explore.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv('data/yelp_ratings.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 44530 entries, 0 to 44529\n",
      "Data columns (total 3 columns):\n",
      " #   Column     Non-Null Count  Dtype  \n",
      "---  ------     --------------  -----  \n",
      " 0   text       44530 non-null  object \n",
      " 1   stars      44530 non-null  float64\n",
      " 2   sentiment  44530 non-null  int64  \n",
      "dtypes: float64(1), int64(1), object(1)\n",
      "memory usage: 1.0+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "\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>text</th>\n",
       "      <th>stars</th>\n",
       "      <th>sentiment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Total bill for this horrible service? Over $8G...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>I *adore* Travis at the Hard Rock's new Kelly ...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>I have to say that this office really has it t...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Went in for a lunch. Steak sandwich was delici...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Today was my second out of three sessions I ha...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                text  stars  sentiment\n",
       "0  Total bill for this horrible service? Over $8G...    1.0          0\n",
       "1  I *adore* Travis at the Hard Rock's new Kelly ...    5.0          1\n",
       "2  I have to say that this office really has it t...    5.0          1\n",
       "3  Went in for a lunch. Steak sandwich was delici...    5.0          1\n",
       "4  Today was my second out of three sessions I ha...    1.0          0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "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>stars</th>\n",
       "      <th>sentiment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>44530.000000</td>\n",
       "      <td>44530.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.834449</td>\n",
       "      <td>0.748507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.513973</td>\n",
       "      <td>0.433876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              stars     sentiment\n",
       "count  44530.000000  44530.000000\n",
       "mean       3.834449      0.748507\n",
       "std        1.513973      0.433876\n",
       "min        1.000000      0.000000\n",
       "25%        2.000000      0.000000\n",
       "50%        5.000000      1.000000\n",
       "75%        5.000000      1.000000\n",
       "max        5.000000      1.000000"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Get length of each text (in words)  \n",
    "text_lengths = df['text'].str.split().str.len()\n",
    "\n",
    "# Plot histogram \n",
    "plt.hist(text_lengths, bins=50)\n",
    "\n",
    "# Add label and title\n",
    "plt.xlabel('Text length (words)')\n",
    "plt.ylabel('Frequency') \n",
    "plt.title('Distribution of text lengths')\n",
    "\n",
    "# Show plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "text         0\n",
       "stars        0\n",
       "sentiment    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.stars.value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.sentiment.value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='sentiment'>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Check average stars by sentiment\n",
    "df.groupby('sentiment').stars.mean().plot(kind='bar')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "05-transformer-encoder",
   "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": "3.10.9"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}