14-baby-names.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# US - Baby Names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 1. Import the necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 2. Import the dataset from this address.\n",
    "\n",
    "#### Step 3. Assign it to a variable called baby_names."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "url = 'https://raw.githubusercontent.com/guipsamora/pandas_exercises/master/06_Stats/US_Baby_Names/US_Baby_Names_right.csv'\n",
    "\n",
    "baby_names = pd.read_csv(url)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 4. See the first 10 entries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Id</th>\n",
       "      <th>Name</th>\n",
       "      <th>Year</th>\n",
       "      <th>Gender</th>\n",
       "      <th>State</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11349</td>\n",
       "      <td>11350</td>\n",
       "      <td>Emma</td>\n",
       "      <td>2004</td>\n",
       "      <td>F</td>\n",
       "      <td>AK</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11350</td>\n",
       "      <td>11351</td>\n",
       "      <td>Madison</td>\n",
       "      <td>2004</td>\n",
       "      <td>F</td>\n",
       "      <td>AK</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11351</td>\n",
       "      <td>11352</td>\n",
       "      <td>Hannah</td>\n",
       "      <td>2004</td>\n",
       "      <td>F</td>\n",
       "      <td>AK</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11352</td>\n",
       "      <td>11353</td>\n",
       "      <td>Grace</td>\n",
       "      <td>2004</td>\n",
       "      <td>F</td>\n",
       "      <td>AK</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11353</td>\n",
       "      <td>11354</td>\n",
       "      <td>Emily</td>\n",
       "      <td>2004</td>\n",
       "      <td>F</td>\n",
       "      <td>AK</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>11354</td>\n",
       "      <td>11355</td>\n",
       "      <td>Abigail</td>\n",
       "      <td>2004</td>\n",
       "      <td>F</td>\n",
       "      <td>AK</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>11355</td>\n",
       "      <td>11356</td>\n",
       "      <td>Olivia</td>\n",
       "      <td>2004</td>\n",
       "      <td>F</td>\n",
       "      <td>AK</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>11356</td>\n",
       "      <td>11357</td>\n",
       "      <td>Isabella</td>\n",
       "      <td>2004</td>\n",
       "      <td>F</td>\n",
       "      <td>AK</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>11357</td>\n",
       "      <td>11358</td>\n",
       "      <td>Alyssa</td>\n",
       "      <td>2004</td>\n",
       "      <td>F</td>\n",
       "      <td>AK</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>11358</td>\n",
       "      <td>11359</td>\n",
       "      <td>Sophia</td>\n",
       "      <td>2004</td>\n",
       "      <td>F</td>\n",
       "      <td>AK</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0     Id      Name  Year Gender State  Count\n",
       "0       11349  11350      Emma  2004      F    AK     62\n",
       "1       11350  11351   Madison  2004      F    AK     48\n",
       "2       11351  11352    Hannah  2004      F    AK     46\n",
       "3       11352  11353     Grace  2004      F    AK     44\n",
       "4       11353  11354     Emily  2004      F    AK     41\n",
       "5       11354  11355   Abigail  2004      F    AK     37\n",
       "6       11355  11356    Olivia  2004      F    AK     33\n",
       "7       11356  11357  Isabella  2004      F    AK     30\n",
       "8       11357  11358    Alyssa  2004      F    AK     29\n",
       "9       11358  11359    Sophia  2004      F    AK     28"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "baby_names.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 5. Delete the column 'Unnamed: 0' and 'Id'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "del baby_names['Unnamed: 0']\n",
    "del baby_names['Id']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Year</th>\n",
       "      <th>Gender</th>\n",
       "      <th>State</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Emma</td>\n",
       "      <td>2004</td>\n",
       "      <td>F</td>\n",
       "      <td>AK</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Madison</td>\n",
       "      <td>2004</td>\n",
       "      <td>F</td>\n",
       "      <td>AK</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Hannah</td>\n",
       "      <td>2004</td>\n",
       "      <td>F</td>\n",
       "      <td>AK</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Grace</td>\n",
       "      <td>2004</td>\n",
       "      <td>F</td>\n",
       "      <td>AK</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Emily</td>\n",
       "      <td>2004</td>\n",
       "      <td>F</td>\n",
       "      <td>AK</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "      Name  Year Gender State  Count\n",
       "0     Emma  2004      F    AK     62\n",
       "1  Madison  2004      F    AK     48\n",
       "2   Hannah  2004      F    AK     46\n",
       "3    Grace  2004      F    AK     44\n",
       "4    Emily  2004      F    AK     41"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "baby_names.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 6. Is there more male or female names in the dataset?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "F    558846\n",
       "M    457549\n",
       "Name: Gender, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "baby_names['Gender'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 7. Group the dataset by name and assign to names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "del baby_names['Year']\n",
    "names = baby_names.groupby('Name')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 8. How many different names exist in the dataset?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gender</th>\n",
       "      <th>State</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Aaban</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aadan</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aadarsh</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aaden</th>\n",
       "      <td>196</td>\n",
       "      <td>196</td>\n",
       "      <td>196</td>\n",
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       "    <tr>\n",
       "      <th>Aadhav</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>Zyra</th>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zyrah</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zyren</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zyria</th>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zyriah</th>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>17632 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Gender  State  Count\n",
       "Name                         \n",
       "Aaban         2      2      2\n",
       "Aadan         4      4      4\n",
       "Aadarsh       1      1      1\n",
       "Aaden       196    196    196\n",
       "Aadhav        1      1      1\n",
       "...         ...    ...    ...\n",
       "Zyra          7      7      7\n",
       "Zyrah         2      2      2\n",
       "Zyren         1      1      1\n",
       "Zyria        10     10     10\n",
       "Zyriah        9      9      9\n",
       "\n",
       "[17632 rows x 3 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names.count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 9. What is the name with most occurrences?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Count    242874\n",
       "Name: Jacob, dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names.sum().sort_values(by='Count', ascending=False).iloc[0]\n",
    "\n",
    "# names.sum().Count.idxmax()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 10. How many different names have the least occurrences?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2578"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names_group = names.sum()\n",
    "\n",
    "filt = names_group['Count'] == names_group.Count.min()\n",
    "res = names_group[filt]\n",
    "len(res)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 11. What is the median name occurrence?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Count</th>\n",
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       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Aishani</th>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alara</th>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alysse</th>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ameir</th>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Anely</th>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sriram</th>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Trinton</th>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vita</th>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yoni</th>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zuleima</th>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "<p>66 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Count\n",
       "Name          \n",
       "Aishani     49\n",
       "Alara       49\n",
       "Alysse      49\n",
       "Ameir       49\n",
       "Anely       49\n",
       "...        ...\n",
       "Sriram      49\n",
       "Trinton     49\n",
       "Vita        49\n",
       "Yoni        49\n",
       "Zuleima     49\n",
       "\n",
       "[66 rows x 1 columns]"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filt = names_group['Count'] == names_group['Count'].median()\n",
    "names_group[filt]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 12. What is the standard deviation of names?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Count    11006.069468\n",
       "dtype: float64"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names_group.std() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 13. Get a summary with the mean, min, max, std and quartiles."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
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       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>17632.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2008.932169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>11006.069468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>11.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>49.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>337.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>242874.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Count\n",
       "count   17632.000000\n",
       "mean     2008.932169\n",
       "std     11006.069468\n",
       "min         5.000000\n",
       "25%        11.000000\n",
       "50%        49.000000\n",
       "75%       337.000000\n",
       "max    242874.000000"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names_group.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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