babe 0.0.3

Data access and analysis of baby names statistics

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.

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.

names_all_us_states

		popularity
name	year
Aaban	2013	6
2014	6
Aadam	2019	6
Aadan	2008	12
2009	6
...	...	...
Zyriah	2013	7
2014	6
2016	5
Zyron	2015	5
Zyshonne	1998	5

594681 rows × 1 columns

names = set(names_all_us_states.reset_index()['name'].values)
print(f"{len(names)} unique names")

31862 unique names

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)

names_all_us_states.loc['Vanessa'].plot(figsize=(15, 4), style='-o', grid=True)

names_all_us_states.loc['Cora'].plot(figsize=(15, 4), style='-o', grid=True)

names_by_us_states

This dataframe provides the same as above, but by state. 51 US states are covered.

names_by_us_states

			gender	popularity
state	name	year
AK	Mary	1910	F	14
Annie	1910	F	12
Anna	1910	F	10
Margaret	1910	F	8
Helen	1910	F	7
...	...	...	...	...
WY	Theo	2019	M	5
Tristan	2019	M	5
Vincent	2019	M	5
Warren	2019	M	5
Waylon	2019	M	5

6122890 rows × 2 columns

states = set(names_by_us_states.reset_index()['state'])
print(f"{len(states)} states")

51 states

names_by_us_states.loc['CA']

<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>

		gender	popularity
name	year
Mary	1910	F	295
Helen	1910	F	239
Dorothy	1910	F	220
Margaret	1910	F	163
Frances	1910	F	134
...	...	...	...
Zayvion	2019	M	5
Zeek	2019	M	5
Zhaire	2019	M	5
Zian	2019	M	5
Ziyad	2019	M	5

387781 rows × 2 columns

names_by_us_states.loc['CA'].loc['Cora']

<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>

	gender	popularity
year
1911	F	8
1912	F	9
1913	F	15
1914	F	15
1915	F	17
...	...	...
2015	F	269
2016	F	244
2017	F	284
2018	F	282
2019	F	256

109 rows × 2 columns

names_by_us_states.loc['CA'].loc['Cora'].plot(figsize=(15, 4), style='-o', grid=True)

names_by_us_states.loc['GA'].loc['Cora'].plot(figsize=(15, 4), style='-o', grid=True)