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2019-08-13-MATH05-Time-series.md

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MATH05

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Contents

{:.no_toc}

  • ToC {:toc}

pandas basic about time series

import pandas as pd
import numpy as np

t_series01 = pd.date_range("2015-1-1", periods=31)
T_series01 = pd.Series(np.arange(31), index=t_series01)
print(t_series01)
print(T_series01)
OUTPUT
``` DatetimeIndex(['2015-01-01', '2015-01-02', '2015-01-03', '2015-01-04', '2015-01-05', '2015-01-06', '2015-01-07', '2015-01-08', '2015-01-09', '2015-01-10', '2015-01-11', '2015-01-12', '2015-01-13', '2015-01-14', '2015-01-15', '2015-01-16', '2015-01-17', '2015-01-18', '2015-01-19', '2015-01-20', '2015-01-21', '2015-01-22', '2015-01-23', '2015-01-24', '2015-01-25', '2015-01-26', '2015-01-27', '2015-01-28', '2015-01-29', '2015-01-30', '2015-01-31'], dtype='datetime64[ns]', freq='D') 2015-01-01 0 2015-01-02 1 2015-01-03 2 2015-01-04 3 2015-01-05 4 2015-01-06 5 2015-01-07 6 2015-01-08 7 2015-01-09 8 2015-01-10 9 2015-01-11 10 2015-01-12 11 2015-01-13 12 2015-01-14 13 2015-01-15 14 2015-01-16 15 2015-01-17 16 2015-01-18 17 2015-01-19 18 2015-01-20 19 2015-01-21 20 2015-01-22 21 2015-01-23 22 2015-01-24 23 2015-01-25 24 2015-01-26 25 2015-01-27 26 2015-01-28 27 2015-01-29 28 2015-01-30 29 2015-01-31 30 Freq: D, dtype: int32 ```
SUPPLEMENT
```python # timestamp object print(T_series01.index[2]) print(T_series01.index[2].year, T_series01.index[2].month, T_series01.index[2].day, T_series01.index[2].nanosecond)

datetime object

print(T_series01.index[2].to_pydatetime())

timestamp object

2015-01-03 00:00:00 2015 1 3 0

datetime object

2015-01-03 00:00:00

<br><br><br>
<span class="frame3">datetime object</span>
```python
import pandas as pd
import numpy as np
import datetime

T_series = pd.Series(np.random.rand(2),
                     index=[datetime.datetime(2015, 1, 1), datetime.datetime(2015, 2, 1)])
print(T_series)

2015-01-01    0.972084
2015-02-01    0.301809
dtype: float64


import pandas as pd
import numpy as np

t_series02 = pd.date_range("2015-1-1 00:00", "2015-1-1 12:00", freq="H")
T_series02 = pd.Series(np.arange(13), index=t_series02)
print(t_series02)
print(T_series02)
OUTPUT
``` DatetimeIndex(['2015-01-01 00:00:00', '2015-01-01 01:00:00', '2015-01-01 02:00:00', '2015-01-01 03:00:00', '2015-01-01 04:00:00', '2015-01-01 05:00:00', '2015-01-01 06:00:00', '2015-01-01 07:00:00', '2015-01-01 08:00:00', '2015-01-01 09:00:00', '2015-01-01 10:00:00', '2015-01-01 11:00:00', '2015-01-01 12:00:00'], dtype='datetime64[ns]', freq='H') 2015-01-01 00:00:00 0 2015-01-01 01:00:00 1 2015-01-01 02:00:00 2 2015-01-01 03:00:00 3 2015-01-01 04:00:00 4 2015-01-01 05:00:00 5 2015-01-01 06:00:00 6 2015-01-01 07:00:00 7 2015-01-01 08:00:00 8 2015-01-01 09:00:00 9 2015-01-01 10:00:00 10 2015-01-01 11:00:00 11 2015-01-01 12:00:00 12 Freq: H, dtype: int32 ``` 

import pandas as pd
import numpy as np

t_series03 = pd.PeriodIndex([pd.Period('2015-01'), pd.Period('2015-02'), pd.Period('2015-03')])
T_series03 = pd.Series(np.random.rand(3), index=t_series03)
print(T_series03)
OUTPUT
``` 2015-01 0.075913 2015-02 0.550537 2015-03 0.971680 Freq: M, dtype: float64 ```
SUPPLEMENT
```python # PeriodIndex object print(ts2.to_period('M')) ``` ``` 2015-01 0.683801 2015-02 0.916209 Freq: M, dtype: float64 ```




Example

[temperature_indoor_2014.tsv][1], [temperature_outdoor_2014.tsv][2]

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Reference

[1]:{{ site.url }}/download/MATH05/temperature_indoor_2014.tsv [2]:{{ site.url }}/download/MATH05/temperature_outdoor_2014.tsv

OUTPUT