In most data science and machine learning tutorials you typically encounter csv files. Either you connect to them locally, something like this:
import pandas as pd df = pd.read_csv("my_local_data.csv")
Or you access them via a direct url like this:
import pandas as pd df = pd.read_csv("http://www.lotsofdata.com/hosted_data.csv")
What I rarely see though is connecting to slightly more obscure data sources. You will probably end up doing this once you go out into the real world of data science.
One useful data source is Google Sheets. If you have a spreadsheet hosted on Google Drive, which is made available for public access, and want to access it, it’s not immediately clear how to do that.
Let’s go through an example of how to connect to one. I’ll use a spreadsheet that has the Hacker News salary survey results from a couple of years ago.
You can’t use the url directly, because the url isn’t just pointing to the data, it’s pointing to the entire Google Sheets interface.
Instead you need the sheet’s export link.
To do this simply take the url until the /d/ part, and the unique ID that comes after, so this much:
and add /export at the end with some parameters.
You can specify the sheet number (zero-indexed) using gid, and the format to be csv using format.
The full url then becomes:
Try that in your browser and it will download the csv file directly.
You can then read it into pandas and it will be treated as a regular csv file.
Here is the associated Jupyter notebook to see it all in action.
Footnote: This is the 13th entry in my 30 day blog challenge.