معرفی شرکت ها
babe-0.0.7
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشترتوضیحات
Data access and analysis of baby names statistics
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | babe-0.0.7 |
| نام | babe |
| نسخه کتابخانه | 0.0.7 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Thor Whalen |
| ایمیل نویسنده | - |
| آدرس صفحه اصلی | https://github.com/thorwhalen/babe |
| آدرس اینترنتی | https://pypi.org/project/babe/ |
| مجوز | mit |
# 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.
To install:
```pip install babe```
Then in a python console or notebook...
```python
from babe import UsNames
d = UsNames()
```
# Intro to the data
The 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).
```python
d.data
```
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>state</th>
<th>gender</th>
<th>year</th>
<th>name</th>
<th>popularity</th>
<th>name_g</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>AK</td>
<td>F</td>
<td>1910</td>
<td>Mary</td>
<td>14</td>
<td>Mary_F</td>
</tr>
<tr>
<th>1</th>
<td>AK</td>
<td>F</td>
<td>1910</td>
<td>Annie</td>
<td>12</td>
<td>Annie_F</td>
</tr>
<tr>
<th>2</th>
<td>AK</td>
<td>F</td>
<td>1910</td>
<td>Anna</td>
<td>10</td>
<td>Anna_F</td>
</tr>
<tr>
<th>3</th>
<td>AK</td>
<td>F</td>
<td>1910</td>
<td>Margaret</td>
<td>8</td>
<td>Margaret_F</td>
</tr>
<tr>
<th>4</th>
<td>AK</td>
<td>F</td>
<td>1910</td>
<td>Helen</td>
<td>7</td>
<td>Helen_F</td>
</tr>
<tr>
<th>...</th>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
</tr>
<tr>
<th>28277</th>
<td>WY</td>
<td>M</td>
<td>2019</td>
<td>Theo</td>
<td>5</td>
<td>Theo_M</td>
</tr>
<tr>
<th>28278</th>
<td>WY</td>
<td>M</td>
<td>2019</td>
<td>Tristan</td>
<td>5</td>
<td>Tristan_M</td>
</tr>
<tr>
<th>28279</th>
<td>WY</td>
<td>M</td>
<td>2019</td>
<td>Vincent</td>
<td>5</td>
<td>Vincent_M</td>
</tr>
<tr>
<th>28280</th>
<td>WY</td>
<td>M</td>
<td>2019</td>
<td>Warren</td>
<td>5</td>
<td>Warren_M</td>
</tr>
<tr>
<th>28281</th>
<td>WY</td>
<td>M</td>
<td>2019</td>
<td>Waylon</td>
<td>5</td>
<td>Waylon_M</td>
</tr>
</tbody>
</table>
<p>6122890 rows × 6 columns</p>
</div>
```python
print(f"{len(d.names)} unique names")
```
31862 unique names
But 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`):
```python
print(f"{len(d.name_gs)} unique names_g (gendered names)")
```
34952 unique names_g (gendered names)
You 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.
```python
d.resolve_name_g('Cora')
```
'Cora_F'
```python
try:
d.resolve_name_g('Vanessa')
except AssertionError as e:
print(e)
```
The Vanessa can be used for both genders. Specify Vanessa_F or Vanessa_M
## by_state data
In some cases, it's more convenient to have a view indexed by `(state, name_g, year)`.
The `by_state` attribute provides that.
```python
d.by_state
```
state name_g year
AK Mary_F 1910 14
Annie_F 1910 12
Anna_F 1910 10
Margaret_F 1910 8
Helen_F 1910 7
..
WY Theo_M 2019 5
Tristan_M 2019 5
Vincent_M 2019 5
Warren_M 2019 5
Waylon_M 2019 5
Name: popularity, Length: 6122890, dtype: int64
This allows one to do things like getting the data for a given state only:
```python
d.by_state['CA']
```
name_g year
Mary_F 1910 295
Helen_F 1910 239
Dorothy_F 1910 220
Margaret_F 1910 163
Frances_F 1910 134
...
Zayvion_M 2019 5
Zeek_M 2019 5
Zhaire_M 2019 5
Zian_M 2019 5
Ziyad_M 2019 5
Name: popularity, Length: 387781, dtype: int64
... within a state, getting the 'by year popularity' for a given name:
```python
d.by_state['CA']['Cora_F'] # or d.by_state['CA', 'Cora_F']
```
year
1911 8
1912 9
1913 15
1914 15
1915 17
...
2015 269
2016 244
2017 284
2018 282
2019 256
Name: popularity, Length: 109, dtype: int64
... if you wanted to get the data for a given name (really `name_g`), for all states, you can do it using "slicing".
For example, if you're wondering how many little boys were called "Vanessa", and more specifically, when and where?...
```python
d.by_state[:, 'Vanessa_M']
```
state year
AZ 1988 8
CA 1980 7
1981 5
1982 20
1983 19
1984 14
1985 12
1986 13
1987 13
1988 26
1989 17
1990 16
1991 18
1992 17
1993 17
1994 10
1995 9
1996 10
1997 11
1998 7
DC 1989 11
NY 1982 5
1983 9
1986 6
1988 6
1989 6
TX 1981 5
1982 7
1983 12
1984 9
1985 10
1986 8
1987 9
1988 8
1989 5
1990 6
1991 5
1992 5
1994 5
Name: popularity, dtype: int64
## national data
A national aggregation is available through the `national` attribute
```python
d.national
```
name_g year
Aaban_M 2013 6
2014 6
Aadam_M 2019 6
Aadan_M 2008 12
2009 6
..
Zyriah_F 2013 7
2014 6
2016 5
Zyron_M 2015 5
Zyshonne_M 1998 5
Name: popularity, Length: 633239, dtype: int64
The interface is as with the `by_state` attribute, but without the state specification.
```python
d.national.loc['Vanessa_F']
```
year
1935 5
1947 24
1948 32
1949 16
1950 41
...
2015 1687
2016 1633
2017 1486
2018 1345
2019 1188
Name: popularity, Length: 74, dtype: int64
# Plotting stuff
```python
d.plot_popularity('Cora');
```
```python
d.plot_popularity('Cora', 'GA');
```
```python
d.plot_popularity(['Cora', 'Vanessa_F']);
```
```python
d.plot_popularity('Cora', ['CA', 'GA']);
```
```python
d.plot_popularity(['Cora', 'Vanessa_F'], ['CA', 'GA']);
```
# Misc
## gender-ambiguous names
We'll call the "femininity" of a name be the proportion of times it was used (all states, all time) to name a girl,
and the "masculinity" of a name be defined accordingly.
```python
d.femininity_of_name.iloc[12000:12010]
```
Lemmie 0.161290
Kashmere 0.161290
Clary 0.162162
Sung 0.162393
Kyrie 0.163527
Cedar 0.163686
Masyn 0.163895
Naveen 0.165605
Chai 0.166667
Atlee 0.167382
dtype: float64
```python
d.femininity_of_name.plot(figsize=(17, 5), ylabel='femininity');
```
```python
d.masculinity_of_name.iloc[19000:19010]
```
Berkley 0.108889
Dasani 0.110092
Sharone 0.111111
Ifeoluwa 0.111111
Rama 0.111111
Scout 0.111486
Brownie 0.111732
Lashon 0.113158
Indigo 0.113364
Argie 0.113636
dtype: float64
```python
d.masculinity_of_name.plot(figsize=(17, 5), ylabel='masculinity');
```
The (gender-)"ambiguity" of a name can thus be defined as twice the minimum of it's femininity and masculinity.
By 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.
Note 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.
```python
d.ambiguity_of_name
```
Munachiso 1.0
Addis 1.0
Deshone 1.0
Gal 1.0
Rajdeep 1.0
...
Sharelle 0.0
Analy 0.0
Sharayah 0.0
Sharaya 0.0
Aaban 0.0
Length: 31862, dtype: float64
```python
t = d.ambiguity_of_name
print(f"There are {len(t[t > 0])} (gender-)ambiguous names")
```
There are 3090 (gender-)ambiguous names
```python
t = d.ambiguity_of_name
t[t > 0].plot(figsize=(17, 5), ylabel='gender-ambiguity');
```
```python
t = list(d.ambiguous_names)
print(f"{len(t)} (gender-)ambiguous names:")
print(*t[:9], '...', sep=', ')
```
3090 (gender-)ambiguous names:
Nolie, Tyrese, Linn, Savannah, Bryn, Rei, Abby, Shilo, Tracy, ...
```python
```
نیازمندی
| مقدار | نام |
|---|---|
| - | pandas |
| - | py2store |
| - | graze |
نحوه نصب
نصب پکیج whl babe-0.0.7:
pip install babe-0.0.7.whl
نصب پکیج tar.gz babe-0.0.7:
pip install babe-0.0.7.tar.gz