Time Intervals

Process Time Intervals and Group by Task Code

This script is used to process a list of time intervals, load the data into a Pandas DataFrame, calculate the duration of each interval, and group the intervals by task code.

Input Data Format

The input data is a string containing a list of time intervals. Each time interval is formatted as follows:

{end_date_and_time}
; {task_code} {comment}
{start_date_and_time}

Where:

• {end_date_and_time}: The date and time when the task ended, in DD.MM.YYYY HH:MM:SS format (e.g., 24.03.2025 23:29:38).

• {task_code}: A unique identifier for the task (e.g., TASK-1234).

• {comment}: A brief description of the task (e.g., Estimate new features).

• {start_date_and_time}: The date and time when the task started, in DD.MM.YYYY HH:MM:SS format (e.g., 24.03.2025 22:50:13).

Example Input

24.03.2025 23:29:38
; TASK-1234 Estimate new features
24.03.2025 22:50:13

25.03.2025 10:15:00
; TASK-1234 Implement feature A
25.03.2025 09:00:00

26.03.2025 12:00:00
; TASK-5678 Bug fixing
26.03.2025 11:00:00

Expected Output

The expected output is a Pandas DataFrame grouped by task_code. Optionally, the output can include aggregated duration statistics for each task code.

Processing Steps

import streamlit as st
import pandas as pd
import re
from datetime import datetime

Print banner

@st.cache_data
def print_banner():
    print("""
    .___________.       __  .__   __. .___________.
    |           |      |  | |  \\ |  | |           |
    `---|  |----`______|  | |   \\|  | `---|  |----`
        |  |    |______|  | |  . `  |     |  |
        |  |           |  | |  |\\   |     |  |
        |__|           |__| |__| \\__|     |__|

    """)
    return 1

print_banner()

Input data

data = st.text_area("Time Intervals", height=300)

Regular expression pattern to extract intervals

pattern = re.compile(r'(\d{2}\.\d{2}\.\d{4} \d{2}:\d{2}:\d{2})\n; ([\w\-]+) (.*?)\n(\d{2}\.\d{2}\.\d{4} \d{2}:\d{2}:\d{2})', re.DOTALL)

Process input

def process():
    # Extract matches
    matches = pattern.findall(data)

    if len(matches) == 0:
        st.error('Time Intervals not found', icon='❌')
        # st.stop()
        return

Convert extracted data into a DataFrame

records = []
for end_dt, task_code, comment, start_dt in matches:
    start = datetime.strptime(start_dt, '%d.%m.%Y %H:%M:%S')
    end = datetime.strptime(end_dt, '%d.%m.%Y %H:%M:%S')
    duration = (end - start).total_seconds() / 3600
    records.append({
        'start_datetime': start,
        'end_datetime': end,
        'task_code': task_code,
        'comment': comment,
        'duration_hours': duration
    })

# Create DataFrame
df = pd.DataFrame(records)

Group by task_code, sum durations, and join comments

grouped_df = df.groupby('task_code', as_index=False).agg({
    'duration_hours': 'sum',
    'comment': lambda x: ' // '.join(x)
})

Display results

# st.write("### Detailed DataFrame:")
# st.table(df)

st.write("### Grouped DataFrame (Total Duration by Task):")
st.table(grouped_df)

Click button

if st.button("Process", type='primary', use_container_width=True):
    process()