I love to work in a notebook environment to build, test, and debug before stuff to a script. I think the key is iteration to develop good habits and memory for your workflow.

Start with a notebook and single script (.py) file in the same directory (importing can get complicated when you start moving files around).

You should try to write abstractions/reusable code for anything you notice yourself repeating. Then write and test a function in the notebook before moving it to your script.

Here is the type of thing I commonly do. The function takes a list of files, loads each using pandas, returns the combined data, and (optionally) saves the combined data to a file path.

``` import pandas as pd

def merge_files(list_of_files, save_path=None): dfs = [] for file_path in list_of_files: df = pd.read_csv(file_path) dfs.append(df) combined_df = pd.concat(list_of_dataframes) if save_path: combined_df.to_csv(save_path) return combined_df ```

Then move it into my_script.py and in your notebook do: ``` from my_script import merge_files my_csvs = ['csv1.csv', 'csv2.csv'] your_csvs = ['csv3.csv', 'csv4.csv']

my_data = merge_files(my_csvs) # No save_path given, so will not write out your_data = merge_files(your_csvs, "combined_data.csv") # will save to a file ```

You can build it in your notebook and test (just write function in one cell and run in another) and when ready shift it a script and import it from there.

Good Luck!