Select every nth row as a Pandas DataFrame without reading the entire file

Question

I am reading a large file that contains ~9.5 million rows x 16 cols.

I am interested in retrieving a representative sample, and since the data is organized by time, I want to do this by selecting every 500th element.

I am able to load the data, and then select every 500th row.

My question: Can I immediately read every 500th element (using.pd.read_csv() or some other method), without having to read first and then filter my data?

Question 2: How would you approach this problem if the date column was not ordered? At the moment, I am assuming it's ordered by date, but all data is prone to errors.

Here is a snippet of what the data looks like (first five rows) The first 4 rows are out of order, bu the remaining dataset looks ordered (by time):

VendorID    tpep_pickup_datetime    tpep_dropoff_datetime   passenger_count trip_distance   RatecodeID  store_and_fwd_flag  PULocationID    DOLocationID    payment_type    fare_amount extra   mta_tax tip_amount  tolls_amount    improvement_surcharge   total_amount
0   1   2017-01-09 11:13:28 2017-01-09 11:25:45 1   3.30    1   N   263 161 1   12.5    0.0 0.5 2.00    0.00    0.3 15.30
1   1   2017-01-09 11:32:27 2017-01-09 11:36:01 1   0.90    1   N   186 234 1   5.0 0.0 0.5 1.45    0.00    0.3 7.25
2   1   2017-01-09 11:38:20 2017-01-09 11:42:05 1   1.10    1   N   164 161 1   5.5 0.0 0.5 1.00    0.00    0.3 7.30
3   1   2017-01-09 11:52:13 2017-01-09 11:57:36 1   1.10    1   N   236 75  1   6.0 0.0 0.5 1.70    0.00    0.3 8.50
4   2   2017-01-01 00:00:00 2017-01-01 00:00:00 1   0.02    2   N   249 234 2   52.0    0.0 0.5 0.00    0.00    0.3 52.80

Answer 1

Can I immediately read every 500th element (using.pd.read_csv() or some other method), without having to read first and then filter my data?

Something you could do is to use the skiprows parameter in read_csv, which accepts a list-like argument to discard the rows of interest (and thus, also select). So you could create a np.arange with a length equal to the amount of rows to read, and remove every 500th element from it using np.delete, so this way we'll only be reading every 500th row:

n_rows = 9.5e6
skip = np.arange(n_rows)
skip = np.delete(skip, np.arange(0, n_rows, 500))
df = pd.read_csv('my_file.csv', skiprows = skip)