前n列数据帧

时间:2016-05-02 11:57:14

标签: python pandas

我有一张桌子:

simple_table

我想切前三列,我该怎么做?

我已经查看了另一篇文章:Finding top N columns for each row in data frame,但这对我来说太过分了。

我试过了:

df1 = df.iloc[:,0:3]

但这会产生错误: IndexingError:索引器太多

修改

添加了 ix

的更详细代码
    cols = [col for col in df.columns if col != 'stream']
    candidates = df.loc[url,cols]

    dfSorted = candidates.sort_values(ascending=False)

    big_three = dfSorted.ix[:,0:3]

1 个答案:

答案 0 :(得分:1)

我认为您可以使用applynlargest一起选择3 DataFrame的前candidates列和值:

import pandas as pd

df = pd.DataFrame({'A': {'a': 1, 'c': 5, 'b': 2, 'd': 3}, 
                   'C': {'a': 8, 'c': 7, 'b': 8, 'd': 7}, 
                   'B': {'a': 4, 'c': 1, 'b': 5, 'd': 4}, 
                   'D': {'a': 5, 'c': 3, 'b': 9, 'd': 1}, 
                   'stream': {'a': 1, 'c': 2, 'b': 2, 'd': 3}})
print df
   A  B  C  D  stream
a  1  4  8  5       1
b  2  5  8  9       2
c  5  1  7  3       2
d  3  4  7  1       3


cols = [col for col in df.columns if col != 'stream']
candidates = df.ix['a':'c', cols]

print candidates
   A  B  C  D
a  1  4  8  5
b  2  5  8  9
c  5  1  7  3

print candidates.apply(lambda x: zip(x.nlargest(3).index, x.nlargest(3).values), axis=1)
a    [(C, 8), (D, 5), (B, 4)]
b    [(D, 9), (C, 8), (B, 5)]
c    [(C, 7), (A, 5), (D, 3)]
dtype: object

与...相同:

def f(x):
    #print x.nlargest(3)
    #print zip(x.nlargest(3).index, x.nlargest(3).values)
    return zip(x.nlargest(3).index, x.nlargest(3).values)

print candidates.apply(f, axis=1)
a    [(C, 8), (D, 5), (B, 4)]
b    [(D, 9), (C, 8), (B, 5)]
c    [(C, 7), (A, 5), (D, 3)]
dtype: object