ExperimentSession.read_csv_todict()

ExperimentSession.read_csv_todict(path: Union[str, pathlib.Path], encoding: str = 'utf-8', **kwargs) → Iterator[dict]

Iterates over the rows in a .csv file, yielding dictionaries.

Parameters

• path – The path to the .csv file. Usually, you want this to be a relative path to a file in a subdirectory of the experiment directory.

• encoding – Encoding of the .csv file. Defaults to ‘utf-8’.

• **kwargs – Further arguments passed on to csv.DictReader

Yields

dict – A dictionary in which the keys are the column names.

Examples

Consider the following csv-file, located at files/data.csv in your experiment directory:

col1    ,   col2    ,   col3
text_a  ,   text_b  ,   text_c
text_d  ,   text_e  ,   text_f

When building a page, usual usage would be:

import alfred3 as al
exp = al.Experiment()

@exp.member
class CSVDemoPage(al.Page):     # this could also be a Section
    name = "csv_demo"

    def on_exp_access(self):

        for row in self.exp.read_csv_todict("files/data.csv"):
            print(row)

The output would be the following:

{"col1": "text_a", "col2": "text_b", "col3": "text_c"}  # first iteration
{"col1": "text_d", "col2": "text_e", "col3": "text_f"}  # second iteration

If you need a full list of the rows, you can wrap the function call in list():

import alfred3 as al
exp = al.Experiment()

@exp.member
class CSVDemoPage(al.Page):     # this could also be a Section
    name = "csv_demo"

    def on_exp_access(self):

        data = list(self.exp.read_csv_todict("files/data.csv"))
        print(data)

The output would be the following:

[{"col1": "text_a", "col2": "text_b", "col3": "text_c"},
{"col1": "text_d", "col2": "text_e", "col3": "text_f"}]