pandas计算数据帧的每列中的值

Question

我正在寻找一种方法来计算一列中的值的数量，并证明它比我原先想象的要复杂。

       Percentile   Percentile1 Percentile2 Percentile3
0       mediocre    contender   contender   mediocre
69      mediocre    bad         mediocre    mediocre
117     mediocre    mediocre    mediocre    mediocre
144     mediocre    none        mediocre    contender
171     mediocre    mediocre    contender   mediocre

我试图创建类似于以下输出的内容。它需要四个选项并按列计算。它本质上是每列的pd.value.counts。任何帮助肯定会受到赞赏。

         Percentile     Percentile1     Percentile2     Percentile3
mediocre:    5               2               3               4
contender:   0               1               2               1
bad:         0               1               0               0
none:        0               1               0               0

Answer 1

它有助于使您的数据“整洁”＃34;先(PDF)。这意味着列应代表变量，行应代表观察。

In [98]: df
Out[98]: 
    Percentile Percentile1 Percentile2 Percentile3
0     mediocre   contender   contender    mediocre
69    mediocre         bad    mediocre    mediocre
117   mediocre    mediocre    mediocre    mediocre
144   mediocre        none    mediocre   contender
171   mediocre    mediocre   contender    mediocre

[5 rows x 4 columns]

在这种情况下，melting DataFrame使其整洁：

In [125]: melted = pd.melt(df); melted
Out[125]: 
       variable      value
0    Percentile   mediocre
1    Percentile   mediocre
2    Percentile   mediocre
3    Percentile   mediocre
4    Percentile   mediocre
5   Percentile1  contender
6   Percentile1        bad
7   Percentile1   mediocre
8   Percentile1       none
9   Percentile1   mediocre
10  Percentile2  contender
11  Percentile2   mediocre
12  Percentile2   mediocre
13  Percentile2   mediocre
14  Percentile2  contender
15  Percentile3   mediocre
16  Percentile3   mediocre
17  Percentile3   mediocre
18  Percentile3  contender
19  Percentile3   mediocre

[20 rows x 2 columns]

然后使用crosstab制作频率表：

In [127]: pd.crosstab(index=[melted['value']], columns=[melted['variable']])
Out[127]: 
variable   Percentile  Percentile1  Percentile2  Percentile3
value                                                       
bad                 0            1            0            0
contender           0            1            2            1
mediocre            5            2            3            4
none                0            1            0            0

[4 rows x 4 columns]