jsonToCsv 1.0.0

The Problem

The problem with converting json to csv is that json is a dynamic, multi-typed, nested format. Csv on the other hand is a fixed single-type format.

JsonToCsv solves this by defining a meta language (format string) which can be used to define repeatable and fixed-format steps, allowing the flattening of the wide json domain space into the slim csv space.

Module PyDoc

You can access the pydoc here: http://htmlpreview.github.io/?https://github.com/kata198/jsonToCsv/blob/master/doc/index.html

Module Usage Example

See: https://github.com/kata198/jsonToCsv/blob/master/example.py and https://github.com/kata198/jsonToCsv/blob/master/example_mutli.py.

For a basic example of using the module directly for extraction and reformatting into various formats (CSV, TSV, a text table)

Extracting Data

Once you’ve written your formatStr and created the JsonToCsv object, you’re ready to start parsing!

extractData

extractData is the “core” method of JsonToCsv. It performs the actual work of taking the json and following the format string to create a series of lines.

The output of this method is a list of lists, the outer list is each line, and each line is a list where each element represents a field.

Some more complicated use-cases where “extractData” is required are:

• Creating alternate formats of output (like TSV or a text table, or plugging into a GUI)

• Analysis of the data, i.e. filtering or modifying

• Joining data from multiple JSON entries (see that section for more info)

• Whatever you need to do

You can pass the output of this function to the “dataToStr” method to convert it into a printable string.

dataToStr

dataToStr provides the means to convert data (from extractData) to a printable string.

The first argument is the list-of-lists that extractData provides

It then has the following optional arguments:

• separator - Defaults to comma, may use tab for TSV, or whatever you want

• lineSeparator - Defaults to CRLF (rn) which is the RFC4180 standard, but you may use something else (like n).

• quoteFields - This you can set to True or False to explicitly quote or not quote data per RFC4180 standards. The default is the string “smart”, which means the data will be scanned to see if it needs quoting, and if so, it will quote the data. Otherwise, it will not. Generally you will want to keep this at the default.

convertToCsv

The most basic and direct method is the “convertToCsv” function. You can pass in a string (raw data) or a dict (already parsed e.g. by ‘json’ module ), and you’ll be output the csv lines, ready to be passed to the “print” function.

This is the same as calling extractData and passing it to dataToStr, except you can only use comma as a separator through this function.

This function takes the same “lineSeparator” and “quoteFields” arguments described in “dataToStr” above.

findDuplicates

This function can help you identify when multiple lines contain the same data in the same field.

You pass in the data extracted by extractData, pick a zero-origin “fieldNum”, which dictates which field to check on each line for duplicate values.

If the “flat” argument is False (default), the output is a map where the keys are all the field values which had duplicate entries.

If “flat” is True, the output is just a list of list-of field values. Basically, the data from extractData, but ONLY included if it has a duplicate in the chosen field.

joinCsv

joinCsv will take in two sets of list<list<str>> (i.e. returned frmo “extractData”), and two 0-origin numbers, joinFieldNum1 (what is the index of the “join field” in the first dataset) and joinFieldNum2.

So for example, you may have two sets of data, both describing people. “Social Security Number” could be the 4th field from zero on one of them, and the 0th on another dataset. So if you want to combine these two datasets, you can use this method to do so, bt joining those fields (i.e. any instances where there’s a field match between the two joinFieldNum columns, that index is removed from the second dataset, sand the second dataset is appended to the first.

multiJoinCsv

Same as joinCsv, but joinCsv allows no duplicates within a dataset itself. So going with the data above, imagine if the same social security number had two people’s names in one dataset…. well which one is rght? A computer can’t determine that.

So this function will give a “best effort”, in the above example, you’d get person X’s dataset attached to whoemver had that social security number listed. So if you have a field duplicated twice in both csvData1 and csvData2, you’ll end up with 4 lines total:

• A1 B1

• A2 B1

• A1 B2

• A2 B3

This matches very eagerly, but you may start to get some invalid data at this point.

FULL EXAMPLE:

.”Data”[ +”Instances”[ “hostname”, /”attrs”[“key”=”role” “value”], /”attrs”[“key”=”created_at” “value”, “who_set”], .”Performance”[ “cpus”, “memory” ] ] ]

Explanation:

The given json object will first be descended by the “Data” key, where a map is expected.

In this map, “Instances” will be the “line item”, i.e. we will iterate over each item in the “Instances” list to generate each line of the csv.

So, for each map in “Instances”:

• We print the “hostname” key as the first csv element

• We descend into a list of maps under the key “attrs”,

• Search for where one of those maps has an entry “key” with the value “role”, and we print the value of the “value” key of that map as the second csv element.

Then, we return to previous level.

We descend again into that list of maps under the key “attrs”,

• Search for where one of those maps has an entry “key” with the value “created_at”, and we print the value of the “value” key of that map as the third csv element.

• We then print value of the “who_set” key of that same map as the fourth csv element.

Then, we return to the previous level

We then descend into a map under the key ‘Performance’

• we print the value of the key “cpus” at this level as the fifth csv element.

• we print the value of the key “memory” at this level as the sixth csv element.

Then, we return to the previous level

We return to the previous level

(we are done iterating at this point)

We return to the previous level

Example with inline comments:

The following is the meant to parse the following json: https://github.com/kata198/jsonToCsv/blob/master/example_multi.json

PARSE_STR = ‘’’

“date”, # First element of every line will be the value of

# “date” at the top level

+”results”[ # Iterate over each member of the list under “results”

“myBeforeKey”, # Include “myBeforeKey” as the next item in every line

+”instances”[ # Iterate over each member of the list under “instances”

“hostname”, # Include “hostname” under “instances” in each line

“ip” # Next key to add is “ip”

/”attributes”[“name”=”status” # Descend into a list-of-maps under “attributes” and look

# for where the key “name” has the value “status”

“value” # In the matched-map, print the value of the key “value”

], # Leave this matched map, return to one level up

.”puppet_data”[ # Descend into map found at “puppet_data” key

“hostgroup” # Print the “hostgroup” key at this level

], # Return to previous level

/”attributes”[“name”=”domain” # Descend into a list-of-maps under “attributes” and look

# for where the key “name” has the value “domain”

“value” # Print the “value” key in this matched map

], # Go back up to previous level

/”attributes”[“name”=”owner” # Descend into a list-of-maps at “attributes” and look

# for where the key “name” has the value “owner”

“value” # Print the key “value” at this level

] # Go back to previous level

] # Go back to previous level

“myAfterKey” # Append to all previous lines the value of key “myAfterKey”

], # Go back up a level

“name” # Append to all previous lines the value of key “name”

‘’’