Group common code into functions

When the same code is repeated across different functions like this:

def insert_l1_file(new_lst):
    data = pd.read_csv(filepath)
    data = data.fillna('')
    data = data.rename(columns=lambda x: str(x).replace('\r', ''))
    insertion_time = time.strftime("%d/%m/%Y %H:%M:%S")
    # ... more code

def insert_l2_file(psu_name, value_lst, filepath, header_lst, new_package, id):
    data = pd.read_csv(filepath)
    data = data.fillna('')
    data = data.rename(columns=lambda x: str(x).replace('\r', ''))
    insertion_time = time.strftime("%d/%m/%Y %H:%M:%S")
    # ... more code

def insert_key_details(psu_name, value_lst, filepath, header_lst):
    data = pd.read_csv(filepath)
    data = data.fillna('')
    data = data.rename(columns=lambda x: str(x).replace('\r', ''))
    insertion_time = time.strftime("%d/%m/%Y %H:%M:%S")
    # ... more code

… create a common function and call it.

def load_data(filepath):
    data = pd.read_csv(filepath)
    data = data.fillna('')
    data = data.rename(columns=lambda x: str(x).replace('\r', ''))
    insertion_time = time.strftime("%d/%m/%Y %H:%M:%S")
    return data, insertion_time

def insert_l1_file(new_lst):
    data, insertion_time = load_data(filepath)
    # ... more code

def insert_l2_file(psu_name, value_lst, filepath, header_lst, new_package, id):
    data, insertion_time = load_data(filepath)
    # ... more code

def insert_key_details(psu_name, value_lst, filepath, header_lst):
    data, insertion_time = load_data(filepath)
    # ... more code

But operations are still different

For PSU_Personnel.csv, we want to sort the records. Not for the others.

In that case, this is a BAD thing do do.

data = {key: pd.read_csv(info['file']) for key, info in lookup.items()}
data['l3'].sort()

A better thing to do is:

lookup = {                             # Define a transformation for each file
    'l1': dict(file='PSU_l1.csv',        transform=lambda x: x),
    'l2': dict(file='PSU_l2.csv',        transform=lambda x: x),
    'l3': dict(file='PSU_Personnel.csv', transform=lambda x: x.sort()),
}
data = {
    key: info['transform'](pd.read_csv(info['file']))
    for key, info in lookup.items()
}
result = data[form_type][:lookup[form_type]['row']]

This lets you define arbitrary transformations for each dataset.

But variable names are a given

If you cannot control the variable names (e.g. someone else has written that code), and you must use the given variables data_l1, data_l2, etc., you could use locals() like this:

result = locals().get('data_' + form_type)[:-1]

But re-structuring the code, if you can, is much better.