为什么我的df正在搜索列索引而不是检查列名？

Question

我的df矩阵看起来像这样：

           rating
id       10153337 10183250 10220967   ...    99808270 99816554 99821259
user_id                               ...
10003869      NaN      8.0      NaN   ...         NaN      NaN      NaN
10022889      NaN      NaN      3.0   ...         NaN      1.0      NaN

我无法获得我需要的专栏，因为它会返回超出范围的索引＆＃39;错误

specificID = ratings_matrix[[99816554]]
...
     raise IndexError("indices are out-of-bounds")
IndexError: indices are out-of-bounds

为什么不搜索为列提供的值？

一些可运行的代码：

ratings = pd.read_json(
''.join(
    ['{"columns":["id","rating","user_id"],"index":[0,1,2],"data":[[',
     '67728134,4,10003869],[57495823,9,10060085],[99816554,1,10022889]]}']
), orient='split')

ratings
ratings.dtypes

ratings_matrix = ratings.pivot_table(index=['user_id'], columns=['id'], values=['rating'])
ratings_matrix.columns.map(type)
ratings_matrix[[67728134]] #here! searches column numbers rather than values

Answer 1

请注意，在创建数据透视表时，您将列表传递给values参数：

ratings_matrix = ratings.pivot_table( # |<--- here --->|
    index=['user_id'], columns=['id'], values=['rating'])

这告诉大熊猫要创建一个pd.MultiIndex。这就是为什么您的结果中包含rating列的级别的原因。

选项1
使用multiindex

specificID = ratings_matrix[[('rating', 99816554)]]

选项2
不要创建多索引

ratings_matrix = ratings.pivot_table( # see what I did?
    index=['user_id'], columns=['id'], values='rating')

然后

specificID = ratings_matrix[[99816554]]

设置

df = pd.read_json(
    ''.join(
        ['{"columns":["id","rating","user_id"],"index":[0,1,2],"data":[[',
         '67728134,4,10003869],[57495823,9,10060085],[99816554,1,10022889]]}']
    ), orient='split'
)

df

ratings_matrix = ratings.pivot_table( # |<--- here --->|
    index=['user_id'], columns=['id'], values=['rating'])
ratings_matrix[[('rating', 67728134)]]

ratings_matrix = ratings.pivot_table( # see what I did?
    index=['user_id'], columns=['id'], values='rating')
ratings_matrix[[67728134]]