Analysing New York's Popular Baby Names Dataset.

Popular_BabyNames_Project
In [1]:
import numpy as np
import pandas as pd
from pandas import Series, DataFrame
pd.set_option("display.precision", 2)
In [2]:
# Answering some questions about this Popular Baby Names Dataset in New York.
# This dataset was extracted from https://www.data.gov, the site of the U.S. 
# Government’s open data.
In [3]:
data = pd.read_csv('Popular_Baby_Names_New_York_City.csv')
data.head()
Out[3]:
Year of Birth Gender Ethnicity Child's First Name Count Rank
0 2011 FEMALE HISPANIC GERALDINE 13 75
1 2011 FEMALE HISPANIC GIA 21 67
2 2011 FEMALE HISPANIC GIANNA 49 42
3 2011 FEMALE HISPANIC GISELLE 38 51
4 2011 FEMALE HISPANIC GRACE 36 53
In [4]:
# Taking a look at data dimensionality:
In [5]:
print(data.shape)

(19418, 6)
In [6]:
# From the output, we can see that the table contains 19418 rows and 6 columns.
In [7]:
# How many female and male (Gender feature) are represented in this dataset?
In [8]:
data['Gender'].value_counts()
Out[8]:
FEMALE    9933
MALE      9485
Name: Gender, dtype: int64
In [9]:
# 9933 females and 9485 males are represented in this dataset.
In [10]:
# Exploring more the dataset, we sort the values on it by Year of Birth:
In [11]:
data.sort_values(by='Year of Birth', ascending=False)
Out[11]:
Year of Birth Gender Ethnicity Child's First Name Count Rank
16007 2016 MALE ASIAN AND PACIFIC ISLANDER Stanley 12 57
17686 2016 MALE HISPANIC Valentino 14 87
17710 2016 FEMALE WHITE NON HISPANIC Alma 18 76
16188 2016 FEMALE WHITE NON HISPANIC Yitty 58 40
17707 2016 FEMALE HISPANIC Sasha 16 68
16189 2016 MALE ASIAN AND PACIFIC ISLANDER Rayyan 19 50
16190 2016 MALE BLACK NON HISPANIC Isaac 20 48
17704 2016 FEMALE HISPANIC Hailey 81 22
17702 2016 FEMALE WHITE NON HISPANIC Dylan 19 75
16192 2016 MALE HISPANIC Felix 10 91
17700 2016 FEMALE ASIAN AND PACIFIC ISLANDER Queenie 20 31
17699 2016 FEMALE WHITE NON HISPANIC Ashley 10 84
16194 2016 FEMALE BLACK NON HISPANIC Zariah 15 38
17694 2016 MALE ASIAN AND PACIFIC ISLANDER Jace 11 58
17691 2016 MALE BLACK NON HISPANIC Christian 61 16
17690 2016 FEMALE BLACK NON HISPANIC Emily 26 27
16199 2016 FEMALE ASIAN AND PACIFIC ISLANDER Allison 20 31
16186 2016 MALE HISPANIC Ivan 36 67
17713 2016 FEMALE WHITE NON HISPANIC Liv 14 80
17714 2016 FEMALE HISPANIC Belen 10 74
16179 2016 MALE WHITE NON HISPANIC Maximilian 24 85
16174 2016 FEMALE BLACK NON HISPANIC Sasha 12 41
17733 2016 MALE BLACK NON HISPANIC Jaxon 16 51
17732 2016 FEMALE HISPANIC Layla 57 33
17728 2016 FEMALE WHITE NON HISPANIC Mindy 21 73
17727 2016 FEMALE WHITE NON HISPANIC Leila 24 70
16178 2016 FEMALE WHITE NON HISPANIC Nicole 35 59
17724 2016 FEMALE WHITE NON HISPANIC Lila 45 49
17715 2016 MALE HISPANIC Max 25 76
16180 2016 MALE WHITE NON HISPANIC Rocco 30 79
... ... ... ... ... ... ...
2591 2011 FEMALE BLACK NON HISPANIC BRIANNA 38 17
2619 2011 FEMALE BLACK NON HISPANIC GRACE 15 40
2606 2011 FEMALE BLACK NON HISPANIC EMILY 30 25
2618 2011 FEMALE BLACK NON HISPANIC GENESIS 15 40
2617 2011 FEMALE BLACK NON HISPANIC GABRIELLE 55 8
2616 2011 FEMALE BLACK NON HISPANIC GABRIELLA 24 31
2615 2011 FEMALE BLACK NON HISPANIC FATOUMATA 51 11
2614 2011 FEMALE BLACK NON HISPANIC FATOU 12 43
2613 2011 FEMALE BLACK NON HISPANIC FATIMA 24 31
2612 2011 FEMALE BLACK NON HISPANIC FANTA 15 40
2611 2011 FEMALE BLACK NON HISPANIC FAITH 46 12
2610 2011 FEMALE BLACK NON HISPANIC EVA 11 44
2609 2011 FEMALE BLACK NON HISPANIC ESSENCE 11 44
2608 2011 FEMALE BLACK NON HISPANIC ERIN 10 45
2607 2011 FEMALE BLACK NON HISPANIC EMMA 15 40
2605 2011 FEMALE BLACK NON HISPANIC ELIZABETH 22 33
2592 2011 FEMALE BLACK NON HISPANIC BRIELLE 35 20
2604 2011 FEMALE BLACK NON HISPANIC EGYPT 15 40
2603 2011 FEMALE BLACK NON HISPANIC EDEN 11 44
2602 2011 FEMALE BLACK NON HISPANIC DYLAN 11 44
2601 2011 FEMALE BLACK NON HISPANIC DESTINY 35 20
2600 2011 FEMALE BLACK NON HISPANIC DANIELLE 20 35
2599 2011 FEMALE BLACK NON HISPANIC DAKOTA 15 40
2598 2011 FEMALE BLACK NON HISPANIC CHRISTINA 17 38
2597 2011 FEMALE BLACK NON HISPANIC CHLOE 72 4
2596 2011 FEMALE BLACK NON HISPANIC CHEYENNE 10 45
2595 2011 FEMALE BLACK NON HISPANIC CHELSEA 37 18
2594 2011 FEMALE BLACK NON HISPANIC CAMILLE 12 43
2593 2011 FEMALE BLACK NON HISPANIC BROOKE 12 43
0 2011 FEMALE HISPANIC GERALDINE 13 75

19418 rows × 6 columns

In [12]:
#What are the ethnicities represented in this dataset?
In [13]:
data['Ethnicity'].value_counts()
Out[13]:
HISPANIC                      5714
WHITE NON HISPANIC            5473
BLACK NON HISPANIC            2826
ASIAN AND PACIFIC ISLANDER    2693
WHITE NON HISP                1338
ASIAN AND PACI                 693
BLACK NON HISP                 681
Name: Ethnicity, dtype: int64
In [14]:
# As we can see, we have a very diverse ethnicity representation.
In [15]:
# What is the percentage of Hispanic females and males (Ethnicity feature)?
In [16]:
float((data['Ethnicity'] == 'HISPANIC').sum()) / data.shape[0]
Out[16]:
0.29426305489751775
In [17]:
np.around([0.29426305489751775], decimals=2)
Out[17]:
array([0.29])
In [18]:
# The percentage of Hispanic females and males is around the 29%.
In [19]:
# What is the percentage of White Non Hispanic females and males (Ethnicity feature)?
In [20]:
float((data['Ethnicity'] == 'WHITE NON HISPANIC').sum()) / data.shape[0]
Out[20]:
0.28185188999897004
In [21]:
np.around([0.28185188999897004], decimals=2)
Out[21]:
array([0.28])
In [22]:
# The percentage of Non White Hispanic females and males is around the 28%.
In [23]:
# What is the average Year of Birth of females in this dataset?
In [24]:
data[data['Gender']=='FEMALE']['Year of Birth'].mean()
Out[24]:
2013.1224202154435
In [25]:
#The year 2013
In [26]:
# What is the average Year of Birth of males in this dataset?
In [27]:
data[data['Gender']=='MALE']['Year of Birth'].mean()
Out[27]:
2013.1263046916183
In [28]:
#Also the year 2013.
In [29]:
#What are the some of the names among the BLACK NON HISPANIC ethnicity?
In [30]:
data[data['Ethnicity'] == 'BLACK NON HISPANIC']['Child\'s First Name']
Out[30]:
696          AARON
697         ABDOUL
698      ABDOULAYE
699           ADAM
700           ADEN
           ...    
19389         Levi
19392       Jahmir
19397         Liam
19400        Fatou
19407        Layla
Name: Child's First Name, Length: 2826, dtype: object
In [31]:
#Looking the above results, we can see that some of the names among the BLACK NON HISPANIC ethnicity had a Middle East-Jewish
# influence and origin. For example, names like Fatou, Layla, Abdoul, Levi, Aaron.
In [40]:
# We will analyse more the popular names among the ASIAN AND PACIFIC ISLANDER babies.
# For this propose we are extracting a portion of the dataset and selecting this ethnicity.
gb = data.groupby("Ethnicity")
asian_pac_islander = gb.get_group("ASIAN AND PACIFIC ISLANDER")
asian_pac_islander[546:576]
Out[40]:
Year of Birth Gender Ethnicity Child's First Name Count Rank
2490 2011 FEMALE ASIAN AND PACIFIC ISLANDER KATE 10 38
2491 2011 FEMALE ASIAN AND PACIFIC ISLANDER KATELYN 14 34
2492 2011 FEMALE ASIAN AND PACIFIC ISLANDER KATHERINE 21 27
2493 2011 FEMALE ASIAN AND PACIFIC ISLANDER KATIE 18 30
2494 2011 FEMALE ASIAN AND PACIFIC ISLANDER KAYLA 21 27
2495 2011 FEMALE ASIAN AND PACIFIC ISLANDER KAYLEE 20 28
2496 2011 FEMALE ASIAN AND PACIFIC ISLANDER KELLY 22 26
2497 2011 FEMALE ASIAN AND PACIFIC ISLANDER KHLOE 13 35
2498 2011 FEMALE ASIAN AND PACIFIC ISLANDER KYLIE 11 37
2499 2011 FEMALE ASIAN AND PACIFIC ISLANDER LAUREN 13 35
2500 2011 FEMALE ASIAN AND PACIFIC ISLANDER LEAH 16 32
2501 2011 FEMALE ASIAN AND PACIFIC ISLANDER LEELA 10 38
2502 2011 FEMALE ASIAN AND PACIFIC ISLANDER LILLIAN 16 32
2503 2011 FEMALE ASIAN AND PACIFIC ISLANDER LILY 15 33
2504 2011 FEMALE ASIAN AND PACIFIC ISLANDER MADISON 19 29
2505 2011 FEMALE ASIAN AND PACIFIC ISLANDER MAGGIE 10 38
2506 2011 FEMALE ASIAN AND PACIFIC ISLANDER MANDY 12 36
2507 2011 FEMALE ASIAN AND PACIFIC ISLANDER MARYAM 17 31
2508 2011 FEMALE ASIAN AND PACIFIC ISLANDER MAYA 27 21
2509 2011 FEMALE ASIAN AND PACIFIC ISLANDER MEGAN 10 38
2510 2011 FEMALE ASIAN AND PACIFIC ISLANDER MELODY 13 35
2511 2011 FEMALE ASIAN AND PACIFIC ISLANDER MIA 36 14
2512 2011 FEMALE ASIAN AND PACIFIC ISLANDER MICHELLE 42 12
2513 2011 FEMALE ASIAN AND PACIFIC ISLANDER MILA 17 31
2514 2011 FEMALE ASIAN AND PACIFIC ISLANDER MINA 12 36
2515 2011 FEMALE ASIAN AND PACIFIC ISLANDER NATALIE 20 28
2516 2011 FEMALE ASIAN AND PACIFIC ISLANDER NICOLE 33 16
2517 2011 FEMALE ASIAN AND PACIFIC ISLANDER NINA 21 27
2518 2011 FEMALE ASIAN AND PACIFIC ISLANDER OLIVIA 89 4
2519 2011 FEMALE ASIAN AND PACIFIC ISLANDER PHOEBE 18 30
In [42]:
# We can have some insides about the anglo-saxon origins and influences of names like Mia, Melody, Phoebe and Megan.

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