Analysis of popular baby names from public databases from the USA, Canada and Spain, by using phonetic translators in Python to compare how well a first name sounds with a middle and family names in English and Spanish.
Elisa on the keyboard. Image by Victor Cuspinera.
Getting a name for a baby is not as trivial as people could think, or at least not for my wife and me.
When we were expecting our first baby girl, during the last five months of the pregnancy, we looked for options of names on different sources as websites and books with popular baby names, websites with international names, asked recommendations between our friends, or even wrote down names of movies and tv series characters.
After months of searching names, on early April of 2021 we came with a list of our favorite names for the baby girl:
| Mom's choices | Dad's choices |
|---|---|
| Elisa | Aisha |
| Macarena | Amanda |
| Carlina | |
| Gina | |
| Victoria |
However, we didn't agree in a name for the baby. So, I started to wonder how we could make the best selection of the name for our baby? Which are the most popular names? Is there a way to measure how well a surname sounds combined with the family name?
The first step was to look for names. In our case, because we live in Mexico we were interested on names in Spanish; however, we also were open for a name from an English-speaking country.
I was interested in looking for a database of names in Mexico 🇲🇽. I found that the Registro Nacional de Población (National Population Registry) has this information, but it is not public available.
However, I found that Instituto Nacional de Estadística (Statistics National Institute) from Spain 🇪🇸 has open data of the most popular 100 names in this country from 2002 to 2019, and also data of names with frequency equal to or greater than 20 people in 2019.
For names from English speaking countries, I found two databases of popular baby names: one from the U.S. 🇺🇸 published by the Social Security Agency, and another from British Columbia, Canada 🇨🇦, shared by the Government of B.C.
While three of the four databases contain similar information, they have different formats. For example, the databases with names from Spain have one Excel file for each year, while Canadian data is divided in male and female files, and finally the information from the U.S. have one text file for each year since 1880 until 2019.
To make life easier, in the merge_clean.ipynb notebook from this Repository, I merge the different files for each database and unify the format by using the next four columns: name, year, sex and number, and save these files.
When comparing the databases of popular names from Spain, the U.S. and Canada, it would be observed that the number of distinct names were very different among databases. These differences probably could be explained by the structure of the databases because the range of years and their characteristics:
| Country | Number of different names | Number of years | Years | Charactersitics of database |
|---|---|---|---|---|
| Spain | 379 | 18 | 2002-2019 | Most popular 100 names |
| B.C., Canada | 4,340 | 100 | 1920-2019 | Names with 5 or more observations |
| USA | 99,444 | 140 | 1880-2019 | Names with 5 or more observations |
While the growth in the average number of people per name is stable for the USA and Canada around 0% level, it is not for Spain, probably because it contains only the 100 most popular names in Spain, compared with the extensive list of names in the USA and Canada data.
⭐️ click here to see the notebook with the complete EDA and main analysis.
This section I would present the main finds of the descriptive analysis of the perform of our favourite names: Aisha, Amanda, Carlina, Elisa, Gina, Macarena and Victoria, in the names databases from Spain, the U.S. and Canada.
Between the favorite names of our baby, while using the Spain database, the most used name is victoria with 59.6 thousand females, followed by elisa with 36.3 thousand people, macarena with 14.4 thousand, amanda with 12.9 thousand, gina with 2.3 thousand, aisha with 2.0 thousand, and carlina with 147 people.
In the other hand, the youngest name is aisha with an average age of 9.9 years old, followed by gina with an average of 21.4 years old, amanda with 24.8 years old, macarena with 29.9 years old, victoria with 46.7 years old, elisa with 48.7 years old, and carlina with 61.2 years old.
Until now we haven't talk much about the second source from the Instituto Nacional de Estadística from Spain that contain the data of names with frequency equal to or greater than 20 people in 2019. The beauty of this source is that it contains the complete list of names considering also compound names, which was helpful to identify the most common compound names associated with the list of our favorite names, and its average age. For example, the name maria victoria contains the name victoria and it is the most popular name that contain one of our favorite options for our baby girl, however, its average age was 53 years old which means it is a name for older people.
In this sense, in the databases we could find 243 compound names that includes victoria, 82 with elisa, 31 with macarena, 28 with amanda, 10 with gina, and 3 with aisha and 2 with carlina.
The next plot shows the comparison between frequency and average age for all single and compound names that contain one of our seven favorite names for our baby girl.
Finally, the following word cloud presents the compound names with frequency equal to 75 or more observations. While the nodes represent each name, the arrows show the order of connection between names and the darker the arrow, the stronger is the connection between names. For example, the names maria and victoria are connected in both ways and forms the compound names maria victoria and victoria maria.
For the U.S. plot, I am presenting the trend of newborn babies per year from 1950 to 2019 that had any of our seven favorite names.
Here we find that while victoria has been popular for all years, gina became the most popular name in the late 60's and amanda in the 80's. The name elisa has been in the middle range of our favorite names with values around 500 observations per year, while aisha popularity increased from around 10 observations on the 60's to 1,000 on the 70's and became steady on that level. Finally, the names carlina and macarena are the least popular from our list in records from the U.S.
In this case the following plot presents the trend of newborn babies per year from 1950 to 2019 in British Columbia, Canada, that had any of our favorite names. In this case, the database has information of five from our seven selected names: victoria, elisa, amanda, gina and aisha.
Similar to the plot from the U.S., here we find that victoria has been popular for all years and amanda became the most popular name in the 80's and 90's. The popularity of gina decreased over time. And finally, the names elisa and aisha have low records.
Scoring names using International Phonetic Alphabet (IPA)
The last effort of this analysis is to construct a tool that measures how well a surname combines with anothers surname and/or family names.
For this purpose, I transform the names to their Spanish and English phonetic notation using the IPA, and look for ryhmes in English and Spanish for each comparison between each two strings (names). For example, in the name maria victoria smith this function gets the score for the comparison between (1) maria and victoria, (2) maria and smith, and (3) victoria with smith.
In each of this two-strings comparison, I get three scores: Spanish, English and Total. Finally, for complete names with more than two strings I average all scores for Spanish, all scores for English, and all Total scores getting also three final scores (Spanish, English and Total) for a complete name.
⭐️ click here to look into the class used to get scores of rhymes of names.
😅 Fun fact: when I finally arrived to this point of the analysis on mid-April 2021, my wife already choose a name for our baby girl: elisa. From this point, it was on my side to choose if I wanted a compound name for the baby.
| Name | IPA Spanish | IPA English | Score Spanish | Score English | Score Total |
|---|---|---|---|---|---|
| elisa cuspinera martinez | elisa kuspineɾa maɾtines | əˈlizə kʊzpɪˈnirə mɑrˈtinɛz | 0.0556 | 0.3334 | 0.1944 |
Among the compound female names that includes elisa combined with our family names cuspinera and martinez (this is because in Mexico and Latin America we use two family names), the top of possible second names with higher scores were: aisa, akira, alisa, ariza, corisa, delisa, elfrida, elissa, elvina, elyria, elysia, erisa, isa, jazeera, lisa, liza, louisa, luisa, macarena, maeda, magdalena, makita, malina, malinda, malvina, marcelia, marcellina, marchita, margarita, marilda, marina, marquita, martina, martita, mathea, matthea, maurita, mayeda, miera, misa, raisa, riera, risa, shakira, viera.
As you could realize, one of these names is macarena. So, as in the movie Inception, I felt that my wife implanted on my head the idea of this name and, among the top options of compound names combined with elisa, this one was my favorite mainly becase the way it sounds when combining macarena and cuspinera. Actually, macarena cuspinera scores 0.5 in Spanish and 0.6667 in English.
Finally, we decide that the name of our baby girl would be elisa macarena cuspinera martinez, which has an even better score than elisa cuspinera martinez.
| Name | IPA Spanish | IPA English | Score Spanish | Score English | Score Total |
|---|---|---|---|---|---|
| elisa macarena cuspinera martinez | elisa makaɾena kuspineɾa maɾtines | əˈlizə mækəˈrinə kʊzpɪˈnirə mɑrˈtinɛz | 0.1667 | 0.4167 | 0.2917 |
Choosing the name of your baby could be not as easy as people think. The aim of this project was to build a tool that measures how well a complete name would sound assigning a score to make it easier finding possible names for a baby. However, as a tool, this model is only a piece of the puzzle and this could (and should) be complemented with other qualitative variables, for example, meaning of names, regional customs, family traditions, trends and/or other ideas from the future parents.
| Python packages | Python packages |
|---|---|
| altair | eng_to_ipa |
| epitran | numpy |
| pandas | re |
| rpy2 | time |
| word_tokenize |
├── README.md <- The top-level README
├── data
│ ├── names <- Files that merges raw data, and the results with scores from the
│ │ customed Class named ad Names_rhyme.py
│ └── raw <- Contains the raw data of the names from Spain, the US and Canada
│
├── src <- Source code for use in this project
│ ├── merge_clean.ipynb <- Merge the raw databases
│ ├── analysis.ipynb <- Performs the EDA and main analysis
│ ├── Names_rhyme.py <- Class that returns the scores (Total, Spanish and English)
│ └── Cuspinera-Martinez_baby.ipynb <- Example of using the customed class Names_rhyme.py
│ with the name of our baby
│
└── img <- Images for the main README file
-
Popular names in Spain
- Most frequent first names and surnames by Instituto Nacional de Estadística.
-
Popular names in the USA
- Popular Baby Names by the Social Security Agency.
- Social Security Agency official webpage.
- US Baby Names dashboard by Elias Dabbas.
-
Popular names in British Columbia, Canada
- B.C.'s Most Popular Baby Names by the Government of British Columbia.
-
Tools
- David Mortensen. (2021).
epitranPython library. - ICHI PRO. (2020). Analyzing Poetry with Python. Original article in Spanish.
- Julia Silge and David Robinson. (2017). Text Mining with R: A Tidy Approach. Chapter 4 Relationships between words: n-grams and correlations.
- mphilli, Mitchellpkt, CanadianCommander, timvancann. (2020). English to IPA
eng-to-ipaPython library. - toPhonetics. Online converter of English text to IPA phonetic transcription.
- David Mortensen. (2021).