utils.normalise_datetime

Contains the utility function that allows for ease of testing the ingestion functions.

 1"""
 2Contains the utility function that allows for ease of testing the ingestion functions.
 3"""
 4
 5from datetime import datetime
 6
 7
 8def normalise_datetimes(rows):
 9    """
10    Iterates through a list of dictionaries and converts datetime objects to string in the format 'YYYY-MM-DD HH:MM:SS.sss' so they can be compared with expected string values.
11
12    # Arguments:
13        rows: A list of dictionaries; each dictionary represents a data record.
14
15    # Return:
16        A list of dictionaries where the datetime values are formatted as strings.
17
18    """
19    for row in rows:
20        for key, value in row.items():
21            if isinstance(value, datetime):
22                row[key] = value.strftime("%Y-%m-%d %H:%M:%S.%f")[:-3]
23    return rows
def normalise_datetimes(rows): 
 9def normalise_datetimes(rows):
10    """
11    Iterates through a list of dictionaries and converts datetime objects to string in the format 'YYYY-MM-DD HH:MM:SS.sss' so they can be compared with expected string values.
12
13    # Arguments:
14        rows: A list of dictionaries; each dictionary represents a data record.
15
16    # Return:
17        A list of dictionaries where the datetime values are formatted as strings.
18
19    """
20    for row in rows:
21        for key, value in row.items():
22            if isinstance(value, datetime):
23                row[key] = value.strftime("%Y-%m-%d %H:%M:%S.%f")[:-3]
24    return rows

Iterates through a list of dictionaries and converts datetime objects to string in the format 'YYYY-MM-DD HH:MM:SS.sss' so they can be compared with expected string values.

Arguments:

rows: A list of dictionaries; each dictionary represents a data record.

Return:

A list of dictionaries where the datetime values are formatted as strings.