def agg(file,groupby, applyname, func):
"""Operates on a groupby column in a csv file and applies a function
Example Usage:
./csvcli.py cvsops --file ext/input.csv --groupby last_name --applyname count --func npmedian
Processing csvfile: ext/input.csv and groupby name: last_name and applyname: count
2017-06-22 14:07:52,532 - nlib.utils - INFO - Loading appliable functions/plugins: npmedian
2017-06-22 14:07:52,533 - nlib.utils - INFO - Loading appliable functions/plugins: npsum
2017-06-22 14:07:52,533 - nlib.utils - INFO - Loading appliable functions/plugins: numpy
2017-06-22 14:07:52,533 - nlib.utils - INFO - Loading appliable functions/plugins: tanimoto
last_name
eagle 17.0
lee 3.0
smith 13.5
Name: count, dtype: float64
"""
if not file and not groupby and not applyname and not func:
click.echo("--file and --column and --applyname --func are required")
sys.exit(1)
click.echo("Processing csvfile: {file} and groupby name: {groupby} and applyname: {applyname}".\
format(file=file, groupby=groupby, applyname=applyname))
#Load Plugins and grab correct one
plugins = utils.plugins_map()
appliable_func = plugins[func]
res = csvops.group_by_operations(data=file,
groupby_column_name=groupby, apply_column_name=applyname,
func=appliable_func)
click.echo(res)
def csv_aggregate_columns(groupbyop):
"""Aggregate column in an uploaded csv
---
consumes: application/json
parameters:
- in: path
name: Appliable Function (i.e. npsum, npmedian)
type: string
required: true
description: appliable function, which must be registered (check /api/funcs)
- in: query
name: column
type: string
description: The column to process in an aggregation
required: True
- in: query
name: group_by
type: string
description: The column to group_by in an aggregation
required: True
- in: header
name: Content-Type
type: string
description: Requires "Content-Type:application/json" to be set
required: True
- in: body
name: payload
type: string
description: base64 encoded csv file
required: True
responses:
200:
description: Returns an aggregated CSV.
"""
#TO DO?: Make this into a helper function
#Return 415 if not valid content type
content_type = request.headers.get('Content-Type')
content_type_log_msg = "Content-Type is set to: {content_type}".\
format(content_type=content_type)
log.info(content_type_log_msg)
if not content_type == "application/json":
wrong_method_log_msg =\
"Wrong Content-Type in request: {content_type} sent, but requires application/json".\
format(content_type=content_type)
log.info(wrong_method_log_msg)
return jsonify({
"content_type": content_type,
"error_msg": wrong_method_log_msg
}), status.HTTP_415_UNSUPPORTED_MEDIA_TYPE
#Parse Query Parameters and Retrieve Values
query_string = request.query_string
query_string_msg = "Request Query String: {query_string}".format(
query_string=query_string)
log.info(query_string_msg)
column = request.args.get("column")
group_by = request.args.get("group_by")
#Query Parameter logging and handling
query_parameters_log_msg = "column: [{column}] and group_by: [{group_by}] Query Parameter values".\
format(column=column, group_by=group_by)
log.info(query_parameters_log_msg)
if not column or not group_by:
error_msg = "Query Parameter column or group_by not set"
log.info(error_msg)
return jsonify({
"column": column,
"group_by": group_by,
"error_msg": error_msg
}), status.HTTP_400_BAD_REQUEST
#Load Plugins and grab correct one
plugins = utils.plugins_map()
appliable_func = plugins[groupbyop]
#TO DO?: Add some additional error handling (invalid column name, etc)
#Unpack data and operate on it
data, _ = _b64decode_helper(request)
#Returns Pandas Series
res = csvops.group_by_operations(data,
groupby_column_name=group_by,
apply_column_name=column,
func=appliable_func)
log.info(res)
return res.to_json(), status.HTTP_200_OK
