如何从数据框中检索出自己要的数据：
data = {'year': [2010, 2011, 2012, 2011, 2012, 2010, 2011, 2012],
'team': ['Bears', 'Bears', 'Bears', 'Packers', 'Packers', 'Lions',
'Lions', 'Lions'],
'wins': [11, 8, 10, 15, 11, 6, 10, 4],
'losses': [5, 8, 6, 1, 5, 10, 6, 12]}
football = pd.DataFrame(data)
print football['year']
print ''
print football.year # shorthand for football['year']
print ''
print football[['year', 'wins', 'losses']]
这里我们用两种方式检索了‘year’这列的数据，也可同时列出好几列的数据：
看结果：
0 2010
1 2011
2 2012
3 2011
4 2012
5 2010
6 2011
7 2012
Name: year, dtype: int64
0 2010
1 2011
2 2012
3 2011
4 2012
5 2010
6 2011
7 2012
Name: year, dtype: int64
year wins losses
0 2010 11 5
1 2011 8 8
2 2012 10 6
3 2011 15 1
4 2012 11 5
5 2010 6 10
6 2011 10 6
7 2012 4 12
- 获取某一行的数据：
football.iloc[[x]],football.loc[[]x]
#获取第一行的
football.loc[[0]]
football.iloc[0]
- 获取一个区间的行数：
football[x:y]
比如说第2行到底5行的：
football[2:6]
- 根据某个条件查询：
football[条件]
比如获取胜利场次大于10场的：
football[football.wins > 10]
获取‘Pachers’队胜利超过10场数据：
football[(football.wins>10) & (football.team == 'Packers')]
下面来跑下数据验证上面的方法：
import pandas as pd
data = {'year': [2010, 2011, 2012, 2011, 2012, 2010, 2011, 2012],
'team': ['Bears', 'Bears', 'Bears', 'Packers', 'Packers', 'Lions',
'Lions', 'Lions'],
'wins': [11, 8, 10, 15, 11, 6, 10, 4],
'losses': [5, 8, 6, 1, 5, 10, 6, 12]}
football = pd.DataFrame(data)
print football.iloc[[0]]
print "---------------------"
print football.loc[[0]]
print "----------------------"
print football[3:5]
print "----------------------"
print football[football.wins > 10]
print "----------------------"
print football[(football.wins > 10) & (football.team == "Packers")]
查看下运行结果:
losses team wins year
0 5 Bears 11 2010
--------------------------------------
losses team wins year
0 5 Bears 11 2010
-----------------------------------
losses team wins year
3 1 Packers 15 2011
4 5 Packers 11 2012
-------------------------------------
losses team wins year
0 5 Bears 11 2010
3 1 Packers 15 2011
4 5 Packers 11 2012
-------------------------------------
losses team wins year
3 1 Packers 15 2011
4 5 Packers 11 2012
Perfect
