trends.ipynb

{
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   "cell_type": "markdown",
   "id": "touched-bumper",
   "metadata": {},
   "source": [
    "# Exploring trends in baby names\n",
    "\n",
    "When my wife and I were expecting our firstborn, I naturally turned to data visualization to help us choose a name. [Exploring baby name trends](http://hint.fm/papers/final-baby-margin-nocomments.pdf) has already received attention frpm the visualization community, and the visualization system [NameGrapher](https://namerology.com/baby-name-grapher) (previously known as Name Voyager) is still running and still popular. However, this tool was too clunky or not able to answer some questions I had with this data: \n",
    "\n",
    "* What are the most popular names within a given time period?\n",
    "* Which names are becoming more popular?\n",
    "* Which names are going out of style?\n",
    "* What are the most gender neutral baby names?\n",
    "\n",
    "So I went straight to the source, the [Popular Baby Name](https://www.ssa.gov/oact/babynames/index.html) dataset, from the US Social Security Administration (SSA). This notebook does a little exploratory data analysis based on this dataset to answer these questions."
   ]
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   "cell_type": "markdown",
   "id": "684482d1-c9f2-41a3-a7b9-579729df145a",
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   "source": [
    "## Notebook Settings\n",
    "This notebook deals with a lot of data, 7 MB, and can be slow to run on some laptops. Modifying `year_range` to filter the data can help with this issue. Remember that the end of a `range` in Python is not inclusive.\n",
    "\n",
    "*Note: This notebook expects that you've already downloaded the national data product and uncompressed it into a directory called `names` that contains all of the year of birth (yob) `.txt` files in your current working directory.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "funny-invitation",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import altair as alt\n",
    "from altair import datum\n",
    "import numpy as np\n",
    "import glob, re\n",
    "from IPython.display import IFrame\n",
    "\n",
    "# Filter data by range\n",
    "year_range = range(2010, 2021)\n",
    "\n",
    "# Setup some color settings used in charts\n",
    "# Classic pink and blue for girls and boys, respectively\n",
    "color_domain = ['Female', 'Male']\n",
    "color_range = ['#ff9da7', '#4e79a7']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a9216182-6d6c-4dd2-b6e6-10f0fcaf2e25",
   "metadata": {},
   "source": [
    "## Data Preparation\n",
    "\n",
    "I make some slight modifications to what is already a very clean dataset. \n",
    "\n",
    "1. Take data from files in the `names` directory into a single Pandas DataFrame. \n",
    "2. While each file sorts the names by rank, there are many ties. Therefore, I use [`rank` set to *dense*](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.rank.html).\n",
    "3. Map the dataset's code for sex, (*M*, *F*) to more verbose categories, (*Male*, *Female*)."
   ]
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   "execution_count": 2,
   "id": "12d1d78d-3078-40ab-8f4c-677cf77a61f8",
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       "\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>name</th>\n",
       "      <th>sex</th>\n",
       "      <th>total</th>\n",
       "      <th>year</th>\n",
       "      <th>rank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>362627</td>\n",
       "      <td>362627</td>\n",
       "      <td>362627.000000</td>\n",
       "      <td>362627.000000</td>\n",
       "      <td>362627.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>56013</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>Isabella</td>\n",
       "      <td>Female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>22</td>\n",
       "      <td>207141</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>109.033500</td>\n",
       "      <td>2014.922915</td>\n",
       "      <td>877.053614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>658.574663</td>\n",
       "      <td>3.155369</td>\n",
       "      <td>134.670436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2010.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>2012.000000</td>\n",
       "      <td>881.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>2015.000000</td>\n",
       "      <td>911.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>2018.000000</td>\n",
       "      <td>936.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22924.000000</td>\n",
       "      <td>2020.000000</td>\n",
       "      <td>958.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "            name     sex          total           year           rank\n",
       "count     362627  362627  362627.000000  362627.000000  362627.000000\n",
       "unique     56013       2            NaN            NaN            NaN\n",
       "top     Isabella  Female            NaN            NaN            NaN\n",
       "freq          22  207141            NaN            NaN            NaN\n",
       "mean         NaN     NaN     109.033500    2014.922915     877.053614\n",
       "std          NaN     NaN     658.574663       3.155369     134.670436\n",
       "min          NaN     NaN       5.000000    2010.000000       1.000000\n",
       "25%          NaN     NaN       7.000000    2012.000000     881.000000\n",
       "50%          NaN     NaN      11.000000    2015.000000     911.000000\n",
       "75%          NaN     NaN      30.000000    2018.000000     936.000000\n",
       "max          NaN     NaN   22924.000000    2020.000000     958.000000"
      ]
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     "execution_count": 2,
     "metadata": {},
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   ],
   "source": [
    "files = 'names/*.txt'\n",
    "\n",
    "data = []\n",
    "for fn in glob.glob(files):\n",
    "    year = int(re.search(\"\\d{4}\", fn)[0])\n",
    "    if year not in year_range:  # Skip files for years outside of our range\n",
    "        continue;\n",
    "    df = pd.read_csv(fn, index_col=False, names=['name', 'sex', 'total'])\n",
    "    df['year'] = year\n",
    "    data.append(df)\n",
    "    \n",
    "# Union these datasets together\n",
    "data = pd.concat(data, axis=0, ignore_index=True)\n",
    "data.describe(include='all')\n",
    "\n",
    "# Calculate ranks for each group within year and sex\n",
    "data['rank'] = data.groupby(['year', 'sex'])['total'].rank(method='dense', ascending=False)\n",
    "\n",
    "# Expand categorical values for sex\n",
    "data['sex'] = data.sex.apply(lambda s: 'Male' if s == 'M' else 'Female')\n",
    "\n",
    "data.describe(include='all')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b30e0e7f-f0c9-4349-9b30-bd2d6a02da6f",
   "metadata": {},
   "source": [
    "### Quality Assurance\n",
    "\n",
    "This cell performs these quality assurance checks with the raw dataset:\n",
    "\n",
    "- There should never be more than 1000 occurance of any given rank within sex."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9267a7e7-e16c-4677-98a2-0d22233460b3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "All quality tests were passed\n"
     ]
    }
   ],
   "source": [
    "assert data['rank'].max() < 1000\n",
    "\n",
    "print('All quality tests were passed')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85da3a12-ff0b-4ff2-a588-4c2b361f01d4",
   "metadata": {},
   "source": [
    "## Exploratory Data Analysis\n",
    "In this section, I explore a few questions related to this data."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "activated-cursor",
   "metadata": {},
   "source": [
    "### Which names are popular?\n",
    "\n",
    "We can answer this question with a humble bar plot. Changing the value of `name_limit` changes the value of $n$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5fc78440-4f3f-4051-905e-01cae488f457",
   "metadata": {},
   "outputs": [],
   "source": [
    "name_limit = 10"
   ]
  },
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   "execution_count": 5,
   "id": "chief-linux",
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    "def multiColBarChart(df, x_field, y_field, grp_field, color_field, limit=10, ascending=False):\n",
    "    grp = data.groupby([grp_field, y_field]).agg({x_field: sum}).sort_values(x_field, ascending=ascending).reset_index().groupby(grp_field)\n",
    "    grps = [ grp.get_group(x) for x in grp.groups ]\n",
    "    \n",
    "    charts = []\n",
    "    for g in grps:\n",
    "        g = g.head(limit)\n",
    "        title = '{order} popular {limit} {sex} names ({min_year} - {max_year})'.format(**{\n",
    "            'order': 'Most' if not ascending else 'Least',\n",
    "            'limit': limit,\n",
    "            'sex': 'male' if g.sex.unique()[0] == 'Male' else 'female',\n",
    "            'min_year': min(df.year.unique()),\n",
    "            'max_year': max(df.year.unique())\n",
    "        })\n",
    "        chart = alt.Chart(g).mark_bar().encode(\n",
    "            x=x_field,\n",
    "            y=alt.Y(y_field, sort=g[y_field].to_list()),\n",
    "            color=alt.Color(color_field, scale=alt.Scale(domain=color_domain, range=color_range))\n",
    "        ).properties(title=title)\n",
    "        charts.append(chart)\n",
    "    \n",
    "    return alt.hconcat(*charts)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f609f997-8aa3-4dcb-8380-393f886a7172",
   "metadata": {},
   "source": [
    "My wife and I knew were were having a boy, and at one time *Liam* was on our shortlist. Knowing that it was one of the most popular names definitely made it less appealing to us."
   ]
  },
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    "multiColBarChart(data, x_field='total', y_field='name', color_field='sex', grp_field='sex', limit=name_limit)"
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   "source": [
    "### Which names are unpopular?\n",
    "\n",
    "I'm also interested in what are the least popular names. \"Least popular\" is a bit of a misnomer since the dataset includes the top 1000 popular datasets by design."
   ]
  },
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       "  })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300}}, \"hconcat\": [{\"data\": {\"name\": \"data-c43e21dc42c3bd063eb5e7c11f460988\"}, \"mark\": \"bar\", \"encoding\": {\"color\": {\"field\": \"sex\", \"scale\": {\"domain\": [\"Female\", \"Male\"], \"range\": [\"#ff9da7\", \"#4e79a7\"]}, \"type\": \"nominal\"}, \"x\": {\"field\": \"total\", \"type\": \"quantitative\"}, \"y\": {\"field\": \"name\", \"sort\": [\"Sohany\", \"Danykah\", \"Zaeya\", \"Zaelie\", \"Daphine\", \"Zaeley\", \"Zaedyn\", \"Zadi\", \"Zadey\", \"Daphny\"], \"type\": \"nominal\"}}, \"title\": \"Least popular 10 female names (2010 - 2020)\"}, {\"data\": {\"name\": \"data-e0a4537f24f1a78df9d27eb7de34df40\"}, \"mark\": \"bar\", \"encoding\": {\"color\": {\"field\": \"sex\", \"scale\": {\"domain\": [\"Female\", \"Male\"], \"range\": [\"#ff9da7\", \"#4e79a7\"]}, \"type\": \"nominal\"}, \"x\": {\"field\": \"total\", \"type\": \"quantitative\"}, \"y\": {\"field\": \"name\", \"sort\": [\"Aizel\", \"Aito\", \"Airyn\", \"Airo\", \"Airius\", \"Airin\", \"Airik\", \"Airic\", \"Ailin\", \"Ailen\"], \"type\": \"nominal\"}}, \"title\": \"Least popular 10 male names (2010 - 2020)\"}], \"$schema\": \"https://vega.github.io/schema/vega-lite/v4.17.0.json\", \"datasets\": {\"data-c43e21dc42c3bd063eb5e7c11f460988\": [{\"sex\": \"Female\", \"name\": \"Sohany\", \"total\": 5}, {\"sex\": \"Female\", \"name\": \"Danykah\", \"total\": 5}, {\"sex\": \"Female\", \"name\": \"Zaeya\", \"total\": 5}, {\"sex\": \"Female\", \"name\": \"Zaelie\", \"total\": 5}, {\"sex\": \"Female\", \"name\": \"Daphine\", \"total\": 5}, {\"sex\": \"Female\", \"name\": \"Zaeley\", \"total\": 5}, {\"sex\": \"Female\", \"name\": \"Zaedyn\", \"total\": 5}, {\"sex\": \"Female\", \"name\": \"Zadi\", \"total\": 5}, {\"sex\": \"Female\", \"name\": \"Zadey\", \"total\": 5}, {\"sex\": \"Female\", \"name\": \"Daphny\", \"total\": 5}], \"data-e0a4537f24f1a78df9d27eb7de34df40\": [{\"sex\": \"Male\", \"name\": \"Aizel\", \"total\": 5}, {\"sex\": \"Male\", \"name\": \"Aito\", \"total\": 5}, {\"sex\": \"Male\", \"name\": \"Airyn\", \"total\": 5}, {\"sex\": \"Male\", \"name\": \"Airo\", \"total\": 5}, {\"sex\": \"Male\", \"name\": \"Airius\", \"total\": 5}, {\"sex\": \"Male\", \"name\": \"Airin\", \"total\": 5}, {\"sex\": \"Male\", \"name\": \"Airik\", \"total\": 5}, {\"sex\": \"Male\", \"name\": \"Airic\", \"total\": 5}, {\"sex\": \"Male\", \"name\": \"Ailin\", \"total\": 5}, {\"sex\": \"Male\", \"name\": \"Ailen\", \"total\": 5}]}}, {\"mode\": \"vega-lite\"});\n",
       "</script>"
      ],
      "text/plain": [
       "alt.HConcatChart(...)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "multiColBarChart(data, x_field='total', y_field='name', color_field='sex', grp_field='sex', limit=name_limit, ascending=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "promotional-polyester",
   "metadata": {},
   "source": [
    "### Which names are on the rise?\n",
    "\n",
    "To determine increasing in popularity the most, we need to remove names that are not ranked for any year in our time frame. To determine which names are continuously within the range of years we're exploring, I remove names where yearly occurrence in the dataset is less than the number of years in the current `year_range`. I do this by creating a separate dataset of all valid names, then do a *left join* to take the difference between these two DataFrames."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "tracked-budget",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sex</th>\n",
       "      <th>name</th>\n",
       "      <th>total</th>\n",
       "      <th>year</th>\n",
       "      <th>rank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Female</td>\n",
       "      <td>Isabella</td>\n",
       "      <td>22924</td>\n",
       "      <td>2010</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Female</td>\n",
       "      <td>Isabella</td>\n",
       "      <td>19919</td>\n",
       "      <td>2011</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Female</td>\n",
       "      <td>Isabella</td>\n",
       "      <td>19113</td>\n",
       "      <td>2012</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Female</td>\n",
       "      <td>Isabella</td>\n",
       "      <td>17654</td>\n",
       "      <td>2013</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Female</td>\n",
       "      <td>Isabella</td>\n",
       "      <td>17108</td>\n",
       "      <td>2014</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      sex      name  total  year  rank\n",
       "0  Female  Isabella  22924  2010   1.0\n",
       "1  Female  Isabella  19919  2011   2.0\n",
       "2  Female  Isabella  19113  2012   3.0\n",
       "3  Female  Isabella  17654  2013   4.0\n",
       "4  Female  Isabella  17108  2014   4.0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "name_freq_by_year = data.sort_values('year', ascending=True) \\\n",
    "    .groupby(['sex', 'name'], sort=False) \\\n",
    "    .agg({'year': lambda x: len(x.tolist()) }) \\\n",
    "    .reset_index() \\\n",
    "    .rename(columns={'year': 'count'})\n",
    "name_freq_by_year = name_freq_by_year[name_freq_by_year['count'] == len(year_range)]\n",
    "\n",
    "# Merge data\n",
    "data_continuous = pd.merge(name_freq_by_year, data, left_on=['sex', 'name'], right_on=['sex', 'name'])\n",
    "\n",
    "# Remove extraneous columns\n",
    "data_continuous.drop(columns=['count'], inplace=True)\n",
    "\n",
    "# Peek at the data\n",
    "data_continuous.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "hired-definition",
   "metadata": {},
   "source": [
    "Next, I sanity check the group order. For some reason, sorting before grouping is waaaaaay faster than sorting after grouping. This cell performs a few quick sanity checks with the group object:\n",
    "\n",
    "- Test that the values within the groups are in chronological order.\n",
    "- Test that there are no gap years in results, e.g. 2000, 2001, 2003, ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "accepted-croatia",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "All sanity checks passed\n"
     ]
    }
   ],
   "source": [
    "def testOrder(df):\n",
    "    def isAscending(s):\n",
    "        s = s.to_list()\n",
    "        for i in range(len(s) - 1):\n",
    "            if s[i] >= s[i + 1]:\n",
    "                return False\n",
    "        return True\n",
    "    \n",
    "    def isSequential(s):\n",
    "        s = s.to_list()\n",
    "        for i in range(len(s) - 1):\n",
    "            if s[i] != s[i + 1] - 1:\n",
    "                return False\n",
    "        return True\n",
    "    \n",
    "    def test(results):\n",
    "        exp = results.shape[0]\n",
    "        reality = sum(results.year)\n",
    "        assert (exp == reality)\n",
    "\n",
    "    grp = df.sort_values('year', ascending=True).groupby(['sex', 'name'], sort=False)\n",
    "    \n",
    "    # Test ascending\n",
    "    yearCount = grp.agg({'year': isAscending })\n",
    "    test(yearCount)\n",
    "    \n",
    "    # Test sequential\n",
    "    yearSeq = grp.agg({'year': isSequential })\n",
    "    test(yearSeq)\n",
    "    \n",
    "    print('All sanity checks passed')\n",
    "\n",
    "\n",
    "testOrder(data_continuous)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "figured-indianapolis",
   "metadata": {},
   "source": [
    "Finally, I calculate the slope for each name within this time frame. I'm just going to fit date and rank linearly, $y=mx+b$. I also flip the sign of the slope because lower ranks are better than higher ranks, first place is better than second place."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "earlier-instruction",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sex</th>\n",
       "      <th>name</th>\n",
       "      <th>slope</th>\n",
       "      <th>total</th>\n",
       "      <th>year</th>\n",
       "      <th>rank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>92597</th>\n",
       "      <td>Female</td>\n",
       "      <td>Alaia</td>\n",
       "      <td>82.090909</td>\n",
       "      <td>2254</td>\n",
       "      <td>2020</td>\n",
       "      <td>118.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92596</th>\n",
       "      <td>Female</td>\n",
       "      <td>Alaia</td>\n",
       "      <td>82.090909</td>\n",
       "      <td>1600</td>\n",
       "      <td>2019</td>\n",
       "      <td>179.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92595</th>\n",
       "      <td>Female</td>\n",
       "      <td>Alaia</td>\n",
       "      <td>82.090909</td>\n",
       "      <td>526</td>\n",
       "      <td>2018</td>\n",
       "      <td>493.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92594</th>\n",
       "      <td>Female</td>\n",
       "      <td>Alaia</td>\n",
       "      <td>82.090909</td>\n",
       "      <td>460</td>\n",
       "      <td>2017</td>\n",
       "      <td>544.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92593</th>\n",
       "      <td>Female</td>\n",
       "      <td>Alaia</td>\n",
       "      <td>82.090909</td>\n",
       "      <td>487</td>\n",
       "      <td>2016</td>\n",
       "      <td>520.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99752</th>\n",
       "      <td>Female</td>\n",
       "      <td>Jayden</td>\n",
       "      <td>-60.372727</td>\n",
       "      <td>575</td>\n",
       "      <td>2014</td>\n",
       "      <td>482.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99751</th>\n",
       "      <td>Female</td>\n",
       "      <td>Jayden</td>\n",
       "      <td>-60.372727</td>\n",
       "      <td>697</td>\n",
       "      <td>2013</td>\n",
       "      <td>399.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99750</th>\n",
       "      <td>Female</td>\n",
       "      <td>Jayden</td>\n",
       "      <td>-60.372727</td>\n",
       "      <td>825</td>\n",
       "      <td>2012</td>\n",
       "      <td>354.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99749</th>\n",
       "      <td>Female</td>\n",
       "      <td>Jayden</td>\n",
       "      <td>-60.372727</td>\n",
       "      <td>1075</td>\n",
       "      <td>2011</td>\n",
       "      <td>276.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99748</th>\n",
       "      <td>Female</td>\n",
       "      <td>Jayden</td>\n",
       "      <td>-60.372727</td>\n",
       "      <td>1463</td>\n",
       "      <td>2010</td>\n",
       "      <td>208.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>187022 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          sex    name      slope  total  year   rank\n",
       "92597  Female   Alaia  82.090909   2254  2020  118.0\n",
       "92596  Female   Alaia  82.090909   1600  2019  179.0\n",
       "92595  Female   Alaia  82.090909    526  2018  493.0\n",
       "92594  Female   Alaia  82.090909    460  2017  544.0\n",
       "92593  Female   Alaia  82.090909    487  2016  520.0\n",
       "...       ...     ...        ...    ...   ...    ...\n",
       "99752  Female  Jayden -60.372727    575  2014  482.0\n",
       "99751  Female  Jayden -60.372727    697  2013  399.0\n",
       "99750  Female  Jayden -60.372727    825  2012  354.0\n",
       "99749  Female  Jayden -60.372727   1075  2011  276.0\n",
       "99748  Female  Jayden -60.372727   1463  2010  208.0\n",
       "\n",
       "[187022 rows x 6 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def calcSlope(x):\n",
    "    slope = np.polyfit(range(len(x)), x, 1)[0]\n",
    "    return slope * -1\n",
    "\n",
    "data_slopes = data_continuous \\\n",
    "    .sort_values('year', ascending=True) \\\n",
    "    .groupby(['sex', 'name'], sort=False) \\\n",
    "    .agg({'rank': calcSlope}) \\\n",
    "    .reset_index() \\\n",
    "    .rename(columns={'rank': 'slope'})\n",
    "\n",
    "data_slopes = pd.merge(data_slopes, data, left_on=['sex', 'name'], right_on=['sex', 'name'])\n",
    "data_slopes.sort_values(['slope', 'year'], ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6bb2570-7a8b-4946-93dd-b0f80fe2612a",
   "metadata": {},
   "source": [
    "Finally, we can see these trends with a multi series line chart. The legend is sorted by the names with the greatest slope. Both charts support interactive zooming, and tooltips over the dots in each line."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "dimensional-account",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plotTrends(df, limit=10, top=True):\n",
    "    nameFilter = df.groupby(['sex', 'name']) \\\n",
    "                 .slope.unique().apply(lambda x: x[0]) \\\n",
    "                 .sort_values(ascending=not top) \\\n",
    "                 .groupby('sex') \\\n",
    "                 .head(limit) \\\n",
    "                 .to_frame().reset_index()[['sex', 'name']]\n",
    "    df = pd.merge(nameFilter, df, on=['sex', 'name'])\n",
    "    df_grp = df.groupby('sex')    \n",
    "    charts = []\n",
    "    rankDomain = [df['rank'].max(), 1]\n",
    "    for grp in [ df_grp.get_group(x) for x in df_grp.groups ]:\n",
    "        name_order = list(grp.sort_values('slope', ascending=False)['name'].unique())\n",
    "        chart = alt.Chart(grp).mark_line(point=True).encode(\n",
    "            x='year:O',\n",
    "            y=alt.Y('rank:Q', scale=alt.Scale(domain=rankDomain), axis=alt.Axis(tickCount=10)),\n",
    "            color=alt.Color('name', sort=name_order),\n",
    "            tooltip=['name', 'rank'],\n",
    "        ).properties(title='Top {} {} {} Names'.format(limit, 'Rising' if top else 'Falling', grp.sex.unique()[0]), width=750).interactive()\n",
    "        charts.append(chart)\n",
    "    \n",
    "    return alt.vconcat(*charts) \\\n",
    "              .resolve_scale(color='independent')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "6d3e31f9-08e2-428c-8f60-6e806853e77f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    let outputDiv = document.currentScript.previousElementSibling;\n",
       "    if (outputDiv.id !== \"altair-viz-077be75b51b24d7dae45639ed436ec40\") {\n",
       "      outputDiv = document.getElementById(\"altair-viz-077be75b51b24d7dae45639ed436ec40\");\n",
       "    }\n",
       "    const paths = {\n",
       "      \"vega\": \"https://cdn.jsdelivr.net/npm//vega@5?noext\",\n",
       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm//vega-lib?noext\",\n",
       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm//vega-lite@4.17.0?noext\",\n",
       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm//vega-embed@6?noext\",\n",
       "    };\n",
       "\n",
       "    function maybeLoadScript(lib, version) {\n",
       "      var key = `${lib.replace(\"-\", \"\")}_version`;\n",
       "      return (VEGA_DEBUG[key] == version) ?\n",
       "        Promise.resolve(paths[lib]) :\n",
       "        new Promise(function(resolve, reject) {\n",
       "          var s = document.createElement('script');\n",
       "          document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
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       "          s.onload = () => {\n",
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       "          };\n",
       "          s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
       "          s.src = paths[lib];\n",
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       "    function showError(err) {\n",
       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
       "      throw err;\n",
       "    }\n",
       "\n",
       "    function displayChart(vegaEmbed) {\n",
       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
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       "\n",
       "    if(typeof define === \"function\" && define.amd) {\n",
       "      requirejs.config({paths});\n",
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       "      maybeLoadScript(\"vega\", \"5\")\n",
       "        .then(() => maybeLoadScript(\"vega-lite\", \"4.17.0\"))\n",
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       "        .catch(showError)\n",
       "        .then(() => displayChart(vegaEmbed));\n",
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    "Looking at this data by rank makes the most sense to me, because it's relative and that makes sense to me when comparing year-to-year. But I imagine you'd find different results if you look at total babies born, but those numbers might be affected by some other factors."
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    "### Which names are going out of style?\n",
    "\n",
    "We can easily explore which names are decreasing in popularity by sorting the data the opposite way. You could include names that drop out of the top 1000 at any year within the selected range, but not including them provides a more consistent view the data.\n",
    "\n",
    "Results will vary depending on how you've selected the year, but with the default settings, you should see that the female name *Isis* really took a popularity hit when ISIS leader Abu Bakr al Baghdadi announced the formation of a caliphate in 2014."
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\"total\": 654, \"year\": 2013, \"rank\": 407.0}, {\"sex\": \"Male\", \"name\": \"Trent\", \"slope\": -38.481818181818184, \"total\": 566, \"year\": 2014, \"rank\": 462.0}, {\"sex\": \"Male\", \"name\": \"Trent\", \"slope\": -38.481818181818184, \"total\": 517, \"year\": 2015, \"rank\": 496.0}, {\"sex\": \"Male\", \"name\": \"Trent\", \"slope\": -38.481818181818184, \"total\": 412, \"year\": 2016, \"rank\": 555.0}, {\"sex\": \"Male\", \"name\": \"Trent\", \"slope\": -38.481818181818184, \"total\": 390, \"year\": 2017, \"rank\": 556.0}, {\"sex\": \"Male\", \"name\": \"Trent\", \"slope\": -38.481818181818184, \"total\": 281, \"year\": 2018, \"rank\": 638.0}, {\"sex\": \"Male\", \"name\": \"Trent\", \"slope\": -38.481818181818184, \"total\": 265, \"year\": 2019, \"rank\": 664.0}, {\"sex\": \"Male\", \"name\": \"Trent\", \"slope\": -38.481818181818184, \"total\": 233, \"year\": 2020, \"rank\": 679.0}, {\"sex\": \"Male\", \"name\": \"Zackary\", \"slope\": -38.30909090909091, \"total\": 695, \"year\": 2010, \"rank\": 378.0}, {\"sex\": \"Male\", \"name\": \"Zackary\", \"slope\": -38.30909090909091, \"total\": 598, \"year\": 2011, \"rank\": 419.0}, {\"sex\": \"Male\", \"name\": \"Zackary\", \"slope\": -38.30909090909091, \"total\": 522, \"year\": 2012, \"rank\": 454.0}, {\"sex\": \"Male\", \"name\": \"Zackary\", \"slope\": -38.30909090909091, \"total\": 466, \"year\": 2013, \"rank\": 498.0}, {\"sex\": \"Male\", \"name\": \"Zackary\", \"slope\": -38.30909090909091, \"total\": 434, \"year\": 2014, \"rank\": 539.0}, {\"sex\": \"Male\", \"name\": \"Zackary\", \"slope\": -38.30909090909091, \"total\": 321, \"year\": 2015, \"rank\": 618.0}, {\"sex\": \"Male\", \"name\": \"Zackary\", \"slope\": -38.30909090909091, \"total\": 312, \"year\": 2016, \"rank\": 623.0}, {\"sex\": \"Male\", \"name\": \"Zackary\", \"slope\": -38.30909090909091, \"total\": 251, \"year\": 2017, \"rank\": 659.0}, {\"sex\": \"Male\", \"name\": \"Zackary\", \"slope\": -38.30909090909091, \"total\": 206, \"year\": 2018, \"rank\": 704.0}, {\"sex\": \"Male\", \"name\": \"Zackary\", \"slope\": -38.30909090909091, \"total\": 191, \"year\": 2019, \"rank\": 736.0}, {\"sex\": \"Male\", \"name\": \"Zackary\", \"slope\": -38.30909090909091, \"total\": 173, \"year\": 2020, \"rank\": 736.0}]}}, {\"mode\": \"vega-lite\"});\n",
       "</script>"
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      "text/plain": [
       "alt.VConcatChart(...)"
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     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "plotTrends(data_slopes, limit=name_limit, top=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "621508ff-4a8d-44be-9617-e589d17ca316",
   "metadata": {},
   "source": [
    "### Which names the most gender neutral?\n",
    "\n",
    "Some gender neutral names are less neutral than others. Johnny Cash articulates a good case in point."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "710f5922-c30f-49e0-ac5a-51f4768ee440",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"560\"\n",
       "            height=\"315\"\n",
       "            src=\"https://www.youtube.com/embed/WOHPuY88Ry4\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "            \n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x7fcadc647190>"
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    "IFrame('https://www.youtube.com/embed/WOHPuY88Ry4', 560, 315)"
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   "cell_type": "markdown",
   "id": "5767d56c-ae03-4f22-8574-78d781dceb24",
   "metadata": {},
   "source": [
    "So I look at names that were assigned to both male and female babies where the proportion of male to female babies doesn't lean too far towards one end of the other. I also filter out some of the less popular names."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "81dfb6e6-b4a9-471f-9058-c1aad02c0565",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>leaning</th>\n",
       "      <th>percent</th>\n",
       "      <th>sex</th>\n",
       "      <th>total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Amari</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.366044</td>\n",
       "      <td>Male</td>\n",
       "      <td>1249.181818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Arden</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.706303</td>\n",
       "      <td>Male</td>\n",
       "      <td>100.818182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ari</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.295511</td>\n",
       "      <td>Male</td>\n",
       "      <td>716.272727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Arie</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.641457</td>\n",
       "      <td>Male</td>\n",
       "      <td>58.181818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Aries</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.308666</td>\n",
       "      <td>Male</td>\n",
       "      <td>163.909091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>285</th>\n",
       "      <td>Tru</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.354113</td>\n",
       "      <td>Female</td>\n",
       "      <td>63.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>286</th>\n",
       "      <td>True</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.465826</td>\n",
       "      <td>Female</td>\n",
       "      <td>63.818182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>287</th>\n",
       "      <td>Unknown</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.428273</td>\n",
       "      <td>Female</td>\n",
       "      <td>55.909091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>288</th>\n",
       "      <td>Yael</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.350042</td>\n",
       "      <td>Female</td>\n",
       "      <td>113.636364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>289</th>\n",
       "      <td>Yuri</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.563283</td>\n",
       "      <td>Female</td>\n",
       "      <td>81.727273</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>290 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        name leaning   percent     sex        total\n",
       "0      Amari    Male  0.366044    Male  1249.181818\n",
       "1      Arden  Female  0.706303    Male   100.818182\n",
       "2        Ari    Male  0.295511    Male   716.272727\n",
       "3       Arie  Female  0.641457    Male    58.181818\n",
       "4      Aries    Male  0.308666    Male   163.909091\n",
       "..       ...     ...       ...     ...          ...\n",
       "285      Tru    Male  0.354113  Female    63.000000\n",
       "286     True    Male  0.465826  Female    63.818182\n",
       "287  Unknown    Male  0.428273  Female    55.909091\n",
       "288     Yael    Male  0.350042  Female   113.636364\n",
       "289     Yuri  Female  0.563283  Female    81.727273\n",
       "\n",
       "[290 rows x 5 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a DataFrame that only includes names that have been ranked in both the Male and Female categories\n",
    "neutral_names = pd.pivot_table(data, index=['name'], columns=['sex'], values=['total'], aggfunc=np.mean).droplevel(0, axis=1).reset_index().dropna(axis=0)\n",
    "\n",
    "# Calculate the difference between male and female ranks to create an index of difference in popularity called 'diff'\n",
    "neutral_names['percent'] = neutral_names['Female'] / (neutral_names['Female'] + neutral_names['Male'])\n",
    "\n",
    "# Filter out low count names\n",
    "min_count = 50\n",
    "neutral_names = neutral_names[(neutral_names['Female'] > min_count) & (neutral_names['Male'] > min_count)]\n",
    "\n",
    "# Filter out names predominantly male or female\n",
    "min_percent, max_percent = 0.25, 0.75\n",
    "neutral_names = neutral_names[(neutral_names['percent'] > min_percent) & (neutral_names['percent'] < max_percent)]\n",
    "\n",
    "# Classify each name as more girl or more boy\n",
    "neutral_names['leaning'] = neutral_names['percent'].apply(lambda x: 'Female' if x > 0.5 else 'Male')\n",
    "\n",
    "# Tidy data\n",
    "neutral_names = pd.melt(neutral_names, id_vars=['name', 'leaning', 'percent'], value_vars=['Male', 'Female'], value_name='total')\n",
    "\n",
    "neutral_names"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9ed5043-f43f-472d-9084-dc34b4e941a7",
   "metadata": {},
   "source": [
    "Within these two groups, the most gender neutral names are at the top of the chart. Remember that these plots don't say anything about the popularity of these names, just whether they tend to be assign to more babies who were assigned male or female at birth."
   ]
  },
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   "cell_type": "code",
   "execution_count": 16,
   "id": "57da9852-490b-4469-8f48-bfe4a53707a9",
   "metadata": {},
   "outputs": [
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    }
   ],
   "source": [
    "def plotDistribution(df, grp_var):\n",
    "    charts = []\n",
    "    grp = df.groupby(grp_var)\n",
    "    for grp in [ grp.get_group(x) for x in grp.groups ]:\n",
    "        sex = grp['leaning'].unique()[0]\n",
    "        asc = sex == 'Female'\n",
    "        chart = alt.Chart(grp).mark_bar().encode(\n",
    "            x=alt.X('sum(total)', stack='normalize'),\n",
    "            y=alt.Y('name', sort=list(neutral_names.sort_values('percent', ascending=asc).name.unique())),\n",
    "            color=alt.Color('sex', scale=alt.Scale(domain=color_domain, range=color_range)),\n",
    "        ).properties(title='Distribution of predominantly {} names'.format(sex.lower()))\n",
    "        text = alt.Chart(grp).mark_text(dx=-15, dy=0, color='white').encode(\n",
    "            x=alt.X('percent'),\n",
    "            y=alt.Y('name', sort=list(neutral_names.sort_values('percent', ascending=asc).name.unique())),\n",
    "            detail='sex',\n",
    "            text=alt.Text('percent', format='.2f')\n",
    "        ).transform_filter(\n",
    "            (datum.sex == 'Female')\n",
    "        )\n",
    "        \n",
    "        charts.append(chart + text)\n",
    "    \n",
    "    return alt.hconcat(*charts)\n",
    "\n",
    "plotDistribution(neutral_names, 'leaning')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f1fb9896-488a-49b5-9bda-3ee0091b329e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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