{"id":30807,"date":"2024-10-28T06:03:58","date_gmt":"2024-10-28T06:03:58","guid":{"rendered":"http:\/\/atmokpo.com\/w\/?p=30807"},"modified":"2024-11-26T06:47:21","modified_gmt":"2024-11-26T06:47:21","slug":"%ed%8c%8c%ec%9d%b4%ec%8d%ac-%ec%9e%90%eb%8f%99%eb%a7%a4%eb%a7%a4-%ea%b0%9c%eb%b0%9c-pandas%eb%a5%bc-%ec%9d%b4%ec%9a%a9%ed%95%9c-%eb%8d%b0%ec%9d%b4%ed%84%b0-%eb%b6%84%ec%84%9d-%ea%b8%b0%ec%b4%88","status":"publish","type":"post","link":"https:\/\/atmokpo.com\/w\/30807\/","title":{"rendered":"\ud30c\uc774\uc36c \uc790\ub3d9\ub9e4\ub9e4 \uac1c\ubc1c, pandas\ub97c \uc774\uc6a9\ud55c \ub370\uc774\ud130 \ubd84\uc11d \uae30\ucd08"},"content":{"rendered":"

<\/p>\n

\n

\n \uc624\ub298\ub0a0 \uae08\uc735\uc2dc\uc7a5\uc5d0\uc11c\ub294 \uc790\ub3d9\ub9e4\ub9e4 \uc2dc\uc2a4\ud15c\uc774 \uc810\uc810 \ub354 \ub9ce\uc774 \uc0ac\uc6a9\ub418\uace0 \uc788\uc2b5\ub2c8\ub2e4.
\n \uc790\ub3d9\ub9e4\ub9e4\ub294 \ucf54\ub4dc\uc640 \uc54c\uace0\ub9ac\uc998\uc744 \ud1b5\ud574 \uc778\uac04\uc758 \uac1c\uc785 \uc5c6\uc774 \uac70\ub798\ub97c \uc218\ud589\ud560 \uc218 \uc788\uac8c \ud574\uc8fc\uba70,
\n \uc774\ub97c \ud1b5\ud574 \ubcf4\ub2e4 \uc815\uad50\ud558\uace0 \uc2e0\uc18d\ud55c \uac70\ub798\uac00 \uac00\ub2a5\ud569\ub2c8\ub2e4.
\n \uc774 \uae00\uc5d0\uc11c\ub294 \ud30c\uc774\uc36c\uc744 \uc0ac\uc6a9\ud558\uc5ec \uc790\ub3d9\ub9e4\ub9e4 \uc2dc\uc2a4\ud15c\uc744 \uac1c\ubc1c\ud558\ub294 \ub370 \ud544\uc694\ud55c
\n Pandas<\/strong>\ub97c \uc774\uc6a9\ud55c \ub370\uc774\ud130 \ubd84\uc11d\uc758 \uae30\ucd08\ub97c \uc124\uba85\ud558\uaca0\uc2b5\ub2c8\ub2e4.\n <\/p>\n

1. \uc790\ub3d9\ub9e4\ub9e4 \uc2dc\uc2a4\ud15c\uc758 \uac1c\uc694<\/h2>\n

\n \uc790\ub3d9\ub9e4\ub9e4 \uc2dc\uc2a4\ud15c\uc740 \ud2b9\uc815 \uc870\uac74\uc744 \ub9cc\uc871\ud560 \ub54c \uc790\ub3d9\uc73c\ub85c \ub9e4\uc218 \ubc0f \ub9e4\ub3c4\ub97c \uc218\ud589\ud558\ub294 \uc2dc\uc2a4\ud15c\uc785\ub2c8\ub2e4.
\n \uc774 \uc2dc\uc2a4\ud15c\uc740 \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc758 \uc77c\ud658\uc73c\ub85c, \uc2dc\uc7a5\uc758 \ub370\uc774\ud130\ub97c \uc2e4\uc2dc\uac04\uc73c\ub85c \ubd84\uc11d\ud558\uc5ec
\n \ucd5c\uc801\uc758 \uc21c\uac04\uc5d0 \uac70\ub798\ub97c \uc2e4\ud589\ud569\ub2c8\ub2e4.
\n \uc774\ub7ec\ud55c \uc2dc\uc2a4\ud15c\uc744 \uad6c\ud604\ud558\uae30 \uc704\ud574\uc11c\ub294 \ub370\uc774\ud130 \uc218\uc9d1, \ubd84\uc11d, \uadf8\ub9ac\uace0 \ub9e4\ub9e4 \uc804\ub7b5\uc774 \ud544\uc694\ud569\ub2c8\ub2e4.\n <\/p>\n

2. \ub370\uc774\ud130 \ubd84\uc11d\uc758 \uc911\uc694\uc131<\/h2>\n

\n \uc790\ub3d9\ub9e4\ub9e4\uc758 \uc131\uacf5\uc740 \ub370\uc774\ud130 \ubd84\uc11d\uc758 \uc815\ud655\uc131\uc5d0 \ub2ec\ub824 \uc788\uc2b5\ub2c8\ub2e4.
\n \ub370\uc774\ud130 \ubd84\uc11d\uc744 \ud1b5\ud574 \uc2dc\uc7a5\uc758 \ucd94\uc138\ub97c \ud30c\uc545\ud558\uace0, \ud2b9\uc815 \uc790\uc0b0\uc758 \uac00\uaca9 \uc6c0\uc9c1\uc784\uc744 \uc608\uce21\ud558\ub294 \uac83\uc774 \uac00\ub2a5\ud569\ub2c8\ub2e4.
\n \uc774 \uacfc\uc815\uc5d0\uc11c \ud30c\uc774\uc36c\uc758 Pandas<\/strong> \ub77c\uc774\ube0c\ub7ec\ub9ac\ub294 \ub9e4\uc6b0 \uc720\uc6a9\ud569\ub2c8\ub2e4.
\n Pandas\ub294 \ub370\uc774\ud130 \uc870\uc791 \ubc0f \ubd84\uc11d\uc744 \uc704\ud55c \uac15\ub825\ud55c \ub3c4\uad6c\ub85c,
\n \ub370\uc774\ud130\ud504\ub808\uc784\uc744 \uc0ac\uc6a9\ud558\uc5ec \ub2e4\uc591\ud55c \ud615\ud0dc\uc758 \ub370\uc774\ud130\ub97c \ud6a8\uc728\uc801\uc73c\ub85c \ucc98\ub9ac\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n <\/p>\n

3. Pandas \uc18c\uac1c<\/h2>\n

\n Pandas\ub294 Python \ud504\ub85c\uadf8\ub798\ubc0d \uc5b8\uc5b4\uc758 \ub77c\uc774\ube0c\ub7ec\ub9ac\ub85c,
\n \uace0\uc131\ub2a5\uc758 \ub370\uc774\ud130 \uad6c\uc870\uc640 \ubd84\uc11d \ub3c4\uad6c\ub97c \uc81c\uacf5\ud569\ub2c8\ub2e4.
\n \uc8fc\ub85c \ub370\uc774\ud130 \ud504\ub808\uc784(DataFrame)\uc774\ub77c\ub294 2\ucc28\uc6d0 \ub370\uc774\ud130 \uad6c\uc870\ub97c \uc0ac\uc6a9\ud558\uba70,
\n \uc774\ub294 \ud45c \ud615\uc2dd\uc758 \ub370\uc774\ud130\ub97c \uc27d\uac8c \ucc98\ub9ac\ud558\uace0 \ubd84\uc11d\ud560 \uc218 \uc788\uac8c \ub3c4\uc640\uc90d\ub2c8\ub2e4.
\n Pandas\ub294 \ub370\uc774\ud130\ub97c \uc218\uc9d1(\uc6f9 \ud06c\ub864\ub9c1, API \ud638\ucd9c)\ud558\uace0,
\n \ub370\uc774\ud130\ub97c \uc815\uc81c\ud558\uace0 \ubcc0\ud658\ud558\uba70,
\n \ubcf5\uc7a1\ud55c \ub370\uc774\ud130 \ubd84\uc11d \uc791\uc5c5\uc744 \uc218\ud589\ud560 \uc218 \uc788\ub294 \uae30\ub2a5\ub4e4\uc744 \uc81c\uacf5\ud569\ub2c8\ub2e4.\n <\/p>\n

4. Pandas \uc124\uce58\ud558\uae30<\/h2>\n

\uba3c\uc800, Pandas\ub97c \uc124\uce58\ud574\uc57c \ud569\ub2c8\ub2e4. \ub2e4\uc74c\uc758 \uba85\ub839\uc5b4\ub97c \uc0ac\uc6a9\ud558\uc5ec Pandas\ub97c \uc124\uce58\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4:<\/p>\n

pip install pandas<\/code><\/pre>\n
5. Pandas\ub97c \ud65c\uc6a9\ud55c \ub370\uc774\ud130 \ubd84\uc11d \uae30\ucd08<\/h2>\n
5.1 \ub370\uc774\ud130\ud504\ub808\uc784 \uc0dd\uc131\ud558\uae30<\/h3>\n
\n            Pandas\uc758 \ub370\uc774\ud130\ud504\ub808\uc784\uc740 \ub370\uc774\ud130 \ubd84\uc11d\uc758 \uae30\ubcf8 \uad6c\uc870\uccb4\uc785\ub2c8\ub2e4.
\n            \ub370\uc774\ud130\ud504\ub808\uc784\uc744 \uc0dd\uc131\ud558\ub294 \ubc29\ubc95\uc740 \uc5ec\ub7ec \uac00\uc9c0\uac00 \uc788\uc73c\uba70,
\n            \uadf8 \uc911 \uac00\uc7a5 \uac04\ub2e8\ud55c \ubc29\ubc95\uc740 \ub515\uc154\ub108\ub9ac\ub97c \uc0ac\uc6a9\ud558\ub294 \uac83\uc785\ub2c8\ub2e4.\n        <\/p>\n
import pandas as pd\n\ndata = {\n    '\ub0a0\uc9dc': ['2021-01-01', '2021-01-02', '2021-01-03'],\n    '\uc885\uac00': [100, 102, 105],\n    '\uac70\ub798\ub7c9': [1000, 1200, 1500]\n}\ndf = pd.DataFrame(data)\nprint(df)<\/code><\/pre>\n
5.2 \ub370\uc774\ud130\ud504\ub808\uc784\uc758 \uae30\ubcf8 \uc774\ud574<\/h3>\n
\n            \uc0dd\uc131\ub41c \ub370\uc774\ud130\ud504\ub808\uc784\uc740 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4:\n        <\/p>\n
         \ub0a0\uc9dc  \uc885\uac00   \uac70\ub798\ub7c9\n        0  2021-01-01  100  1000\n        1  2021-01-02  102  1200\n        2  2021-01-03  105  1500<\/code><\/pre>\n
\n            \uac01 \uc5f4\uc740 \ub2e4\uc591\ud55c \ub370\uc774\ud130 \uc720\ud615\uc744 \uac00\uc9c8 \uc218 \uc788\uc73c\uba70,
\n            \uc778\ub371\uc2a4\ub97c \ud1b5\ud574 \uac01 \ud589\uc5d0 \uc811\uadfc\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n        <\/p>\n
5.3 \ub370\uc774\ud130 \uc77d\uae30\uc640 \uc800\uc7a5<\/h3>\n
\n            Pandas\ub294 CSV, Excel, SQL \ub4f1 \ub2e4\uc591\ud55c \ud615\uc2dd\uc758 \ub370\uc774\ud130\ub97c \uc77d\uace0 \uc4f8 \uc218 \uc788\ub294 \uae30\ub2a5\uc744 \uc81c\uacf5\ud569\ub2c8\ub2e4.
\n            \uc608\ub97c \ub4e4\uc5b4, CSV \ud30c\uc77c\uc744 \uc77d\ub294 \uac83\uc740 \ub2e4\uc74c\uacfc \uac19\uc774 \ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4:\n        <\/p>\n
df = pd.read_csv('data.csv')<\/code><\/pre>\n
\n            \uadf8\ub9ac\uace0 \ub370\uc774\ud130\ud504\ub808\uc784\uc744 CSV \ud30c\uc77c\ub85c \uc800\uc7a5\ud558\ub294 \ubc29\ubc95\uc740 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4:\n        <\/p>\n
df.to_csv('output.csv', index=False)<\/code><\/pre>\n
5.4 \uae30\ubcf8\uc801\uc778 \ub370\uc774\ud130 \uc870\uc791<\/h3>\n
5.4.1 \uc5f4 \uc120\ud0dd\uacfc \ucd94\uac00<\/h4>\n
\n            \ub370\uc774\ud130\ud504\ub808\uc784\uc5d0\uc11c \ud2b9\uc815 \uc5f4\uc744 \uc120\ud0dd\ud558\ub294 \ubc29\ubc95\uc740 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4:\n        <\/p>\n
closing_prices = df['\uc885\uac00']<\/code><\/pre>\n
\n            \uc5f4\uc744 \ucd94\uac00\ud558\ub294 \ubc29\ubc95\uc740 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4.
\n            \uc608\ub97c \ub4e4\uc5b4 \uc885\uac00\uc758 5% \uc99d\uac00\ub97c \ub098\ud0c0\ub0b4\ub294 \uc0c8 \uc5f4\uc744 \ucd94\uac00\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4:\n        <\/p>\n
df['5% \uc99d\uac00'] = df['\uc885\uac00'] * 1.05<\/code><\/pre>\n
5.5 \ub370\uc774\ud130 \ud544\ud130\ub9c1<\/h3>\n
\n            \ud2b9\uc815 \uc870\uac74\uc744 \ub9cc\uc871\ud558\ub294 \ub370\uc774\ud130\ub97c \ud544\ud130\ub9c1\ud558\ub294 \ubc29\ubc95\uc740 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4:\n        <\/p>\n
high_volume = df[df['\uac70\ub798\ub7c9'] > 1200]<\/code><\/pre>\n
6. \uae30\ubcf8\uc801\uc778 \uc2dc\uac01\ud654<\/h2>\n
\n            Pandas\ub294 Matplotlib\uc640 \ud1b5\ud569\ub418\uc5b4 \ub370\uc774\ud130 \uc2dc\uac01\ud654\ub97c \uc190\uc27d\uac8c \uc218\ud589\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.
\n            \ub2e4\uc74c\uc740 \uc885\uac00\uc5d0 \ub300\ud55c \uac04\ub2e8\ud55c \ub77c\uc778 \ucc28\ud2b8\ub97c \uadf8\ub9ac\ub294 \uc608\uc2dc\uc785\ub2c8\ub2e4:\n        <\/p>\n
import matplotlib.pyplot as plt\n\ndf['\ub0a0\uc9dc'] = pd.to_datetime(df['\ub0a0\uc9dc'])\nplt.plot(df['\ub0a0\uc9dc'], df['\uc885\uac00'], marker='o')\nplt.title('\uc885\uac00 \ubcc0\ud654')\nplt.xlabel('\ub0a0\uc9dc')\nplt.ylabel('\uc885\uac00')\nplt.show()<\/code><\/pre>\n
7. \uc8fc\uc2dd \ub370\uc774\ud130 \uc218\uc9d1\ud558\uae30<\/h2>\n
\n            \uc774\uc81c Pandas\uc758 \uae30\ub2a5\uc744 \ud65c\uc6a9\ud558\uc5ec \uc2e4\uc2dc\uac04 \uc8fc\uc2dd \ub370\uc774\ud130\ub97c \uc218\uc9d1\ud574 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4.
\n            \uc774\ub97c \uc704\ud574 yfinance<\/strong> \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.
\n            yfinance\ub97c \uc124\uce58\ud558\ub824\uba74 \ub2e4\uc74c \uba85\ub839\uc5b4\ub97c \uc0ac\uc6a9\ud569\ub2c8\ub2e4:\n        <\/p>\n
pip install yfinance<\/code><\/pre>\n
7.1 yfinance\ub97c \uc774\uc6a9\ud55c \ub370\uc774\ud130 \uc218\uc9d1<\/h3>\n
\ub2e4\uc74c \ucf54\ub4dc\ub97c \ud1b5\ud574 \ud2b9\uc815 \uc8fc\uc2dd\uc758 \ub370\uc774\ud130\ub97c \uc218\uc9d1\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4:<\/p>\n
import yfinance as yf\n\nstock_data = yf.download('AAPL', start='2020-01-01', end='2021-01-01')\nprint(stock_data)<\/code><\/pre>\n
8. \uacb0\ub860<\/h2>\n
\n            \uc774 \uae00\uc5d0\uc11c\ub294 \ud30c\uc774\uc36c\uc758 Pandas \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc774\uc6a9\ud55c \ub370\uc774\ud130 \ubd84\uc11d\uc758 \uae30\ucd08\ub97c \ub2e4\ub918\uc2b5\ub2c8\ub2e4.
\n            \ub370\uc774\ud130 \ud504\ub808\uc784\uc758 \uc0dd\uc131, \ub370\uc774\ud130 \uc870\uc791 \ubc0f \uc2dc\uac01\ud654\ub294 \uc790\ub3d9\ub9e4\ub9e4 \uc2dc\uc2a4\ud15c\uc744 \uad6c\ucd95\ud558\ub294 \ub370 \ud544\uc218\uc801\uc778 \uc694\uc18c\uc785\ub2c8\ub2e4.
\n            \ub370\uc774\ud130\ub97c \ubd84\uc11d\ud558\ub294 \ub2a5\ub825\uc740 \uc131\uacf5\uc801\uc778 \uc790\ub3d9\ub9e4\ub9e4 \uc804\ub7b5\uc744 \uc218\ub9bd\ud558\ub294 \ub370 \ub9e4\uc6b0 \uc911\uc694\ud558\uba70,
\n            \uc774 \uacfc\uc815\uc5d0\uc11c Pandas\ub294 \ub9e4\uc6b0 \uc720\uc6a9\ud55c \ub3c4\uad6c\uac00 \ub420 \uac83\uc785\ub2c8\ub2e4.
\n            \uc55e\uc73c\ub85c \ub354 \uace0\uae09 \uc790\ub3d9\ub9e4\ub9e4 \uc804\ub7b5\uacfc \uc54c\uace0\ub9ac\uc998\uc5d0 \ub300\ud574 \uae4a\uc774 \uc788\ub294 \ub0b4\uc6a9\uc744 \ub2e4\ub904\ubcfc \uc608\uc815\uc785\ub2c8\ub2e4.\n        <\/p>\n<\/article>\n
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\uc624\ub298\ub0a0 \uae08\uc735\uc2dc\uc7a5\uc5d0\uc11c\ub294 \uc790\ub3d9\ub9e4\ub9e4 \uc2dc\uc2a4\ud15c\uc774 \uc810\uc810 \ub354 \ub9ce\uc774 \uc0ac\uc6a9\ub418\uace0 \uc788\uc2b5\ub2c8\ub2e4. \uc790\ub3d9\ub9e4\ub9e4\ub294 \ucf54\ub4dc\uc640 \uc54c\uace0\ub9ac\uc998\uc744 \ud1b5\ud574 \uc778\uac04\uc758 \uac1c\uc785 \uc5c6\uc774 \uac70\ub798\ub97c \uc218\ud589\ud560 \uc218 \uc788\uac8c \ud574\uc8fc\uba70, \uc774\ub97c \ud1b5\ud574 \ubcf4\ub2e4 \uc815\uad50\ud558\uace0 \uc2e0\uc18d\ud55c \uac70\ub798\uac00 \uac00\ub2a5\ud569\ub2c8\ub2e4. \uc774 \uae00\uc5d0\uc11c\ub294 \ud30c\uc774\uc36c\uc744 \uc0ac\uc6a9\ud558\uc5ec \uc790\ub3d9\ub9e4\ub9e4 \uc2dc\uc2a4\ud15c\uc744 \uac1c\ubc1c\ud558\ub294 \ub370 \ud544\uc694\ud55c Pandas\ub97c \uc774\uc6a9\ud55c \ub370\uc774\ud130 \ubd84\uc11d\uc758 \uae30\ucd08\ub97c \uc124\uba85\ud558\uaca0\uc2b5\ub2c8\ub2e4. 1. \uc790\ub3d9\ub9e4\ub9e4 \uc2dc\uc2a4\ud15c\uc758 \uac1c\uc694 \uc790\ub3d9\ub9e4\ub9e4 \uc2dc\uc2a4\ud15c\uc740 \ud2b9\uc815 \uc870\uac74\uc744 \ub9cc\uc871\ud560 \ub54c \uc790\ub3d9\uc73c\ub85c \ub9e4\uc218 … \ub354 \ubcf4\uae30 “\ud30c\uc774\uc36c \uc790\ub3d9\ub9e4\ub9e4 \uac1c\ubc1c, pandas\ub97c \uc774\uc6a9\ud55c \ub370\uc774\ud130 \ubd84\uc11d \uae30\ucd08”<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[37],"tags":[],"class_list":["post-30807","post","type-post","status-publish","format-standard","hentry","category-37"],"yoast_head":"\n\ud30c\uc774\uc36c \uc790\ub3d9\ub9e4\ub9e4 \uac1c\ubc1c, pandas\ub97c \uc774\uc6a9\ud55c \ub370\uc774\ud130 \ubd84\uc11d \uae30\ucd08 - 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