surveyflow

A Python library for processing survey data — parse survey definitions and responses into structured outputs ready for analysis.

Features

Parse survey definition (question structure, types, positions) into metadata.json
Parse survey response rows into rawdata.csv with numeric codes
- Single-choice → integer code (e.g. 1)
- Multi-choice / ranking → semicolon-separated codes (e.g. "1;3;5")
- Open-ended / matrix / number → raw text
Filter responses by status (default: approved only)
Consistent columns between rawdata.csv and metadata.json

Installation

pip install surveyflow

Quick Start

from surveyflow.steps.ingestion import IngestionStep

# definition: dict from your survey platform's definition API
# rows_pages: list of paginated response pages from your survey platform

step = IngestionStep()
context = step.run({
    "definition":  definition,
    "rows_pages":  rows_pages,
    "output_dir":  "./output",
})

df       = context["rawdata"]      # pandas DataFrame
metadata = context["metadata"]     # dict with question info + value labels

Output

`rawdata.csv`

task_id	date_time	q6	q7	q10	q18
task_001	2026-03-01	1	2	1	1;3
task_002	2026-03-01	2	1	2	2

`metadata.json`

{
  "survey_id": 12345,
  "questions": {
    "q6": {
      "position": 6,
      "english_question": "Please provide your current address",
      "answer_type": "singlechoice",
      "values": { "1": "Ward 1", "2": "Ward 2", "3": "Ward 3" }
    },
    "q18": {
      "position": 18,
      "english_question": "Who do you live with",
      "answer_type": "multiplechoice",
      "values": { "1": "Spouse", "2": "Parents", "3": "Children" }
    }
  }
}

Input Format

`definition`

{
    "survey": { "survey_id": 12345, "title": "...", ... },
    "questions": [
        {
            "question_id": 1001,
            "position": 6,
            "question": "...",
            "english_question": "Please provide your current address",
            "type": 2,        # 2=singlechoice, 3=multiplechoice, 6=ranking, 4=matrix, ...
            "input_type": 0,
            "mandatory": True,
            "status": 1
        },
        ...
    ]
}

`rows_pages`

[
    {   # page 1
        "rows": [
            {
                "task_id": "task_001",
                "date_time": "2026-03-01 09:00:00",
                "profile_status": "approved",
                "questions": [
                    { "type": "singlechoice", "question": "Please provide your current address", "answer": "Ward 1" },
                    { "type": "multiplechoice", "question": "Who do you live with",
                      "answer": [{"answer_name": "Spouse"}, {"answer_name": "Children"}] },
                    ...
                ]
            },
            ...
        ]
    },
    # page 2, page 3, ...
]

Answer Types

`type` value	`answer_type`	Encoded in rawdata?
2	`singlechoice`	Yes → int
3	`multiplechoice`	Yes → `"1;3;5"`
6	`ranking`	Yes → `"2;1;3"`
4	`matrix`	No → `"row:col\|row:col"`
1 + input_type=100	`multiplenumber`	No → `"label:num\|label:num"`
1	`freetext`	No → raw text
1 + input_type=3	`singlenumber`	No → raw number
1109	`area`	No → raw text

Excluded from output: audio, user-name, user-phone, instruction, reward.

Profile Status Filter

# Default: approved only
step.run({ ..., "profile_status": ["approved"] })

# Include all statuses
step.run({ ..., "profile_status": [] })

# Custom filter
step.run({ ..., "profile_status": ["approved", "pending"] })

Requirements

Python >= 3.10
pandas >= 2.0
openpyxl >= 3.1

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