Metadata-Version: 2.1
Name: jello
Version: 0.5.0
Summary: Filter JSON and JSON Lines data with Python syntax.
Home-page: https://github.com/kellyjonbrazil/jello
Author: Kelly Brazil
Author-email: kellyjonbrazil@gmail.com
License: MIT
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Topic :: Utilities
Requires-Python: >=3.6
Description-Content-Type: text/markdown

# jello
Filter JSON and JSON Lines data with Python syntax

`jello` is similar to `jq` in that it processes JSON and JSON Lines data except `jello` uses standard python dict and list syntax.

JSON or JSON Lines can be piped into `jello` (JSON Lines are automatically slurped into a list of dictionaries) and are available as the variable `_`. Assign the output the the variable `r` to print as JSON or simple lines.

For more information on the motivations for this project, see my [blog post](https://blog.kellybrazil.com/2020/03/25/jello-the-jq-alternative-for-pythonistas/).

## Install
```
pip3 install --upgrade jello
```

### Usage
```
<JSON Data> | jello [OPTIONS] query
``` 
`query` can be most any valid python code as long as the result is assigned to `r`. `_` is the sanitized JSON from STDIN presented as a python dict or list of dicts. For example:
```
$ cat data.json | jello 'r = _["key"]'
```

**Options**
- `-c` compact print JSON output instead of pretty printing
- `-i` initialize environment with a custom config file
- `-l` lines output (suitable for bash array assignment)
- `-r` raw output of selected keys (no quotes)
- `-n` print selected null values
- `-h` help
- `-v` version info

> Note: The `lines()` convenience function has been deprecated and will be removed in a future version. Use the `-l` option instead to generate output suitable for assignment to a bash variable or array. Use of the `lines()` function will generate a warning message to `STDERR`.

**Custom Configuration File**

You can use the `-i` option to initialize the `jello` environment with your own configuration file. The configuration file accepts valid python code and can be as simple as adding `import` statements for your favorite libraries.

The filename must be `.jelloconf.py` and must be located in the proper directory based on the OS platform:
- Linux: `~/`
- Windows: `%appdata%/`

To simply import a module (e.g. `glom`) your `.jelloconf.py` file would look like this:
```
from glom import *
```
Then you could use `glom` in your `jello` filters:
```
$ jc -a | jello -i 'r = glom(_, "parsers.25.name")'

"lsblk"
```

Alternatively, if you wanted to initialize your `jello` environment to substitute `glom` syntax for `_` your `.jelloconf.py` file could look like this:
```
def _(q, data=_):
    import glom
    return glom.glom(data, q)
```
Then you could use the following syntax to filter the JSON data:
```
$ jc -a | jello -i 'r = _("parsers.6.compatible")'

[
  "linux",
  "darwin",
  "cygwin",
  "win32",
  "aix",
  "freebsd"
]
```

## Examples:
### lambda functions and math
```
$ echo '{"t1":-30, "t2":-20, "t3":-10, "t4":0}' | jello '\
keys = _.keys()
vals = _.values()
cel = list(map(lambda x: (float(5)/9)*(x-32), vals))
r = dict(zip(keys, cel))'

{
  "t1": -34.44444444444444,
  "t2": -28.88888888888889,
  "t3": -23.333333333333336,
  "t4": -17.77777777777778
}

```
```
$ jc -a | jello 'r = len([entry for entry in _["parsers"] if "darwin" in entry["compatible"]])'

32
```
### for loops
Output as JSON array
```
jc -a | jello '\
r = []
for entry in _["parsers"]:
  if "darwin" in entry["compatible"]:
    r.append(entry["name"])'

[
  "airport",
  "airport_s",
  "arp",
  "crontab",
  "crontab_u",
  ...
]
```
Output as bash array
```
jc -a | jello -rl '\
r = []
for entry in _["parsers"]:
  if "darwin" in entry["compatible"]:
    r.append(entry["name"])'

airport
airport_s
arp
crontab
crontab_u
...
```
### List and Dictionary Comprehension
Output as JSON array
```
$ jc -a | jello 'r = [entry["name"] for entry in _["parsers"] if "darwin" in entry["compatible"]]'

[
  "airport",
  "airport_s",
  "arp",
  "crontab",
  "crontab_u",
  ...
]
```
Output as bash array
```
$ jc -a | jello -rl 'r = [entry["name"] for entry in _["parsers"] if "darwin" in entry["compatible"]]'

airport
airport_s
arp
crontab
crontab_u
...
```
### Environment Variables
```
$ echo '{"login_name": "joeuser"}' | jello '\
r = True if os.getenv("LOGNAME") == _["login_name"] else False'

true
```
### Using 3rd Party Libraries
You can import and use your favorite libraries to manipulate the data.  For example, using `glom`:
```
$ jc -a | jello '\
from glom import *
r = glom(_, ("parsers", ["name"]))'

[
  "airport",
  "airport_s",
  "arp",
  "blkid",
  "crontab",
  "crontab_u",
  "csv",
  ...
]
```
### Complex JSON Manipulation
The data from this example comes from https://programminghistorian.org/assets/jq_twitter.json

Under **Grouping and Counting**, Matthew describes an advanced `jq` filter against a sample Twitter dataset that includes JSON Lines data. There he describes the following query:

“We can now create a table of users. Let’s create a table with columns for the user id, user name, followers count, and a column of their tweet ids separated by a semicolon.”

https://programminghistorian.org/en/lessons/json-and-jq

Here is a simple solution using `jello`:
```
$ cat jq_twitter.json | jello -l '\
user_ids = set()
r = []
for tweet in _:
    user_ids.add(tweet["user"]["id"])
for user in user_ids:
    user_profile = {}
    tweet_ids = []
    for tweet in _:
        if tweet["user"]["id"] == user:
            user_profile.update({
                "user_id": user,
                "user_name": tweet["user"]["screen_name"],
                "user_followers": tweet["user"]["followers_count"]})
            tweet_ids.append(str(tweet["id"]))
    user_profile["tweet_ids"] = ";".join(tweet_ids)
    r.append(user_profile)'
...
{"user_id": 2696111005, "user_name": "EGEVER142", "user_followers": 1433, "tweet_ids": "619172303654518784"}
{"user_id": 42226593, "user_name": "shirleycolleen", "user_followers": 2114, "tweet_ids": "619172281294655488;619172179960328192"}
{"user_id": 106948003, "user_name": "MrKneeGrow", "user_followers": 172, "tweet_ids": "501064228627705857"}
{"user_id": 18270633, "user_name": "ahhthatswhy", "user_followers": 559, "tweet_ids": "501064204661850113"}
{"user_id": 14331818, "user_name": "edsu", "user_followers": 4220, "tweet_ids": "615973042443956225;618602288781860864"}
{"user_id": 2569107372, "user_name": "SlavinOleg", "user_followers": 35, "tweet_ids": "501064198973960192;501064202794971136;501064214467731457;501064215759568897;501064220121632768"}
{"user_id": 22668719, "user_name": "nodehyena", "user_followers": 294, "tweet_ids": "501064222772445187"}
...
```

