Python Tutorial: Using the Census API

Key Takeaways

the data frame used in the first lesson was downloaded from the census API server now we'll learn how to construct an API request and load the response into a panda's data frame this is a basic census API request the part up to the question mark is referred to as the base URL and specifies the host year and data set the part after the question mark is referred to as the query string this is where we specify parameters such as the variables being requested and the desired geography although the URL can be constructed using simple string manipulation we will use the request library imported here to construct the URL we define variables for the host year and data set then build the base URL by joining these variables with a slash dataset equals Dec / SF one stands for summary file one and refers to the full count data from the decennial census the requests get method accepts query parameters as a dictionary the Census API documentation refers to these parameters as predicates so we name the dictionary predicates the variables to request are assigned to a list get bars these include the name of the geographic unit the land area in square meters and the full population count don't worry about the obscure name p zero zero one zero zero one will learn where to find these variable names later a dictionary key get is created by joining the variable names into a comma separated string set the four key to the geographic level we use state followed by the asterisk wildcard to request all states finally execute the request and store the return value in the response object our now inspect the text attribute of the response object is a string with the form of a list of lists each sub list is a row of data with the first row being the table header notice that all values including numerix are double quoted when we load this into pandas we will have to fix these data types in case of a poorly formed request the API server will return an error message one common error is specifying the variable name incorrectly the json method of the response object returns a list of lists the first sub list contains the column names every api call returns geographic identifiers this one returned the column named state throughout this course we will be renaming columns to make them easier to work with begin by creating a list containing the new column names all lowercase now construct the data frame using PD data frame the new column names get passed to the columns parameter the JSON list of Lists gets passed to the data parameter use slicing to skip row 0 which contains the header 2 columns have integer data currently stored as text fix the data types by passing the correct type int to the as type method use head to view the result the state identifier like a zip code has leading zeros and should be left as text now that the data is loaded let's see an example analysis create a new column pop per km2 by dividing the population by the land area the result is in square meters multiplied by 1000 squared to get the density in square kilometers then use n largest to return the three top records the first parameter is the number of rows to return the second is the column to sort by you've seen key elements of the census API now it's your turn to try