \n",
"... ... ... ... \n",
"2572 Vifon Hu Tiu Nam Vang [\"Phnom Penh\" style] Asian Sty... Bowl \n",
"2573 Wai Wai Oriental Style Instant Noodles Pack \n",
"2574 Wai Wai Tom Yum Shrimp Pack \n",
"2575 Wai Wai Tom Yum Chili Flavor Pack \n",
"2576 Westbrae Miso Ramen Pack \n",
"\n",
" Country Stars \n",
"0 Japan 3.75 \n",
"1 Taiwan 1.00 \n",
"2 USA 2.25 \n",
"3 Taiwan 2.75 \n",
"4 India 3.75 \n",
"... ... ... \n",
"2572 Vietnam 3.50 \n",
"2573 Thailand 1.00 \n",
"2574 Thailand 2.00 \n",
"2575 Thailand 2.00 \n",
"2576 USA 0.50 \n",
"\n",
"[2577 rows x 5 columns]"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 1\n",
"data = pd.read_csv('ramen-ratings.csv')\n",
"data"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "WCQ9qeXaknU6"
},
"outputs": [],
"source": [
"# 2\n",
"data.rename(columns={'Stars':'Rating'}, inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "JuiO4-YGknU6"
},
"outputs": [],
"source": [
"# 3\n",
"data.Style = data.Style.astype('category')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "jb7cnEFWknU7"
},
"outputs": [],
"source": [
"# 4\n",
"data.drop(columns=['Country'], inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "vA8o47TyknU7",
"outputId": "86951a95-495d-4223-89cd-48500982baee"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 2577 entries, 0 to 2576\n",
"Data columns (total 4 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 Brand 2577 non-null object \n",
" 1 Variety 2577 non-null object \n",
" 2 Style 2575 non-null category\n",
" 3 Rating 2577 non-null float64 \n",
"dtypes: category(1), float64(1), object(2)\n",
"memory usage: 63.4+ KB\n"
]
}
],
"source": [
"# 5\n",
"data.info()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "oO0lLbLXknU7"
},
"outputs": [],
"source": [
"# 6\n",
"data.rename(columns={'Brand':'Company','Variety':'Product'}, inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "cgfLohlmknU8",
"outputId": "b7f254d9-951a-4260-a654-f66a875733ea"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
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" }\n",
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" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Company</th>\n",
" <th>Product</th>\n",
" <th>Style</th>\n",
" <th>Rating</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Nissin</td>\n",
" <td>Cup Noodles Chicken Vegetable</td>\n",
" <td>Cup</td>\n",
" <td>2.25</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>Ching's Secret</td>\n",
" <td>Hot Garlic Instant Noodles</td>\n",
" <td>Pack</td>\n",
" <td>4.25</td>\n",
" </tr>\n",
" <tr>\n",
" <th>46</th>\n",
" <td>Nongshim</td>\n",
" <td>Shin Ramyun Black</td>\n",
" <td>Pack</td>\n",
" <td>5.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>69</th>\n",
" <td>MyKuali</td>\n",
" <td>Penang Red Tom Yum Goong Noodle</td>\n",
" <td>Pack</td>\n",
" <td>5.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75</th>\n",
" <td>Nongshim</td>\n",
" <td>Shin Ramyun</td>\n",
" <td>Pack</td>\n",
" <td>3.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2545</th>\n",
" <td>Koka</td>\n",
" <td>Mi Hai Cua Crab Flavor</td>\n",
" <td>Pack</td>\n",
" <td>3.50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2552</th>\n",
" <td>Nissin</td>\n",
" <td>Demae Ramen Spicy Flavor</td>\n",
" <td>Pack</td>\n",
" <td>3.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2556</th>\n",
" <td>Nongshim</td>\n",
" <td>Champong Oriental Noodles</td>\n",
" <td>Pack</td>\n",
" <td>4.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2558</th>\n",
" <td>Nongshim</td>\n",
" <td>Shin Ramyun</td>\n",
" <td>Pack</td>\n",
" <td>4.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2565</th>\n",
" <td>Sapporo Ichiban</td>\n",
" <td>Shrimp Flavor</td>\n",
" <td>Pack</td>\n",
" <td>2.50</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>118 rows × 4 columns</p>\n",
"</div>"
],
"text/plain": [
" Company Product Style Rating\n",
"2 Nissin Cup Noodles Chicken Vegetable Cup 2.25\n",
"24 Ching's Secret Hot Garlic Instant Noodles Pack 4.25\n",
"46 Nongshim Shin Ramyun Black Pack 5.00\n",
"69 MyKuali Penang Red Tom Yum Goong Noodle Pack 5.00\n",
"75 Nongshim Shin Ramyun Pack 3.00\n",
"... ... ... ... ...\n",
"2545 Koka Mi Hai Cua Crab Flavor Pack 3.50\n",
"2552 Nissin Demae Ramen Spicy Flavor Pack 3.00\n",
"2556 Nongshim Champong Oriental Noodles Pack 4.00\n",
"2558 Nongshim Shin Ramyun Pack 4.00\n",
"2565 Sapporo Ichiban Shrimp Flavor Pack 2.50\n",
"\n",
"[118 rows x 4 columns]"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 7\n",
"data[data.duplicated(subset=['Company','Product'], keep=False)]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "u7hZfhWdknU9",
"outputId": "c12e87b2-872a-42a6-8235-7fbc7a6eb7c6"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: top;\n",
" }\n",
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" .dataframe thead th {\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Company</th>\n",
" <th>Product</th>\n",
" <th>Style</th>\n",
" <th>Rating</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>New Touch</td>\n",
" <td>T's Restaurant Tantanmen</td>\n",
" <td>Cup</td>\n",
" <td>3.75</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Just Way</td>\n",
" <td>Noodles Spicy Hot Sesame Spicy Hot Sesame Guan...</td>\n",
" <td>Pack</td>\n",
" <td>1.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Nissin</td>\n",
" <td>Cup Noodles Chicken Vegetable</td>\n",
" <td>Cup</td>\n",
" <td>2.25</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Wei Lih</td>\n",
" <td>GGE Ramen Snack Tomato Flavor</td>\n",
" <td>Pack</td>\n",
" <td>2.75</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Ching's Secret</td>\n",
" <td>Singapore Curry</td>\n",
" <td>Pack</td>\n",
" <td>3.75</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2572</th>\n",
" <td>Vifon</td>\n",
" <td>Hu Tiu Nam Vang [\"Phnom Penh\" style] Asian Sty...</td>\n",
" <td>Bowl</td>\n",
" <td>3.50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2573</th>\n",
" <td>Wai Wai</td>\n",
" <td>Oriental Style Instant Noodles</td>\n",
" <td>Pack</td>\n",
" <td>1.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2574</th>\n",
" <td>Wai Wai</td>\n",
" <td>Tom Yum Shrimp</td>\n",
" <td>Pack</td>\n",
" <td>2.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2575</th>\n",
" <td>Wai Wai</td>\n",
" <td>Tom Yum Chili Flavor</td>\n",
" <td>Pack</td>\n",
" <td>2.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2576</th>\n",
" <td>Westbrae</td>\n",
" <td>Miso Ramen</td>\n",
" <td>Pack</td>\n",
" <td>0.50</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>2517 rows × 4 columns</p>\n",
"</div>"
],
"text/plain": [
" Company Product Style \\\n",
"0 New Touch T's Restaurant Tantanmen Cup \n",
"1 Just Way Noodles Spicy Hot Sesame Spicy Hot Sesame Guan... Pack \n",
"2 Nissin Cup Noodles Chicken Vegetable Cup \n",
"3 Wei Lih GGE Ramen Snack Tomato Flavor Pack \n",
"4 Ching's Secret Singapore Curry Pack \n",
"... ... ... ... \n",
"2572 Vifon Hu Tiu Nam Vang [\"Phnom Penh\" style] Asian Sty... Bowl \n",
"2573 Wai Wai Oriental Style Instant Noodles Pack \n",
"2574 Wai Wai Tom Yum Shrimp Pack \n",
"2575 Wai Wai Tom Yum Chili Flavor Pack \n",
"2576 Westbrae Miso Ramen Pack \n",
"\n",
" Rating \n",
"0 3.75 \n",
"1 1.00 \n",
"2 2.25 \n",
"3 2.75 \n",
"4 3.75 \n",
"... ... \n",
"2572 3.50 \n",
"2573 1.00 \n",
"2574 2.00 \n",
"2575 2.00 \n",
"2576 0.50 \n",
"\n",
"[2517 rows x 4 columns]"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 8\n",
"data.drop_duplicates(subset=['Company','Product'], inplace=True)\n",
"data"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "Fob7kMgxknU9"
},
"outputs": [],
"source": [
"# 9\n",
"data.dropna(inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "rgVGWILNknU-",
"outputId": "3ebe8940-6ef1-4422-fac5-e3878e93f224"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"Int64Index: 2515 entries, 0 to 2576\n",
"Data columns (total 4 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 Company 2515 non-null object \n",
" 1 Product 2515 non-null object \n",
" 2 Style 2515 non-null category\n",
" 3 Rating 2515 non-null float64 \n",
"dtypes: category(1), float64(1), object(2)\n",
"memory usage: 81.4+ KB\n"
]
}
],
"source": [
"# 10\n",
"data.info()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.8"
},
"colab": {
"provenance": [],
"include_colab_link": true
}
},
"nbformat": 4,
"nbformat_minor": 0
}
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