Python Pandas:每月或每周拆分 TimeSerie
it1352
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问题说明
我有一个跨越几年的 Timeserie，格式如下:
I have a Timeserie that spans few year, in the following format:
timestamp open high low close volume
0 2009-01-02 05:00:00 900.00 906.75 898.00 904.75 15673.0
1 2009-01-02 05:30:00 904.75 907.75 903.75 905.50 4600.0
2 2009-01-02 06:00:00 905.50 907.25 904.50 904.50 3472.0
3 2009-01-02 06:30:00 904.50 905.00 903.25 904.75 6074.0
4 2009-01-02 07:00:00 904.75 905.50 897.00 898.25 12538.0
将该数据帧拆分为多个数据帧(包含 1 周或 1 个月的数据)的最简单方法是什么?77
What would be the simplest way to split that dataframe into multiple dataframes of 1 week or 1 month worth of data?77
例如，包含 1 年数据的数据帧将被拆分为 52 个包含一周数据的数据帧，并作为 52 个数据帧的列表返回
as an example a dataframe containing 1 year of data would be split in 52 dataframes containing a week of data and returned as a list of 52 dataframes
(数据可以用下面的公式重构)
(the data can be reconstructed with the formula below)
import pandas as pd
from pandas import Timestamp
dikt={'close': {0: 904.75, 1: 905.5, 2: 904.5, 3: 904.75, 4: 898.25}, 'low': {0: 898.0, 1: 903.75, 2: 904.5, 3: 903.25, 4: 897.0}, 'open': {0: 900.0, 1: 904.75, 2: 905.5, 3: 904.5, 4: 904.75}, 'high': {0: 906.75, 1: 907.75, 2: 907.25, 3: 905.0, 4: 905.5}, 'volume': {0: 15673.0, 1: 4600.0, 2: 3472.0, 3: 6074.0, 4: 12538.0}, 'timestamp': {0: Timestamp('2009-01-02 05:00:00'), 1: Timestamp('2009-01-02 05:30:00'), 2: Timestamp('2009-01-02 06:00:00'), 3: Timestamp('2009-01-02 06:30:00'), 4: Timestamp('2009-01-02 07:00:00')}}
pd.DataFrame(dikt, columns=['timestamp', 'open', 'high', 'low', 'close', 'volume'])
正确答案
#1
将 groupby 与 pd.TimeGrouper 和列表推导一起使用
use groupby with pd.TimeGrouper and list comprehensions
weeks = [g for n, g in df.set_index('timestamp').groupby(pd.TimeGrouper('W'))]
months = [g for n, g in df.set_index('timestamp').groupby(pd.TimeGrouper('M'))]
<小时>
如果需要，您可以重置索引
You can reset the index if you need
weeks = [g.reset_index()
for n, g in df.set_index('timestamp').groupby(pd.TimeGrouper('W'))]
months = [g.reset_index()
for n, g in df.set_index('timestamp').groupby(pd.TimeGrouper('M'))]
<小时>
在 dict
weeks = {n: g.reset_index()
for n, g in df.set_index('timestamp').groupby(pd.TimeGrouper('W'))}
months = {n: g.reset_index()
for n, g in df.set_index('timestamp').groupby(pd.TimeGrouper('M'))}
这篇好文章是转载于：学新通技术网
