Pandas Groupby: Groupby conditional statement

Question

I am trying to identify the location of stops from gps data but need to account for some gps drift. I have identified stops and isolated them into a new dataframe: df['Stopped'] = (df.groupby('DAY')['LAT'].diff().abs() <= 0.0005) & (df.groupby('DAY')['LNG'].diff().abs() <= 0.0005) df2 = df.loc[(df['Stopped'] == True)] Now I can label groups that have the exact match in coordinates using: df2['StoppedEvent'] = df2.groupby(['LAT','LNG']).ngroup() But I want to group by the same conditions of Stopped. Something like this but that works: df2['StoppedEvent'] = df2.groupby((['LAT','LNG']).diff().fillna(0).abs() <= 0.0005).ngroup() JavaScript questions and answers, JavaScript questions pdf, JavaScript question bank, JavaScript questions and answers pdf, mcq on JavaScript pdf, JavaScript questions and solutions, JavaScript mcq Test , Interview JavaScript questions, JavaScript Questions for Interview, JavaScript MCQ (Multiple Choice Questions)

Answer 1

I would do something like the following: df['Stopped'] = (df.groupby('DAY')['LAT'].diff().abs() <= 0.0005)\ & (df.groupby('DAY')['LNG'].diff().abs() <= 0.0005) df["Stopped_Group"] = (~df["Stopped"]).cumsum() df2 = df.loc[df['Stopped']] Now you'll have a column, "Stopped_Group", which is constant within a set of rows that are close to each other as determined by your logic. In the original dataframe, df, this column won't have any meaning for rows that correspond to motion. To get your desired output (if I understand you correctly), do something like the following: df2["Stopped_Duration"] = df2.groupby("Stopped_Group").transform("size")