時間序列的索引與重取樣

Manipulating Time Series Data in Python

Stefan Jansen

Founder & Lead Data Scientist at Applied Artificial Intelligence

時間序列轉換

常見的時間序列轉換：

解析日期字串並轉為 datetime64
針對特定子期間選取與切片
設定與變更 DateTimeIndex 的頻率
- 上取樣 vs 下取樣

取得 GOOG 股價

google = pd.read_csv('google.csv')  # import pandas as pd

google.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 504 entries, 0 to 503
Data columns (total 2 columns):
date     504 non-null object
price    504 non-null float64
dtypes: float64(1), object(1)

google.head()

         date   price
0  2015-01-02  524.81
1  2015-01-05  513.87
2  2015-01-06  501.96
3  2015-01-07  501.10
4  2015-01-08  502.68

將日期字串轉為 datetime64

pd.to_datetime()：
- 解析日期字串
- 轉為 datetime64

google.date = pd.to_datetime(google.date)

google.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 504 entries, 0 to 503
Data columns (total 2 columns):
date     504 non-null datetime64[ns]
price    504 non-null float64
dtypes: datetime64[ns](1), float64(1)

將日期字串轉為 datetime64

.set_index()：
- 將日期設為索引
- inplace：
  - 不建立複本

google.set_index('date', inplace=True)

google.info()

<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 504 entries, 2015-01-02 to 2016-12-30
Data columns (total 1 columns):
price    504 non-null float64
dtypes: float64(1)

繪製 Google 股價時間序列

google.price.plot(title='Google Stock Price')

plt.tight_layout(); plt.show()

ch1_2_v2 - Indexing & Resampling Time Series.013.png

部分字串索引

使用可解析為日期的字串進行選取／索引

google['2015'].info() # Pass string for part of date

DatetimeIndex: 252 entries, 2015-01-02 to 2015-12-31
Data columns (total 1 columns):
price    252 non-null float64
dtypes: float64(1)

google['2015-3': '2016-2'].info() # Slice includes last month

DatetimeIndex: 252 entries, 2015-03-02 to 2016-02-29
Data columns (total 1 columns):
price    252 non-null float64
dtypes: float64(1)
memory usage: 3.9 KB

部分字串索引

google.loc('2016-6-1', 'price') # Use full date with .loc[]

734.15

.asfreq()：設定頻率

.asfreq('D')：
- 將 DateTimeIndex 設為曆日頻率

google.asfreq('D').info() # set calendar day frequency

DatetimeIndex: 729 entries, 2015-01-02 to 2016-12-30
Freq: D
Data columns (total 1 columns):
price    504 non-null float64
dtypes: float64(1)

.asfreq()：設定頻率

上取樣：
- 頻率提高會產生新日期 ⇒ 出現遺漏值

google.asfreq('D').head()

             price
date              
2015-01-02  524.81
2015-01-03     NaN
2015-01-04     NaN
2015-01-05  513.87
2015-01-06  501.96

.asfreq()：重設頻率

.asfreq('B')：
- 將 DateTimeIndex 設為營業日頻率

google = google.asfreq('B') # Change to calendar day frequency

google.info()

DatetimeIndex: 521 entries, 2015-01-02 to 2016-12-30
Freq: B
Data columns (total 1 columns):
price    504 non-null float64
dtypes: float64(1)

.asfreq()：重設頻率

google[google.price.isnull()] # Select missing 'price' values

            price
date             
2015-01-19    NaN
2015-02-16    NaN
...
2016-11-24    NaN
2016-12-26    NaN

非交易日的營業日

一起來練習吧！

Manipulating Time Series Data in Python