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@glasnt glasnt/ Secret
Created May 12, 2019

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# Using the PreTalx API, export Submission and Review data, pre-process slightly,
# and save as a large XLSX file for further data manipulation in Google Sheets, Microsoft Excel, Apple Numbers, etc.
# This code probably won't *just work* for you, but serves as a assistant file to a personal blog post, only.
# Licence: BSD-3
import requests
import json
import os
import csv
import glob
import pandas as pd
import sys
EVENT = "YourEventNameHere"
PRETALX_TOKEN = os.environ.get("PRETALX_API", None)
def slug(s):
return "" % (EVENT, s)
def get(uri):
resp = requests.get(uri, headers={"Authorization": "Token %s" % PRETALX_TOKEN})
if resp.ok:
return resp.json()
return {}
def get_data(name):
resp = get(uri=slug(name))
results = resp["results"]
while resp["next"]:
resp = get(uri=resp["next"])
results += resp["results"]
return resp
submissions = get_data("submissions")
reviews = get_data("reviews")
# Preprocess submissions
subs = {}
for s in submissions:
s["total_score"] = 0
s["count_score"] = 0
speakers = []
for x in s["speakers"]:
s["speakers"] = " & ".join(speakers)
s["track"] = s["track"]["en"]
subs[s["code"]] = s
# Preprocess reviewers
reviewers = {}
for r in reviews:
subs[r["submission"]]["total_score"] += r["score"]
subs[r["submission"]]["count_score"] += 1
if r["user"] not in reviewers.keys():
reviewers[r["user"]] = []
r["code"] = subs[r["submission"]]["code"]
r["title"] = subs[r["submission"]]["title"]
r["speakers"] = subs[r["submission"]]["speakers"]
r["track"] = subs[r["submission"]]["track"]
# Write reviewer data to csv files, separated by reviewer
for idx, r in enumerate(reviewers):
fn = "data/%s_%s.csv" % (str(idx + 1).zfill(2), r.replace(" ", ""))
f = csv.writer(open(fn, "w"))
keys = ["code", "title", "track", "speakers", "score", "text"]
for x in reviewers[r]:
data = []
for k in keys:
print(r, len(reviewers[r]))
# write submission data to csv file
f = csv.writer(open("data/00_submissions.csv", "w"))
keys = ["code", "title", "track", "speakers", "total_score", "count_score"]
f.writerow(keys + ["avg"])
for idx, s in enumerate(subs):
data = []
for k in keys:
t = subs[s]["total_score"]
c = subs[s]["count_score"]
if c == 0:
# Save all the files as one XLSX, with each file as a different sheet/tab
writer = ExcelWriter("compiled.xlsx")
for filename in sorted(glob.glob("data/*.csv")):
df_csv = pd.read_csv(filename)
(_, f_name) = os.path.split(filename)
(f_shortname, _) = os.path.splitext(f_name)
if not f_shortname:
f_shortname = f_name
df_csv.to_excel(writer, f_shortname, index=False)
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