As part of the birth statistics, the most popular first names are determined annually. The dataset includes the available reporting years from 2010. Phonetically identical first names were summarized. Improved phonetic groupings may slightly change previous year's data. First names that occur less than 5 times per grouping are not listed in the table.

This statistic shows the most frequent combinations of first name and last name in the United States, as of 2013. According to this ranking, the name "James Smith" occurs most often and is most popular in the United States.

Popular Baby Names by Sex and Ethnic Group Data were collected through civil birth registration. Each record represents the ranking of a baby name in the order of frequency. Data can be used to represent the popularity of a name. Caution should be used when assessing the rank of a baby name if the frequency count is close to 10; the ranking may vary year to year.

Context

Cultural diversity in the U.S. has led to great variations in names and naming traditions and names have been used to express creativity, personality, cultural identity, and values. Source: https://en.wikipedia.org/wiki/Naming_in_the_United_States

Content

This public dataset was created by the Social Security Administration and contains all names from Social Security card applications for births that occurred in the United States after 1879. Note that many people born before 1937 never applied for a Social Security card, so their names are not included in this data. For others who did apply, records may not show the place of birth, and again their names are not included in the data.

All data are from a 100% sample of records on Social Security card applications as of the end of February 2015. To safeguard privacy, the Social Security Administration restricts names to those with at least 5 occurrences.

Fork this kernel to get started with this dataset.

Acknowledgements

https://bigquery.cloud.google.com/dataset/bigquery-public-data:usa_names

https://cloud.google.com/bigquery/public-data/usa-names

Dataset Source: Data.gov. This dataset is publicly available for anyone to use under the following terms provided by the Dataset Source — http://www.data.gov/privacy-policy#data_policy — and is provided "AS IS" without any warranty, express or implied, from Google. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset.

Banner Photo by @dcp from Unplash.

Inspiration

What are the most common names?

What are the most common female names?

Are there more female or male names?

Female names by a wide margin?

The dataset contains first name statistics for the year 2016.

United States Baby Names Count

United States Baby Names Dataset

By Amber Thomas [source]

About this dataset

The data is based on a complete sample of records on Social Security card applications as of March 2021 and is presented in three main files: baby-names-national.csv, baby-names-state.csv, and baby-names-territories.csv. These files contain detailed information about names given to babies at the national level (50 states and District of Columbia), state level (individual states), and territory level (including American Samoa, Guam, Northern Mariana Islands Puerto Rico and U.S. Virgin Islands) respectively.

Each entry in the dataset includes several key attributes such as state_abb or territory_code representing the abbreviation or code indicating the specific state or territory where the baby was born. The sex attribute denotes the gender of each baby – either male or female – while year represents the specific birth year when each baby was born.

Another important attribute is name which indicates given name selected for each individual newborn.The count attribute provides numerical data about how many babies received a particular name within a specific state/territory, gender combination for a given year.

It's also worth noting that all names included have at least two characters in length to ensure high data quality standards.

How to use the dataset

- Understanding the Columns

The dataset consists of multiple columns with specific information about each baby name entry. Here are the key columns in this dataset:

• state_abb: The abbreviation of the state or territory where the baby was born.
• sex: The gender of the baby.
• year: The year in which the baby was born.
• name: The given name of the baby.

• count: The number of babies with a specific name born in a certain state, gender, and year.

- Exploring National Data

To analyze national trends or overall popularity across all states and years: a) Focus on baby-names-national.csv. b) Use columns like name, sex, year, and count to study trends over time.

- Analyzing State-Level Data

To examine specific states' data: a) Utilize baby-names-state.csv file. b) Filter data by desired states using state_abb column values. c) Combine analysis with other relevant attributes like gender, year, etc., for detailed insights.

- Understanding Territory Data

For insights into United States territories (American Samoa, Guam, Northern Mariana Islands, Puerto Rico, U.S Virgin Islands): a) Access informative data from baby-names-territories.csv. b) Analyze based on similar principles as state-level data but considering unique territory factors.

- Gender-Specific Analysis

You can study names' popularity specifically among males or females by filtering the data using the sex column. This will allow you to explore gender-specific naming trends and preferences.

- Identifying Regional Patterns

To identify naming patterns in specific regions: a) Analyze state-level or territory-level data. b) Look for variations in name popularity across different states or territories.

- Analyzing Name Popularity over Time

Track the popularity of specific names over time using the name, year, and count columns. This can help uncover trends, fluctuations, and changes in names' usage and popularity.

- Comparing Names and Variations

Use this

Research Ideas

• Tracking Popularity Trends: This dataset can be used to analyze the popularity of baby names over time. By examining the count of babies with a specific name born in different years, trends and shifts in naming preferences can be identified.
• Gender Analysis: The dataset includes information on the gender of each baby. It can be used to study gender patterns and differences in naming choices. For example, it would be possible to compare the frequency and popularity of certain names among males and females.
• Regional Variations: With state abbreviations provided, it is possible to explore regional variations in baby naming trends within the United States. Researchers could examine how certain names are more popular or unique to specific states or territories, highlighting cultural or geographical factors that influence naming choices

Acknowledgements

If you use this dataset in your research, please credit the original a...

Visualization of first name statistics of Statistik Austria. As part of the application, two or more male or female first names can be selected and compared with each other. It shows a line chart with the number of children to whom the selected names were assigned between 1984 and 2022, as well as an Austria map in which the districts are colored according to the popularity of the names.

Popular Baby Names by Sex and Ethnic Group Data were collected through civil birth registration. Each record represents the ranking of a baby name in the order of frequency. Data can be used to represent the popularity of a name. Caution should be used when assessing the rank of a baby name if the frequency count is close to 10; the ranking may vary year to year.

Rank and count of the top names for baby girls, changes in rank since the previous year and breakdown by country, region, mother's age and month of birth.

The Bureau of the Census has released Census 2000 Summary File 1 (SF1) 100-Percent data. The file includes the following population items: sex, age, race, Hispanic or Latino origin, household relationship, and household and family characteristics. Housing items include occupancy status and tenure (whether the unit is owner or renter occupied). SF1 does not include information on incomes, poverty status, overcrowded housing or age of housing. These topics will be covered in Summary File 3. Data are available for states, counties, county subdivisions, places, census tracts, block groups, and, where applicable, American Indian and Alaskan Native Areas and Hawaiian Home Lands. The SF1 data are available on the Bureau's web site and may be retrieved from American FactFinder as tables, lists, or maps. Users may also download a set of compressed ASCII files for each state via the Bureau's FTP server. There are over 8000 data items available for each geographic area. The full listing of these data items is available here as a downloadable compressed data base file named TABLES.ZIP. The uncompressed is in FoxPro data base file (dbf) format and may be imported to ACCESS, EXCEL, and other software formats. While all of this information is useful, the Office of Community Planning and Development has downloaded selected information for all states and areas and is making this information available on the CPD web pages. The tables and data items selected are those items used in the CDBG and HOME allocation formulas plus topics most pertinent to the Comprehensive Housing Affordability Strategy (CHAS), the Consolidated Plan, and similar overall economic and community development plans. The information is contained in five compressed (zipped) dbf tables for each state. When uncompressed the tables are ready for use with FoxPro and they can be imported into ACCESS, EXCEL, and other spreadsheet, GIS and database software. The data are at the block group summary level. The first two characters of the file name are the state abbreviation. The next two letters are BG for block group. Each record is labeled with the code and name of the city and county in which it is located so that the data can be summarized to higher-level geography. The last part of the file name describes the contents . The GEO file contains standard Census Bureau geographic identifiers for each block group, such as the metropolitan area code and congressional district code. The only data included in this table is total population and total housing units. POP1 and POP2 contain selected population variables and selected housing items are in the HU file. The MA05 table data is only for use by State CDBG grantees for the reporting of the racial composition of beneficiaries of Area Benefit activities. The complete package for a state consists of the dictionary file named TABLES, and the five data files for the state. The logical record number (LOGRECNO) links the records across tables.

Count of popularity of adult first names (forenames, given names) in Peru, from an approximately 7% sample of the adult population.

In Peru, many people are registered as supporters of political parties, and their names are published by the Registro de Organizaciones Políticas. The lists include a DNI (national identity number) for each person to avoid duplicates. The 1,572,002 people on these lists (excluding the regional movements) represent around 7% of the adult population of Peru.

The first and middle names have been sorted and counted (there are an average of 1.6 first names for each person).

These 2,538,011 first (and middle) names represent 76,720 different names, most of which are infrequent. The file has been limited to names that occur ten or more times in the sample, which is 7,250 unique names (2,417,750 names, more than 95% of the total).

Each row in the file contains the rank, a percentage of that name in the entire set of 2,538,011 names, a count of the times the name occurs in the sample, and the name.

Description: First names of the total population by municipality Period: 2015 Metadata: We only publish figures from 5 occurrences. More information, data and publications on this topic on Statbel

With 603 name registrations, Astrid was the most popular name given to newborn girls in Sweden in 2022. Maja, Alma, and Vera were other popular names during this year. As for newborn boys, William was the most popular name in 2022.

Analysis of ‘First name statistics for newborns in Moers 2020 ’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/527d7456-db9a-482f-984c-43a0eb3eb700 on 12 January 2022.

--- Dataset description provided by original source is as follows ---

The dataset contains the forenames given in 2020 for boys and girls. A new dataset is created each year for this domain.

--- Original source retains full ownership of the source dataset ---

This dataset contains ranks and counts for the top 25 baby names by sex for live births that occurred in California (by occurrence) based on information entered on birth certificates.

This statistic illustrates the ranking of most popular female baby names in France in 2022. It appears that Jade was the most given name for baby girls in 2022, with 3,420 new registrations. The same year the most popular name for baby boys was Gabriel.

The data (name, year of birth, sex, and number) are from a 100 percent sample of Social Security card applications for 1880 onward.

Popular Baby Names by Sex and Ethnic Group Data were collected through civil birth registration. Each record represents the ranking of a baby name in the order of frequency. Data can be used to represent the popularity of a name. Caution should be used when assessing the rank of a baby name if the frequency count is close to 10; the ranking may vary year to year.

The data set contains the distribution of first names for newborns of 2012.

The dataset contains statistical information on the number of persons with a specific combination of personal names and personal names (multiple names) included in the Register of Natural Persons (until 06.28.2021). Population Register). It should be noted that the Register of Natural Persons also includes personal names of foreigners in the Latin alphabet transliteration according to the travel document issued by the foreign state (for example, Nicola, Alex), which does not comply with the norms of the Latvian literary language.

As of 2023.10.01, the dataset contains information on gender (male, female) of combinations of names and personal names of persons registered in the Register of Natural Persons.