So I’m working on this website that uses selenium to generate certificates and download them. I installed selenium yesterday as well as webdriver and when I tried to import selenium into my codebase, the compiler doesn’t seem to recognize it. Here is the code to better understand its use:

import pandas as pd
import re
from selenium import webdriver
from selenium.webdriver.common.by import By
from selenium.webdriver.support.ui import WebDriverWait
from selenium.webdriver.support import expected_conditions as EC
from selenium.common.exceptions import TimeoutException, NoSuchElementException
from selenium.webdriver.common.keys import Keys
import datetime
import time

# Load the data
cumulativeSummary = pd.read_excel('cumulativeSummary.xls', header=1)
awards = pd.read_csv('specialAwards.csv')

# Remove rows where the 'Scout' column exactly matches 'Guest, Troop 226 Scout'
cumulativeSummary = cumulativeSummary[cumulativeSummary.iloc[:, 0] != 'Guest, Troop 226 Scout']
awards = awards[awards['Scout'] != 'Guest, Troop 226 Scout']

# Remove unnecessary columns from the awards DataFrame
awards = awards.drop(columns=['COH', 'Started'])

# Function to add hyphens where necessary
def add_hyphens(award):
    return re.sub(r'(d+)s+(w+)', r'1-2', award)

# Apply the function to the 'Award' column
awards['Award'] = awards['Award'].apply(add_hyphens)

# Filter the awards DataFrame for camping and service awards
awards = awards[awards['Award'].str.contains('Nights Camping|Hours Service', na=False)]

# Remove rows where both "Earned" and "Awarded" columns are not empty
awards = awards[~(awards['Earned'].notna() & awards['Awarded'].notna())]

# Drop rows where "Awarded" column is not NaN
awards = awards[awards['Awarded'].isna()]

# Drop the "Earned" column
awards = awards.drop(columns=['Earned'])

# Organize awards into categories
camping_awards = awards[awards['Award'].str.contains('Nights Camping', na=False)]
service_awards = awards[awards['Award'].str.contains('Hours Service', na=False)]

def print_awards(category, awards_df):
    results_list = []
    if category == 'camping':
        print("nCamping Awards:")
        days_list = [300, 275, 250, 225, 200, 175, 150, 125, 100, 75, 50, 25, 10]
        awarded_scouts = set()
    elif category == 'service':
        print("nService Awards:")
        hours_list = [100, 75, 50, 25]
        awarded_scouts = set()
    else:
        return results_list

    for award in (days_list if category == 'camping' else hours_list):
        eligible_scouts = awards_df[awards_df['Award'].str.contains(f"{award}-", na=False)]
        if not eligible_scouts.empty:
            print(f"- {award}-{'Nights Camping' if category == 'camping' else 'Hours Service'}:")
            for index, row in eligible_scouts.iterrows():
                if row['Scout'] not in awarded_scouts:
                    print(f"  - {row['Scout']}")
                    awarded_scouts.add(row['Scout'])
                    results_list.append({'Scout': row['Scout'], 'Awards': f"{award}-{'Nights Camping' if category == 'camping' else 'Hours Service'}"})

    return results_list

def determine_camping_awards(cumulative_df, awards_df):
    results_list = []

    # Convert columns to numeric, coerce errors to NaN
    cumulative_df['Camping Nights'] = pd.to_numeric(cumulative_df['Unnamed: 2'], errors='coerce')

    for index, row in cumulative_df.iterrows():
        scout_name = row.iloc[0]
        camping_nights = row['Camping Nights']

        print(f"Processing {scout_name} with {camping_nights} camping nights")

        # Initialize lists for the awards
        highest_award = None
        awarded_lower_awards = []

        # Check for camping awards
        for days in sorted([300, 275, 250, 225, 200, 175, 150, 125, 100, 75, 50, 25, 10], reverse=True):
            award_name = f"{days}-Nights Camping"
            scout_award = awards_df[(awards_df['Scout'] == scout_name) & (awards_df['Award'].str.contains(f"{days}-Nights Camping", na=False))]
            print(f"Checking for {award_name}: Found {not scout_award.empty}")
            if scout_award.empty:
                if not highest_award:
                    highest_award = award_name
            else:
                awarded_lower_awards.append(award_name)

        # Store the results
        if highest_award:
            results_list.append({'Scout': scout_name, 'Awards': highest_award})
            print(f"Assigned {highest_award} to {scout_name}")
        elif awarded_lower_awards:
            results_list.append({'Scout': scout_name, 'Awards': ', '.join(awarded_lower_awards)})
            print(f"Assigned lower awards {', '.join(awarded_lower_awards)} to {scout_name}")

    return results_list

def determine_service_awards(cumulative_df, awards_df):
    results_list = []

    # Convert columns to numeric, coerce errors to NaN
    cumulative_df['Service Hours'] = pd.to_numeric(cumulative_df['Unnamed: 4'], errors='coerce')

    for index, row in cumulative_df.iterrows():
        scout_name = row.iloc[0]
        service_hours = row['Service Hours']

        # Initialize lists for the awards
        highest_award = None
        awarded_lower_awards = []

        # Check for service awards
        for hours in sorted([25, 50, 75, 100], reverse=True):
            award_name = f"{hours}-Hours Service"
            scout_award = awards_df[(awards_df['Scout'] == scout_name) & (awards_df['Award'].str.contains(f"{hours}-Hours Service", na=False))]
            if scout_award.empty:
                if not highest_award:
                    highest_award = award_name
            else:
                awarded_lower_awards.append(award_name)

        # Store the results
        if highest_award:
            results_list.append({'Scout': scout_name, 'Awards': highest_award})
        elif awarded_lower_awards:
            results_list.append({'Scout': scout_name, 'Awards': ', '.join(awarded_lower_awards)})

    return results_list

def create_certificates(awards_list):
    driver = webdriver.Chrome()
    wait = WebDriverWait(driver, 20)

    try:
        for award in awards_list:
            scout_name = award['Scout']
            award_name = award['Awards']
            award_details = f"For {award_name.split('-')[0]} nights."
            today_date = datetime.datetime.today().strftime('%B %d, %Y')

            driver.get("https://certificatemagic.com/personalize.php?design=1")
            print(f"Loaded page for {scout_name}")

            #Add on more code automation

    finally:
        driver.quit()

def main():
    # Display prompt for awards
    print("nWould you like to see the 'camping' awards or 'service' awards? (Type 'camping' or 'service')")
    choice = input().strip().lower()

    # Determine awards based on user choice
    if choice == 'camping':
        camping_awards_list = print_awards('camping', camping_awards)
        create_certificates(camping_awards_list)
    elif choice == 'service':
        service_awards_list = print_awards('service', service_awards)
        create_certificates(service_awards_list)
    else:
        print("Invalid choice. Please type 'camping' or 'service'.")

    # Save the filtered awards DataFrame to a new CSV file
    formatted_csv_path = 'formatted_awards.csv'
    awards.to_csv(formatted_csv_path, index=False)
    print(f"Filtered awards have been saved to {formatted_csv_path}")

if __name__ == "__main__":
    main()

As you can see the only time that I used selenium to automate interactions was in the create_certtificates() function.

I tried to use #type: ignore in order to just ignore the error, but that didn’t seem to work because when i tried to use driver to find for an element, none of the methods for driver didn’t work.

This is what the import section looks like

Any help on how to fix this would be greatly appreciated 🙂