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Download, summarise and process LimeSurvey data

Project description

LimeSurvey is open-source survey software. Using pandas, the limepy package simplifies a number of tasks when working with LimeSurvey data:

  • Downloading survey data. This requires that LimeSurvey’s RemoteControl 2 API is enabled, as explained here.
  • Creating a list of al the questions in the survey, with metadata.
  • Summarising data, e.g. creating value counts for multi-column items such as multiple-choice questions; calculating averages for number arrays; or creating scores for a ranking question.
  • Printing answers to open-ended questions.
  • Printing the answers of an individual respondent.

Note that limepy uses f-strings and therefore requires Python 3.6 or higher.

Use at your own risk and please make sure to check the results.


$ pip install limepy

How is it different

There are various python packages for managing the LimeSurvey RemoteControl 2 API. While limepy can help you download survey data, the emphasis is on processing and summarising the data.


Download survey data

You can download survey data with the RemoteControl 2 API (provided the api is enabled in your LimeSurvey installation).

For a one-off download, you can of course do this manually. However, you may want to use the api if you want to write a preliminary report based on the first responses, and then automatically update it as new responses come in.

from pathlib import Path
from limepy import download

csv = download.get_responses(base_url, user_name, password, user_id, sid)
path = Path('../data/responses.csv')

Create Survey object

A Survey object contains the data and metadata of a survey. To create a Survey object, you need:

  • A csv containing the survey results. You can download it manually or use the api as described above. Make sure to set heading type to 'Question code' and reponse type to 'Answer codes'. If using the api to download, the file will be delimited with ; rather than ,.
  • An .lss file containing the survey structure. You can download this manually.
from limepy.wrangle import Survey, Question
import pandas as pd

df = pd.read_csv('../data/responses.csv', sep=';')
with open('../data/structure.lss', encoding="utf8") as f:
    my_structure =

my_survey = Survey(df, my_structure)

If you wish to remove html tags from the questions, set strip_tags=True.

If you have a multilingual questionnaire, then you can select the language the group names, questions, answers and help texts should be presented in, e.g. language='nl' for Dutch.

Note: if you use a merged dataframe (for example, data from various versions of the same questionnaire), you should reset the index before creating a Survey object.

Get list of questions with metadata


Print results for individual respondent

The respondent method will return a string listing the answers of an individual respondent. You need the respondent’s row index.


Create a readable dataframe

Create a dataframe with full questions as column names and ‘long’ responses as values.


Create a Question object

A Question object can be used to summarise data. To create a Question oject, you need a Survey object and the question id (find it in the index of the question list).

my_question = Question(my_survey, 3154)

If you want to use a subset of the respondents for your analysis (e.g., exclude respondents that do not meet certain criteria, or drop duplicates), the most practical approach is probably to create a subset first and use that to create your Survey object. However, you can also use a mask if you want to create a Question object for a subset of the respondents.

my_question = Question(my_survey, 3154, mask=pd.notnull(df.iloc[:, 8]))

Summarise answers to a question

For many question types, limepy can summarise the results.

  • In many cases, this will return a dataframe containing value counts (as well as Percent and Valid Percent).
  • In case of a Numerical input question, the output will be a dataframe containing the results of the pandas DataFrame describe method.
  • In case of a Numbers array question, the average will be calculated for each option (but you must specify the method, i.e. 'mean' or 'median').
  • In case of a Ranking question, the result will be a dataframe with scores calculated for each item.
  • If no method has been implemented for a question type, a dataframe will be returned which contains the columns associated with the question.

To show the metadata associated with a question:


Compare groups

Limepy currently has no method to compare groups, but you can write a function to do so (the example below may not work with all question types).

def compare(qid, category_variable, how='Valid Percent'):
    """Compare answers for groups based on category variable"""
    summaries = []
    for group in set(df[category_variable]):
        if pd.isnull(group):
        mask = list(df[category_variable] == group)
        q = Question(my_survey, qid, mask=mask)
        summary = q.summary
        if how in list(summary.columns):
            summary = summary[[how]]
        summary.columns = [group]
    return pd.concat(summaries, axis=1)

Write answers to an open-ended question

The write_open_ended method creates a string listing all the answers to the question. Optionally, you can specify a list of indices of columns that contain background information you want included in the output.


You can also create a folder and store text files containing the answers to all open-ended questions in the survey.

from pathlib import Path

remove = ' _?:/()'

def include(row):
    for string in ['free text', 'comment']:
        if string in row.question_type:
            return True
    if row.other == 'Y':
        return True
    return False

for qid, row in my_survey.question_list.iterrows():
    if include(row):
        question = row.question
        for char in remove:
            question = question.replace(char, ' ')
        question = question[:25]
        path = Path('../data/open_ended') / f'{qid} {question}.md'
        path.write_text(Question(sv, qid).write_open_ended(background_column_indices=[9]))

Create report as html

def add_table(question, question_text=None):
    """Add table summarising question"""

    if not question_text:
        question_text = question.question
    html = f"<div class='tableHeader'>{question_text}</div>\n"
    html += question.summary.to_html() + '\n'
    help_txt = question.metadata['help']
    if help_txt:
        html += f"<div class='tableCaption'>{help_txt}</div>"
    return html

html = """<head>
<link rel="stylesheet" href="styles.css">
<meta charset="utf-8">

my_question = Question(my_survey, 44)
html += add_table(my_question)

html += "</body>"

Inspect original data

If you want to inspect the original data for a specific question, for example because you want to process answers to an ‘other’ option, then you can use the question title (you can look up the title using my_survey.question_list.

title = 'G01Q07'
colnames = [c for c in df.columns if title in c]

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