To get results for more than one geography, you must include the predicate &col+for or &row+for in your API call. This will place the geographies in the row or column or your table.

For example, when you add &col+for to the API query below, it provides data for your geographies in the columns: api.census.gov/data/2019/acs/acs1/pums?tabulate=weight(PWGTP)&col+for&row+SEX&row+DIS&for=state:11,24,51

The results for this query provide sex by disability data for the District of Columbia (state=11) in the first column, Maryland (state=24) in the second column and Virginia (state=51) in the third column.

[[{"state": "11"}, {"state": "24"}, {"state": "51"},"SEX", "DIS"],

[37840,327485,514387,"1","1"],

[46182,377087,544915,"2","1"],

[298100,2601638,3686473,"1","2"],

[323627,2739470,3789744,"2","2"]]

If you omit &col+for from this query, you will get sex by disability totals for the combined area spanning DC, MD, and VA. It will not provide results for each state individually.

The default is that the microdata API will give you results for the total number of people or housing units in your universe. Some variables allow you to also get results for averages by including avg(insert continuous variable) in your API query. For example, adding avg(AGEP) to this query after the weight allows you to look at the average age of men and women with disabilities compared to those without disabilities: api.census.gov/data/2019/acs/acs1/pums?tabulate=weight(PWGTP),avg(AGEP)&col+SEX&row+DIS

Be sure to place the average function after the tabulate parameter, using a comma to separate the weight function from the average function.

The microdata API allows you to calculate averages for continuous variables. It does not support use of the average function for categorical variables, because in many cases, the calculation of an average would not make sense or would be inappropriate for these variables.

You may also need to restrict your universe to calculate a meaningful average. Many variables will have values of 0 or -1 to indicate people or households that are not in the universe, and you will likely want to exclude these values from your universe when calculating an average.

The last consideration of your microdata API query is to determine if you need to recode any variables. This is a process where you can specify custom categories that your statistics represent rather than using the most-detailed, default categories. It is required when working with many continuous variables and is beneficial for categorical variables as well.

For example, suppose you are working with JWMNP, a continuous variable in the ACS PUMS microdata API for travel time to work. If you place it in the columns or rows of the API query without recoding it, you will get results for each possible value of the continuous variable. Consider this query that provides travel time to work broken out by sex.

api.census.gov/data/2019/acs/acs1/pums?tabulate=weight(PWGTP)&col+SEX&row+JWMNP

After running the query, you will notice the default is to provide results for all 201 possible values of travel time to work in 1-minute increments. It begins with 0 for people who are not a worker or work from home and ends with a 200-minute travel time to work. This is likely more detail than what you need for your analysis. Recoding the variable allows you to define higher-level categories to get results for commuting, such as 15-min increments instead of 1-min increments. For example, this query provides results for the number of males and females with that have commute times within one of these 8 categories: 0 (not a worker or work from home), 1-14 min, 15-29 min, 30-44 min, 45-59 min, 60-74 min, 75-89 min, or 90+ min

api.census.gov/data/2019/acs/acs1/pums?tabulate=weight(PWGTP)&col+SEX&row+JWMNP_RECODE&recode+JWMNP_RECODE=%7B%22b%22%3A%22JWMNP%22%2C%22d%22%3A%5B%5B00%5D%2C%5B%7B%22mn%22%3A1%2C%22mx%22%3A14%7D%5D%2C%5B%7B%22mn%22%3A15%2C%22mx%22%3A29%7D%5D%2C%5B%7B%22mn%22%3A30%2C%22mx%22%3A44%7D%5D%2C%5B%7B%22mn%22%3A45%2C%22mx%22%3A59%7D%5D%2C%5B%7B%22mn%22%3A60%2C%22mx%22%3A74%7D%5D%2C%5B%7B%22mn%22%3A75%2C%22mx%22%3A89%7D%5D%2C%5B%7B%22mn%22%3A90%2C%22mx%22%3A200%7D%5D%5D%7D

IMPORTANT: The process to recode variables is complex and is described in detail in the appendix. We are considering ways to simplify the process to recode variables in the microdata API. If we implement a simplified syntax in the future, it will break the current way of recoding variables and users will need to update their microdata API queries to the new syntax for all of their queries that use recoded variables. To stay informed of these important API changes, please sign up for Census Data API email updates.

Some continuous variables like JWMNP or AGEP can be used in microdata API queries without recoding them because they have a relatively small number of possible values. The microdata API will require you to recode any continuous variable that has more than 1,000 possible values before you can place them in the columns or rows of an API query. Otherwise, you will receive an error message. For example, if you try to place household income (HINCP) in the columns/rows without recoding it, you will get an error message: api.census.gov/data/2019/acs/acs1/pums?tabulate=weight(PWGTP)&col+HINCP

This is done because there is no way to present meaningful information to you without requiring you to define your desired categories up front. In the 2019 ACS 1-Year PUMS, HINCP values can range from a minimum of -$59,999 to a maximum of $9,999,999, which is much more than 1,000 possible values.

You can also recode categorical variables to create higher-level categories instead of getting results for the detailed categories that are provided by default. For example, consider the poverty level of persons variable (PERLIS) from the 2020 Current Population Survey Annual Social and Economic Supplement (CPS ASEC) microdata API dataset. PERLIS provides 5 detailed categories of the poverty level of a person: 

• -1 = Not in Poverty Universe
• 1 = Below Poverty Level
• 2 = 100-124 Percent of the Poverty Level
• 3 = 125-149 Percent of the Poverty Level
• 4 = 150 and Above the Poverty Level

You may not need this much detail in your analysis and may be interested in combining some of these categories together. For example, you may want to combine values 2, 3, and 4 together to create a “Not in Poverty” category. You can use the microdata API recode functionality to do this. See the appendix for details.