Question

我有一个数据框，其中set_index()使用了3列。我想提取与每个索引关联的数据类型。我怎样才能有效地做到这一点？我不想做type(df.index.get_level_values())，因为df很大。

MWE：

import pandas as pd
df = pd.DataFrame({"id": [1,2,1,2], "time": [1, 1, 2, 2], "val": [1,2,3,4]})
df.set_index(keys=["id", "time"], inplace=True)
type(df.index.get_level_values(1))
#pandas.core.indexes.numeric.Int64Index

我还想知道索引中实际数据的类型（即看看我知道它的整数但是这样的东西也很好：

type(df.index.get_level_values(1).values[0])
#numpy.int64

Answer 1

您可以使用import pandas as pd df = pd.DataFrame({"id": [1,2,1,2], "time": [1, 1, 2, 2], "val": [1,2,3,4]}) df.set_index(keys=["id", "time"], inplace=True) index = df.index print([lev.dtype.type for lev in index.levels]) # [<class 'numpy.int64'>, <class 'numpy.int64'>] # Alternatively, there is the private attribute, `_inferred_type_levels`, # but this is probably not what you are looking for. print(index._inferred_type_levels) # ['integer', 'integer']：

index.levels

In [172]: list(index.levels) Out[172]: [Int64Index([1, 2], dtype='int64', name='id'), Int64Index([1, 2], dtype='int64', name='time')]是一维索引的冻结列表：

ddd = test.groupby('InvoiceDocNumber', as_index=False).agg({"itemprob": "max"})
ddd= ddd.rename(columns={'itemprob': 'invoiceprob'})
ddd['invoicerank'] =ddd['invoiceprob'].rank(ascending=0)

在pandas

1 个答案: