<div id="readability-page-1" class="page"><div>
<p>
  “10 minutes to pandas” 문서를 따라해보며 작성했습니다.
</p>



<p><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></p>







<p>
  2022. 02. 02  최초작성
</p>







<h2><a id="post-5718-_el8m7y3m87jz"></a>CSV</h2>



<p>
  다음 내용으로 test.csv라는 파일을 작성합니다.
</p>



<p>
  첫줄에 있는 컬럼 헤더에 공백이 없도록 주의하세요. Pandas에서 로드후 공백이 포함된 이름이 됩니다.
</p>



<pre><code>id,alphabet
1,a
2,b
3,c
4,d
5,e</code></pre>







<p>
  다음 코드로 csv 파일 로드 및 저장을 테스트합니다.
</p>



<pre><code>import numpy as np
import pandas as pd
import matplotlib.pyplot as plt



df = pd.read_csv("test.csv")

print(df)
#	id alphabet
# 0   1    	a
# 1   2    	b
# 2   3    	c
# 3   4    	d
# 4   5    	e


df.to_csv('test2.csv', index=None)</code></pre>







<p>
  새로 생성된 test2.csv의 내용은 다음과 같습니다.
</p>



<figure><img data-lazy-fallback="1" data-attachment-id="5719" data-permalink="https://webnautes.blog/5718/word-image-5718-1/" data-orig-file="https://i0.wp.com/webnautes.blog/wp-content/uploads/2023/08/word-image-5718-1.png?fit=364%2C384&amp;ssl=1" data-orig-size="364,384" 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-5718-1" data-image-description="" data-image-caption="" data-medium-file="https://i0.wp.com/webnautes.blog/wp-content/uploads/2023/08/word-image-5718-1.png?fit=284%2C300&amp;ssl=1" data-large-file="https://i0.wp.com/webnautes.blog/wp-content/uploads/2023/08/word-image-5718-1.png?fit=364%2C384&amp;ssl=1" decoding="async" fetchpriority="high" width="364" height="384" src="https://i0.wp.com/webnautes.blog/wp-content/uploads/2023/08/word-image-5718-1.png?resize=364%2C384&amp;ssl=1" alt="" sizes="(max-width: 364px) 100vw, 364px" data-recalc-dims="1" data-old-src="https://i0.wp.com/webnautes.blog/wp-content/uploads/2023/08/word-image-5718-1.png?resize=364%2C384&amp;is-pending-load=1#038;ssl=1" data-lazy-srcset="https://i0.wp.com/webnautes.blog/wp-content/uploads/2023/08/word-image-5718-1.png?w=364&amp;ssl=1 364w, https://i0.wp.com/webnautes.blog/wp-content/uploads/2023/08/word-image-5718-1.png?resize=284%2C300&amp;ssl=1 284w"></figure>







<h2><a id="post-5718-_d5ucizchlq4a"></a>HDF5</h2>



<p>
  Pandas에 저장된 데이터를 HDF5 포맷으로 저장했다가 로드해봅니다.
</p>



<p>
  추가로 pytables 패키지 설치가 필요합니다.
</p>



<pre><code>import numpy as np
import pandas as pd
import matplotlib.pyplot as plt



# 2022년 1월 1일 0시 0분 0초부터 1초 간격으로 20개를 생성합니다.
rng = pd.date_range("1/1/2022", periods=20, freq="S")
print(rng)
# 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 00:00:17',
#            	'2022-01-01 00:00:18', '2022-01-01 00:00:19'],
#           	dtype='datetime64[ns]', freq='S')


# 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)


# HDF5 포맷을 파일로 저장합니다.
ts.to_hdf("foo.h5", "df")
# 2022-01-01 00:00:00	2
# 2022-01-01 00:00:01	6
# 2022-01-01 00:00:02	4
# 2022-01-01 00:00:03	5
# 2022-01-01 00:00:04	5
# 2022-01-01 00:00:05	9
# 2022-01-01 00:00:06	4
# 2022-01-01 00:00:07	0
# 2022-01-01 00:00:08	1
# 2022-01-01 00:00:09	0
# 2022-01-01 00:00:10	5
# 2022-01-01 00:00:11	7
# 2022-01-01 00:00:12	1
# 2022-01-01 00:00:13	7
# 2022-01-01 00:00:14	5
# 2022-01-01 00:00:15	1
# 2022-01-01 00:00:16	1
# 2022-01-01 00:00:17	5
# 2022-01-01 00:00:18	5
# 2022-01-01 00:00:19	1
# Freq: S, dtype: int64

# HDF5 포맷의 파일을 로드합니다.
print(pd.read_hdf("foo.h5", "df"))
# 2022-01-01 00:00:00	2
# 2022-01-01 00:00:01	6
# 2022-01-01 00:00:02	4
# 2022-01-01 00:00:03	5
# 2022-01-01 00:00:04	5
# 2022-01-01 00:00:05	9
# 2022-01-01 00:00:06	4
# 2022-01-01 00:00:07	0
# 2022-01-01 00:00:08	1
# 2022-01-01 00:00:09	0
# 2022-01-01 00:00:10	5
# 2022-01-01 00:00:11	7
# 2022-01-01 00:00:12	1
# 2022-01-01 00:00:13	7
# 2022-01-01 00:00:14	5
# 2022-01-01 00:00:15	1
# 2022-01-01 00:00:16	1
# 2022-01-01 00:00:17	5
# 2022-01-01 00:00:18	5
# 2022-01-01 00:00:19	1
# Freq: S, dtype: int64</code></pre>







<h2><a id="post-5718-_81otwqky84h2"></a>Excel</h2>



<p>
  Pandas에 저장된 데이터를 MS Excel 파일로 저장했다가 로드해봅니다.
</p>



<p>
  추가로 openpyxl 패키지 설치가 필요합니다.
</p>



<pre><code>import numpy as np
import pandas as pd
import matplotlib.pyplot as plt



# 2022년 1월 1일 0시 0분 0초부터 1초 간격으로 20개를 생성합니다.
rng = pd.date_range("1/1/2022", periods=20, freq="S")
print(rng)
# 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 00:00:17',
#            	'2022-01-01 00:00:18', '2022-01-01 00:00:19'],
#           	dtype='datetime64[ns]', freq='S')


# 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)


# Excel 파일로 저장합니다.
ts.to_excel("foo.xlsx", sheet_name="Sheet1")
# 2022-01-01 00:00:00	0
# 2022-01-01 00:00:01	8
# 2022-01-01 00:00:02	6
# 2022-01-01 00:00:03	2
# 2022-01-01 00:00:04	3
# 2022-01-01 00:00:05	7
# 2022-01-01 00:00:06	8
# 2022-01-01 00:00:07	1
# 2022-01-01 00:00:08	9
# 2022-01-01 00:00:09	2
# 2022-01-01 00:00:10	9
# 2022-01-01 00:00:11	8
# 2022-01-01 00:00:12	8
# 2022-01-01 00:00:13	8
# 2022-01-01 00:00:14	1
# 2022-01-01 00:00:15	3
# 2022-01-01 00:00:16	7
# 2022-01-01 00:00:17	0
# 2022-01-01 00:00:18	6
# 2022-01-01 00:00:19	6
# Freq: S, dtype: int64


# EXCEL 파일을 로드합니다.
print(pd.read_excel("foo.xlsx", "Sheet1", index_col=None, na_values=["NA"]))
# 0  2022-01-01 00:00:00  0
# 1  2022-01-01 00:00:01  8
# 2  2022-01-01 00:00:02  6
# 3  2022-01-01 00:00:03  2
# 4  2022-01-01 00:00:04  3
# 5  2022-01-01 00:00:05  7
# 6  2022-01-01 00:00:06  8
# 7  2022-01-01 00:00:07  1
# 8  2022-01-01 00:00:08  9
# 9  2022-01-01 00:00:09  2
# 10 2022-01-01 00:00:10  9
# 11 2022-01-01 00:00:11  8
# 12 2022-01-01 00:00:12  8
# 13 2022-01-01 00:00:13  8
# 14 2022-01-01 00:00:14  1
# 15 2022-01-01 00:00:15  3
# 16 2022-01-01 00:00:16  7
# 17 2022-01-01 00:00:17  0
# 18 2022-01-01 00:00:18  6
# 19 2022-01-01 00:00:19  6</code></pre>





</div></div>

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 CSV 다음 내용으로 test.csv라는 파일을 작성합니다. 첫줄에 있는 컬럼 헤더에 공백이 없도록 주의하세요. Pandas에서 로드후 공백이 포함된 이름이 됩니다. 다음 코드로 csv 파일 로드 및 저장을 테스트합니다. 새로 생성된 test2.csv의 내용은 다음과 같습니다. HDF5 Pandas에 저장된 데이터를 HDF5 포맷으로 저장했다가 로드해봅니다. 추가로 pytables… 더 보기 »Pandas 강좌 8 – Pandas에서 CSV, HDF5, Excel로 저장 및 읽기
The post Pandas 강좌 8 – Pandas에서 CSV, HDF5, Excel로 저장 및 읽기 first appeared on 멈춤보단 천천히라도.

Pandas 강좌 8 – Pandas에서 CSV, HDF5, Excel로 저장 및 읽기

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