Assignment-1.2 - (optional) - Pandas (for basic understanding) Practice Questions.ipynb

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   "source": [
    "**Consider the following Python dictionary data and Python list labels:**\n",
    "\n",
    "data = {'birds': ['Cranes', 'Cranes', 'plovers', 'spoonbills', 'spoonbills', 'Cranes', 'plovers', 'Cranes', 'spoonbills', 'spoonbills'],\n",
    "        'age': [3.5, 4, 1.5, np.nan, 6, 3, 5.5, np.nan, 8, 4],\n",
    "        'visits': [2, 4, 3, 4, 3, 4, 2, 2, 3, 2],\n",
    "        'priority': ['yes', 'yes', 'no', 'yes', 'no', 'no', 'no', 'yes', 'no', 'no']}\n",
    "\n",
    "labels = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>birds</th>\n",
       "      <th>age</th>\n",
       "      <th>visits</th>\n",
       "      <th>priority</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Cranes</td>\n",
       "      <td>3.5</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Cranes</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>plovers</td>\n",
       "      <td>1.5</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>spoonbills</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>spoonbills</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Cranes</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>plovers</td>\n",
       "      <td>5.5</td>\n",
       "      <td>2</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Cranes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>spoonbills</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>spoonbills</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        birds  age  visits priority\n",
       "0      Cranes  3.5       2      yes\n",
       "1      Cranes  4.0       4      yes\n",
       "2     plovers  1.5       3       no\n",
       "3  spoonbills  NaN       4      yes\n",
       "4  spoonbills  6.0       3       no\n",
       "5      Cranes  3.0       4       no\n",
       "6     plovers  5.5       2       no\n",
       "7      Cranes  NaN       2      yes\n",
       "8  spoonbills  8.0       3       no\n",
       "9  spoonbills  4.0       2       no"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "data = {'birds': ['Cranes', 'Cranes', 'plovers', 'spoonbills', 'spoonbills', 'Cranes', 'plovers', 'Cranes', 'spoonbills', 'spoonbills'], 'age': [3.5, 4, 1.5, np.nan, 6, 3, 5.5, np.nan, 8, 4], 'visits': [2, 4, 3, 4, 3, 4, 2, 2, 3, 2], 'priority': ['yes', 'yes', 'no', 'yes', 'no', 'no', 'no', 'yes', 'no', 'no']}\n",
    "labels = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j']\n",
    "\n",
    "df = pd.DataFrame(data)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "QCVMXd9vS6gU"
   },
   "source": [
    "**1. Create a DataFrame birds from this dictionary data which has the index labels.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Jdh7YCdqS6gV"
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "a        Cranes\n",
       "b        Cranes\n",
       "c       plovers\n",
       "d    spoonbills\n",
       "e    spoonbills\n",
       "f        Cranes\n",
       "g       plovers\n",
       "h        Cranes\n",
       "i    spoonbills\n",
       "j    spoonbills\n",
       "Name: birds, dtype: object"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j']\n",
    "df.iloc[:,0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "siLwCXssS6gX"
   },
   "source": [
    "**2. Display a summary of the basic information about birds DataFrame and its data.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "TwAPsgtnS6gZ"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>birds</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>Cranes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>4</td>\n",
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      "text/plain": [
       "         birds\n",
       "count       10\n",
       "unique       3\n",
       "top     Cranes\n",
       "freq         4"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "u7y6Wy-6S6gb"
   },
   "source": [
    "**3. Print the first 2 rows of the birds dataframe **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "hsT0EmgHS6gc"
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    Cranes\n",
       "b    Cranes\n",
       "Name: birds, dtype: object"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[0:2, 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "pQR358iGS6ge"
   },
   "source": [
    "**4. Print all the rows with only 'birds' and 'age' columns from the dataframe**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "TNmw73EYS6gf"
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   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>birds</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>Cranes</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>Cranes</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>plovers</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>spoonbills</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>spoonbills</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f</th>\n",
       "      <td>Cranes</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>g</th>\n",
       "      <td>plovers</td>\n",
       "      <td>5.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>h</th>\n",
       "      <td>Cranes</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>i</th>\n",
       "      <td>spoonbills</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>j</th>\n",
       "      <td>spoonbills</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        birds  age\n",
       "a      Cranes  3.5\n",
       "b      Cranes  4.0\n",
       "c     plovers  1.5\n",
       "d  spoonbills  NaN\n",
       "e  spoonbills  6.0\n",
       "f      Cranes  3.0\n",
       "g     plovers  5.5\n",
       "h      Cranes  NaN\n",
       "i  spoonbills  8.0\n",
       "j  spoonbills  4.0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[:,0:2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "sTZQB4NES6gh"
   },
   "source": [
    "**5. select [2, 3, 7] rows and in columns ['birds', 'age', 'visits']**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "udoUZE1bS6gi"
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   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>birds</th>\n",
       "      <th>age</th>\n",
       "      <th>visits</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>plovers</td>\n",
       "      <td>1.5</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>spoonbills</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Cranes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
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       "  </tbody>\n",
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      ],
      "text/plain": [
       "        birds  age  visits\n",
       "2     plovers  1.5       3\n",
       "3  spoonbills  NaN       4\n",
       "7      Cranes  NaN       2"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index = [0,1,2,3,4,5,6,7,8,9]\n",
    "df.loc[[2,3,7],['birds','age','visits']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "842Sj1Y8S6gl"
   },
   "source": [
    "**6. select the rows where the number of visits is less than 4**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "pzd-cwMfS6gm"
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>visits</th>\n",
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       "      <th>a</th>\n",
       "      <td>2</td>\n",
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       "      <th>c</th>\n",
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       "      <td>2</td>\n",
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      ],
      "text/plain": [
       "   visits\n",
       "a       2\n",
       "c       3\n",
       "e       3\n",
       "g       2\n",
       "h       2\n",
       "i       3\n",
       "j       2"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j']\n",
    "df.loc[df['visits']<4,['visits']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "HXc4s7uAS6gq"
   },
   "source": [
    "**7. select the rows with columns ['birds', 'visits'] where the age is missing i.e NaN**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "_47h5RcjS6gq"
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>birds</th>\n",
       "      <th>visits</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>spoonbills</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>h</th>\n",
       "      <td>Cranes</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "        birds  visits\n",
       "d  spoonbills       4\n",
       "h      Cranes       2"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df['age'].isnull(), ['birds', 'visits']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "PoACk8MtS6gu"
   },
   "source": [
    "**8. Select the rows where the birds is a Cranes and the age is less than 4**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "1HxJe851S6gv"
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   "outputs": [
    {
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       "    .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>birds</th>\n",
       "      <th>age</th>\n",
       "      <th>visits</th>\n",
       "      <th>priority</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>Cranes</td>\n",
       "      <td>3.5</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f</th>\n",
       "      <td>Cranes</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    birds  age  visits priority\n",
       "a  Cranes  3.5       2      yes\n",
       "f  Cranes  3.0       4       no"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df['birds'] == 'Cranes')&(df['age']<4)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "JCVVonwrS6gx"
   },
   "source": [
    "**9. Select the rows the age is between 2 and 4(inclusive)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "wzBhD5uQS6gy"
   },
   "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>birds</th>\n",
       "      <th>age</th>\n",
       "      <th>visits</th>\n",
       "      <th>priority</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>Cranes</td>\n",
       "      <td>3.5</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>Cranes</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f</th>\n",
       "      <td>Cranes</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>j</th>\n",
       "      <td>spoonbills</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        birds  age  visits priority\n",
       "a      Cranes  3.5       2      yes\n",
       "b      Cranes  4.0       4      yes\n",
       "f      Cranes  3.0       4       no\n",
       "j  spoonbills  4.0       2       no"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['age'].between(2,4)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "_6A8jc_3S6g0"
   },
   "source": [
    "**10. Find the total number of visits of the bird Cranes**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "MAaIkwy0S6g0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['birds'] == 'Cranes'].sum()['visits']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "6o0jAL2sS6g2"
   },
   "source": [
    "**11. Calculate the mean age for each different birds in dataframe.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "4GMpzL4eS6g3"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "birds\n",
       "Cranes        3.5\n",
       "plovers       3.5\n",
       "spoonbills    6.0\n",
       "Name: age, dtype: float64"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('birds').mean()['age']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "7Hg9fejkS6g5"
   },
   "source": [
    "**12. Append a new row 'k' to dataframe with your choice of values for each column. Then delete that row to return the original DataFrame.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "DFxYI3SGS6g6"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>birds</th>\n",
       "      <th>age</th>\n",
       "      <th>visits</th>\n",
       "      <th>priority</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>Cranes</td>\n",
       "      <td>3.5</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>Cranes</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>plovers</td>\n",
       "      <td>1.5</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>spoonbills</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>spoonbills</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f</th>\n",
       "      <td>Cranes</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>g</th>\n",
       "      <td>plovers</td>\n",
       "      <td>5.5</td>\n",
       "      <td>2</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>h</th>\n",
       "      <td>Cranes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>i</th>\n",
       "      <td>spoonbills</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>j</th>\n",
       "      <td>spoonbills</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>k</th>\n",
       "      <td>kingfisher</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        birds  age  visits priority\n",
       "a      Cranes  3.5       2      yes\n",
       "b      Cranes  4.0       4      yes\n",
       "c     plovers  1.5       3       no\n",
       "d  spoonbills  NaN       4      yes\n",
       "e  spoonbills  6.0       3       no\n",
       "f      Cranes  3.0       4       no\n",
       "g     plovers  5.5       2       no\n",
       "h      Cranes  NaN       2      yes\n",
       "i  spoonbills  8.0       3       no\n",
       "j  spoonbills  4.0       2       no\n",
       "k  kingfisher  NaN       4      yes"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k = {'birds':'kingfisher', 'age':np.nan, 'visits':4, 'priority': 'yes'}\n",
    "\n",
    "df = df.append(k,ignore_index = True)\n",
    "df.index = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k']\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>birds</th>\n",
       "      <th>age</th>\n",
       "      <th>visits</th>\n",
       "      <th>priority</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>Cranes</td>\n",
       "      <td>3.5</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>Cranes</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>plovers</td>\n",
       "      <td>1.5</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>spoonbills</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>spoonbills</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f</th>\n",
       "      <td>Cranes</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>g</th>\n",
       "      <td>plovers</td>\n",
       "      <td>5.5</td>\n",
       "      <td>2</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>h</th>\n",
       "      <td>Cranes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>i</th>\n",
       "      <td>spoonbills</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>j</th>\n",
       "      <td>spoonbills</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        birds  age  visits priority\n",
       "a      Cranes  3.5       2      yes\n",
       "b      Cranes  4.0       4      yes\n",
       "c     plovers  1.5       3       no\n",
       "d  spoonbills  NaN       4      yes\n",
       "e  spoonbills  6.0       3       no\n",
       "f      Cranes  3.0       4       no\n",
       "g     plovers  5.5       2       no\n",
       "h      Cranes  NaN       2      yes\n",
       "i  spoonbills  8.0       3       no\n",
       "j  spoonbills  4.0       2       no"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop('k')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "x9X8PqTSS6g8"
   },
   "source": [
    "**13. Find the number of each type of birds in dataframe (Counts)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "S8T0jB_US6g9"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "birds\n",
       "Cranes        4\n",
       "plovers       2\n",
       "spoonbills    4\n",
       "dtype: int64"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['birds']).size()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "M3967HavS6hA"
   },
   "source": [
    "**14. Sort dataframe (birds) first by the values in the 'age' in decending order, then by the value in the 'visits' column in ascending order.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "X7MSFaoUS6hC"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "i    spoonbills\n",
       "e    spoonbills\n",
       "g       plovers\n",
       "b        Cranes\n",
       "j    spoonbills\n",
       "a        Cranes\n",
       "f        Cranes\n",
       "c       plovers\n",
       "d    spoonbills\n",
       "h        Cranes\n",
       "k    kingfisher\n",
       "Name: birds, dtype: object"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values('age', ascending = False)['birds']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a        Cranes\n",
       "g       plovers\n",
       "h        Cranes\n",
       "j    spoonbills\n",
       "c       plovers\n",
       "e    spoonbills\n",
       "i    spoonbills\n",
       "b        Cranes\n",
       "d    spoonbills\n",
       "f        Cranes\n",
       "k    kingfisher\n",
       "Name: birds, dtype: object"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values('visits', ascending = True)['birds']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "DSPcHzWCS6hG"
   },
   "source": [
    "**15. Replace the priority column values with'yes' should be 1 and 'no' should be 0**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ZTFU8eG1S6hH"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    1\n",
       "b    1\n",
       "c    0\n",
       "d    1\n",
       "e    0\n",
       "f    0\n",
       "g    0\n",
       "h    1\n",
       "i    0\n",
       "j    0\n",
       "k    1\n",
       "Name: priority, dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.priority.replace(['yes', 'no'], [1, 0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "vVnp5sGWS6hJ"
   },
   "source": [
    "**16. In the 'birds' column, change the 'Cranes' entries to 'trumpeters'.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ubklshemS6hJ"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    trumpeters\n",
       "b    trumpeters\n",
       "c       plovers\n",
       "d    spoonbills\n",
       "e    spoonbills\n",
       "f    trumpeters\n",
       "g       plovers\n",
       "h    trumpeters\n",
       "i    spoonbills\n",
       "j    spoonbills\n",
       "k    kingfisher\n",
       "Name: birds, dtype: object"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.birds.replace('Cranes', 'trumpeters')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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