Virtual competition 1.ipynb

{
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   "cell_type": "markdown",
   "metadata": {
    "id": "Hmt5hLSnFuQ-"
   },
   "source": [
    "Consider the following Python dictionary `data` and Python list `labels`:\n",
    "\n",
    "``` python\n",
    "data = {'animal': ['cat', 'cat', 'snake', 'dog', 'dog', 'cat', 'snake', 'cat', 'dog', 'dog'],\n",
    "        'age': [2.5, 3, 0.5, np.nan, 5, 2, 4.5, np.nan, 7, 3],\n",
    "        'visits': [1, 3, 2, 3, 2, 3, 1, 1, 2, 1],\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",
    "```\n",
    "\n",
    "**1.** Create a DataFrame `df` from this dictionary `data` which has the index `labels`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "colab": {
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    },
    "executionInfo": {
     "elapsed": 13,
     "status": "error",
     "timestamp": 1635478191857,
     "user": {
      "displayName": "Ummar Shaik",
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  animal  age  visits priority\n",
      "a    cat  2.5       1      yes\n",
      "b    cat  3.0       3      yes\n",
      "c  snake  0.5       2       no\n",
      "d    dog  NaN       3      yes\n",
      "e    dog  5.0       2       no\n",
      "f    cat  2.0       3       no\n",
      "g  snake  4.5       1       no\n",
      "h    cat  NaN       1      yes\n",
      "i    dog  7.0       2       no\n",
      "j    dog  3.0       1       no\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "data = {'animal': ['cat', 'cat', 'snake', 'dog', 'dog', 'cat', 'snake', 'cat', 'dog', 'dog'],\n",
    "        'age': [2.5, 3, 0.5, np.nan, 5, 2, 4.5, np.nan, 7, 3],\n",
    "        'visits': [1, 3, 2, 3, 2, 3, 1, 1, 2, 1],\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",
    "\n",
    "df = pd.DataFrame(data, index=labels)\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "IpKnDID_FuRB"
   },
   "source": [
    "**2.** Display a summary of the basic information about this DataFrame and its data (*hint: there is a single method that can be called on the DataFrame*)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "id": "gcovPS3rFuRC"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 10 entries, a to j\n",
      "Data columns (total 4 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   animal    10 non-null     object \n",
      " 1   age       8 non-null      float64\n",
      " 2   visits    10 non-null     int64  \n",
      " 3   priority  10 non-null     object \n",
      "dtypes: float64(1), int64(1), object(2)\n",
      "memory usage: 400.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ab1u4T55FuRC"
   },
   "source": [
    "**3.** Return the first 3 rows of the DataFrame `df`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "id": "uIcy04PCFuRD"
   },
   "outputs": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>animal</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>cat</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>cat</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>snake</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "  animal  age  visits priority\n",
       "a    cat  2.5       1      yes\n",
       "b    cat  3.0       3      yes\n",
       "c  snake  0.5       2       no"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Insh_QRrFuRD"
   },
   "source": [
    "**4.** Display the 'animal' and 'age' columns from the DataFrame `df`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "id": "pJ-PHmJ5FuRE"
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   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>animal</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>cat</td>\n",
       "      <td>2.5</td>\n",
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       "      <th>b</th>\n",
       "      <td>cat</td>\n",
       "      <td>3.0</td>\n",
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       "      <th>c</th>\n",
       "      <td>snake</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>dog</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>dog</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f</th>\n",
       "      <td>cat</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>g</th>\n",
       "      <td>snake</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>h</th>\n",
       "      <td>cat</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>i</th>\n",
       "      <td>dog</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>j</th>\n",
       "      <td>dog</td>\n",
       "      <td>3.0</td>\n",
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      ],
      "text/plain": [
       "  animal  age\n",
       "a    cat  2.5\n",
       "b    cat  3.0\n",
       "c  snake  0.5\n",
       "d    dog  NaN\n",
       "e    dog  5.0\n",
       "f    cat  2.0\n",
       "g  snake  4.5\n",
       "h    cat  NaN\n",
       "i    dog  7.0\n",
       "j    dog  3.0"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['animal','age']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xRORUgdwFuRE"
   },
   "source": [
    "**5.** Display the data in rows `[3, 4, 8]` *and* in columns `['animal', 'age']'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "id": "8Yx-a5yyFuRF"
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>animal</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>dog</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>e</th>\n",
       "      <td>dog</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
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       "      <th>i</th>\n",
       "      <td>dog</td>\n",
       "      <td>7.0</td>\n",
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      "text/plain": [
       "  animal  age\n",
       "d    dog  NaN\n",
       "e    dog  5.0\n",
       "i    dog  7.0"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[[3,4,8], [0,1]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>animal</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>dog</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>dog</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>i</th>\n",
       "      <td>dog</td>\n",
       "      <td>7.0</td>\n",
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      "text/plain": [
       "  animal  age\n",
       "d    dog  NaN\n",
       "e    dog  5.0\n",
       "i    dog  7.0"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[['d','e','i'],['animal','age']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "EwxrthzwFuRF"
   },
   "source": [
    "**6.** Select only the rows where the number of visits is greater than 3."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "id": "J8tm9hpcFuRF"
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>animal</th>\n",
       "      <th>age</th>\n",
       "      <th>visits</th>\n",
       "      <th>priority</th>\n",
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      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [animal, age, visits, priority]\n",
       "Index: []"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df.loc[df['visits'] > 3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2XhJNfmKFuRF"
   },
   "source": [
    "**7.** Select the rows where the age is missing, i.e. it is `NaN`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "id": "0D0NW1Y-FuRG"
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>animal</th>\n",
       "      <th>age</th>\n",
       "      <th>visits</th>\n",
       "      <th>priority</th>\n",
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       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>dog</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>h</th>\n",
       "      <td>cat</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>yes</td>\n",
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      "text/plain": [
       "  animal  age  visits priority\n",
       "d    dog  NaN       3      yes\n",
       "h    cat  NaN       1      yes"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['age'].isnull()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mjT1t30pFuRG"
   },
   "source": [
    "**8.** Select the rows where the animal is a cat *and* the age is less than 3."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "id": "llayTxaVFuRG"
   },
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>animal</th>\n",
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       "      <th>visits</th>\n",
       "      <th>priority</th>\n",
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       "      <th>a</th>\n",
       "      <td>cat</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1</td>\n",
       "      <td>yes</td>\n",
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       "      <th>f</th>\n",
       "      <td>cat</td>\n",
       "      <td>2.0</td>\n",
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       "      <td>no</td>\n",
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      "text/plain": [
       "  animal  age  visits priority\n",
       "a    cat  2.5       1      yes\n",
       "f    cat  2.0       3       no"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df['animal']=='cat') & (df['age']<3)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "TpFIIG6YFuRG"
   },
   "source": [
    "**9.** Select the rows where the age is between 2 and 4 (inclusive)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "id": "DSg6DgMSFuRH"
   },
   "outputs": [
    {
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       "      <th></th>\n",
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       "      <td>cat</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>j</th>\n",
       "      <td>dog</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  animal  age  visits priority\n",
       "a    cat  2.5       1      yes\n",
       "b    cat  3.0       3      yes\n",
       "f    cat  2.0       3       no\n",
       "j    dog  3.0       1       no"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['age'].between(2,4)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hpRQ38reFuRH"
   },
   "source": [
    "**10.** Change the age in row 'f' to 1.5."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "id": "_YMVhJ8vFuRH"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "animal      cat\n",
       "age         1.5\n",
       "visits        3\n",
       "priority     no\n",
       "Name: f, dtype: object"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['f','age']=1.5\n",
    "df.loc['f']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hBWRarrFFuRH"
   },
   "source": [
    "**11.** Calculate the sum of all visits in `df` (i.e. the total number of visits)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "id": "f68JlP6vFuRH"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['visits'].sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pS3mMupIFuRH"
   },
   "source": [
    "**12.** Calculate the mean age for each different animal in `df`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "id": "DssyUV8LFuRI"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "animal\n",
       "cat      2.333333\n",
       "dog      5.000000\n",
       "snake    2.500000\n",
       "Name: age, dtype: float64"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('animal')['age'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "GMR-Ace2FuRI"
   },
   "source": [
    "**13.** Append a new row 'k' to `df` with your choice of values for each column. Then delete that row to return the original DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "id": "P8Qfy0a8FuRI"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>animal</th>\n",
       "      <th>age</th>\n",
       "      <th>visits</th>\n",
       "      <th>priority</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>b</th>\n",
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       "      <th>c</th>\n",
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       "      <td>2</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>dog</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>dog</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f</th>\n",
       "      <td>cat</td>\n",
       "      <td>1.5</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>g</th>\n",
       "      <td>snake</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>h</th>\n",
       "      <td>cat</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>i</th>\n",
       "      <td>dog</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>j</th>\n",
       "      <td>dog</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>k</th>\n",
       "      <td>snake</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  animal  age  visits priority\n",
       "a    cat  2.5       1      yes\n",
       "b    cat  3.0       3      yes\n",
       "c  snake  0.5       2       no\n",
       "d    dog  NaN       3      yes\n",
       "e    dog  5.0       2       no\n",
       "f    cat  1.5       3       no\n",
       "g  snake  4.5       1       no\n",
       "h    cat  NaN       1      yes\n",
       "i    dog  7.0       2       no\n",
       "j    dog  3.0       1       no\n",
       "k  snake  3.0       2       no"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['k']=['snake',3,2,'no']\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  animal  age  visits priority\n",
      "a    cat  2.5       1      yes\n",
      "b    cat  3.0       3      yes\n",
      "c  snake  0.5       2       no\n",
      "d    dog  NaN       3      yes\n",
      "e    dog  5.0       2       no\n",
      "f    cat  1.5       3       no\n",
      "g  snake  4.5       1       no\n",
      "h    cat  NaN       1      yes\n",
      "i    dog  7.0       2       no\n",
      "j    dog  3.0       1       no\n"
     ]
    }
   ],
   "source": [
    "df=df.drop('k')\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "GjMKZ4POFuRI"
   },
   "source": [
    "**14.** Count the number of each type of animal in `df`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "id": "1ydrxeyNFuRI"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cat      4\n",
       "dog      4\n",
       "snake    2\n",
       "Name: animal, dtype: int64"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['animal'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "MNBGtMjoFuRI"
   },
   "source": [
    "**15.** Sort `df` first by the values in the 'age' in *decending* order, then by the value in the 'visits' column in *ascending* order (so row `i` should be first, and row `d` should be last)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "id": "76tGccYfFuRJ"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
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       "      <th>animal</th>\n",
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       "      <th>b</th>\n",
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       "      <th>a</th>\n",
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       "      <td>1</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f</th>\n",
       "      <td>cat</td>\n",
       "      <td>1.5</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>snake</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>h</th>\n",
       "      <td>cat</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>dog</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  animal  age  visits priority\n",
       "i    dog  7.0       2       no\n",
       "e    dog  5.0       2       no\n",
       "g  snake  4.5       1       no\n",
       "j    dog  3.0       1       no\n",
       "b    cat  3.0       3      yes\n",
       "a    cat  2.5       1      yes\n",
       "f    cat  1.5       3       no\n",
       "c  snake  0.5       2       no\n",
       "h    cat  NaN       1      yes\n",
       "d    dog  NaN       3      yes"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.sort_values(by=['age','visits'],ascending=[False,True])\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5VOktn8PFuRJ"
   },
   "source": [
    "**16.** The 'priority' column contains the values 'yes' and 'no'. Replace this column with a column of boolean values: 'yes' should be `True` and 'no' should be `False`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "id": "HUrNuHMfFuRJ"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>b</th>\n",
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       "      <td>3.0</td>\n",
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       "      <th>a</th>\n",
       "      <td>cat</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f</th>\n",
       "      <td>cat</td>\n",
       "      <td>1.5</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>snake</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>h</th>\n",
       "      <td>cat</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>dog</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  animal  age  visits  priority\n",
       "i    dog  7.0       2     False\n",
       "e    dog  5.0       2     False\n",
       "g  snake  4.5       1     False\n",
       "j    dog  3.0       1     False\n",
       "b    cat  3.0       3      True\n",
       "a    cat  2.5       1      True\n",
       "f    cat  1.5       3     False\n",
       "c  snake  0.5       2     False\n",
       "h    cat  NaN       1      True\n",
       "d    dog  NaN       3      True"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['priority'] = df['priority'].replace({'yes':True, 'no':False})\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "T9MVDUniFuRJ"
   },
   "source": [
    "**17.** In the 'animal' column, change the 'snake' entries to 'python'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "id": "pWNj4Vl4FuRJ"
   },
   "outputs": [
    {
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       "      <th>b</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>python</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>h</th>\n",
       "      <td>cat</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>dog</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   animal  age  visits  priority\n",
       "i     dog  7.0       2     False\n",
       "e     dog  5.0       2     False\n",
       "g  python  4.5       1     False\n",
       "j     dog  3.0       1     False\n",
       "b     cat  3.0       3      True\n",
       "a     cat  2.5       1      True\n",
       "f     cat  1.5       3     False\n",
       "c  python  0.5       2     False\n",
       "h     cat  NaN       1      True\n",
       "d     dog  NaN       3      True"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['animal'] = df['animal'].replace('snake','python')\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**18.** Load the ny-flights dataset to Python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "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",
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       "    }\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>fl_date</th>\n",
       "      <th>unique_carrier</th>\n",
       "      <th>airline_id</th>\n",
       "      <th>tail_num</th>\n",
       "      <th>fl_num</th>\n",
       "      <th>origin</th>\n",
       "      <th>dest</th>\n",
       "      <th>dep_time</th>\n",
       "      <th>dep_delay</th>\n",
       "      <th>arr_time</th>\n",
       "      <th>arr_delay</th>\n",
       "      <th>cancelled</th>\n",
       "      <th>arr</th>\n",
       "      <th>dep</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014-01-01 00:00:00</td>\n",
       "      <td>AA</td>\n",
       "      <td>19805</td>\n",
       "      <td>N338AA</td>\n",
       "      <td>1</td>\n",
       "      <td>JFK</td>\n",
       "      <td>LAX</td>\n",
       "      <td>914.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1238.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2014-01-01 12:38:00</td>\n",
       "      <td>2014-01-01 09:14:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014-01-01 00:00:00</td>\n",
       "      <td>AA</td>\n",
       "      <td>19805</td>\n",
       "      <td>N335AA</td>\n",
       "      <td>3</td>\n",
       "      <td>JFK</td>\n",
       "      <td>LAX</td>\n",
       "      <td>1157.0</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>1523.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2014-01-01 15:23:00</td>\n",
       "      <td>2014-01-01 11:57:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-01-01 00:00:00</td>\n",
       "      <td>AA</td>\n",
       "      <td>19805</td>\n",
       "      <td>N327AA</td>\n",
       "      <td>21</td>\n",
       "      <td>JFK</td>\n",
       "      <td>LAX</td>\n",
       "      <td>1902.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2224.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2014-01-01 22:24:00</td>\n",
       "      <td>2014-01-01 19:02:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014-01-01 00:00:00</td>\n",
       "      <td>AA</td>\n",
       "      <td>19805</td>\n",
       "      <td>N3EHAA</td>\n",
       "      <td>29</td>\n",
       "      <td>LGA</td>\n",
       "      <td>PBI</td>\n",
       "      <td>722.0</td>\n",
       "      <td>-8.0</td>\n",
       "      <td>1014.0</td>\n",
       "      <td>-26.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2014-01-01 10:14:00</td>\n",
       "      <td>2014-01-01 07:22:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014-01-01 00:00:00</td>\n",
       "      <td>AA</td>\n",
       "      <td>19805</td>\n",
       "      <td>N319AA</td>\n",
       "      <td>117</td>\n",
       "      <td>JFK</td>\n",
       "      <td>LAX</td>\n",
       "      <td>1347.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1706.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2014-01-01 17:06:00</td>\n",
       "      <td>2014-01-01 13:47:00</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",
       "      <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>20812</th>\n",
       "      <td>2014-01-31 00:00:00</td>\n",
       "      <td>UA</td>\n",
       "      <td>19977</td>\n",
       "      <td>N54711</td>\n",
       "      <td>1253</td>\n",
       "      <td>ROC</td>\n",
       "      <td>ORD</td>\n",
       "      <td>801.0</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>908.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2014-01-31 09:08:00</td>\n",
       "      <td>2014-01-31 08:01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20813</th>\n",
       "      <td>2014-01-31 00:00:00</td>\n",
       "      <td>UA</td>\n",
       "      <td>19977</td>\n",
       "      <td>N77525</td>\n",
       "      <td>1429</td>\n",
       "      <td>LGA</td>\n",
       "      <td>CLE</td>\n",
       "      <td>1522.0</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>1649.0</td>\n",
       "      <td>-31.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2014-01-31 16:49:00</td>\n",
       "      <td>2014-01-31 15:22:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20814</th>\n",
       "      <td>2014-01-31 00:00:00</td>\n",
       "      <td>UA</td>\n",
       "      <td>19977</td>\n",
       "      <td>N37293</td>\n",
       "      <td>1456</td>\n",
       "      <td>LGA</td>\n",
       "      <td>IAH</td>\n",
       "      <td>719.0</td>\n",
       "      <td>-6.0</td>\n",
       "      <td>1006.0</td>\n",
       "      <td>-20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2014-01-31 10:06:00</td>\n",
       "      <td>2014-01-31 07:19:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20815</th>\n",
       "      <td>2014-01-31 00:00:00</td>\n",
       "      <td>UA</td>\n",
       "      <td>19977</td>\n",
       "      <td>N24729</td>\n",
       "      <td>1457</td>\n",
       "      <td>LGA</td>\n",
       "      <td>IAH</td>\n",
       "      <td>852.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1156.0</td>\n",
       "      <td>-6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2014-01-31 11:56:00</td>\n",
       "      <td>2014-01-31 08:52:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20816</th>\n",
       "      <td>2014-01-31 00:00:00</td>\n",
       "      <td>MQ</td>\n",
       "      <td>20398</td>\n",
       "      <td>N609MQ</td>\n",
       "      <td>3699</td>\n",
       "      <td>BUF</td>\n",
       "      <td>ORD</td>\n",
       "      <td>1208.0</td>\n",
       "      <td>-12.0</td>\n",
       "      <td>1251.0</td>\n",
       "      <td>-19.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2014-01-31 12:51:00</td>\n",
       "      <td>2014-01-31 12:08:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20817 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   fl_date unique_carrier  airline_id tail_num  fl_num origin  \\\n",
       "0      2014-01-01 00:00:00             AA       19805   N338AA       1    JFK   \n",
       "1      2014-01-01 00:00:00             AA       19805   N335AA       3    JFK   \n",
       "2      2014-01-01 00:00:00             AA       19805   N327AA      21    JFK   \n",
       "3      2014-01-01 00:00:00             AA       19805   N3EHAA      29    LGA   \n",
       "4      2014-01-01 00:00:00             AA       19805   N319AA     117    JFK   \n",
       "...                    ...            ...         ...      ...     ...    ...   \n",
       "20812  2014-01-31 00:00:00             UA       19977   N54711    1253    ROC   \n",
       "20813  2014-01-31 00:00:00             UA       19977   N77525    1429    LGA   \n",
       "20814  2014-01-31 00:00:00             UA       19977   N37293    1456    LGA   \n",
       "20815  2014-01-31 00:00:00             UA       19977   N24729    1457    LGA   \n",
       "20816  2014-01-31 00:00:00             MQ       20398   N609MQ    3699    BUF   \n",
       "\n",
       "      dest  dep_time  dep_delay  arr_time  arr_delay  cancelled  \\\n",
       "0      LAX     914.0       14.0    1238.0       13.0        0.0   \n",
       "1      LAX    1157.0       -3.0    1523.0       13.0        0.0   \n",
       "2      LAX    1902.0        2.0    2224.0        9.0        0.0   \n",
       "3      PBI     722.0       -8.0    1014.0      -26.0        0.0   \n",
       "4      LAX    1347.0        2.0    1706.0        1.0        0.0   \n",
       "...    ...       ...        ...       ...        ...        ...   \n",
       "20812  ORD     801.0       -4.0     908.0        4.0        0.0   \n",
       "20813  CLE    1522.0      -10.0    1649.0      -31.0        0.0   \n",
       "20814  IAH     719.0       -6.0    1006.0      -20.0        0.0   \n",
       "20815  IAH     852.0        7.0    1156.0       -6.0        0.0   \n",
       "20816  ORD    1208.0      -12.0    1251.0      -19.0        0.0   \n",
       "\n",
       "                       arr                  dep  \n",
       "0      2014-01-01 12:38:00  2014-01-01 09:14:00  \n",
       "1      2014-01-01 15:23:00  2014-01-01 11:57:00  \n",
       "2      2014-01-01 22:24:00  2014-01-01 19:02:00  \n",
       "3      2014-01-01 10:14:00  2014-01-01 07:22:00  \n",
       "4      2014-01-01 17:06:00  2014-01-01 13:47:00  \n",
       "...                    ...                  ...  \n",
       "20812  2014-01-31 09:08:00  2014-01-31 08:01:00  \n",
       "20813  2014-01-31 16:49:00  2014-01-31 15:22:00  \n",
       "20814  2014-01-31 10:06:00  2014-01-31 07:19:00  \n",
       "20815  2014-01-31 11:56:00  2014-01-31 08:52:00  \n",
       "20816  2014-01-31 12:51:00  2014-01-31 12:08:00  \n",
       "\n",
       "[20817 rows x 14 columns]"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nf = pd.read_csv('ny-flights.csv')\n",
    "nf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**19.** Which airline ID is present maximum times in the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['fl_date', 'unique_carrier', 'airline_id', 'tail_num', 'fl_num',\n",
      "       'origin', 'dest', 'dep_time', 'dep_delay', 'arr_time', 'arr_delay',\n",
      "       'cancelled', 'arr', 'dep'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(nf.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20409"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nf['airline_id'].value_counts().idxmax()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**20.** Draw a plot between dep_delay and arr_delay"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "nf.plot(kind='scatter', x='dep_delay', y='arr_delay')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "name": "100-pandas-puzzles-with-solutions.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}