python – 为什么构造multiIndex Dataframe时所有元素都是NaN

2019年8月4日 295次阅读

假设我有这样的Dataframe.我想将其转换为2级multiIndex数据帧.

         dt         st  close  volume
0   20100101  000001.sz      1   10000
1   20100101  000002.sz     10   50000
2   20100101  000003.sz      5    1000
3   20100101  000004.sz     15    7000
4   20100101  000005.sz    100  100000
5   20100102  000001.sz      2   20000
6   20100102  000002.sz     20   60000
7   20100102  000003.sz      6    2000
8   20100102  000004.sz     20    8000
9   20100102  000005.sz    110  110000

但是当我尝试这段代码时：

data = pd.read_csv('data/trial.csv')
print(data)
idx = pd.MultiIndex.from_product([data.dt.unique(),
                                  data.st.unique()],
                                 names=['dt', 'st'])
col = ['close', 'volume']

df = pd.DataFrame(data, idx, col)
print(df)

我发现所有元素都是NaN

                    close  volume
dt       st                      
20100101 000001.sz    NaN     NaN
         000002.sz    NaN     NaN
         000003.sz    NaN     NaN
         000004.sz    NaN     NaN
         000005.sz    NaN     NaN
20100102 000001.sz    NaN     NaN
         000002.sz    NaN     NaN
         000003.sz    NaN     NaN
         000004.sz    NaN     NaN
         000005.sz    NaN     NaN

如何处理这种情况？谢谢.

最佳答案 在
read_csv中只需要参数index_col：

#by positions of columns
data = pd.read_csv('data/trial.csv', index_col=[0,1])

要么：

#by names of columns
data = pd.read_csv('data/trial.csv', index_col=['dt', 'st'])

print (data)
                    close  volume
dt       st                      
20100101 000001.sz      1   10000
         000002.sz     10   50000
         000003.sz      5    1000
         000004.sz     15    7000
         000005.sz    100  100000
20100102 000001.sz      2   20000
         000002.sz     20   60000
         000003.sz      6    2000
         000004.sz     20    8000
         000005.sz    110  110000

Why all element are all NaN when construct a multiIndex Dataframe?

原因在于DataFrame构造函数：

df = pd.DataFrame(data, idx, col)

DataFrame调用数据具有RangeIndex并且不与新的MultiIndex对齐,因此在数据中获取NaN.

如果每个dt始终具有相同的st值,则可能的解决方案是按列名称过滤Dataframe,然后转换为numpy数组,但更好的是index_col和set_index解决方案：

df = pd.DataFrame(data[col].values, idx, col)
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