Adjacent records grouping with the original order

Task:List the team information with the most consecutive NBA titles.


1 import pandas as pd
2 import numpy as np
3 pd.set_option('display.max_columns', None)
4 nba_file = 'E:\\txt\\nba.txt'
5 nba_champion = pd.read_csv(nba_file,sep='\t')
6 nba_champion = nba_champion.sort_values(by = 'Year')
7 arr = np.zeros(len(nba_champion))
8 arr[nba_champion['Champion']!=nba_champion['Champion'].shift(1)]=1
9 arr = np.cumsum(arr)
10 nba_champion['flag']=arr
11 nba_champion_g = nba_champion.groupby(by='flag')
12 max_num = nba_champion_g.size().idxmax()
13 max_champion = nba_champion_g.get_group(max_num)
14 print(max_champion)

Python doesn't have the ability to group by adjacent conditions. You need to create a list of grouping flags.


1 =connect("mysql") Connect to database
2 =A1.query("select * from nba order by year") Sort by year
3 When adjacency is different, start a new group
4 =A3.maxp(~.len()) List the team information with the most consecutive NBA titles.

The set of esProc is ordered, and it is very convenient to merge the adjacent same records into one group and start a new group when the adjacent record is different.