Metadata-Version: 2.1
Name: babe
Version: 0.0.2
Summary: Data access and analysis of baby names statistics
Home-page: https://github.com/thorwhalen/babe
Author: Thor Whalen
License: mit
Description: 
        # babe
        
        Note that the first time you import name, you need to have access to the Internet, and it will take a few seconds (depending on bandwidth) to download the required data.
        
        But this data is automatically saved in a local file so things are faster the next time around.
        
        
        ```python
        from babe import names_by_us_states, names_all_us_states
        ```
        
        ## names_all_us_state
        
        This data frame provides popularity matrix for names of babies born in the US between 1910 and 2019.
        
        
        ```python
        names_all_us_states
        ```
        
        
        
        
        <div>
        <style scoped>
            .dataframe tbody tr th:only-of-type {
                vertical-align: middle;
            }
        
            .dataframe tbody tr th {
                vertical-align: top;
            }
        
            .dataframe thead th {
                text-align: right;
            }
        </style>
        <table border="1" class="dataframe">
          <thead>
            <tr style="text-align: right;">
              <th></th>
              <th></th>
              <th>popularity</th>
            </tr>
            <tr>
              <th>name</th>
              <th>year</th>
              <th></th>
            </tr>
          </thead>
          <tbody>
            <tr>
              <th rowspan="2" valign="top">Aaban</th>
              <th>2013</th>
              <td>6</td>
            </tr>
            <tr>
              <th>2014</th>
              <td>6</td>
            </tr>
            <tr>
              <th>Aadam</th>
              <th>2019</th>
              <td>6</td>
            </tr>
            <tr>
              <th rowspan="2" valign="top">Aadan</th>
              <th>2008</th>
              <td>12</td>
            </tr>
            <tr>
              <th>2009</th>
              <td>6</td>
            </tr>
            <tr>
              <th>...</th>
              <th>...</th>
              <td>...</td>
            </tr>
            <tr>
              <th rowspan="3" valign="top">Zyriah</th>
              <th>2013</th>
              <td>7</td>
            </tr>
            <tr>
              <th>2014</th>
              <td>6</td>
            </tr>
            <tr>
              <th>2016</th>
              <td>5</td>
            </tr>
            <tr>
              <th>Zyron</th>
              <th>2015</th>
              <td>5</td>
            </tr>
            <tr>
              <th>Zyshonne</th>
              <th>1998</th>
              <td>5</td>
            </tr>
          </tbody>
        </table>
        <p>594681 rows × 1 columns</p>
        </div>
        
        
        
        
        ```python
        names = set(names_all_us_states.reset_index()['name'].values)
        print(f"{len(names)} unique names")
        ```
        
            31862 unique names
        
        
        
        ```python
        years = set(names_all_us_states.reset_index()['year'])
        print(f"Popularity stats cover years {min(years)} through {max(years)} (or subset thereof, depending on the name)")
        ```
        
            Popularity stats cover years 1910 through 2019 (or subset thereof, depending on the name)
        
        
        
        ```python
        names_all_us_states.loc['Vanessa'].plot(figsize=(15, 4), style='-o', grid=True)
        ```
        
        
        
        
            <AxesSubplot:xlabel='year'>
        
        
        
        
            
        ![png](img/output_10_1.png)
            
        
        
        
        ```python
        names_all_us_states.loc['Cora'].plot(figsize=(15, 4), style='-o', grid=True)
        ```
        
        
        
        
            <AxesSubplot:xlabel='year'>
        
        
        
        
            
        ![png](img/output_11_1.png)
            
        
        
        ## names_by_us_states
        
        This dataframe provides the same as above, but by state. 51 US states are covered.
        
        
        ```python
        names_by_us_states
        ```
        
        
        
        
        <div>
        <style scoped>
            .dataframe tbody tr th:only-of-type {
                vertical-align: middle;
            }
        
            .dataframe tbody tr th {
                vertical-align: top;
            }
        
            .dataframe thead th {
                text-align: right;
            }
        </style>
        <table border="1" class="dataframe">
          <thead>
            <tr style="text-align: right;">
              <th></th>
              <th></th>
              <th></th>
              <th>gender</th>
              <th>popularity</th>
            </tr>
            <tr>
              <th>state</th>
              <th>name</th>
              <th>year</th>
              <th></th>
              <th></th>
            </tr>
          </thead>
          <tbody>
            <tr>
              <th rowspan="5" valign="top">AK</th>
              <th>Mary</th>
              <th>1910</th>
              <td>F</td>
              <td>14</td>
            </tr>
            <tr>
              <th>Annie</th>
              <th>1910</th>
              <td>F</td>
              <td>12</td>
            </tr>
            <tr>
              <th>Anna</th>
              <th>1910</th>
              <td>F</td>
              <td>10</td>
            </tr>
            <tr>
              <th>Margaret</th>
              <th>1910</th>
              <td>F</td>
              <td>8</td>
            </tr>
            <tr>
              <th>Helen</th>
              <th>1910</th>
              <td>F</td>
              <td>7</td>
            </tr>
            <tr>
              <th>...</th>
              <th>...</th>
              <th>...</th>
              <td>...</td>
              <td>...</td>
            </tr>
            <tr>
              <th rowspan="5" valign="top">WY</th>
              <th>Theo</th>
              <th>2019</th>
              <td>M</td>
              <td>5</td>
            </tr>
            <tr>
              <th>Tristan</th>
              <th>2019</th>
              <td>M</td>
              <td>5</td>
            </tr>
            <tr>
              <th>Vincent</th>
              <th>2019</th>
              <td>M</td>
              <td>5</td>
            </tr>
            <tr>
              <th>Warren</th>
              <th>2019</th>
              <td>M</td>
              <td>5</td>
            </tr>
            <tr>
              <th>Waylon</th>
              <th>2019</th>
              <td>M</td>
              <td>5</td>
            </tr>
          </tbody>
        </table>
        <p>6122890 rows × 2 columns</p>
        </div>
        
        
        
        
        ```python
        states = set(names_by_us_states.reset_index()['state'])
        print(f"{len(states)} states")
        ```
        
            51 states
        
        
        
        ```python
        names_by_us_states.loc['CA']
        ```
        
        
        
        
        <div>
        <style scoped>
            .dataframe tbody tr th:only-of-type {
                vertical-align: middle;
            }
        
            .dataframe tbody tr th {
                vertical-align: top;
            }
        
            .dataframe thead th {
                text-align: right;
            }
        </style>
        <table border="1" class="dataframe">
          <thead>
            <tr style="text-align: right;">
              <th></th>
              <th></th>
              <th>gender</th>
              <th>popularity</th>
            </tr>
            <tr>
              <th>name</th>
              <th>year</th>
              <th></th>
              <th></th>
            </tr>
          </thead>
          <tbody>
            <tr>
              <th>Mary</th>
              <th>1910</th>
              <td>F</td>
              <td>295</td>
            </tr>
            <tr>
              <th>Helen</th>
              <th>1910</th>
              <td>F</td>
              <td>239</td>
            </tr>
            <tr>
              <th>Dorothy</th>
              <th>1910</th>
              <td>F</td>
              <td>220</td>
            </tr>
            <tr>
              <th>Margaret</th>
              <th>1910</th>
              <td>F</td>
              <td>163</td>
            </tr>
            <tr>
              <th>Frances</th>
              <th>1910</th>
              <td>F</td>
              <td>134</td>
            </tr>
            <tr>
              <th>...</th>
              <th>...</th>
              <td>...</td>
              <td>...</td>
            </tr>
            <tr>
              <th>Zayvion</th>
              <th>2019</th>
              <td>M</td>
              <td>5</td>
            </tr>
            <tr>
              <th>Zeek</th>
              <th>2019</th>
              <td>M</td>
              <td>5</td>
            </tr>
            <tr>
              <th>Zhaire</th>
              <th>2019</th>
              <td>M</td>
              <td>5</td>
            </tr>
            <tr>
              <th>Zian</th>
              <th>2019</th>
              <td>M</td>
              <td>5</td>
            </tr>
            <tr>
              <th>Ziyad</th>
              <th>2019</th>
              <td>M</td>
              <td>5</td>
            </tr>
          </tbody>
        </table>
        <p>387781 rows × 2 columns</p>
        </div>
        
        
        
        
        ```python
        names_by_us_states.loc['CA'].loc['Cora']
        ```
        
        
        
        
        <div>
        <style scoped>
            .dataframe tbody tr th:only-of-type {
                vertical-align: middle;
            }
        
            .dataframe tbody tr th {
                vertical-align: top;
            }
        
            .dataframe thead th {
                text-align: right;
            }
        </style>
        <table border="1" class="dataframe">
          <thead>
            <tr style="text-align: right;">
              <th></th>
              <th>gender</th>
              <th>popularity</th>
            </tr>
            <tr>
              <th>year</th>
              <th></th>
              <th></th>
            </tr>
          </thead>
          <tbody>
            <tr>
              <th>1911</th>
              <td>F</td>
              <td>8</td>
            </tr>
            <tr>
              <th>1912</th>
              <td>F</td>
              <td>9</td>
            </tr>
            <tr>
              <th>1913</th>
              <td>F</td>
              <td>15</td>
            </tr>
            <tr>
              <th>1914</th>
              <td>F</td>
              <td>15</td>
            </tr>
            <tr>
              <th>1915</th>
              <td>F</td>
              <td>17</td>
            </tr>
            <tr>
              <th>...</th>
              <td>...</td>
              <td>...</td>
            </tr>
            <tr>
              <th>2015</th>
              <td>F</td>
              <td>269</td>
            </tr>
            <tr>
              <th>2016</th>
              <td>F</td>
              <td>244</td>
            </tr>
            <tr>
              <th>2017</th>
              <td>F</td>
              <td>284</td>
            </tr>
            <tr>
              <th>2018</th>
              <td>F</td>
              <td>282</td>
            </tr>
            <tr>
              <th>2019</th>
              <td>F</td>
              <td>256</td>
            </tr>
          </tbody>
        </table>
        <p>109 rows × 2 columns</p>
        </div>
        
        
        
        
        ```python
        names_by_us_states.loc['CA'].loc['Cora'].plot(figsize=(15, 4), style='-o', grid=True)
        ```
        
        
        
        
            <AxesSubplot:xlabel='year'>
        
        
        
        
            
        ![png](img/output_18_1.png)
            
        
        
        
        ```python
        names_by_us_states.loc['GA'].loc['Cora'].plot(figsize=(15, 4), style='-o', grid=True)
        ```
        
        
        
        
            <AxesSubplot:xlabel='year'>
        
        
        
        
            
        ![png](img/output_19_1.png)
            
        
        
Platform: any
Description-Content-Type: text/markdown
