保存和加载多熊猫数据框并保留列结构

时间：2018-08-31 17:59:29

标签： python pandas csv multi-index

我找不到正确保存和正确检索多索引熊猫数据框的方法，从而无法保留多索引列结构。对于可重现的示例：

enter image description here 

toy_data.to_json()
'{"["GOOG","Shares"]":{"1521849600000":null,"1521936000000":null,"1522368000000":null,"1522454400000":694548763.0,"1522540800000":null},"["GOOG","ROE"]":{"1521849600000":null,"1521936000000":null,"1522368000000":null,"1522454400000":0.1076,"1522540800000":null},"["FB","Shares"]":{"1521849600000":null,"1521936000000":null,"1522368000000":null,"1522454400000":2398606201.0,"1522540800000":null},"["FB","ROE"]":{"1521849600000":null,"1521936000000":null,"1522368000000":null,"1522454400000":0.2465,"1522540800000":null}}'

toy_data.to_csv('toy_data.csv')

toy_data1 = pd.read_csv('toy_data.csv')

enter image description here

您的建议将不胜感激。

2 个答案:

答案 0 :(得分：2)

read_csv

在header中使用index_col和read_csv参数可以满足您的需求。

toy_data.to_csv('sample.csv')
pd.read_csv('sample.csv', header=[0, 1], index_col=[0])

Company       GOOG          FB     
Indicators  Shares  ROE Shares  ROE
Quarter_end                        
2018-03-24     NaN  NaN    NaN  NaN
2018-03-25     NaN  NaN    NaN  NaN
2018-03-30     NaN  NaN    NaN  NaN
2018-03-31     1.0  2.0    3.0  4.0
2018-04-01     NaN  NaN    NaN  NaN

read_hdf

保存到hdf可能是一个更好的选择。

toy_data.to_hdf('sample.h5', 'toy_key')
pd.read_hdf('sample.h5', 'toy_key')

Company       GOOG          FB     
Indicators  Shares  ROE Shares  ROE
Quarter_end                        
2018-03-24     NaN  NaN    NaN  NaN
2018-03-25     NaN  NaN    NaN  NaN
2018-03-30     NaN  NaN    NaN  NaN
2018-03-31     1.0  2.0    3.0  4.0
2018-04-01     NaN  NaN    NaN  NaN

设置

cols = pd.MultiIndex.from_product(
    [['GOOG', 'FB'], ['Shares', 'ROE']],
    names=['Company', 'Indicators']
)
idx = pd.to_datetime(
    ['2018-03-24', '2018-03-25', '2018-03-30',
     '2018-03-31', '2018-04-01']
).rename('Quarter_end')

toy_data = pd.DataFrame([
    [np.nan, np.nan, np.nan, np.nan],
    [np.nan, np.nan, np.nan, np.nan],
    [np.nan, np.nan, np.nan, np.nan],
    [1, 2, 3, 4],
    [np.nan, np.nan, np.nan, np.nan],
], idx, cols)

答案 1 :(得分：0)

您尚未提供可用的示例数据，但我可以肯定的是，您需要做的就是传递header=[0, 1]和index_col=0作为read_csv的参数。

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