How to pivot a pandas df where each column header is a date and column has multiple values

I used python BeautifulSoup and selenium to extract data from Jira’s timesheet in order to get the log work by resource.

this is the result when print my dataframe :

but the result I want to establish is :

is there a way to loop over dataframe headers and append the cell elements ?

here is the python script so far :

chromedriver_path = r"C:selinum driverschromedriver.exe"

driver = webdriver.Chrome(chromedriver_path)

# Login credentials
username = "username"
password = "pwd"

# Login to the website
driver.get("http://*******/login.jsp")
driver.find_element_by_id("login-form-username").send_keys(username)
driver.find_element_by_id("login-form-password").send_keys(password)
driver.find_element_by_id("login-form-submit").click()

# URL to retrieve table
url = "http://********/secure/projecttimesheet!project.jspa"
# Navigate to the URL
driver.get(url)


# Open the dropdown menu
dropdown_menu_button = driver.find_element(By.XPATH, '//button[@ng-init="ts.getFilterProject();"]')
dropdown_menu_button.click()


checkbox_div = driver.find_element(By.CLASS_NAME, "toggleProject")
checkbox_div.click()

# Click on the body of the page to close the dropdown menu
body = driver.find_element(By.TAG_NAME, "body")
body.click()

# Wait for the table to load
time.sleep(2)

resources_button = driver.find_element(By.ID, "sp-group-by-resources")
resources_button.click()


# Wait for the table to load
time.sleep(2)

# Parse the HTML content
soup = BeautifulSoup(driver.page_source, 'html.parser')

# Close the browser
driver.close()

# Find the table element in the HTML
table = soup.find('table')


# Read the table data into a pandas dataframe, starting from the second row
df = pd.read_html(str(table), decimal=',', thousands='.', header=1)[0]

# Remove the last 3 rows
df = df.iloc[:-4]

# Remove the "Unnamed: 22", "∑ Hours", and "∑ Days" columns
df = df.drop(columns=["Unnamed: 1" , "Unnamed: 22", "∑ Hours", "∑ Days"])

# Replace NaN values with 0
df = df.fillna(0)