Tasks/tasks-pandas.ipynb

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     "start_time": "2024-06-09T12:36:33.165598Z"
    }
   },
   "source": "import pandas as pd",
   "outputs": [],
   "execution_count": 3
  },
  {
   "metadata": {
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     "start_time": "2024-06-09T12:36:33.479122Z"
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   },
   "cell_type": "code",
   "source": [
    "spam = pd.read_csv('../assets/spam.csv')\n",
    "iris = pd.read_csv('../assets/iris.csv')"
   ],
   "id": "2f69854f8333c2d4",
   "outputs": [],
   "execution_count": 4
  },
  {
   "metadata": {
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     "start_time": "2024-05-19T08:41:04.948945Z"
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   },
   "cell_type": "code",
   "source": "spam.head()",
   "id": "5c6f643f8c28701c",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "                                                text target\n",
       "0  Go until jurong point, crazy.. Available only ...    ham\n",
       "1                      Ok lar... Joking wif u oni...    ham\n",
       "2  Free entry in 2 a wkly comp to win FA Cup fina...   spam\n",
       "3  U dun say so early hor... U c already then say...    ham\n",
       "4  Nah I don't think he goes to usf, he lives aro...    ham"
      ],
      "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>text</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Go until jurong point, crazy.. Available only ...</td>\n",
       "      <td>ham</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ok lar... Joking wif u oni...</td>\n",
       "      <td>ham</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Free entry in 2 a wkly comp to win FA Cup fina...</td>\n",
       "      <td>spam</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>U dun say so early hor... U c already then say...</td>\n",
       "      <td>ham</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Nah I don't think he goes to usf, he lives aro...</td>\n",
       "      <td>ham</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
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   ],
   "execution_count": 3
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "### Question 1\n",
    "What percentage of the documents in `spam` data are spam?, and creat bar chart"
   ],
   "id": "4205b49325fc65c6"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "<style>\n",
    "    .answer {\n",
    "        display: none;\n",
    "    }\n",
    "</style>\n",
    "\n",
    "<details>\n",
    "    <summary>Click to reveal answer</summary>\n",
    "    <div class=\"answer\">\n",
    "    <pre>\n",
    "    <code>\n",
    "    spam.target.value_counts(normalize=True)\n",
    "    </code>\n",
    "    </pre>\n",
    "    </div>\n",
    "</details>\n"
   ],
   "id": "9aa460aef2aa829f"
  },
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   "source": "",
   "id": "4f1bb29384e6661d",
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   "execution_count": 3
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   "outputs": [],
   "execution_count": 19
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "### Question 2\n",
    "Calculate the length of each text, save it in column called length"
   ],
   "id": "1e3960fcc588ee78"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "<details>\n",
    "    <summary>Click to reveal answer</summary>\n",
    "    <p>spam['length'] = spam.text.str.len()</p>\n",
    "</details>\n"
   ],
   "id": "176730d3c54302c"
  },
  {
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   },
   "cell_type": "code",
   "source": "spam['length'] = spam.text.str.len()",
   "id": "bad3b986cfbe9922",
   "outputs": [],
   "execution_count": 6
  },
  {
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   "id": "61c72d7000c06bae",
   "outputs": [],
   "execution_count": 3
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "### Question 3\n",
    "\n",
    "Find the longest spam message"
   ],
   "id": "2c4e25b1f03d2198"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "<style>\n",
    "    .answer {\n",
    "        display: none;\n",
    "    }\n",
    "</style>\n",
    "\n",
    "<details>\n",
    "    <summary>Click to reveal answer</summary>\n",
    "    <p class=\"answer\">\n",
    "    spam[spam[\"target\"] == \"spam\"].sort_values(by=\"length\", ascending=False).head(1)\n",
    "    </p>\n",
    "</details>\n"
   ],
   "id": "17aa99abb4ec6346"
  },
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   "cell_type": "markdown",
   "source": "",
   "id": "f6ebd72faf35f25b"
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   "cell_type": "code",
   "source": "",
   "id": "2c5de4ee96f40e32",
   "outputs": [],
   "execution_count": 13
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "### Questions 4\n",
    "Find the shortest spam message"
   ],
   "id": "e9bd9c9fecd22511"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "<style>\n",
    "    .answer {\n",
    "        display: none;\n",
    "    }\n",
    "</style>\n",
    "\n",
    "<details>\n",
    "    <summary>Click to reveal answer</summary>\n",
    "    <div class=\"answer\">\n",
    "        <pre><code>\n",
    "        spam[spam[\"target\"] == \"spam\"].sort_values(by=\"length\", ascending=True).head(1)\n",
    "    </code></pre>\n",
    "    </div>\n",
    "</details>\n"
   ],
   "id": "59da8c420d61128f"
  },
  {
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   "cell_type": "code",
   "source": "",
   "id": "688103af02db24d6",
   "outputs": [],
   "execution_count": 13
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "### Question 5\n",
    "\n",
    "Find the longest ham message"
   ],
   "id": "84ace14f2c5db5f9"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "<style>\n",
    "    .answer {\n",
    "        display: none;\n",
    "    }\n",
    "</style>\n",
    "\n",
    "<details>\n",
    "    <summary>Click to reveal answer</summary>\n",
    "    <div class=\"answer\">\n",
    "        <pre><code>\n",
    "        spam[spam[\"target\"] == \"ham\"].sort_values(by=\"length\", ascending=False).head(1)\n",
    "    </code></pre>\n",
    "    </div>\n",
    "</details>\n"
   ],
   "id": "ae0351d33239b133"
  },
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   "source": "",
   "id": "911e648621813447",
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   "execution_count": 13
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "### Questions 6\n",
    "Find the shortest ham message"
   ],
   "id": "8cda0f42f183a929"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "<style>\n",
    "    .answer {\n",
    "        display: none;\n",
    "    }\n",
    "</style>\n",
    "\n",
    "<details>\n",
    "    <summary>Click to reveal answer</summary>\n",
    "    <div class=\"answer\">\n",
    "        <pre><code>\n",
    "        spam[spam[\"target\"] == \"ham\"].sort_values(by=\"length\", ascending=True).head(1)\n",
    "    </code></pre>\n",
    "    </div>\n",
    "</details>\n"
   ],
   "id": "e8b81c91baa2a3c5"
  },
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   "cell_type": "code",
   "source": "",
   "id": "867a37712e40f21d",
   "outputs": [],
   "execution_count": 12
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "### Questions 7\n",
    "Find the average length for ham message"
   ],
   "id": "102463695c8ad787"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "<style>\n",
    "    .answer {\n",
    "        display: none;\n",
    "    }\n",
    "</style>\n",
    "\n",
    "<details>\n",
    "    <summary>Click to reveal answer</summary>\n",
    "    <div class=\"answer\">\n",
    "        <pre><code>\n",
    "        spam[spam[\"target\"] == \"ham\"].length.mean()\n",
    "    </code></pre>\n",
    "    </div>\n",
    "</details>\n"
   ],
   "id": "57f3fb9d4d4da81a"
  },
  {
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   "cell_type": "code",
   "source": "",
   "id": "bbec04237c964bef",
   "outputs": [],
   "execution_count": 12
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "### Questions 8\n",
    "Find the average length for spam message"
   ],
   "id": "92c61d0094cdba8e"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "<style>\n",
    "    .answer { display: none;}\n",
    "</style>\n",
    "\n",
    "<details>\n",
    "    <summary>Click to reveal answer</summary>\n",
    "    <div class=\"answer\">\n",
    "        <pre><code>\n",
    "        spam[spam[\"target\"] == \"spam\"].length.mean()\n",
    "    </code></pre>\n",
    "    </div>\n",
    "</details>\n"
   ],
   "id": "7fde52a5db818270"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-19T08:43:47.887608Z",
     "start_time": "2024-05-19T08:43:47.886157Z"
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   "cell_type": "code",
   "source": "",
   "id": "b4f9f4410164df5",
   "outputs": [],
   "execution_count": 12
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "### Questions 9\n",
    "How many messages spam and ham their length is greater than then average?"
   ],
   "id": "fce5c94a9741fd92"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "<details>\n",
    "    <summary>Click to reveal answer</summary>\n",
    "    <div class=\"answer\">\n",
    "        <pre><code>\n",
    "        only_spam = spam[spam[\"target\"] == \"spam\"]\n",
    "        only_ham = spam[spam[\"target\"] == \"ham\"]\n",
    "        avg_spam = only_spam.length.mean()\n",
    "        avg_ham = only_ham.length.mean()\n",
    "        spam_above_avg = only_spam[only_spam.length > avg_spam]\n",
    "        ham_above_avg = only_ham[only_ham.length > avg_ham]\n",
    "    </code></pre>\n",
    "    </div>\n",
    "</details>"
   ],
   "id": "a5baec9142d0d25d"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "### Questions 10\n",
    "Using the `iris` dataset, find the average sepal length for each species"
   ],
   "id": "60f08edf020c6d5d"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-08T09:36:58.944291Z",
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   "cell_type": "code",
   "source": "",
   "id": "f3d23a60a1ad9d5e",
   "outputs": [],
   "execution_count": 2
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "<details>\n",
    "    <summary>Click to reveal answer</summary>\n",
    "    <div class=\"answer\">\n",
    "        <pre><code>\n",
    "        iris.groupby('species').sepal_length.mean()\n",
    "    </code></pre>\n",
    "    </div>\n",
    "</details>"
   ],
   "id": "237653c86a0dc028"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "### Questions 11\n",
    "Using the `iris` dataset, crate a bar chart of the average sepal length for each species."
   ],
   "id": "d8bbf2d5b62ab2dd"
  },
  {
   "metadata": {
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   "cell_type": "code",
   "source": "",
   "id": "435210f5f0872fa0",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='species'>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 3
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "<details>\n",
    "    <summary>Click to reveal answer</summary>\n",
    "    <div class=\"answer\">\n",
    "        <pre><code>\n",
    "        iris.groupby('species').sepal_length.mean().plot(kind='bar')\n",
    "    </code></pre>\n",
    "    </div>\n",
    "</details>"
   ],
   "id": "863827d66e5d31ef"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "## Questions 12\n",
    "Using the `iris` dataset, Create a scatter plot of sepal length vs sepal width\n"
   ],
   "id": "92aa2164a7659643"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-08T09:43:11.376372Z",
     "start_time": "2024-06-08T09:43:11.292725Z"
    }
   },
   "cell_type": "code",
   "source": "",
   "id": "f14d8c5b83df3f07",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='sepal_length', ylabel='sepal_width'>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 18
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "<details>\n",
    "    <summary>Click to reveal answer</summary>\n",
    "    <div class=\"answer\">\n",
    "        <pre><code>\n",
    "        iris.plot.scatter(x='sepal_length', y='sepal_width')\n",
    "    </code></pre>\n",
    "    </div>\n",
    "</details>"
   ],
   "id": "e162ada9892972ed"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": "",
   "id": "412d3a6c96e8200c"
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}