read-data-from-s3bucket-local_jupyter_notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "140ce694",
   "metadata": {},
   "source": [
    "# Reading data from S3 bucket on local machine using boto"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07b3f21f",
   "metadata": {},
   "source": [
    "Before working on this notebook we have already authenticated using AWS security key and access id on our git bash command prompt.\n",
    "\n",
    "Firstly, activated the conda cloud_lab.\n",
    "\n",
    "Secondly, typed aws configure and authenticated by providing access key and secutity key which we gathered from the security credentials page of our AWS account."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "9bdfe7ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing necessary libraries\n",
    "import boto3\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "533f4735",
   "metadata": {},
   "source": [
    "We have imported the boto3 library and have chosen the cloud lab environment where we had already installed boto.\n",
    "\n",
    "Boto3 makes it easy to integrate Python application, library, or script with AWS services including Amazon S3, Amazon EC2, Amazon DynamoDB, and more. In this notebook, we will be accessing the csv file that we saved in our amazon s3 bucket.\n",
    "\n",
    "We will be starting with instantiating a boto client."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "aac6f148",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<botocore.client.S3 at 0x2166f016f70>"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Instantiate an S3 client\n",
    "s3_client = boto3.client('s3')\n",
    "\n",
    "# Check\n",
    "s3_client"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3de0fd5d",
   "metadata": {},
   "source": [
    "Taking the file from the specific s3 bucket."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "ab183b36",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'ResponseMetadata': {'RequestId': '21SZ8Q8R7FJTMAVM',\n",
       "  'HostId': '/Xk8XV7eojEfkKXmFmEQxCE59ScDo8kZcMkKuIK5qC6jGAkicBeZN+cfmuztozTfZyQLnBDSi8o=',\n",
       "  'HTTPStatusCode': 200,\n",
       "  'HTTPHeaders': {'x-amz-id-2': '/Xk8XV7eojEfkKXmFmEQxCE59ScDo8kZcMkKuIK5qC6jGAkicBeZN+cfmuztozTfZyQLnBDSi8o=',\n",
       "   'x-amz-request-id': '21SZ8Q8R7FJTMAVM',\n",
       "   'date': 'Mon, 06 Feb 2023 01:01:13 GMT',\n",
       "   'last-modified': 'Sun, 05 Feb 2023 23:44:34 GMT',\n",
       "   'etag': '\"fb3cf3d45b1fc32b939cbd13697dbe68\"',\n",
       "   'x-amz-server-side-encryption': 'AES256',\n",
       "   'accept-ranges': 'bytes',\n",
       "   'content-type': 'text/csv',\n",
       "   'server': 'AmazonS3',\n",
       "   'content-length': '7338'},\n",
       "  'RetryAttempts': 0},\n",
       " 'AcceptRanges': 'bytes',\n",
       " 'LastModified': datetime.datetime(2023, 2, 5, 23, 44, 34, tzinfo=tzutc()),\n",
       " 'ContentLength': 7338,\n",
       " 'ETag': '\"fb3cf3d45b1fc32b939cbd13697dbe68\"',\n",
       " 'ContentType': 'text/csv',\n",
       " 'ServerSideEncryption': 'AES256',\n",
       " 'Metadata': {},\n",
       " 'Body': <botocore.response.StreamingBody at 0x2166f0a56d0>}"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the specified file from the specified bucket and assign it to a variable\n",
    "s3_object = s3_client.get_object(Bucket='kk-bstn-bucket-jan30', Key='merged_file.csv')\n",
    "\n",
    "# Check\n",
    "s3_object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "25cf389b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "botocore.response.StreamingBody"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(s3_object['Body'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "1ecf8f35",
   "metadata": {
    "scrolled": true
   },
   "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>token</th>\n",
       "      <th>year</th>\n",
       "      <th>frequency</th>\n",
       "      <th>pages</th>\n",
       "      <th>books</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>token</td>\n",
       "      <td>year</td>\n",
       "      <td>frequency</td>\n",
       "      <td>pages</td>\n",
       "      <td>books</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>data</td>\n",
       "      <td>1584</td>\n",
       "      <td>16</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>data</td>\n",
       "      <td>1614</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>data</td>\n",
       "      <td>1627</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>data</td>\n",
       "      <td>1631</td>\n",
       "      <td>22</td>\n",
       "      <td>18</td>\n",
       "      <td>1</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>312</th>\n",
       "      <td>data</td>\n",
       "      <td>2004</td>\n",
       "      <td>201841</td>\n",
       "      <td>93539</td>\n",
       "      <td>3483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>313</th>\n",
       "      <td>data</td>\n",
       "      <td>2005</td>\n",
       "      <td>197467</td>\n",
       "      <td>88901</td>\n",
       "      <td>3392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>314</th>\n",
       "      <td>data</td>\n",
       "      <td>2006</td>\n",
       "      <td>203669</td>\n",
       "      <td>92960</td>\n",
       "      <td>3449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>315</th>\n",
       "      <td>data</td>\n",
       "      <td>2007</td>\n",
       "      <td>168338</td>\n",
       "      <td>78986</td>\n",
       "      <td>3246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>316</th>\n",
       "      <td>data</td>\n",
       "      <td>2008</td>\n",
       "      <td>105331</td>\n",
       "      <td>47811</td>\n",
       "      <td>2358</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>317 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     token  year  frequency  pages  books\n",
       "0    token  year  frequency  pages  books\n",
       "1     data  1584         16     14      1\n",
       "2     data  1614          3      2      1\n",
       "3     data  1627          1      1      1\n",
       "4     data  1631         22     18      1\n",
       "..     ...   ...        ...    ...    ...\n",
       "312   data  2004     201841  93539   3483\n",
       "313   data  2005     197467  88901   3392\n",
       "314   data  2006     203669  92960   3449\n",
       "315   data  2007     168338  78986   3246\n",
       "316   data  2008     105331  47811   2358\n",
       "\n",
       "[317 rows x 5 columns]"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read the csv into a dataframe; note that we pass in the 'StreamingBody' above\n",
    "s3_df = pd.read_csv(s3_object['Body'])\n",
    "\n",
    "# Check\n",
    "s3_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cbf64696",
   "metadata": {},
   "source": [
    "The first row is same as the column heading. We will remove it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "a3e6c79b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#removing the first row\n",
    "s3_df= s3_df.iloc[1:, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "0f03d651",
   "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>index</th>\n",
       "      <th>token</th>\n",
       "      <th>year</th>\n",
       "      <th>frequency</th>\n",
       "      <th>pages</th>\n",
       "      <th>books</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>data</td>\n",
       "      <td>1584</td>\n",
       "      <td>16</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>data</td>\n",
       "      <td>1614</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>data</td>\n",
       "      <td>1627</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>data</td>\n",
       "      <td>1631</td>\n",
       "      <td>22</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>data</td>\n",
       "      <td>1637</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index token  year frequency pages books\n",
       "0      1  data  1584        16    14     1\n",
       "1      2  data  1614         3     2     1\n",
       "2      3  data  1627         1     1     1\n",
       "3      4  data  1631        22    18     1\n",
       "4      5  data  1637         1     1     1"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#resetting index\n",
    "s3_df.head().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "b8a7e5ef",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 316 entries, 1 to 316\n",
      "Data columns (total 5 columns):\n",
      " #   Column     Non-Null Count  Dtype \n",
      "---  ------     --------------  ----- \n",
      " 0   token      316 non-null    object\n",
      " 1   year       316 non-null    object\n",
      " 2   frequency  316 non-null    object\n",
      " 3   pages      316 non-null    object\n",
      " 4   books      316 non-null    object\n",
      "dtypes: object(5)\n",
      "memory usage: 12.5+ KB\n"
     ]
    }
   ],
   "source": [
    "#quick overview\n",
    "s3_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7fc9e13e",
   "metadata": {},
   "source": [
    "The columns year, frequncey, pages, books need to be corrected to right format."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "bef0b078",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Soniye\\AppData\\Local\\Temp\\ipykernel_14416\\3570885124.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  s3_df['year']=s3_df['year'].astype('int32')\n",
      "C:\\Users\\Soniye\\AppData\\Local\\Temp\\ipykernel_14416\\3570885124.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  s3_df['frequency']=s3_df['frequency'].astype('int32')\n",
      "C:\\Users\\Soniye\\AppData\\Local\\Temp\\ipykernel_14416\\3570885124.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  s3_df['pages']=s3_df['pages'].astype('int32')\n",
      "C:\\Users\\Soniye\\AppData\\Local\\Temp\\ipykernel_14416\\3570885124.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  s3_df['books']=s3_df['books'].astype('int32')\n"
     ]
    }
   ],
   "source": [
    "#convert to correct type\n",
    "s3_df['year']=s3_df['year'].astype('int32')\n",
    "s3_df['frequency']=s3_df['frequency'].astype('int32')\n",
    "s3_df['pages']=s3_df['pages'].astype('int32')\n",
    "s3_df['books']=s3_df['books'].astype('int32')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "828d1d39",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 316 entries, 1 to 316\n",
      "Data columns (total 5 columns):\n",
      " #   Column     Non-Null Count  Dtype \n",
      "---  ------     --------------  ----- \n",
      " 0   token      316 non-null    object\n",
      " 1   year       316 non-null    int32 \n",
      " 2   frequency  316 non-null    int32 \n",
      " 3   pages      316 non-null    int32 \n",
      " 4   books      316 non-null    int32 \n",
      "dtypes: int32(4), object(1)\n",
      "memory usage: 7.5+ KB\n"
     ]
    }
   ],
   "source": [
    "s3_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "d3f70179",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "316"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s3_df['year'].nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01ceac07",
   "metadata": {},
   "source": [
    "We have 316 rows with 316 unique year data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "52c17b72",
   "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>year</th>\n",
       "      <th>frequency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>316.000000</td>\n",
       "      <td>316.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1847.569620</td>\n",
       "      <td>38555.993671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>96.874382</td>\n",
       "      <td>69212.366418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1584.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1771.750000</td>\n",
       "      <td>20.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1850.500000</td>\n",
       "      <td>3004.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1929.250000</td>\n",
       "      <td>41776.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2008.000000</td>\n",
       "      <td>254561.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              year      frequency\n",
       "count   316.000000     316.000000\n",
       "mean   1847.569620   38555.993671\n",
       "std      96.874382   69212.366418\n",
       "min    1584.000000       1.000000\n",
       "25%    1771.750000      20.750000\n",
       "50%    1850.500000    3004.000000\n",
       "75%    1929.250000   41776.750000\n",
       "max    2008.000000  254561.000000"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s3_df[['year', 'frequency']].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d1362fd",
   "metadata": {},
   "source": [
    "Let us plot the frequency column and see its relationship with year."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "200433ae",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1200x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 5))\n",
    "plt.plot(s3_df['year'], s3_df['frequency'] )\n",
    "plt.title(\"Number of occurrences of the token data over the years\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "37be1045",
   "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>token</th>\n",
       "      <th>year</th>\n",
       "      <th>frequency</th>\n",
       "      <th>pages</th>\n",
       "      <th>books</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>312</th>\n",
       "      <td>data</td>\n",
       "      <td>2004</td>\n",
       "      <td>201841</td>\n",
       "      <td>93539</td>\n",
       "      <td>3483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>313</th>\n",
       "      <td>data</td>\n",
       "      <td>2005</td>\n",
       "      <td>197467</td>\n",
       "      <td>88901</td>\n",
       "      <td>3392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>314</th>\n",
       "      <td>data</td>\n",
       "      <td>2006</td>\n",
       "      <td>203669</td>\n",
       "      <td>92960</td>\n",
       "      <td>3449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>315</th>\n",
       "      <td>data</td>\n",
       "      <td>2007</td>\n",
       "      <td>168338</td>\n",
       "      <td>78986</td>\n",
       "      <td>3246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>316</th>\n",
       "      <td>data</td>\n",
       "      <td>2008</td>\n",
       "      <td>105331</td>\n",
       "      <td>47811</td>\n",
       "      <td>2358</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    token  year  frequency  pages  books\n",
       "312  data  2004     201841  93539   3483\n",
       "313  data  2005     197467  88901   3392\n",
       "314  data  2006     203669  92960   3449\n",
       "315  data  2007     168338  78986   3246\n",
       "316  data  2008     105331  47811   2358"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s3_df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0867ebdd",
   "metadata": {},
   "source": [
    "At the starting year the search frequncy of token data was very less almost negligible and it started to rise after 1900.\n",
    "It is littel unclear as to why it is falling after 2000s when the word 'data' started becoming popular."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "56216a0d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0efbb4a9",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "cloud_lab",
   "language": "python",
   "name": "cloud_lab"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}