Retrieving rows from Pandas DataFrame based on month of a date column in a range

Question

I currently have a table called Sales . The Sales table has a column called sale_date which is in the form YYYY-MM-DD and I want to extract rows where the month is within a range.

| seller_id | product_id | buyer_id | sale_date    | quantity | price |
|-----------|------------|----------|--------------|----------|-------|
| 7         | 11         | 49       | '2019-01-21' | 5        | 3330  |
| 13        | 32         | 6        | '2019-02-10' | 9        | 1089  |
| 50        | 47         | 4        | '2019-01-06' | 1        | 1343  |

I've tried something like:

>>> df.loc[df['sale_date'].str.split('-').isin([1, 2, 3])]
>>> df.loc[[int(x[1]) for x in df['sale_date'].str.split('-')][1] in [1, 2, 3]]

but these result in a type error and key error, respectively.

Is there any way that I can extract just the month from the sale_date column and check whether it's in a range? Thanks.

Answer 1

You can convert values to datetimes and then extract months:

df.loc[pd.to_datetime(df['sale_date']).dt.month.isin([1, 2, 3])]

Or modify your solution with extract second values from list by indexing str[1] with casting to integers:

df.loc[df['sale_date'].str.split('-').str[1].astype(int).isin([1, 2, 3])]