Python / Pandas：如何在一列中选择值等于另一列中另一行的行？

Question

Here is a sample of my data:

In[177]:df_data[['Date', 'TeamName', 'Opponent', 'ScoreOff']].head()
Out[177]: 
                     Date              TeamName              Opponent   ScoreOff
4128  2005-09-08 00:00:00  New England Patriots       Oakland Raiders   30
4129  2005-09-08 00:00:00       Oakland Raiders  New England Patriots   20
4130  2005-09-11 00:00:00     Arizona Cardinals       New York Giants   19
4131  2005-09-11 00:00:00      Baltimore Ravens    Indianapolis Colts   7
4132  2005-09-11 00:00:00         Buffalo Bills        Houston Texans   22

For each row, I need to set a new column ['OpponentScoreOff'] equal to that team's opponent's ScoreOff on that day.

I have done it by basically doing the following, but it's slow and I feel like there is a more pythonic/vectorized way to do it.

g1 = df_data.groupby('Date')
for date, teams in g1:
    g2 = teams.groupby('TeamName')
    for teamname, game in teams:
        df_data[(df_data['TeamName'] == teamname) & (dfdata['Date'] == date)]['OppScoreOff'] =     df_data[(df_data['Opponent'] == teamname) & (df_data['Date'] == date)]['ScoreOff']

It worked, but it's slow. Any better way to do this?

Answer 1

You could use sort to take advantage of the bijection between TeamName and Opponent for any given date. Consider the following:

import pandas as pd
import numpy as np

df_data = df_data.sort(['Date', 'TeamName'])
opp_score = np.array(df_data.sort(['Date', 'Opponent'])['ScoreOff'])
df_data['OpponentScoreOff'] = opp_score

The array call is necessary to remove the DataFrame indexing. That way, the array isn't resorted once it's put back into df_data .