{"author":"Thor Whalen","author_email":"","bugtrack_url":null,"classifiers":[],"description":"# babe\n\nNote 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.\n\nBut this data is automatically saved in a local file so things are faster the next time around.\n\nTo install:\n\n```pip install babe```\n\nThen in a python console or notebook...\n\n\n```python\nfrom babe import UsNames\n\nd = UsNames()\n```\n\n# Intro to the data\n\nThe fundamental data provides a popularity score (number of babies recorded) associated to a `(state, gender, name, year)` tuple (that has data -- for names of babies born in the US between 1910 and 2019).\n\n\n```python\nd.data\n```\n\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n

	state	gender	year	name	popularity	name_g
0	AK	F	1910	Mary	14	Mary_F
1	AK	F	1910	Annie	12	Annie_F
2	AK	F	1910	Anna	10	Anna_F
3	AK	F	1910	Margaret	8	Margaret_F
4	AK	F	1910	Helen	7	Helen_F
...	...	...	...	...	...	...
28277	WY	M	2019	Theo	5	Theo_M
28278	WY	M	2019	Tristan	5	Tristan_M
28279	WY	M	2019	Vincent	5	Vincent_M
28280	WY	M	2019	Warren	5	Warren_M
28281	WY	M	2019	Waylon	5	Waylon_M

6122890 rows × 6 columns

\n\n\n\n\n\n```python\nprint(f\"{len(d.names)} unique names\")\n```\n\n 31862 unique names\n\n\nBut some names can be used for both genders, so most of the internals will use `name_g`, the name concatenated with the gender (`_F` or `_M`):\n\n\n```python\nprint(f\"{len(d.name_gs)} unique names_g (gendered names)\")\n```\n\n 34952 unique names_g (gendered names)\n\n\nYou can use `resolve_name_g` to get the `name_g` corresponding to a name as long as the name isn't used for more than one gender.\n\n\n```python\nd.resolve_name_g('Cora')\n```\n\n\n\n\n 'Cora_F'\n\n\n\n\n```python\ntry:\n d.resolve_name_g('Vanessa')\nexcept AssertionError as e:\n print(e)\n```\n\n The Vanessa can be used for both genders. Specify Vanessa_F or Vanessa_M\n\n\n## by_state data\n\nIn some cases, it's more convenient to have a view indexed by `(state, name_g, year)`. \nThe `by_state` attribute provides that.\n\n\n```python\nd.by_state\n```\n\n\n\n\n state name_g year\n AK Mary_F 1910 14\n Annie_F 1910 12\n Anna_F 1910 10\n Margaret_F 1910 8\n Helen_F 1910 7\n ..\n WY Theo_M 2019 5\n Tristan_M 2019 5\n Vincent_M 2019 5\n Warren_M 2019 5\n Waylon_M 2019 5\n Name: popularity, Length: 6122890, dtype: int64\n\n\n\nThis allows one to do things like getting the data for a given state only:\n\n\n```python\nd.by_state['CA']\n```\n\n\n\n\n name_g year\n Mary_F 1910 295\n Helen_F 1910 239\n Dorothy_F 1910 220\n Margaret_F 1910 163\n Frances_F 1910 134\n ... \n Zayvion_M 2019 5\n Zeek_M 2019 5\n Zhaire_M 2019 5\n Zian_M 2019 5\n Ziyad_M 2019 5\n Name: popularity, Length: 387781, dtype: int64\n\n\n\n... within a state, getting the 'by year popularity' for a given name:\n\n\n```python\nd.by_state['CA']['Cora_F'] # or d.by_state['CA', 'Cora_F']\n```\n\n\n\n\n year\n 1911 8\n 1912 9\n 1913 15\n 1914 15\n 1915 17\n ... \n 2015 269\n 2016 244\n 2017 284\n 2018 282\n 2019 256\n Name: popularity, Length: 109, dtype: int64\n\n\n\n... if you wanted to get the data for a given name (really `name_g`), for all states, you can do it using \"slicing\". \n\nFor example, if you're wondering how many little boys were called \"Vanessa\", and more specifically, when and where?...\n\n\n```python\nd.by_state[:, 'Vanessa_M'] \n```\n\n\n\n\n state year\n AZ 1988 8\n CA 1980 7\n 1981 5\n 1982 20\n 1983 19\n 1984 14\n 1985 12\n 1986 13\n 1987 13\n 1988 26\n 1989 17\n 1990 16\n 1991 18\n 1992 17\n 1993 17\n 1994 10\n 1995 9\n 1996 10\n 1997 11\n 1998 7\n DC 1989 11\n NY 1982 5\n 1983 9\n 1986 6\n 1988 6\n 1989 6\n TX 1981 5\n 1982 7\n 1983 12\n 1984 9\n 1985 10\n 1986 8\n 1987 9\n 1988 8\n 1989 5\n 1990 6\n 1991 5\n 1992 5\n 1994 5\n Name: popularity, dtype: int64\n\n\n\n## national data\n\nA national aggregation is available through the `national` attribute\n\n\n```python\nd.national\n```\n\n\n\n\n name_g year\n Aaban_M 2013 6\n 2014 6\n Aadam_M 2019 6\n Aadan_M 2008 12\n 2009 6\n ..\n Zyriah_F 2013 7\n 2014 6\n 2016 5\n Zyron_M 2015 5\n Zyshonne_M 1998 5\n Name: popularity, Length: 633239, dtype: int64\n\n\n\nThe interface is as with the `by_state` attribute, but without the state specification.\n\n\n```python\nd.national.loc['Vanessa_F']\n```\n\n\n\n\n year\n 1935 5\n 1947 24\n 1948 32\n 1949 16\n 1950 41\n ... \n 2015 1687\n 2016 1633\n 2017 1486\n 2018 1345\n 2019 1188\n Name: popularity, Length: 74, dtype: int64\n\n\n\n# Plotting stuff\n\n\n```python\nd.plot_popularity('Cora');\n```\n\n\n\n![png](img/output_29_0.png)\n\n\n\n\n```python\nd.plot_popularity('Cora', 'GA');\n```\n\n\n\n![png](img/output_30_0.png)\n\n\n\n\n```python\nd.plot_popularity(['Cora', 'Vanessa_F']);\n```\n\n\n\n![png](img/output_31_0.png)\n\n\n\n\n```python\nd.plot_popularity('Cora', ['CA', 'GA']);\n```\n\n\n\n![png](img/output_32_0.png)\n\n\n\n\n```python\nd.plot_popularity(['Cora', 'Vanessa_F'], ['CA', 'GA']);\n```\n\n\n\n![png](img/output_33_0.png)\n\n\n\n# Misc\n\n## gender-ambiguous names\n\nWe'll call the \"femininity\" of a name be the proportion of times it was used (all states, all time) to name a girl, \nand the \"masculinity\" of a name be defined accordingly. \n\n\n```python\nd.femininity_of_name.iloc[12000:12010]\n```\n\n\n\n\n Lemmie 0.161290\n Kashmere 0.161290\n Clary 0.162162\n Sung 0.162393\n Kyrie 0.163527\n Cedar 0.163686\n Masyn 0.163895\n Naveen 0.165605\n Chai 0.166667\n Atlee 0.167382\n dtype: float64\n\n\n\n\n```python\nd.femininity_of_name.plot(figsize=(17, 5), ylabel='femininity');\n```\n\n\n\n![png](img/output_38_0.png)\n\n\n\n\n```python\nd.masculinity_of_name.iloc[19000:19010]\n```\n\n\n\n\n Berkley 0.108889\n Dasani 0.110092\n Sharone 0.111111\n Ifeoluwa 0.111111\n Rama 0.111111\n Scout 0.111486\n Brownie 0.111732\n Lashon 0.113158\n Indigo 0.113364\n Argie 0.113636\n dtype: float64\n\n\n\n\n```python\nd.masculinity_of_name.plot(figsize=(17, 5), ylabel='masculinity');\n```\n\n\n\n![png](img/output_40_0.png)\n\n\n\nThe (gender-)\"ambiguity\" of a name can thus be defined as twice the minimum of it's femininity and masculinity. \n\nBy defining the ambiguity thusly, we have a score that is between 0 and 1. It is maximal (1) when an equal proportion of boys and girls were named with the name. It is minimal (0) when only one gender was named with it.\n\nNote that this score is raw (or \"un-smoothed\"). It's computed with the raw counts, so the extreme scores will usually be for names with very low counts. \n\n\n```python\nd.ambiguity_of_name\n```\n\n\n\n\n Munachiso 1.0\n Addis 1.0\n Deshone 1.0\n Gal 1.0\n Rajdeep 1.0\n ... \n Sharelle 0.0\n Analy 0.0\n Sharayah 0.0\n Sharaya 0.0\n Aaban 0.0\n Length: 31862, dtype: float64\n\n\n\n\n```python\nt = d.ambiguity_of_name\nprint(f\"There are {len(t[t > 0])} (gender-)ambiguous names\")\n```\n\n There are 3090 (gender-)ambiguous names\n\n\n\n```python\nt = d.ambiguity_of_name\nt[t > 0].plot(figsize=(17, 5), ylabel='gender-ambiguity');\n```\n\n\n\n![png](img/output_44_0.png)\n\n\n\n\n```python\nt = list(d.ambiguous_names)\nprint(f\"{len(t)} (gender-)ambiguous names:\")\nprint(*t[:9], '...', sep=', ')\n```\n\n 3090 (gender-)ambiguous names:\n Nolie, Tyrese, Linn, Savannah, Bryn, Rei, Abby, Shilo, Tracy, ...\n\n\n\n```python\n\n```\n\n\n","description_content_type":"text/markdown","docs_url":null,"download_url":"","downloads":{"last_day":-1,"last_month":-1,"last_week":-1},"dynamic":null,"home_page":"https://github.com/thorwhalen/babe","keywords":"","license":"mit","license_expression":null,"license_files":null,"maintainer":"","maintainer_email":"","name":"babe","package_url":"https://pypi.org/project/babe/","platform":"any","project_url":"https://pypi.org/project/babe/","project_urls":{"Homepage":"https://github.com/thorwhalen/babe"},"provides_extra":null,"release_url":"https://pypi.org/project/babe/0.0.7/","requires_dist":["pandas","py2store","graze"],"requires_python":"","summary":"Data access and analysis of baby names statistics","version":"0.0.7","yanked":false,"yanked_reason":null}

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graze	false
pandas	false
py2store	false

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