如何使用 Pandas 根据值和连续时间段将带时间戳的 CSV 数据拆分为多个 CSV

Question

I am trying to analyse a ships AIS data. I have a CSV with ~20,000 rows, with columns for lat / long / speed / time stamp.

I have loaded the data in a pandas data frame, in a Jupyter notebook.

What I want to do is split the CSV into smaller CSVs based on the time stamp and the speed, so I want an individual CSV for each period of time the vessel speed was less than say 2 knots, eg if the vessel transited at 10 knots for 6hrs, then slowed down to 1 knot for a period of 3 hrs, sped back up 10 knots, then slowed down again to 1 knot for a period of 4 hrs, I would want to the output to be two CSVs, one for the 3hr period and one for the 4hr period. This is so I can review these periods individually in my mapping software.

I can filter the data easily to show all the periods where it is <1 knot but I can't break it down to output the continuous periods as separate CSVs / data frames. EDIT

Here is an example of the data

I've tried to show more clearly what I want to achieve here

Answer 1

Here is something to maybe get you started.

First filter out all values that meets the criteria (for example below 2):

df = pd.DataFrame({'speed':[2,1,4,5,4,1,1,1,3,4,5,6], 'time':[4,5,6,7,8,9,10,11,12,13,14,15]})
df_below2 = df[df['speed']<=2].reset_index(drop=True)

Now we need to split the frame if there is too long gap btw values in time. For example:

threshold = 2
df_below2['not_continuous']  = df_below2['time'].diff() > threshold

Distinguish between the groups using cums:

df_below2['group_id'] = df_below2['not_continuous'].cumsum()

From here it should be easy to split the frame based on the group id.