Getting started with API requests in Python

This is an introductory blogpost on how to work with Application Programming Interfaces (APIs) in Python. Seeing as this is our blog’s first post on this topic I’ll spend some time explaining some basic information I’ve had to learn in the process of doing this and why it might be useful in your research. There are several blogs and websites with tutorials online and there’s no point repeating, but I’ll try to explain this for an audience like me, i.e., (a) has no formal training in computer science/web servers/HTTP but competent with scripting, and (b) interested in using this for research purposes.

What is an API?

Many sites (e.g., Facebook, Twitter, many many more) make their data available through what’s called Application Programming Interfaces or APIs. APIs basically allow software to interact, and send and receive data from servers so as to typically provide additional services to businesses, mobile apps and the like, or allow for additional analysis of the data for research or commercial purposes (e.g., collecting all trending Twitter hashtags per location to analyze how news and information propagates).

I am a civil/environmental engineer, what can APIs do for me?

APIs are particularly useful for data that changes often or that involves repeated computation (e.g., daily precipitation measurements used to forecast lake levels). There’s a lot of this kind of data relevant for water resources systems analysts, easily accessible through APIs:

I’m interested, how do I do it?

I’ll demonstrate some basic information scripts using Python and the Requests library in the section below. There are many different API requests one can perform, but the most common one is GET, which is a request to retrieve data (not to modify it in any way). To retrieve data from an API you need two things: a base URL and an endpoint. The base URL is basically the static address to the API you’re interested in and an endpoint is appended to the end of it as the server route used to collect a specific set of data from the API. Collecting different kinds of data from an API is basically a process of manipulating that endpoint to retrieve exactly what is needed for a specific computation. I’ll demo this using the USGS’s API, but be aware that it varies for each one, so you’d need to figure out how to construct your URLs for the specific API you’re trying to access. They most often come with a documentation page and example URL generators that help you figure out how to construct them.

The base URL for the USGS API is

I am, for instance, interested in collecting streamflow data from a gage near my house for last May. In Python I would set up the specific URL for these data like so:

response = requests.get("¶meterCd=00060&siteType=ST&siteStatus=all")

For some interpretation of how this was constructed, every parameter option is separated by &, and they can be read like so: data in json format; indented so I can read them more easily; site number is the USGS gage number; start and end dates; a USGS parameter code to indicate streamflow; site type ‘stream’; any status (active or inactive). This is obviously the format that works for this API, for a different API you’d have to figure out how to structure these arguments for the data you need. If you paste the URL in your browser you’ll see the same data I’m using Python to retrieve.

Object response now contains several attributes, the most useful of which are response.content and response.status_code. content contains the content of the URL, i.e. your data and other stuff, as a bytes object. status_code contains the status of your request, with regards to whether the server couldn’t find what you asked for or you weren’t authenticated to access that data, etc. You can find what the codes mean here.

To access the data contained in your request (most usually in json format) you can use the json function contained in the library to return a dictionary object:

data = response.json()

The Python json library is also very useful here to help you manipulate the data you retrieve.

How can I use this for multiple datasets with different arguments?

So obviously, the utility of APIs comes when one needs multiple datasets of something. Using a Python script we can iterate through the arguments needed and generate the respective endpoints to retrieve our data. An easier way of doing this without manipulating and appending strings is to set up a dictionary with the parameter values and pass that to the get function:

parameters = {"format": 'json', "indent": 'on',
              "sites": '04234000', "startDT": '2020-05-01',
              "endDT": '2020-05-31', "parameterCd":'00060',
              "siteType": 'ST', "siteStatus":'all'}
response = requests.get("", 

This produces the same data, but we now have an easier way to manipulate the arguments in the dictionary. Using simple loops and lists to iterate through one can retrieve and store multiple different datasets from this API.

Other things to be aware of

Multiple other people use these APIs and some of them carry datasets that are very large so querying them takes time. If the server needs to process something before producing it for you, that takes time too. Some APIs limit the number of requests you can make as a result and it is general good practice to try to be as efficient as possible with your requests. This means not requesting large datasets you only need parts of, but being specific to what you need. Depending on the database, this might mean adding several filters to your request URL to be as specific as possible to only the dates and attributes needed, or combining several attributes (e.g., multiple gages in the above example) in a single request.

Web Scraping: Constructing URLs, Downloading and Unpacking Zipped Files in Python and R


Being able to automate data retrieval helps alleviate encountering irritating, repetitive tasks. Often times, these data files can be separated by a range of times, site locations, or even type of measurement, causing them to be cumbersome to download manually. This blog post outlines how to download multiple zipped csv files from a webpage using both R and Python.

We will specifically explore downloading historical hourly locational marginal pricing (LMP) data files from PJM, a regional transmission organization coordinating a wholesale electricity market across 13 Midwestern, Southeastern, and Northeastern states. The files in question are located here.

First, Using Python

A distinct advantage of using Python over R is being able to write fewer lines for the same results. However, the differences in the time required to execute the code are generally minimal between the two languages.

Constructing URLs

The URL for the most recent file from the webpage for August 2017 has the following format: “
Notably, the URL is straightforward in its structure and allows for a fairly simple approach in attempting to create this URL from scratch.

To begin, we can deconstruct the URL into three aspects: the base URL, the date, and the file extension.
The base URL is fairly straightforward because it is constant: “
As is the file extension: “-da.csv”

However, the largest issue presented is how to approach recreating the date: “201708”
It is in the form “yyyymm”, requiring a reconstruction of a 4-digit year (luckily these records don’t go back to the first millennium) and a 2-digit month.

In Python, this is excessively easy to reconstruct using the .format() string modifying method. All that is required is playing around with the format-specification mini-language within the squiggly brackets. Note that the squiggly brackets are inserted within the string with the following form: {index:modifyer}

In this example, we follow the format above and deconstruct the URL then reassemble it within one line in Python. Assigning this string to a variable allows for easy iterations through the code by simply changing the inputs. So let’s create a quick function named url_creator that allows for the input of a date in the format (yyyy, mm) and returns the constructed URL as a string:

def url_creator(date):
    Input is list [yyyy, mm]
    yyyy, mm = date

    return "{0:}{1:02d}".format(yyyy, mm)

To quickly generate a list of the dates we’re interested in, creating a list of with entries in the format (yyyy, mm) for each of the month’s we’re interested in is easy enough using a couple of imbedded for loops. If you need alternative dates, you can easily alter this loop around or manually create a list of lists to accommodate your needs:

#for loop method
dates = []
for i in range(17):
    for j in range(12):
        dates.append([2001 + i, j + 1])

#alternative manual method
dates = [[2011, 1], [2011, 2], [2011, 3]]

Retrieving and Unpacking Zipped Files

Now that we can create the URL for this specific file type by calling the url_creator function and have all of the dates we may be interested in, we need to be able to access, download, unzip/extract, and subsequently save these files. Utilizing the urllib library, we can request and download the zipped file. Note that urllib.request.urlretrieve only retrieves the files and does not attempt to read it. While we could simply save the zipped file at this step, it is preferred to extract it to prevent headaches down the line.

Utilizing the urllib library, we can extract the downloaded files to the specified folder. Notably, I use the operating system interface method getcwd while extracting the file to save the resulting csv file into the directory where this script is running. Following this, the extracted file is closed.

import zipfile, urllib, os
from urllib.request import Request,urlopen, urlretrieve

for date in dates:
    baseurl = url_creator(date)

    local_filename, headers = urllib.request.urlretrieve(url = baseurl)
    zip_ref = zipfile.ZipFile(file = local_filename, mode = 'r')
    zip_ref.extractall(path = os.getcwd())     #os.getcwd() directs to current working directory

At this point, the extracted csv files will be located in the directory where this script was running.

Alternatively, Using R

We will be using an csv files to specify date ranges instead of for loops as shown in the Python example above. The advantage of using the csv files rather than indexing i=2001:2010 is that if you have a list of non-consecutive months or years, it is sometimes easier to just make a list of the years and cycle through all of the elements of the list rather than trying to figure out how to index the years directly. However, in this example, it would be just as easy to index through the years and month rather than the csv file.

This first block of code sets the working directory and reads in two csv files from the directory. The first csv file contains a list of the years 2001-2010. The second CSV file lists out the months January-December as numbers.

#Set working directory
#specifically specified for the directory where we want to "dump" the data

#Create csv files with a list of the relevant years and months

The breakdown of the URL was previously shown. To create this in R, we will generate a for-loop that will change this link to cycle through all the years and months listed in the csv files. The index i and j iterate through the elements in the year and month csv file respectively. (The if statement will be discussed at the end of this section). We then use the “downloadurl” and “paste” functions to turn the entire link into a string. The parts of the link surrounded in quotes are unchanging and denoted in green. The “as.character” function is used to cycle in the elements from the csv files and turn them into characters. Finally, the sep=’’ is used to denote that there should be no space separating the characters. The next two lines download the file into R as a temporary file.

#i indexes through the years and j indexes through the months
for (i in (1:16)){
  for( j in (1:12)){
      if (j>9){

    #dowload the url and save it as a temporary file


Next, we unzip the temporary file and finds the csv (which is in the form: 200101-da.csv), reading in the csv file into the R environment as “x”. We assign the name “200101-da” to “x”. Finally, it is written as a csv file and stored in the working directory, the temporary file is deleted, and the for-loop starts over again.

    #read in the csv into the global environment
    #Assign a name to the csv "x"
    #create a csv that is stored in your working directory

    unlink(temp) #deletetempfile

    #If j is between 1 and 10, the only difference is that a "0" has to be added
    #in front of the month number

Overcoming Formatting

The reason for the “if” statement is that it is not a trivial process to properly format the dates in Excel when writing to a csv to create the list of dates. When inputing “01”, the entry is simply changed to “1” when the file is saved as a csv. However, note that in the name of the files, the first nine months have a 0 preceding their number. Therefore, the URL construct must be modified for these months. The downloadurl has been changed so that a 0 is added before the month number if j < 10. Aside from the hard-coded zeroes, the block of code is the same as the one above.

else { 




        unlink(temp) #deletetempfile



Many thanks to Rohini Gupta for contributing to this post by defining the problem and explaining her approach in R.

Versions: R = 3.4.1 Python = 3.6.2
To see the code in its entirety, please visit the linked GitHub Repository.