CODEWITHSUNDEEP
pythonpandas
flyingmeatball
flyingmeatball

Reputation: 7997

Pandas combining rows based on dates

I have a dataframe of customers with records for shipments they received. Unfortunately, these can overlap. I'm trying to reduce rows so that I can see dates of consecutive use. Is there any way to do this besides a brute force iterrows implementation?

Here's a sample and what I'd like to do:

df = pd.DataFrame([['A','2011-02-07','2011-02-22',1],['A','2011-02-14','2011-03-10',2],['A','2011-03-07','2011-03-15',3],['A','2011-03-18','2011-03-25',4]], columns = ['Cust','startDate','endDate','shipNo'])
df

enter image description here

condensedDf = df.groupby(['Cust']).apply(reductionFunction)
condensedDF

enter image description here

the reductionFunction will group the first 3 records into one, because in each case the start date of the next is before the end date of the prior. I'm essentially turning multiple records that overlap into one record.

Thoughts on a good "pythonic" implementation? I could do a nasty while loop within each group, but I'd prefer not to...

Upvotes: 5

Views: 3234

Answers (3)

Mahdi Abadinia
Mahdi Abadinia

Reputation: 75

I used below a bit messy but does it Faster.

Thanks to Anurag Dabas for the help.

I merge df on its own and check the overlaps then remove extra rows, do this till there is no overlaps left.

Please note that I added all "shipNo".

import pandas as pd
import numpy as np
df = pd.DataFrame([['A','2011-02-07','2011-02-22',1],['A','2011-02-14','2011-03-10',2],['A','2011-03-07','2011-03-15',3],['A','2011-03-18','2011-03-25',4]], columns = ['Cust','startDate','endDate','shipNo'])
df['startDate'] = pd.to_datetime(df['startDate'])
df['endDate'] = pd.to_datetime(df['endDate'])

def overlap_checker(data):        
        data['CuststartDateendDate']=data['Cust'].map(str)+data['startDate'].map(str)+data['endDate'].map(str)
        df2=pd.merge(data,data,on='Cust')
        df2['Overlap']=np.where((df2['startDate_x']<=df2['endDate_y'])&(df2['endDate_x']>=df2['startDate_y']) & (df2['CuststartDateendDate_x'] != df2['CuststartDateendDate_y']), 'Overlapped','not overlapped')
        df2['startDate_x']=np.where(df2['Overlap'].eq('Overlapped'),df2[['startDate_x','startDate_y']].min(axis=1),df2['startDate_x'])
        df2['endDate_x']=np.where(df2['Overlap'].eq('Overlapped'),df2[['endDate_x','endDate_y']].max(axis=1),df2['endDate_x'])
        df2['shipNo']=df2['shipNo_x'].map(str)+df2['shipNo_y'].map(str)
        df2['shipNo'] = df2['shipNo'].apply(lambda x: ' '.join(sorted(set(x))))
        df2.rename(columns = {'startDate_x':'startDate','endDate_x':'endDate'}, inplace = True)
        return df2, data
    
def overlap_remover(df, data):
        df2= df[(df['Overlap']=="Overlapped")]
        data1=data[~data['CuststartDateendDate'].isin(df2['CuststartDateendDate_x'])]
        df2 = df2.drop(columns=['startDate_y','endDate_y','Overlap','CuststartDateendDate_x','CuststartDateendDate_y','shipNo_x','shipNo_y'])
        df2 = df2.drop_duplicates()
        bigdata = data1.append(df2, ignore_index=True,sort=False)
        return bigdata


dftmp, data = overlap_checker(df)
while dftmp['Overlap'].str.contains('Overlapped').any():
    df = overlap_remover(dftmp,data)
    dftmp, data = overlap_checker(df)

df = df.drop(columns=['CuststartDateendDate'])
df = df[['Cust','startDate','endDate','shipNo']]
print(df)

Upvotes: 1

Lim H.
Lim H.

Reputation: 10060

If you are open to use an auxiliary data frame to hold the result, you can just loop through all the rows to be honest

from time import strptime

results = [df.iloc[0]]

for i, (_, current_row) in enumerate(df1.iterrows()):
    try:
        next_row = df.iloc[i+1]        
        if strptime(current_row['endDate'], '%Y-%M-%d') < strptime(next_row['startDate'], '%Y-%M-%d'):
            results[-1]['endDate'] = current_row['endDate']
            results.append(next_row)
    except IndexError:
        pass

print pd.DataFrame(results).reset_index(drop=True)

Upvotes: 0

jakevdp
jakevdp

Reputation: 86533

Fundamentally, I think this is a graph connectivity problem: a fast way of solving it will be some manner of graph connectivity algorithm. Pandas doesn't include such tools, but scipy does. You can use the compressed sparse graph (csgraph) submodule in scipy to solve your problem like this:

from scipy.sparse.csgraph import connected_components

# convert to datetime, so min() and max() work
df.startDate = pd.to_datetime(df.startDate)
df.endDate = pd.to_datetime(df.endDate)

def reductionFunction(data):
    # create a 2D graph of connectivity between date ranges
    start = data.startDate.values
    end = data.endDate.values
    graph = (start <= end[:, None]) & (end >= start[:, None])

    # find connected components in this graph
    n_components, indices = connected_components(graph)

    # group the results by these connected components
    return data.groupby(indices).aggregate({'startDate': 'min',
                                            'endDate': 'max',
                                            'shipNo': 'first'})

df.groupby(['Cust']).apply(reductionFunction).reset_index('Cust')

enter image description here

If you want to do something different with shipNo from here, it should be pretty straightforward.

Note that the connected_components() function above is not brute force, but uses a fast algorithm to find the connections.

Upvotes: 15

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