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 “10 minutes to pandas” 문서를 따라해보며 작성했습니다. 




<a href="https://pandas.pydata.org/pandas-docs/stable/user_guide/10min.html">https://pandas.pydata.org/pandas-docs/stable/user_guide/10min.html</a>








 2022. 02. 02 최초작성





 2022. 3. 20








<h2><a id="post-5708-_9lk218ntem3z"></a>샘플링</h2>




 특정 주기로 샘플링된 데이터를 원하는 주기로 리샘플링을 할 수 있습니다.




<pre><code>import numpy as np
import pandas as pd


# 2022년 1월 1일 0시 0분 0초부터 1초 간격으로 20개를 생성합니다.
rng = pd.date_range("1/1/2022", periods=20, freq="S")
print(rng)
print('\n\n')


# 앞에서 생성한 시간정보를 인덱스로 하여 0 ~ 10 사이의 숫자를 20개 생성합니다.
ts = pd.Series(np.random.randint(0, 10, len(rng)), index=rng)
print(ts)
print('\n\n')


# sum 메소드를 사용하여 2초 간격으로 리샘플링합니다. 예를 들어 0초, 1초 값을 더해서 0초의 값으로, 2초, 3초 값을 더해서 2초의 값으로 ... 사용합니다.
ts_new = ts.resample("2s").sum()
print(ts_new)
print('\n\n')


# first 메소드를 사용하여 2초 간격으로 리샘플링합니다. 예를 들어 0초, 1초 중 0초의 값을 사용하고, 2초, 3초 값 중 2초의 값을 사용하고 ... 사용합니다.
ts_new = ts.resample("2s").first()
print(ts_new)</code></pre>



<figure><div>실행 결과DatetimeIndex([‘2022-01-01 00:00:00’, ‘2022-01-01 00:00:01’, &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;‘2022-01-01 00:00:02’, ‘2022-01-01 00:00:03’, &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;‘2022-01-01 00:00:04’, ‘2022-01-01 00:00:05’, &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;‘2022-01-01 00:00:06’, ‘2022-01-01 00:00:07’, &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;‘2022-01-01 00:00:08’, ‘2022-01-01 00:00:09’, &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;‘2022-01-01 00:00:10’, ‘2022-01-01 00:00:11’, &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;‘2022-01-01 00:00:12’, ‘2022-01-01 00:00:13’, &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;‘2022-01-01 00:00:14’, ‘2022-01-01 00:00:15’, &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;‘2022-01-01 00:00:16’, ‘2022-01-01 00:00:17’, &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;‘2022-01-01 00:00:18’, ‘2022-01-01 00:00:19’], &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;dtype=’datetime64[ns]’, freq=’S’)2022-01-01 00:00:00&nbsp; &nbsp; 5 2022-01-01 00:00:01&nbsp; &nbsp; 5 2022-01-01 00:00:02&nbsp; &nbsp; 8 2022-01-01 00:00:03&nbsp; &nbsp; 6 2022-01-01 00:00:04&nbsp; &nbsp; 2 2022-01-01 00:00:05&nbsp; &nbsp; 2 2022-01-01 00:00:06&nbsp; &nbsp; 7 2022-01-01 00:00:07&nbsp; &nbsp; 8 2022-01-01 00:00:08&nbsp; &nbsp; 6 2022-01-01 00:00:09&nbsp; &nbsp; 7 2022-01-01 00:00:10&nbsp; &nbsp; 0 2022-01-01 00:00:11&nbsp; &nbsp; 0 2022-01-01 00:00:12&nbsp; &nbsp; 3 2022-01-01 00:00:13&nbsp; &nbsp; 4 2022-01-01 00:00:14&nbsp; &nbsp; 1 2022-01-01 00:00:15&nbsp; &nbsp; 5 2022-01-01 00:00:16&nbsp; &nbsp; 1 2022-01-01 00:00:17&nbsp; &nbsp; 1 2022-01-01 00:00:18&nbsp; &nbsp; 7 2022-01-01 00:00:19&nbsp; &nbsp; 8 Freq: S, dtype: int642022-01-01 00:00:00&nbsp; &nbsp; 10 2022-01-01 00:00:02&nbsp; &nbsp; 14 2022-01-01 00:00:04 &nbsp; &nbsp; 4 2022-01-01 00:00:06&nbsp; &nbsp; 15 2022-01-01 00:00:08&nbsp; &nbsp; 13 2022-01-01 00:00:10 &nbsp; &nbsp; 0 2022-01-01 00:00:12 &nbsp; &nbsp; 7 2022-01-01 00:00:14 &nbsp; &nbsp; 6 2022-01-01 00:00:16 &nbsp; &nbsp; 2 2022-01-01 00:00:18&nbsp; &nbsp; 15 Freq: 2S, dtype: int642022-01-01 00:00:00&nbsp; &nbsp; 5 2022-01-01 00:00:02&nbsp; &nbsp; 8 2022-01-01 00:00:04&nbsp; &nbsp; 2 2022-01-01 00:00:06&nbsp; &nbsp; 7 2022-01-01 00:00:08&nbsp; &nbsp; 6 2022-01-01 00:00:10&nbsp; &nbsp; 0 2022-01-01 00:00:12&nbsp; &nbsp; 3 2022-01-01 00:00:14&nbsp; &nbsp; 1 2022-01-01 00:00:16&nbsp; &nbsp; 1 2022-01-01 00:00:18&nbsp; &nbsp; 7 Freq: 2S, dtype: int64</div></figure>







<h2><a id="post-5708-_uqv3wuo9o7t2"></a>시간대(Time zone)</h2>



<pre><code>import numpy as np
import pandas as pd


# 2022년 1월 1일 0시 0분 부터 일 간격으로 5개를 생성합니다.
rng = pd.date_range("1/1/2022 02:00", periods=5, freq="D")
print(rng)
print()

# 앞에서 생성한 시간정보를 인덱스로 하여 무작위 숫자를 5개 생성합니다.
ts = pd.Series(np.random.randn(len(rng)), rng)
print(ts)
print()

# UTC 시간대로 설정합니다.
ts_utc = ts.tz_localize("UTC")
print(ts_utc)
print()

# 다른 시간대로 변경합니다.
# 11:00:00은 서울 시간대로 11시라는 의미이며
# +09:00은 서울 시간대는 UTC 시간 + 9라는 의미입니다.
print(ts_utc.tz_convert('Asia/Seoul'))</code></pre>



<figure><img data-lazy-fallback="1" data-attachment-id="5709" data-permalink="https://webnautes.blog/5708/word-image-5708-1/" data-orig-file="https://i0.wp.com/webnautes.blog/wp-content/uploads/2023/08/word-image-5708-1.png?fit=938%2C1112&amp;ssl=1" data-orig-size="938,1112" data-comments-opened="1" data-image-meta="{&quot;aperture&quot;:&quot;0&quot;,&quot;credit&quot;:&quot;&quot;,&quot;camera&quot;:&quot;&quot;,&quot;caption&quot;:&quot;&quot;,&quot;created_timestamp&quot;:&quot;0&quot;,&quot;copyright&quot;:&quot;&quot;,&quot;focal_length&quot;:&quot;0&quot;,&quot;iso&quot;:&quot;0&quot;,&quot;shutter_speed&quot;:&quot;0&quot;,&quot;title&quot;:&quot;&quot;,&quot;orientation&quot;:&quot;0&quot;}" data-image-title="word-image-5708-1" data-image-description="" data-image-caption="" data-medium-file="https://i0.wp.com/webnautes.blog/wp-content/uploads/2023/08/word-image-5708-1.png?fit=253%2C300&amp;ssl=1" data-large-file="https://i0.wp.com/webnautes.blog/wp-content/uploads/2023/08/word-image-5708-1.png?fit=864%2C1024&amp;ssl=1" decoding="async" fetchpriority="high" width="938" height="1112" src="https://i0.wp.com/webnautes.blog/wp-content/uploads/2023/08/word-image-5708-1.png?resize=938%2C1112&amp;ssl=1" alt="" sizes="(max-width: 938px) 100vw, 938px" data-recalc-dims="1" data-old-src="https://i0.wp.com/webnautes.blog/wp-content/uploads/2023/08/word-image-5708-1.png?resize=938%2C1112&amp;is-pending-load=1#038;ssl=1" data-lazy-srcset="https://i0.wp.com/webnautes.blog/wp-content/uploads/2023/08/word-image-5708-1.png?w=938&amp;ssl=1 938w, https://i0.wp.com/webnautes.blog/wp-content/uploads/2023/08/word-image-5708-1.png?resize=253%2C300&amp;ssl=1 253w, https://i0.wp.com/webnautes.blog/wp-content/uploads/2023/08/word-image-5708-1.png?resize=864%2C1024&amp;ssl=1 864w, https://i0.wp.com/webnautes.blog/wp-content/uploads/2023/08/word-image-5708-1.png?resize=768%2C910&amp;ssl=1 768w"></figure>







<h2><a id="post-5708-_7qahiakkddr9"></a>시간 범위 표현 간 변환</h2>



<pre><code>import numpy as np
import pandas as pd


# 2022년 1월 1일부터 한달 단위로 5개의 날짜를 생성합니다.
rng = pd.date_range("1/1/2022", periods=5, freq="M")
print(rng)
print()


# 앞에서 생성한 시간정보를 인덱스로 하여 숫자 5개를 생성합니다.
ts = pd.Series(np.arange(len(rng)), index=rng)
print(ts)
print()


# 날짜를 한달 간격으로 변경합니다.
ps = ts.to_period(freq='M')
print(ps)
print()


# 날짜를 매달의 첫날로 변경합니다.
print(ps.to_timestamp())</code></pre>



<figure><img data-lazy-fallback="1" data-attachment-id="5710" data-permalink="https://webnautes.blog/5708/word-image-5708-2/" data-orig-file="https://i0.wp.com/webnautes.blog/wp-content/uploads/2023/08/word-image-5708-2.png?fit=1108%2C1076&amp;ssl=1" data-orig-size="1108,1076" data-comments-opened="1" data-image-meta="{&quot;aperture&quot;:&quot;0&quot;,&quot;credit&quot;:&quot;&quot;,&quot;camera&quot;:&quot;&quot;,&quot;caption&quot;:&quot;&quot;,&quot;created_timestamp&quot;:&quot;0&quot;,&quot;copyright&quot;:&quot;&quot;,&quot;focal_length&quot;:&quot;0&quot;,&quot;iso&quot;:&quot;0&quot;,&quot;shutter_speed&quot;:&quot;0&quot;,&quot;title&quot;:&quot;&quot;,&quot;orientation&quot;:&quot;0&quot;}" data-image-title="word-image-5708-2" data-image-description="" data-image-caption="" data-medium-file="https://i0.wp.com/webnautes.blog/wp-content/uploads/2023/08/word-image-5708-2.png?fit=300%2C291&amp;ssl=1" data-large-file="https://i0.wp.com/webnautes.blog/wp-content/uploads/2023/08/word-image-5708-2.png?fit=1024%2C994&amp;ssl=1" decoding="async" width="1108" height="1076" src="https://i0.wp.com/webnautes.blog/wp-content/uploads/2023/08/word-image-5708-2.png?resize=1108%2C1076&amp;ssl=1" alt="" sizes="(max-width: 1108px) 100vw, 1108px" data-recalc-dims="1" data-old-src="https://i0.wp.com/webnautes.blog/wp-content/uploads/2023/08/word-image-5708-2.png?resize=1108%2C1076&amp;is-pending-load=1#038;ssl=1" data-lazy-srcset="https://i0.wp.com/webnautes.blog/wp-content/uploads/2023/08/word-image-5708-2.png?w=1108&amp;ssl=1 1108w, https://i0.wp.com/webnautes.blog/wp-content/uploads/2023/08/word-image-5708-2.png?resize=300%2C291&amp;ssl=1 300w, https://i0.wp.com/webnautes.blog/wp-content/uploads/2023/08/word-image-5708-2.png?resize=1024%2C994&amp;ssl=1 1024w, https://i0.wp.com/webnautes.blog/wp-content/uploads/2023/08/word-image-5708-2.png?resize=768%2C746&amp;ssl=1 768w"></figure>





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멈춤보단 천천히라도

webnautes

“10 minutes to pandas” 문서를 따라해보며 작성했습니다. https://pandas.pydata.org/pandas-docs/stable/user_guide/10min.html 2022. 02. 02 최초작성 2022. 3. 20 샘플링 특정 주기로 샘플링된 데이터를 원하는 주기로 리샘플링을 할 수 있습니다. 실행 결과 DatetimeIndex([‘2022-01-01 00:00:00’, ‘2022-01-01 00:00:01’,               ‘2022-01-01 00:00:02’, ‘2022-01-01 00:00:03’,               ‘2022-01-01 00:00:04’, ‘2022-01-01 00:00:05’,               ‘2022-01-01 00:00:06’, ‘2022-01-01 00:00:07’,               ‘2022-01-01 00:00:08’, ‘2022-01-01 00:00:09’,               ‘2022-01-01 00:00:10’, ‘2022-01-01 00:00:11’,               ‘2022-01-01 00:00:12’, ‘2022-01-01 00:00:13’,               ‘2022-01-01 00:00:14’, ‘2022-01-01 00:00:15’,               ‘2022-01-01 00:00:16’, ‘2022-01-01… 더 보기 »Pandas 강좌 6 – 시계열(Time series)
The post Pandas 강좌 6 – 시계열(Time series) first appeared on 멈춤보단 천천히라도.

Pandas 강좌 6 – 시계열(Time series)

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