CODEWITHSUNDEEP
pythonpandasmergetime-series
ymoiseev
ymoiseev

Reputation: 436

Merging two pandas dataframes by interval

I have two pandas dataframes with following format:

df_ts = pd.DataFrame([
        [10, 20, 1,  'id1'],
        [11, 22, 5,  'id1'],
        [20, 54, 5,  'id2'],
        [22, 53, 7,  'id2'],
        [15, 24, 8,  'id1'],
        [16, 25, 10, 'id1']
    ], columns = ['x', 'y', 'ts', 'id'])


df_statechange = pd.DataFrame([
        ['id1', 2, 'ok'],
        ['id2', 4, 'not ok'],
        ['id1', 9, 'not ok']
    ], columns = ['id', 'ts', 'state'])

I am trying to get it to the format, such as: 

df_out = pd.DataFrame([
        [10, 20, 1,  'id1', None    ],
        [11, 22, 5,  'id1', 'ok'    ],
        [20, 54, 5,  'id2', 'not ok'],
        [22, 53, 7,  'id2', 'not ok'],
        [15, 24, 8,  'id1', 'ok'    ],
        [16, 25, 10, 'id1', 'not ok']
    ], columns = ['x', 'y', 'ts', 'id', 'state'])

I understand how to accomplish it iteratively by grouping by id and then iterating through each row and changing status when it appears. Is there a pandas build-in more scalable way of doing this?

Upvotes: 3

Views: 3780

Answers (2)

Dimgold
Dimgold

Reputation: 2944

Unfortunately pandas merge support only equality joins. See more details at the following thread: merge pandas dataframes where one value is between two others if you want to merge by interval you'll need to overcome the issue, for example by adding another filter after the merge:

joined = a.merge(b,on='id')
joined = joined[joined.ts.between(joined.ts1,joined.ts2)]

Upvotes: 2

SillyPerson
SillyPerson

Reputation: 619

You can merge pandas data frames on two columns:

pd.merge(df_ts,df_statechange, how='left',on=['id','ts'])

in df_statechange that you shared here there is no common values on ts in both dataframes. Apparently you just copied not complete data frame here. So i got this output:

    x   y  ts   id state
0  10  20   1  id1   NaN
1  11  22   5  id1   NaN
2  20  54   5  id2   NaN
3  22  53   7  id2   NaN
4  15  24   8  id1   NaN
5  16  25  10  id1   NaN

But indeed if you have common ts in the data frames it will have your desired output. For example:

df_statechange = pd.DataFrame([
        ['id1', 5, 'ok'],
        ['id1', 8, 'ok'],
        ['id2', 5, 'not ok'],
        ['id2',7, 'not ok'],
        ['id1', 9, 'not ok']
    ], columns = ['id', 'ts', 'state'])

the output:

  x   y  ts   id   state
0  10  20   1  id1     NaN
1  11  22   5  id1      ok
2  20  54   5  id2  not ok
3  22  53   7  id2  not ok
4  15  24   8  id1      ok
5  16  25  10  id1     NaN

Upvotes: 0

Related Questions