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EricPostMaster/ramen-ratings-practicing-pandas-pandasql.ipynb

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Ramen Ratings - Practicing Pandas, Pandasql
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ramen-ratings-practicing-pandas-pandasql.ipynb
{
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"metadata": {
"colab": {
"name": "Ramen Ratings - Practicing Pandas, Pandasql",
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"cell_type": "markdown",
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"<a href=\"https://colab.research.google.com/gist/EricPostMaster/19d5a374898767e421a916b52500f707/ramen-ratings-practicing-pandas-pandasql.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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{
"cell_type": "code",
"metadata": {
"id": "gPlaS3zWN18G",
"colab_type": "code",
"colab": {}
},
"source": [
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"%matplotlib inline\n",
"import seaborn as sns\n",
"import pandas as pd\n",
"from scipy import stats"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "7b9Xq2NBN8vV",
"colab_type": "code",
"colab": {}
},
"source": [
"ramen_ratings = pd.read_csv(\"ramen-ratings.csv\")"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "2vsVoiRSOQpL",
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"outputId": "34aa9aa2-9156-4903-f489-977d976999d8",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 390
}
},
"source": [
"ramen_ratings.describe(include='all')"
],
"execution_count": 3,
"outputs": [
{
"output_type": "execute_result",
"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Review #</th>\n",
" <th>Brand</th>\n",
" <th>Variety</th>\n",
" <th>Style</th>\n",
" <th>Country</th>\n",
" <th>Stars</th>\n",
" <th>Top Ten</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>2580.000000</td>\n",
" <td>2580</td>\n",
" <td>2580</td>\n",
" <td>2578</td>\n",
" <td>2580</td>\n",
" <td>2580</td>\n",
" <td>41</td>\n",
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" <tr>\n",
" <th>unique</th>\n",
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" <td>355</td>\n",
" <td>2413</td>\n",
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" <td>38</td>\n",
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" <td>38</td>\n",
" </tr>\n",
" <tr>\n",
" <th>top</th>\n",
" <td>NaN</td>\n",
" <td>Nissin</td>\n",
" <td>Beef</td>\n",
" <td>Pack</td>\n",
" <td>Japan</td>\n",
" <td>4</td>\n",
" <td>\\n</td>\n",
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" <tr>\n",
" <th>freq</th>\n",
" <td>NaN</td>\n",
" <td>381</td>\n",
" <td>7</td>\n",
" <td>1531</td>\n",
" <td>352</td>\n",
" <td>384</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>1290.500000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>744.926171</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>1.000000</td>\n",
" <td>NaN</td>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>645.750000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>1290.500000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <th>75%</th>\n",
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" <td>NaN</td>\n",
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" <td>2580.000000</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
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"</table>\n",
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],
"text/plain": [
" Review # Brand Variety Style Country Stars Top Ten\n",
"count 2580.000000 2580 2580 2578 2580 2580 41\n",
"unique NaN 355 2413 7 38 51 38\n",
"top NaN Nissin Beef Pack Japan 4 \\n\n",
"freq NaN 381 7 1531 352 384 4\n",
"mean 1290.500000 NaN NaN NaN NaN NaN NaN\n",
"std 744.926171 NaN NaN NaN NaN NaN NaN\n",
"min 1.000000 NaN NaN NaN NaN NaN NaN\n",
"25% 645.750000 NaN NaN NaN NaN NaN NaN\n",
"50% 1290.500000 NaN NaN NaN NaN NaN NaN\n",
"75% 1935.250000 NaN NaN NaN NaN NaN NaN\n",
"max 2580.000000 NaN NaN NaN NaN NaN NaN"
]
},
"metadata": {
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},
"execution_count": 3
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "lBy8p3hwNwEs",
"colab_type": "code",
"colab": {}
},
"source": [
"import pandas_profiling"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "K4n3i38qO_gg",
"colab_type": "code",
"outputId": "3ee807b0-4292-4b9d-bf76-80892c91b0bd",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 153
}
},
"source": [
"ramen_ratings.dtypes"
],
"execution_count": 12,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"Review # int64\n",
"Brand object\n",
"Variety object\n",
"Style object\n",
"Country object\n",
"Stars object\n",
"Top Ten object\n",
"dtype: object"
]
},
"metadata": {
"tags": []
},
"execution_count": 12
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "DpE5XGtNPYP0",
"colab_type": "code",
"colab": {}
},
"source": [
"ramen_ratings[\"Stars\"] = pd.to_numeric(ramen_ratings[\"Stars\"], errors='coerce')"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "CFUBK1NeQrai",
"colab_type": "code",
"outputId": "a4b3135b-fb72-4281-db9e-7a76b192bf26",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 419
}
},
"source": [
"ramen_ratings.dropna(subset=['Stars'])"
],
"execution_count": 32,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\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>Review #</th>\n",
" <th>Brand</th>\n",
" <th>Variety</th>\n",
" <th>Style</th>\n",
" <th>Country</th>\n",
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" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2580</td>\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",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2579</td>\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",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2578</td>\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",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2577</td>\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",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2576</td>\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",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
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" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2575</th>\n",
" <td>5</td>\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",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2576</th>\n",
" <td>4</td>\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",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2577</th>\n",
" <td>3</td>\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",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2578</th>\n",
" <td>2</td>\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",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2579</th>\n",
" <td>1</td>\n",
" <td>Westbrae</td>\n",
" <td>Miso Ramen</td>\n",
" <td>Pack</td>\n",
" <td>USA</td>\n",
" <td>0.50</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>2577 rows × 7 columns</p>\n",
"</div>"
],
"text/plain": [
" Review # Brand ... Stars Top Ten\n",
"0 2580 New Touch ... 3.75 NaN\n",
"1 2579 Just Way ... 1.00 NaN\n",
"2 2578 Nissin ... 2.25 NaN\n",
"3 2577 Wei Lih ... 2.75 NaN\n",
"4 2576 Ching's Secret ... 3.75 NaN\n",
"... ... ... ... ... ...\n",
"2575 5 Vifon ... 3.50 NaN\n",
"2576 4 Wai Wai ... 1.00 NaN\n",
"2577 3 Wai Wai ... 2.00 NaN\n",
"2578 2 Wai Wai ... 2.00 NaN\n",
"2579 1 Westbrae ... 0.50 NaN\n",
"\n",
"[2577 rows x 7 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 32
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "fNDQmsmXOVRD",
"colab_type": "code",
"outputId": "f595e6da-0937-4e3e-8119-2e522c34a518",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 279
}
},
"source": [
"plot = sns.violinplot(x=\"Style\", y=\"Stars\", data = ramen_ratings)"
],
"execution_count": 33,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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w3nvvjaiZrVu30rZvH3OHSOecbwKpncEvvfBCwTbnLViQXNFo79o9rOdH4iGO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\nNZQ7ADOzhnIHYGbWUO4AzMwa6j9Krr/XBjbw3AAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "5yLpjGIFTIRH",
"colab_type": "code",
"outputId": "2406bc59-3cd8-42e4-ec41-4b15213740dd",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 697
}
},
"source": [
"rating_mean = ramen_ratings.groupby(\"Country\")[\"Stars\"].mean()\n",
"rating_mean"
],
"execution_count": 101,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"Country\n",
"Australia 3.138636\n",
"Bangladesh 3.714286\n",
"Brazil 4.350000\n",
"Cambodia 4.200000\n",
"Canada 2.243902\n",
"China 3.421893\n",
"Colombia 3.291667\n",
"Dubai 3.583333\n",
"Estonia 3.500000\n",
"Fiji 3.875000\n",
"Finland 3.583333\n",
"Germany 3.638889\n",
"Ghana 3.500000\n",
"Holland 3.562500\n",
"Hong Kong 3.801825\n",
"Hungary 3.611111\n",
"India 3.395161\n",
"Indonesia 4.067460\n",
"Japan 3.981605\n",
"Malaysia 4.154194\n",
"Mexico 3.730000\n",
"Myanmar 3.946429\n",
"Nepal 3.553571\n",
"Netherlands 2.483333\n",
"Nigeria 1.500000\n",
"Pakistan 3.000000\n",
"Philippines 3.329787\n",
"Poland 3.625000\n",
"Sarawak 4.333333\n",
"Singapore 4.126147\n",
"South Korea 3.790554\n",
"Sweden 3.250000\n",
"Taiwan 3.665402\n",
"Thailand 3.384817\n",
"UK 2.997101\n",
"USA 3.457043\n",
"United States 3.750000\n",
"Vietnam 3.187963\n",
"Name: Stars, dtype: float64"
]
},
"metadata": {
"tags": []
},
"execution_count": 101
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "wImfWREHdGVx",
"colab_type": "code",
"outputId": "4b297fb9-8fed-4c1c-b71c-6596d60b4ce2",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 408
}
},
"source": [
"rating_mean.plot.bar(figsize=(10,5))"
],
"execution_count": 36,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f16189604e0>"
]
},
"metadata": {
"tags": []
},
"execution_count": 36
},
{
"output_type": "display_data",
"data": {
"image/png": 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/t2H7h8ql7a2WzA57PTZ5HTqX51Y0bbSa6awhbUfluFWu1Gk6eucOq6/6jvk8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\nAAAAFf4/CJk11HiQXYsAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 720x360 with 1 Axes>"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "P8TUowCXhirI",
"colab_type": "code",
"outputId": "ec5319d5-84a1-4311-c465-d89efefc908c",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 408
}
},
"source": [
"rating_count.plot.bar(figsize=(10,5))"
],
"execution_count": 37,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f16188c5f28>"
]
},
"metadata": {
"tags": []
},
"execution_count": 37
},
{
"output_type": "display_data",
"data": {
"image/png": 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Ac4B1Jf0aeA9wBHCipAOBXwGvKA8/HdgVuB64BzhgDHU2MzMzm0hDA6uI2KvPXTv1eGwA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\nlSRJUksMrCRJklpiYCVJktSSPwEmNel9cZFvywAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 720x360 with 1 Axes>"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "5WiH6sJDRTYl",
"colab_type": "code",
"outputId": "8538c752-1219-4c51-d97a-c3f9bbe452f4",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 221
}
},
"source": [
"japan_mean_stars = ramen_ratings.query(\"Country=='Japan'\")['Stars']\n",
"japan_mean_stars"
],
"execution_count": 38,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"0 3.75\n",
"6 4.00\n",
"7 3.75\n",
"8 0.25\n",
"13 4.50\n",
" ... \n",
"2554 4.00\n",
"2555 3.00\n",
"2556 2.00\n",
"2567 5.00\n",
"2568 2.50\n",
"Name: Stars, Length: 352, dtype: float64"
]
},
"metadata": {
"tags": []
},
"execution_count": 38
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "C5T0KcQsRq7p",
"colab_type": "code",
"outputId": "8a77bc07-6ee2-4350-a6ca-8a06c4037756",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 221
}
},
"source": [
"us_mean_stars = ramen_ratings.query(\"Country=='USA' or Country=='United States'\")['Stars']\n",
"us_mean_stars"
],
"execution_count": 39,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"2 2.25\n",
"11 5.00\n",
"17 5.00\n",
"21 5.00\n",
"23 4.75\n",
" ... \n",
"2516 3.50\n",
"2546 2.00\n",
"2557 4.50\n",
"2570 1.50\n",
"2579 0.50\n",
"Name: Stars, Length: 324, dtype: float64"
]
},
"metadata": {
"tags": []
},
"execution_count": 39
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "HWZpa1v5T_X_",
"colab_type": "code",
"colab": {}
},
"source": [
"taiwan_mean_stars = ramen_ratings.query(\"Country=='Taiwan'\")['Stars']"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "lBQyKcGZUORj",
"colab_type": "code",
"outputId": "222a6b63-c8ba-46a5-9d3e-8e3e1f19b098",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
}
},
"source": [
"stats.normaltest(taiwan_mean_stars)"
],
"execution_count": 104,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"NormaltestResult(statistic=38.428897909882785, pvalue=4.521378304590872e-09)"
]
},
"metadata": {
"tags": []
},
"execution_count": 104
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "wSzQL8ELalWY",
"colab_type": "text"
},
"source": [
"The p-value is very low, so we reject the null hypothesis. These reviews are not normally distributed."
]
},
{
"cell_type": "code",
"metadata": {
"id": "UMcl0nJcav0u",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 316
},
"outputId": "d5c3085c-ca1c-465c-e731-50bd39da20bd"
},
"source": [
"plt.hist(x=taiwan_mean_stars)"
],
"execution_count": 117,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(array([ 5., 0., 8., 8., 7., 13., 30., 51., 41., 61.]),\n",
" array([0. , 0.5, 1. , 1.5, 2. , 2.5, 3. , 3.5, 4. , 4.5, 5. ]),\n",
" <a list of 10 Patch objects>)"
]
},
"metadata": {
"tags": []
},
"execution_count": 117
},
{
"output_type": "display_data",
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAXAAAAD4CAYAAAD1jb0+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "XujglteMbIob",
"colab_type": "text"
},
"source": [
"This confirms that the data are not normal."
]
},
{
"cell_type": "code",
"metadata": {
"id": "301uG4h0TTG7",
"colab_type": "code",
"outputId": "58b875ee-9e4b-4687-c8ad-ce5683c37e72",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
}
},
"source": [
"stats.normaltest(japan_mean_stars)"
],
"execution_count": 42,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"NormaltestResult(statistic=94.7163166275466, pvalue=2.7077787222528916e-21)"
]
},
"metadata": {
"tags": []
},
"execution_count": 42
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "8eG0-T0_bXGq",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 316
},
"outputId": "ccfb7f59-7dc4-45cb-963a-6de01a1be114"
},
"source": [
"plt.hist(x=japan_mean_stars)"
],
"execution_count": 118,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(array([ 3., 1., 4., 3., 7., 13., 35., 70., 86., 130.]),\n",
" array([0. , 0.5, 1. , 1.5, 2. , 2.5, 3. , 3.5, 4. , 4.5, 5. ]),\n",
" <a list of 10 Patch objects>)"
]
},
"metadata": {
"tags": []
},
"execution_count": 118
},
{
"output_type": "display_data",
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAD4CAYAAAAXUaZHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nf7qnw8UAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "YxVnS3qOTFS7",
"colab_type": "code",
"outputId": "9b468b73-71c9-42f0-8038-a2102b871e65",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
}
},
"source": [
"stats.normaltest(us_mean_stars)"
],
"execution_count": 43,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"NormaltestResult(statistic=61.61382839688504, pvalue=4.175679516382677e-14)"
]
},
"metadata": {
"tags": []
},
"execution_count": 43
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "8157YYiXbZmw",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 316
},
"outputId": "1e1e6b94-4971-4b0a-d04f-4cfcb6641e82"
},
"source": [
"plt.hist(x=us_mean_stars)"
],
"execution_count": 119,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(array([ 9., 1., 3., 14., 17., 23., 49., 91., 68., 49.]),\n",
" array([0. , 0.5, 1. , 1.5, 2. , 2.5, 3. , 3.5, 4. , 4.5, 5. ]),\n",
" <a list of 10 Patch objects>)"
]
},
"metadata": {
"tags": []
},
"execution_count": 119
},
{
"output_type": "display_data",
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAXAAAAD4CAYAAAD1jb0+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjIsIGh0\ndHA6Ly9tYXRwbG90bGliLm9yZy8li6FKAAALp0lEQVR4nO3dX6jfd33H8edrSUs1zrXaQ8iSshOw\nOETYKofOkeFFu43OFJsLkY5NggRy47a6DjTuRnaXwvDPxRiExpGxopa2o8WAW6mRUXDRkzauNvFP\n6FJNSM2R2Wl34zLfuzhf15ic7vya8/ud33mf83xAOOf7+/d9fxPy5Mv39/uck6pCktTPL017AEnS\ntTHgktSUAZekpgy4JDVlwCWpqc2rubObb765ZmdnV3OXktTeiRMnflhVM1fevqoBn52dZX5+fjV3\nKUntJXlxqdu9hCJJTRlwSWrKgEtSUwZckpoy4JLUlAGXpKYMuCQ1ZcAlqSkDLklNrepKTElrx+yB\no1Pb99mDu6e27/XEM3BJasqAS1JTBlySmjLgktSUAZekpgy4JDVlwCWpKQMuSU0ZcElqyoBLUlMG\nXJKaMuCS1JQBl6SmDLgkNWXAJakpAy5JTRlwSWrKgEtSUwZckpoy4JLUlAGXpKZGCniSP0/yfJJv\nJvlckhuS7ExyPMmZJF9Icv2kh5UkvWrZgCfZDvwZMFdV7wQ2AfcCDwCfqqq3AT8C9k1yUEnSLxr1\nEspm4A1JNgNvBC4AdwCPDPcfAfaMfzxJ0mtZNuBVdR74a+B7LIb7P4ETwMtVdWl42Dlg+1LPT7I/\nyXyS+YWFhfFMLUka6RLKTcA9wE7gV4EtwF2j7qCqDlXVXFXNzczMXPOgkqRfNMollN8F/r2qFqrq\nv4HHgF3AjcMlFYAdwPkJzShJWsIoAf8e8O4kb0wS4E7gFHAMeP/wmL3A45MZUZK0lFGugR9n8c3K\nZ4DnhuccAj4G3J/kDPBW4PAE55QkXWHz8g+BqvoE8Ikrbn4BuH3sE0mSRuJKTElqyoBLUlMGXJKa\nMuCS1JQBl6SmDLgkNWXAJakpAy5JTRlwSWrKgEtSUwZckpoy4JLUlAGXpKYMuCQ1ZcAlqSkDLklN\nGXBJasqAS1JTBlySmjLgktSUAZekpkb6rfSSNE6zB45OZb9nD+6eyn4nxTNwSWrKgEtSUwZckpoy\n4JLUlAGXpKYMuCQ1ZcAlqSkDLklNGXBJasqAS1JTBlySmjLgktSUAZekpgy4JDU1UsCT3JjkkSTf\nSnI6yW8neUuSJ5N8d/h606SHlSS9atQz8M8AX6qqXwd+AzgNHACeqqpbgaeGbUnSKlk24El+BXgP\ncBigqn5aVS8D9wBHhocdAfZMakhJ0tVGOQPfCSwAf5fk2SQPJtkCbK2qC8NjXgK2LvXkJPuTzCeZ\nX1hYGM/UkqSRAr4ZeBfwt1V1G/BfXHG5pKoKqKWeXFWHqmququZmZmZWOq8kaTBKwM8B56rq+LD9\nCItB/0GSbQDD14uTGVGStJRlA15VLwHfT/L24aY7gVPAE8De4ba9wOMTmVCStKRRfyv9nwIPJbke\neAH4EIvxfzjJPuBF4AOTGVGStJSRAl5VJ4G5Je66c7zjSJJG5UpMSWrKgEtSUwZckpoy4JLUlAGX\npKYMuCQ1ZcAlqSkDLklNGXBJasqAS1JTBlySmjLgktSUAZekpgy4JDVlwCWpKQMuSU0ZcElqyoBL\nUlMGXJKaGvWXGkuakNkDR6c9woYxrb/rswd3T+R1PQOXpKYMuCQ1ZcAlqSkDLklNGXBJasqAS1JT\nBlySmjLgktSUAZekpgy4JDVlwCWpKQMuSU0ZcElqyoBLUlMGXJKaMuCS1JQBl6SmRg54kk1Jnk3y\nxWF7Z5LjSc4k+UKS6yc3piTpSq/nDPw+4PRl2w8An6qqtwE/AvaNczBJ0v9vpIAn2QHsBh4ctgPc\nATwyPOQIsGcSA0qSljbqGfingY8CPxu23wq8XFWXhu1zwPalnphkf5L5JPMLCwsrGlaS9KplA57k\nbuBiVZ24lh1U1aGqmququZmZmWt5CUnSEjaP8JhdwPuSvBe4AXgz8BngxiSbh7PwHcD5yY0pSbrS\nsmfgVfXxqtpRVbPAvcCXq+qPgGPA+4eH7QUen9iUkqSrrORz4B8D7k9yhsVr4ofHM5IkaRSjXEL5\nP1X1FeArw/cvALePfyRJ0ihciSlJTRlwSWrKgEtSUwZckpoy4JLUlAGXpKYMuCQ1ZcAlqSkDLklN\nGXBJasqAS1JTBlySmjLgktSUAZekpgy4JDVlwCWpKQMuSU0ZcElqyoBLUlMGXJKaMuCS1JQBl6Sm\nDLgkNWXAJakpAy5JTRlwSWrKgEtSUwZckpoy4JLUlAGXpKYMuCQ1ZcAlqanN0x5AWgtmDxyd9gjS\n6+YZuCQ1ZcAlqSkDLklNLRvwJLckOZbkVJLnk9w33P6WJE8m+e7w9abJjytJ+rlRzsAvAX9RVe8A\n3g18OMk7gAPAU1V1K/DUsC1JWiXLBryqLlTVM8P3PwFOA9uBe4Ajw8OOAHsmNaQk6Wqv6xp4klng\nNuA4sLWqLgx3vQRsfY3n7E8yn2R+YWFhBaNKki43csCTvAl4FPhIVf348vuqqoBa6nlVdaiq5qpq\nbmZmZkXDSpJeNVLAk1zHYrwfqqrHhpt/kGTbcP824OJkRpQkLWWUT6EEOAycrqpPXnbXE8De4fu9\nwOPjH0+S9FpGWUq/C/gg8FySk8NtfwkcBB5Osg94EfjAZEaUJC1l2YBX1dNAXuPuO8c7jiRpVK7E\nlKSm/GmEWlP8qYDS6DwDl6SmDLgkNWXAJakpAy5JTRlwSWrKgEtSU36MUFfxo3xSD56BS1JTBlyS\nmjLgktSUAZekpgy4JDVlwCWpKQMuSU0ZcElqyoBLUlMGXJKaarOUflrLu88e3D2V/UrScjwDl6Sm\nDLgkNWXAJakpAy5JTRlwSWrKgEtSUwZckpoy4JLUlAGXpKYMuCQ1ZcAlqSkDLklNGXBJaqrNTyPc\niKb1Exgl9eAZuCQ1ZcAlqSkDLklNrSjgSe5K8u0kZ5IcGNdQkqTlXfObmEk2AX8D/B5wDvh6kieq\n6tS4hlsLfCNR0lq1kjPw24EzVfVCVf0U+Dxwz3jGkiQtZyUfI9wOfP+y7XPAb135oCT7gf3D5itJ\nvn2N+7sZ+OE1Prcrj3lj8JjXuTyw4uP9taVunPjnwKvqEHBopa+TZL6q5sYwUhse88bgMa9/kzre\nlVxCOQ/cctn2juE2SdIqWEnAvw7cmmRnkuuBe4EnxjOWJGk513wJpaouJfkT4J+ATcBnq+r5sU12\ntRVfhmnIY94YPOb1byLHm6qaxOtKkibMlZiS1JQBl6SmWgR8oy3ZT/LZJBeTfHPas6yGJLckOZbk\nVJLnk9w37ZkmLckNSb6W5BvDMf/VtGdaLUk2JXk2yRenPctqSHI2yXNJTiaZH+trr/Vr4MOS/e9w\n2ZJ94A/X25L9yyV5D/AK8PdV9c5pzzNpSbYB26rqmSS/DJwA9qzzf+MAW6rqlSTXAU8D91XVv055\ntIlLcj8wB7y5qu6e9jyTluQsMFdVY1+41OEMfMMt2a+qfwH+Y9pzrJaqulBVzwzf/wQ4zeJK33Wr\nFr0ybF43/FnbZ1NjkGQHsBt4cNqzrAcdAr7Ukv11/Z97I0syC9wGHJ/uJJM3XEo4CVwEnqyqdX/M\nwKeBjwI/m/Ygq6iAf05yYvjRImPTIeDaIJK8CXgU+EhV/Xja80xaVf1PVf0mi6uYb0+yri+XJbkb\nuFhVJ6Y9yyr7nap6F/AHwIeHS6Rj0SHgLtnfAIbrwI8CD1XVY9OeZzVV1cvAMeCuac8yYbuA9w3X\nhD8P3JHkH6Y70uRV1fnh60XgH1m8LDwWHQLukv11bnhD7zBwuqo+Oe15VkOSmSQ3Dt+/gcU36b81\n3akmq6o+XlU7qmqWxf/HX66qP57yWBOVZMvwxjxJtgC/D4zt02VrPuBVdQn4+ZL908DDE16yP3VJ\nPgd8FXh7knNJ9k17pgnbBXyQxTOyk8Of9057qAnbBhxL8m8snqQ8WVUb4mN1G8xW4Okk3wC+Bhyt\nqi+N68XX/McIJUlLW/Nn4JKkpRlwSWrKgEtSUwZckpoy4JLUlAGXpKYMuCQ19b9XbIzKc1Ke+AAA\nAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "VJSFhFghSeoV",
"colab_type": "code",
"outputId": "83815778-2f9a-4187-cd65-93e45ecff724",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
}
},
"source": [
"stats.ttest_ind(japan_mean_stars,us_mean_stars)"
],
"execution_count": 44,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"Ttest_indResult(statistic=6.915127070208027, pvalue=1.0871375195674504e-11)"
]
},
"metadata": {
"tags": []
},
"execution_count": 44
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "KqeBE9m-VLMc",
"colab_type": "code",
"outputId": "7af77a74-aac2-4166-f64f-497fffde9ec1",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
}
},
"source": [
"stats.ttest_ind(japan_mean_stars,taiwan_mean_stars)"
],
"execution_count": 45,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"Ttest_indResult(statistic=3.646396261793695, pvalue=0.00029012082325444264)"
]
},
"metadata": {
"tags": []
},
"execution_count": 45
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "5U88Bd0UiaV0",
"colab_type": "code",
"outputId": "04e55145-19a1-4537-9d37-8a43e755b1a9",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 419
}
},
"source": [
"ramen_ratings.sort_values(by=\"Review #\", ascending=True, inplace=True)\n",
"ramen_ratings"
],
"execution_count": 47,
"outputs": [
{
"output_type": "execute_result",
"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>Review #</th>\n",
" <th>Brand</th>\n",
" <th>Variety</th>\n",
" <th>Style</th>\n",
" <th>Country</th>\n",
" <th>Stars</th>\n",
" <th>Top Ten</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2579</th>\n",
" <td>1</td>\n",
" <td>Westbrae</td>\n",
" <td>Miso Ramen</td>\n",
" <td>Pack</td>\n",
" <td>USA</td>\n",
" <td>0.50</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2578</th>\n",
" <td>2</td>\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",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2577</th>\n",
" <td>3</td>\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",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2576</th>\n",
" <td>4</td>\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",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2575</th>\n",
" <td>5</td>\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",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2576</td>\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",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2577</td>\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",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2578</td>\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",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2579</td>\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",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2580</td>\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",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>2580 rows × 7 columns</p>\n",
"</div>"
],
"text/plain": [
" Review # Brand ... Stars Top Ten\n",
"2579 1 Westbrae ... 0.50 NaN\n",
"2578 2 Wai Wai ... 2.00 NaN\n",
"2577 3 Wai Wai ... 2.00 NaN\n",
"2576 4 Wai Wai ... 1.00 NaN\n",
"2575 5 Vifon ... 3.50 NaN\n",
"... ... ... ... ... ...\n",
"4 2576 Ching's Secret ... 3.75 NaN\n",
"3 2577 Wei Lih ... 2.75 NaN\n",
"2 2578 Nissin ... 2.25 NaN\n",
"1 2579 Just Way ... 1.00 NaN\n",
"0 2580 New Touch ... 3.75 NaN\n",
"\n",
"[2580 rows x 7 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 47
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "779Tuc7WIxKq",
"colab_type": "code",
"outputId": "626ea907-f794-4cc5-b54c-94614c422c67",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 153
}
},
"source": [
"ramen_ratings.dtypes"
],
"execution_count": 48,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"Review # int64\n",
"Brand object\n",
"Variety object\n",
"Style object\n",
"Country object\n",
"Stars float64\n",
"Top Ten object\n",
"dtype: object"
]
},
"metadata": {
"tags": []
},
"execution_count": 48
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "zh7NrZWKJU_1",
"colab_type": "code",
"outputId": "3cfe3451-5247-45f6-ab5e-57f7a7297a37",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 292
}
},
"source": [
"pip install pandasql"
],
"execution_count": 50,
"outputs": [
{
"output_type": "stream",
"text": [
"Collecting pandasql\n",
" Downloading https://files.pythonhosted.org/packages/6b/c4/ee4096ffa2eeeca0c749b26f0371bd26aa5c8b611c43de99a4f86d3de0a7/pandasql-0.7.3.tar.gz\n",
"Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from pandasql) (1.17.5)\n",
"Requirement already satisfied: pandas in /usr/local/lib/python3.6/dist-packages (from pandasql) (0.25.3)\n",
"Requirement already satisfied: sqlalchemy in /usr/local/lib/python3.6/dist-packages (from pandasql) (1.3.13)\n",
"Requirement already satisfied: python-dateutil>=2.6.1 in /usr/local/lib/python3.6/dist-packages (from pandas->pandasql) (2.6.1)\n",
"Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.6/dist-packages (from pandas->pandasql) (2018.9)\n",
"Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.6/dist-packages (from python-dateutil>=2.6.1->pandas->pandasql) (1.12.0)\n",
"Building wheels for collected packages: pandasql\n",
" Building wheel for pandasql (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
" Created wheel for pandasql: filename=pandasql-0.7.3-cp36-none-any.whl size=26819 sha256=139b57e88e01d1f934e3000a6695c8c4e61272406643bc70befa05c3d2ef21c9\n",
" Stored in directory: /root/.cache/pip/wheels/53/6c/18/b87a2e5fa8a82e9c026311de56210b8d1c01846e18a9607fc9\n",
"Successfully built pandasql\n",
"Installing collected packages: pandasql\n",
"Successfully installed pandasql-0.7.3\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "Dcw-SCpijXaG",
"colab_type": "code",
"colab": {}
},
"source": [
"import pandasql"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "pa3pjYpbjg4X",
"colab_type": "code",
"outputId": "44e66edc-9846-43f5-ae62-d3ab398811b6",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 390
}
},
"source": [
"over_100_reviews2 = pandasql.sqldf(\"SELECT Country, count(Stars) as 'Total Ratings', avg(Stars) as 'Average Rating' FROM ramen_ratings GROUP BY Country ORDER BY count(Stars) DESC LIMIT 11;\", globals())\n",
"over_100_reviews2"
],
"execution_count": 61,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Country</th>\n",
" <th>Total Ratings</th>\n",
" <th>Average Rating</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Japan</td>\n",
" <td>352</td>\n",
" <td>3.981605</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>USA</td>\n",
" <td>323</td>\n",
" <td>3.457043</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>South Korea</td>\n",
" <td>307</td>\n",
" <td>3.790554</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Taiwan</td>\n",
" <td>224</td>\n",
" <td>3.665402</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Thailand</td>\n",
" <td>191</td>\n",
" <td>3.384817</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>China</td>\n",
" <td>169</td>\n",
" <td>3.421893</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>Malaysia</td>\n",
" <td>155</td>\n",
" <td>4.154194</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>Hong Kong</td>\n",
" <td>137</td>\n",
" <td>3.801825</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>Indonesia</td>\n",
" <td>126</td>\n",
" <td>4.067460</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>Singapore</td>\n",
" <td>109</td>\n",
" <td>4.126147</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>Vietnam</td>\n",
" <td>108</td>\n",
" <td>3.187963</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Country Total Ratings Average Rating\n",
"0 Japan 352 3.981605\n",
"1 USA 323 3.457043\n",
"2 South Korea 307 3.790554\n",
"3 Taiwan 224 3.665402\n",
"4 Thailand 191 3.384817\n",
"5 China 169 3.421893\n",
"6 Malaysia 155 4.154194\n",
"7 Hong Kong 137 3.801825\n",
"8 Indonesia 126 4.067460\n",
"9 Singapore 109 4.126147\n",
"10 Vietnam 108 3.187963"
]
},
"metadata": {
"tags": []
},
"execution_count": 61
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "00Xhxg7bjvEz",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 419
},
"outputId": "70b59f42-1111-408a-9fd8-d76e82e86eca"
},
"source": [
"over_100_reviews = pandasql.sqldf(\"SELECT ramen_ratings.* FROM ramen_ratings JOIN over_100_reviews2 ON ramen_ratings.Country = over_100_reviews2.Country WHERE ramen_ratings.Country = over_100_reviews2.Country;\", globals())\n",
"over_100_reviews"
],
"execution_count": 72,
"outputs": [
{
"output_type": "execute_result",
"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>Review #</th>\n",
" <th>Brand</th>\n",
" <th>Variety</th>\n",
" <th>Style</th>\n",
" <th>Country</th>\n",
" <th>Stars</th>\n",
" <th>Top Ten</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>Westbrae</td>\n",
" <td>Miso Ramen</td>\n",
" <td>Pack</td>\n",
" <td>USA</td>\n",
" <td>0.50</td>\n",
" <td>None</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\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",
" <td>None</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\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",
" <td>None</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\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",
" <td>None</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\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",
" <td>None</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2199</th>\n",
" <td>2575</td>\n",
" <td>Samyang Foods</td>\n",
" <td>Kimchi song Song Ramen</td>\n",
" <td>Pack</td>\n",
" <td>South Korea</td>\n",
" <td>4.75</td>\n",
" <td>None</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2200</th>\n",
" <td>2577</td>\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",
" <td>None</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2201</th>\n",
" <td>2578</td>\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",
" <td>None</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2202</th>\n",
" <td>2579</td>\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",
" <td>None</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2203</th>\n",
" <td>2580</td>\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",
" <td>None</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>2204 rows × 7 columns</p>\n",
"</div>"
],
"text/plain": [
" Review # Brand ... Stars Top Ten\n",
"0 1 Westbrae ... 0.50 None\n",
"1 2 Wai Wai ... 2.00 None\n",
"2 3 Wai Wai ... 2.00 None\n",
"3 4 Wai Wai ... 1.00 None\n",
"4 5 Vifon ... 3.50 None\n",
"... ... ... ... ... ...\n",
"2199 2575 Samyang Foods ... 4.75 None\n",
"2200 2577 Wei Lih ... 2.75 None\n",
"2201 2578 Nissin ... 2.25 None\n",
"2202 2579 Just Way ... 1.00 None\n",
"2203 2580 New Touch ... 3.75 None\n",
"\n",
"[2204 rows x 7 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 72
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "Xqdqe3DoSPQR",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 506
},
"outputId": "120199f7-b3de-400c-8135-372d77318ec3"
},
"source": [
"plot = sns.violinplot(x='Country', y='Stars', data=over_100_reviews)\n",
"plot.set_xticklabels(plot.get_xticklabels(), rotation=45, horizontalalignment='right')"
],
"execution_count": 91,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[Text(0, 0, 'USA'),\n",
" Text(0, 0, 'Thailand'),\n",
" Text(0, 0, 'Vietnam'),\n",
" Text(0, 0, 'Taiwan'),\n",
" Text(0, 0, 'South Korea'),\n",
" Text(0, 0, 'Japan'),\n",
" Text(0, 0, 'Singapore'),\n",
" Text(0, 0, 'Indonesia'),\n",
" Text(0, 0, 'China'),\n",
" Text(0, 0, 'Hong Kong'),\n",
" Text(0, 0, 'Malaysia')]"
]
},
"metadata": {
"tags": []
},
"execution_count": 91
},
{
"output_type": "display_data",
"data": {
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k2vjdvJzB84kIF0dv89zzzxuKMoNHVABoySe1Lk/GMTVOd0mKSMHDZCfbivvO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Ew30AwZMmTI0IhMAGTIkCFDiyITABkyZMjQosgEQIYMGTK0KDIBkCFDhgwtikwAZGhpiMgiEfm4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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "QgtGRNLoS9QW",
"colab_type": "code",
"colab": {}
},
"source": [
"from scipy.stats import shapiro"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "laLkT0ytU-75",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 51
},
"outputId": "93d95684-fb1c-4ed3-f421-953be9ec120d"
},
"source": [
"stat, p = shapiro(japan_mean_stars)\n",
"print('Statistics=%.3f, p=%.3f' % (stat, p))\n",
"# interpret\n",
"alpha = 0.05\n",
"if p > alpha:\n",
"\tprint('Sample looks Gaussian (fail to reject H0)')\n",
"else:\n",
"\tprint('Sample does not look Gaussian (reject H0)')"
],
"execution_count": 131,
"outputs": [
{
"output_type": "stream",
"text": [
"Statistics=0.881, p=0.000\n",
"Sample does not look Gaussian (reject H0)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "u7aKmL7XYq_V",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 316
},
"outputId": "e297e851-1c37-4d8b-f110-146be7cc6be4"
},
"source": [
"plt.hist(x=japan_mean_stars)"
],
"execution_count": 111,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(array([ 3., 1., 4., 3., 7., 13., 35., 70., 86., 130.]),\n",
" array([0. , 0.5, 1. , 1.5, 2. , 2.5, 3. , 3.5, 4. , 4.5, 5. ]),\n",
" <a list of 10 Patch objects>)"
]
},
"metadata": {
"tags": []
},
"execution_count": 111
},
{
"output_type": "display_data",
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAD4CAYAAAAXUaZHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjIsIGh0\ndHA6Ly9tYXRwbG90bGliLm9yZy8li6FKAAANr0lEQVR4nO3df6jd9X3H8edridbWrlObS8gS2RUa\nOpxsUy7O4ShF98OqmPxRROm6rAuEgdvsHNi4/SH7o6Bs9MdgKwR1TZloxVoMzdY1pCkiTO2NWn8k\nWoPVmhDNLda2rrAu7Xt/3G/HJd40957vOfd4P/f5gHDP+X6/53zfB8nTL597zkmqCklSW35p3ANI\nkobPuEtSg4y7JDXIuEtSg4y7JDVo9bgHAFizZk1NTk6OewxJWlb279//vaqamG/f2yLuk5OTTE9P\nj3sMSVpWkrx8sn0uy0hSg4y7JDXIuEtSg4y7JDXIuEtSg4y7JDXIuEtSg4y7JDXIuEtSg94Wn1CV\npHGa3L57bOd+6barRvK8XrlLUoOMuyQ1yLhLUoOMuyQ1yLhLUoOMuyQ1yLhLUoOMuyQ1yLhLUoOM\nuyQ1yLhLUoNOGfckdyU5luSZOdv+IclzSZ5K8uUkZ83Zd0uSQ0meT/JHoxpcknRyC7ly/zxwxQnb\n9gAXVNVvAt8GbgFIcj5wHfAb3WP+JcmqoU0rSVqQU8a9qh4CXj9h29eq6nh39xFgQ3d7E3BvVf1P\nVX0HOARcPMR5JUkLMIw19z8D/qO7vR54Zc6+w902SdIS6hX3JH8HHAfuHuCx25JMJ5memZnpM4Yk\n6QQDxz3JnwJXAx+pquo2HwHOnXPYhm7bW1TVjqqaqqqpiYmJQceQJM1joLgnuQK4Gbimqn48Z9cu\n4Lok70hyHrAReKz/mJKkxTjlP7OX5B7gg8CaJIeBW5l9d8w7gD1JAB6pqj+vqmeT3AccYHa55oaq\n+umohpckze+Uca+q6+fZfOcvOP6TwCf7DCVJ6sdPqEpSg4y7JDXIuEtSg4y7JDXIuEtSg4y7JDXI\nuEtSg4y7JDXIuEtSg4y7JDXIuEtSg4y7JDXIuEtSg4y7JDXIuEtSg4y7JDXIuEtSg4y7JDXIuEtS\ng4y7JDXIuEtSg4y7JDXIuEtSg04Z9yR3JTmW5Jk5285JsifJC93Ps7vtSfJPSQ4leSrJRaMcXpI0\nv4VcuX8euOKEbduBvVW1Edjb3Qf4ELCx+7MN+NxwxpQkLcYp415VDwGvn7B5E7Czu70T2Dxn+xdq\n1iPAWUnWDWtYSdLCDLrmvraqjna3XwXWdrfXA6/MOe5wt02StIR6/0K1qgqoxT4uybYk00mmZ2Zm\n+o4hSZpj0Li/9vPllu7nsW77EeDcOcdt6La9RVXtqKqpqpqamJgYcAxJ0nwGjfsuYEt3ewvw4Jzt\nf9K9a+YS4Adzlm8kSUtk9akOSHIP8EFgTZLDwK3AbcB9SbYCLwPXdof/O3AlcAj4MfCxEcwsSTqF\nU8a9qq4/ya7L5zm2gBv6DiVJ6sdPqEpSg4y7JDXIuEtSg4y7JDXIuEtSg4y7JDXIuEtSg4y7JDXI\nuEtSg4y7JDXIuEtSg4y7JDXIuEtSg4y7JDXIuEtSg4y7JDXIuEtSg075LzFJ0lKZ3L573CM0wyt3\nSWqQcZekBhl3SWqQcZekBhl3SWpQr7gn+eskzyZ5Jsk9Sc5Icl6SR5McSvLFJKcPa1hJ0sIMHPck\n64G/Aqaq6gJgFXAdcDvw6ap6H/B9YOswBpUkLVzfZZnVwDuTrAbeBRwFLgPu7/bvBDb3PIckaZEG\njntVHQH+Efgus1H/AbAfeKOqjneHHQbWz/f4JNuSTCeZnpmZGXQMSdI8+izLnA1sAs4DfhU4E7hi\noY+vqh1VNVVVUxMTE4OOIUmaR59lmd8HvlNVM1X1v8ADwKXAWd0yDcAG4EjPGSVJi9Qn7t8FLkny\nriQBLgcOAPuAD3fHbAEe7DeiJGmx+qy5P8rsL04fB57unmsH8AngpiSHgPcCdw5hTknSIvT6Vsiq\nuhW49YTNLwIX93leSVI/fkJVkhpk3CWpQcZdkhpk3CWpQcZdkhpk3CWpQcZdkhpk3CWpQcZdkhpk\n3CWpQb2+fkBSeya37x73CBoCr9wlqUHGXZIaZNwlqUHGXZIaZNwlqUHGXZIaZNwlqUHGXZIaZNwl\nqUHGXZIaZNwlqUHGXZIa1CvuSc5Kcn+S55IcTPK7Sc5JsifJC93Ps4c1rCRpYfpeuX8W+GpV/Trw\nW8BBYDuwt6o2Anu7+5KkJTRw3JP8CvAB4E6AqvpJVb0BbAJ2doftBDb3HVKStDh9rtzPA2aAf03y\nRJI7kpwJrK2qo90xrwJr53twkm1JppNMz8zM9BhDknSiPnFfDVwEfK6qLgT+mxOWYKqqgJrvwVW1\no6qmqmpqYmKixxiSpBP1ifth4HBVPdrdv5/Z2L+WZB1A9/NYvxElSYs1cNyr6lXglSTv7zZdDhwA\ndgFbum1bgAd7TShJWrS+/4bqXwJ3JzkdeBH4GLP/w7gvyVbgZeDanueQJC1Sr7hX1ZPA1Dy7Lu/z\nvJKkfvyEqiQ1yLhLUoOMuyQ1yLhLUoOMuyQ1yLhLUoOMuyQ1yLhLUoOMuyQ1yLhLUoOMuyQ1yLhL\nUoOMuyQ1yLhLUoOMuyQ1yLhLUoOMuyQ1yLhLUoOMuyQ1yLhLUoOMuyQ1yLhLUoOMuyQ1qHfck6xK\n8kSSr3T3z0vyaJJDSb6Y5PT+Y0qSFmMYV+43Agfn3L8d+HRVvQ/4PrB1COeQJC1Cr7gn2QBcBdzR\n3Q9wGXB/d8hOYHOfc0iSFq/vlftngJuBn3X33wu8UVXHu/uHgfXzPTDJtiTTSaZnZmZ6jiFJmmvg\nuCe5GjhWVfsHeXxV7aiqqaqampiYGHQMSdI8Vvd47KXANUmuBM4A3gN8Fjgryeru6n0DcKT/mJKk\nxRj4yr2qbqmqDVU1CVwHfL2qPgLsAz7cHbYFeLD3lJKkRRnF+9w/AdyU5BCza/B3juAckqRfoM+y\nzP+rqm8A3+huvwhcPIznlSQNxk+oSlKDjLskNWgoyzKShm9y++5xj6BlzCt3SWqQcZekBhl3SWqQ\ncZekBhl3SWqQcZekBhl3SWqQcZekBhl3SWqQcZekBhl3SWqQcZekBhl3SWqQcZekBhl3SWqQcZek\nBhl3SWqQcZekBhl3SWqQcZekBg0c9yTnJtmX5ECSZ5Pc2G0/J8meJC90P88e3riSpIXoc+V+HPib\nqjofuAS4Icn5wHZgb1VtBPZ29yVJS2jguFfV0ap6vLv9I+AgsB7YBOzsDtsJbO47pCRpcYay5p5k\nErgQeBRYW1VHu12vAmtP8phtSaaTTM/MzAxjDElSp3fck7wb+BLw8ar64dx9VVVAzfe4qtpRVVNV\nNTUxMdF3DEnSHL3inuQ0ZsN+d1U90G1+Lcm6bv864Fi/ESVJi9Xn3TIB7gQOVtWn5uzaBWzpbm8B\nHhx8PEnSIFb3eOylwEeBp5M82W37W+A24L4kW4GXgWv7jShJWqyB415VDwM5ye7LB31eSVJ/fkJV\nkhpk3CWpQcZdkhpk3CWpQcZdkhrU562Q0oowuX33uEeQFs0rd0lqkHGXpAYZd0lqkHGXpAYZd0lq\nkHGXpAYZd0lqkHGXpAYZd0lqkJ9Q1bLgp0SlxfHKXZIaZNwlqUHGXZIa5Jr7MjTO9eeXbrtqbOeW\ntHBeuUtSg5b9lbtXsUvLd61Iy4NX7pLUoJHFPckVSZ5PcijJ9lGdR5L0ViNZlkmyCvhn4A+Aw8A3\nk+yqqgOjON+4uEQh6e1qVFfuFwOHqurFqvoJcC+waUTnkiSdYFS/UF0PvDLn/mHgd+YekGQbsK27\n+2aS5wc81xrgewM+drnyNa8MvuYVILf3es2/drIdY3u3TFXtAHb0fZ4k01U1NYSRlg1f88rga14Z\nRvWaR7UscwQ4d879Dd02SdISGFXcvwlsTHJektOB64BdIzqXJOkEI1mWqarjSf4C+E9gFXBXVT07\ninMxhKWdZcjXvDL4mleGkbzmVNUonleSNEZ+QlWSGmTcJalByzruK+0rDpLcleRYkmfGPctSSXJu\nkn1JDiR5NsmN455p1JKckeSxJN/qXvPfj3umpZBkVZInknxl3LMshSQvJXk6yZNJpof+/Mt1zb37\nioNvM+crDoDrW/uKg7mSfAB4E/hCVV0w7nmWQpJ1wLqqejzJLwP7gc2N/3cOcGZVvZnkNOBh4Maq\nemTMo41UkpuAKeA9VXX1uOcZtSQvAVNVNZIPbS3nK/cV9xUHVfUQ8Pq451hKVXW0qh7vbv8IOMjs\nJ6CbVbPe7O6e1v1ZnldhC5RkA3AVcMe4Z2nFco77fF9x0PRf+pUuySRwIfDoeCcZvW6J4kngGLCn\nqlp/zZ8BbgZ+Nu5BllABX0uyv/s6lqFaznHXCpLk3cCXgI9X1Q/HPc+oVdVPq+q3mf1098VJml2G\nS3I1cKyq9o97liX2e1V1EfAh4IZu2XVolnPc/YqDFaJbd/4ScHdVPTDueZZSVb0B7AOuGPcsI3Qp\ncE23Bn0vcFmSfxvvSKNXVUe6n8eALzO71Dw0yznufsXBCtD9cvFO4GBVfWrc8yyFJBNJzupuv5PZ\nNw08N96pRqeqbqmqDVU1yezf469X1R+PeayRSnJm9wYBkpwJ/CEw1HfBLdu4V9Vx4OdfcXAQuG+E\nX3HwtpDkHuC/gPcnOZxk67hnWgKXAh9l9mruye7PleMeasTWAfuSPMXsRcyeqloRbw9cQdYCDyf5\nFvAYsLuqvjrMEyzbt0JKkk5u2V65S5JOzrhLUoOMuyQ1yLhLUoOMuyQ1yLhLUoOMuyQ16P8Ac6xF\nf7qnw8UAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "qVXnbFzbY5xb",
"colab_type": "code",
"colab": {}
},
"source": [
""
],
"execution_count": 0,
"outputs": []
}
]
}
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