Pandas过滤 - 非索引列的between_time

Question

I need to filter out data with specific hours. The DataFrame function between_time seems to be the proper way to do that, however, it only works on the index column of the dataframe; but I need to have the data in the original format (eg pivot tables will expect the datetime column to be with the proper name, not as the index).

This means that each filter looks something like this:

df.set_index(keys='my_datetime_field').between_time('8:00','21:00').reset_index()

Which implies that there are two reindexing operations every time such a filter is run.

Is this a good practice or is there a more appropriate way to do the same thing?

Answer 1

Create a DatetimeIndex , but store it in a variable, not the DataFrame. Then call it's indexer_between_time method. This returns an integer array which can then be used to select rows from df using iloc :

import pandas as pd
import numpy as np

N = 100
df = pd.DataFrame(
    {'date': pd.date_range('2000-1-1', periods=N, freq='H'),
     'value': np.random.random(N)})

index = pd.DatetimeIndex(df['date'])
df.iloc[index.indexer_between_time('8:00','21:00')]