Comparing values in Pandas with "diff" and "pct_change" 

I'd suggest not writing anything for a 1-column data frame. Either write for a data frame (regardless of size), or for a series / column. I think that the latter is probably a better way to go, overall.

You can totally do this! Just use "diff" on the result of a "groupby". For example: df = DataFrame({'category': ['wheat', 'corn', 'rice', 'wheat', 'corn', 'rice', 'wheat', 'corn', 'rice'], 'price': [10, 8, 6, 11, 7, 5, 15, 9, 4]}) df.groupby('category')[['price']].diff() You'll get a new data frame back (thanks to the double square brackets around 'price'), showing the difference for each row from the previous occurrence of that category. However, if you want to know which category is which, you'll probably want to join it back to the original data frame: df.groupby('category')[['price']].diff().join(df, rsuffix='_df')