fix: Date format when importing international timestamps

[michael-s-molina]

SUMMARY

This PR fixes a problem when importing CSV data that contains international timestamps (days come before months). We use Pandas read_csv to handle this operation and this PR exposes the dayfirst configuration to address the issue.

BEFORE/AFTER SCREENSHOTS OR ANIMATED GIF

Before: using the following CSV, we can see that the dates are not parsed correctly.

Date Time,Group,Team
01-08-2023 02:01:00,A,2
02-08-2023 02:01:00,A,2
03-08-2023 02:01:00,A,2
04-08-2023 02:01:00,A,2
05-08-2023 02:01:00,A,2
06-08-2023 02:01:00,B,1
07-08-2023 02:01:00,B,1
08-08-2023 02:01:00,B,1
09-08-2023 02:01:00,B,1
10-08-2023 02:01:00,B,1
11-08-2023 02:01:00,B,3
12-08-2023 02:01:00,B,3
13-08-2023 02:01:00,B,3
14-08-2023 02:01:00,A,4
15-08-2023 02:01:00,C,4
16-08-2023 02:01:00,C,4
17-08-2023 02:01:00,C,4
18-08-2023 02:01:00,C,4
19-08-2023 02:01:00,C,4
20-08-2023 02:01:00,C,4
21-08-2023 02:01:00,C,4

After: We provide the Day First configuration for international formats.

TESTING INSTRUCTIONS

1 - Import the example CSV
2 - Check the Day First configuration
3 - Make sure data is imported correctly

ADDITIONAL INFORMATION

• Has associated issue:
• Required feature flags:
• Changes UI
• Includes DB Migration (follow approval process in SIP-59)
  • Migration is atomic, supports rollback & is backwards-compatible
  • Confirm DB migration upgrade and downgrade tested
  • Runtime estimates and downtime expectations provided
• Introduces new feature or API
• Removes existing feature or API

[michael-s-molina]

This is the relevant change. The rest is just formatting.

[eschutho]

LGTM!