Short note space

Here, I used a different method to represent hourly data across various dates. I converted specific dates to common dates and then plotted the time series data on a 24-hour time frame along the x-axis, with traffic patterns on the y-axis.  import os import pandas as pd import numpy as np import matplotlib.pyplot as plt from datetime import datetime import matplotlib.dates import matplotlib.dates as md path = 'datasheet.csv' data = pd.read_csv(path) data.head() # Convert data to DataFrame df = pd.DataFrame({'Date&Time': data['Date'] , 'DL': data['DL']/1000000000, 'UL': data['UL']/1000000000}) # Replace all Date&Time with '02/02/2024' df['Date&Time'] = '02/02/2024 ' + df['Date&Time'].str.split(' ', expand=True)[1] # Convert 'Date&Time' column to datetime format df['Formated_Date'] = pd.to_datetime(df['Date&Time'], format='%m/%d/%Y %H:%M...

First, we must identify the various activities that must be carried out in constructing a two-story house and group them into suitable work breakdown structures (WBS). The following figure indicates the WBS of this sample project and appropriate implementation periods for each activity. Note that the project has to complete within 12 months as a condition. Therefore, we have to be very careful when scheduling the duration of activities in order to complete the project on time. The next step is to build a project using a computer program. Here I used MS Project software and the following figure shows the Project plan and the status of activities completed. The red color activities and arrows indicate the critical path of the project. Path activities have to be completed between the exact period in order to hand over the project on time.