data_input.py

import pandas a?s pd
import pickle

data = pd.read_pickle('dataframe.pkl')

# IF YOU WANT TO EXTEND THE SAVED DATAFRAMEINPUT USE THIS INSTEAD:
#data = pd.read_pickle('dataframeInput.pkl')

print(data)
print(data.dtypes)

# Suppose 'data' is your DataFrame and arr_col_names is a list of column names in 'data'
input_cols = data.drop(['Station abbreviation'], axis =1)

arr_col_names = list(input_cols.columns)

# Initialize new_row_dict with string indices and NA values
new_row_dict = {col_name: pd.NA for col_name in arr_col_names}

def convert_input_to_type(user_input, wanted_type):
    try:
        return wanted_type.type(user_input)
    except ValueError:
        raise ValueError(f"Wrong datatype. Please input a {wanted_type}.")


def input_in_dict(arr_col_name, input_func=input):
    wanted_type = data[arr_col_name].dtype
    print(f"Expecting type {wanted_type} for column '{arr_col_name}'")
    
    while True:
        user_input = input_func(f"Input {arr_col_name}: ")
        try:
            converted_input = convert_input_to_type(user_input, wanted_type)
            new_row_dict[arr_col_name] = converted_input
            break  # Break out of the loop if the conversion succeeds
        except ValueError as e:
            print(e)
            print(f"Error occurred for column '{arr_col_name}' with input '{user_input}'")

# Iterate over each column name in arr_col_names and prompt for user input
while True:
    for col_name in arr_col_names:
        input_in_dict(col_name)

    # Append the dictionary to the DataFrame
    data = data._append(new_row_dict, ignore_index=True)
    
    # Ask the user if they want to input another row
    more_rows = input("Do you want to input another row? (y/n): ")
    if more_rows.lower() != 'y':
        break

print(data)
print("Thanks for your input, it has been saved.")

data.to_pickle('dataframeInput.pkl')