Write a Python (console) program which: Accepts the URL of the CDC data set, your MongoDB login and your MongoDB password as command-line pararameters.

BA371 Assignment 3: Python/MongoDB — store and retrieve CDC data
This assignment assumes that you are familiar with the various topics demonstrated and practiced in the following labs:
Python — Refresher
Python — Database Communications (Part 2)
Python — XML/JSON
The CDC (Centers for Disease Control) makes available lots of data; much of it in JSON or XML form.
The CDC dataset linked here covers 1071 data records on life expectancy (at birth) for black, white and ‘all races’ males, females and ‘all genders’ for the period 1900-2018. (note: the data do not contain any information for other races/ethnicities and other gender types).
In addition to these demographic data, a lot of metadata (data describing the data) are provided.
Take a look at the data to develop a feeling for how the data are structured.
Note how the dataset contains a single JSON structure ({}), which itself contains two (2) structures, one called meta and one called data ({meta: {}, data {}}). Our data resides in the data structure.
Notice how the data structure itself is a list of lists ([ […], […], …, […] ]) with each of the inner lists representing a single row of data. Here is the first row of data:
[ “row-rhmg~ppfg~kih8”, “00000000-0000-0000-20F5-93F72E08E03C”, 0, 1599577329, null, 1599577329, null, “{ }”, “1900”, “All Races”, “Both Sexes”, “47.3”, “2518.0” ]
We are interested in storing only the following variables in MongoDB: year, race, gender, and life expectancy.
year: field 8
race: field 9
gender: field 10
life expectancy: field 11
Your assignment
Write a Python (console) program which:
Accepts the URL of the CDC data set, your MongoDB login and your MongoDB password as command-line pararameters. For instance, if your program is stored in the file foo.py, you should be able to run your program on the command-line as follows:
python foo.py https://data.cdc.gov/api/views/w9j2-ggv5/rows.json Your_MongoDB_Login Your_MongoDB_Password
Explanation: since we do not want to hardcode our MongoDB login and password in the code, we need another way to let the code ‘know’ these two items of information. We have several options to do this:
One way would be to let the code prompt the user for this information. However, doing it that way would imply that the user must be present at the keyboard every time the program runs. This is not feasible for programs which must run automatically, e.g. on a schedule.
A second way to do this would be to store the items in a file and let the program read the file.
A third way —the way we will use here— is to pass the items on the command-line when the program is called to run as shown above.
Downloads the CDC data from the web.
Extracts year, race, gender, and life expectancy from the 1071 records and stores them as 1071 documents (records) in an empty(!!) MongoDB collection named cdcCollection.
After storing the data in cdcCollection, runs the following two (2) MongoDB queries and report their results:
Life expectancy of women (All Races) in 1956 (result: 72.9)
Count of all cases with life expectancy > 75 (result: 217)
Helpful hints
See the XML/JSON lab for how to download a JSON dataset from the web.
Since the data must be stored in an empty(!!) collection with the name cdcCollection, try deleting all records from that collection before storing any in it:
db.cdcCollection.delete_many({})
If we have the whole dataset in a variable json_data, we can get the data list out like this:
our_data = json_data[“data”]
And since our_data will be a list, we can loop through it to get each of the rows out:
for row in our_data:
year = int(row[8])
etc.
When you read JSON field values from a JSON structure or dict, they are read as strings. If you want to treat them as numbers, cast them accordingly.
There may be a few cases in which the life expectancy data is missing and converting to a number will fail. In those cases, simply store the life expectancy as “null”.
Once we have the data extracted from the row, we can make a dict out of it that we can then send to Mongo for storage:
entry = {“year” : year, “race” : race, “gender” : gender, “expect” : expect}
Then insert it into our Mongo collection (see Python — Database Communications (Part 2) lab for how to do that.
Once all the data are in Mongo, we can query them (see the Python — Database Communications (Part 2) lab for how to do that.
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