\n \ucd5c\uadfc \uba87 \ub144\uac04 \uc790\ub3d9\ub9e4\ub9e4 \uc2dc\uc2a4\ud15c\uc740 \uc8fc\uc2dd \ubc0f \uae30\ud0c0 \uae08\uc735 \uc790\uc0b0 \uac70\ub798\uc5d0\uc11c \ub9e4\uc6b0 \uc778\uae30\uac00 \ub192\uc544\uc84c\uc2b5\ub2c8\ub2e4. \n pandas\ub294 Python \ub370\uc774\ud130 \ubd84\uc11d \ub77c\uc774\ube0c\ub7ec\ub9ac\ub85c, \ud45c \ud615\uc2dd\uc758 \ub370\uc774\ud130\ub97c \uc27d\uac8c \ub2e4\ub8f0 \uc218 \uc788\ub3c4\ub85d \ub3c4\uc640\uc90d\ub2c8\ub2e4. \n pandas Series\ub294 1\ucc28\uc6d0 \ubc30\uc5f4 \uad6c\uc870\ub85c, \ub370\uc774\ud130\uc640 \ud574\ub2f9 \ub370\uc774\ud130\uc758 \uc778\ub371\uc2a4\ub97c \ud3ec\ud568\ud569\ub2c8\ub2e4. \n pandas Series\ub294 \ub2e4\uc591\ud55c \ubc29\ubc95\uc73c\ub85c \uc0dd\uc131\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uac00\uc7a5 \uc77c\ubc18\uc801\uc778 \ubc29\ubc95\uc740 \ub9ac\uc2a4\ud2b8\ub098 \ubc30\uc5f4\uc744 \ud65c\uc6a9\ud558\ub294 \uac83\uc785\ub2c8\ub2e4.\n <\/p>\n \n pandas Series\ub294 \ub2e4\uc591\ud55c \uae30\ub2a5\uc744 \uc81c\uacf5\ud569\ub2c8\ub2e4. \uc5ec\uae30\uc11c\ub294 \uba87 \uac00\uc9c0 \uc8fc\uc694 \uae30\ub2a5\uc744 \uc18c\uac1c\ud558\uaca0\uc2b5\ub2c8\ub2e4.\n <\/p>\n \n pandas\ub294 matplotlib\uc640 \ud1b5\ud569\ub418\uc5b4 \uc788\uc5b4 Series\ub97c \uc27d\uac8c \uc2dc\uac01\ud654\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n <\/p>\n \n pandas Series\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc2e4\uc81c \uc790\ub3d9\ub9e4\ub9e4 \uc2dc\uc2a4\ud15c\uc744 \uc5b4\ub5bb\uac8c \uad6c\ucd95\ud560 \uc218 \uc788\ub294\uc9c0 \uc0b4\ud3b4\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \n \ubcf8 \ube14\ub85c\uadf8\uc5d0\uc11c\ub294 \ud30c\uc774\uc36c\uacfc pandas Series\ub97c \ud65c\uc6a9\ud55c \uac04\ub2e8\ud55c \uc790\ub3d9\ub9e4\ub9e4 \uc2dc\uc2a4\ud15c \uac1c\ubc1c \uacfc\uc815\uc744 \uc0b4\ud3b4\ubcf4\uc558\uc2b5\ub2c8\ub2e4. \n pandas\ub97c \ud1b5\ud574 \uae08\uc735 \ub370\uc774\ud130\ub97c \ud6a8\uacfc\uc801\uc73c\ub85c \ub2e4\ub8e8\uace0, \uc5ec\ub7ec\ubd84\uc758 \ud22c\uc790 \uc804\ub7b5\uc744 \ubc1c\uc804\uc2dc\ud0a4\uae38 \ubc14\ub78d\ub2c8\ub2e4.\n <\/p>\n <\/body><\/p>\n","protected":false},"excerpt":{"rendered":"
\n \uc774\ub7ec\ud55c \uc2dc\uc2a4\ud15c\uc744 \uad6c\ucd95\ud558\uae30 \uc704\ud574 \uc5ec\ub7ec \uac00\uc9c0 \ud504\ub85c\uadf8\ub798\ubc0d \uc5b8\uc5b4 \uc911\uc5d0\uc11c\ub3c4 \ud30c\uc774\uc36c<\/strong>\uc774 \ub9ce\uc774 \uc0ac\uc6a9\ub429\ub2c8\ub2e4.
\n \uc774 \uae00\uc5d0\uc11c\ub294 \ud30c\uc774\uc36c\uc5d0\uc11c pandas<\/strong> \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \ud65c\uc6a9\ud558\uc5ec \uc790\ub3d9\ub9e4\ub9e4 \uc2dc\uc2a4\ud15c\uc744 \uac1c\ubc1c\ud558\ub294
\n \uacfc\uc815\uc5d0\uc11c \uc911\uc694\ud55c \uac1c\ub150\uc778 pandas Series<\/strong>\ub97c \uc790\uc138\ud788 \uc0b4\ud3b4\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.\n <\/p>\n1. pandas \ub77c\uc774\ube0c\ub7ec\ub9ac \uc18c\uac1c<\/h2>\n
\n \uc8fc \uc2dd \uac00\uaca9, \uac70\ub798\ub7c9, \uc778\ub371\uc2a4 \ub370\uc774\ud130 \ub4f1\uc744 \ucc98\ub9ac\ud560 \uc218 \uc788\ub294 \uac15\ub825\ud55c \ub3c4\uad6c\uc785\ub2c8\ub2e4.
\n pandas\uc758 \uc8fc\uc694 \ub370\uc774\ud130 \uad6c\uc870\ub294 Series<\/strong>\uc640 DataFrame<\/strong>\uc785\ub2c8\ub2e4.
\n \uba3c\uc800 Series<\/strong>\uc5d0 \ub300\ud574 \uc54c\uc544\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.\n <\/p>\n2. pandas Series\ub780?<\/h2>\n
\n \uc27d\uac8c \uc124\uba85\ud558\uc790\uba74, \ub9ac\uc2a4\ud2b8\uc640 \ub515\uc154\ub108\ub9ac\uc758 \ud2b9\uc9d5\uc744 \uac00\uc9c4 \ub370\uc774\ud130 \uad6c\uc870\ub77c\uace0 \ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.
\n Series\ub294 \ub2e4\uc74c\uacfc \uac19\uc740 \uc7a5\uc810\uc774 \uc788\uc2b5\ub2c8\ub2e4.\n <\/p>\n\n
3. pandas Series \uc0dd\uc131\ud558\uae30<\/h2>\n
3.1 \ub9ac\uc2a4\ud2c0 \uc0ac\uc6a9\ud55c Series \uc0dd\uc131<\/h3>\n
import pandas as pd\n\n# \ub9ac\uc2a4\ud2b8\ub97c \uc0ac\uc6a9\ud558\uc5ec Series \uc0dd\uc131\ndata = [10, 20, 30, 40, 50]\nseries = pd.Series(data)\nprint(series)<\/code><\/pre>\n3.2 \ub515\uc154\ub108\ub9ac\ub97c \uc0ac\uc6a9\ud55c Series \uc0dd\uc131<\/h3>\n
# \ub515\uc154\ub108\ub9ac\ub97c \uc0ac\uc6a9\ud558\uc5ec Series \uc0dd\uc131\ndata_dict = {'a': 10, 'b': 20, 'c': 30}\nseries_dict = pd.Series(data_dict)\nprint(series_dict)<\/code><\/pre>\n4. pandas Series\uc758 \uc8fc\uc694 \uae30\ub2a5<\/h2>\n
4.1 \uae30\ubcf8 \ud1b5\uacc4 \ubd84\uc11d<\/h3>\n
# Series\uc758 \uae30\ubcf8 \ud1b5\uacc4 \ubd84\uc11d\ndata = [10, 20, 30, 40, 50]\nseries = pd.Series(data)\n\nmean_value = series.mean() # \ud3c9\uade0 \uac12\nmedian_value = series.median() # \uc911\uc559\uac12\nstd_value = series.std() # \ud45c\uc900\ud3b8\ucc28\n\nprint(f\"Mean: {mean_value}, Median: {median_value}, Std: {std_value}\")<\/code><\/pre>\n4.2 \uc2dc\uac01\ud654<\/h3>\n
import matplotlib.pyplot as plt\n\n# Series \uc2dc\uac01\ud654\nseries.plot(kind='line')\nplt.title('Series Line Plot')\nplt.xlabel('Index')\nplt.ylabel('Values')\nplt.show()<\/code><\/pre>\n5. \uc2e4\uc81c \uc790\ub3d9\ub9e4\ub9e4 \uc2dc\uc2a4\ud15c\uc5d0 pandas Series \uc801\uc6a9\ud558\uae30<\/h2>\n
\n \uc5ec\uae30\uc11c\ub294 \uac00\uc7a5 \uac04\ub2e8\ud55c \uc774\ub3d9 \ud3c9\uade0 \uad50\ucc28 \uc804\ub7b5\uc744 \uc0ac\uc6a9\ud558\uaca0\uc2b5\ub2c8\ub2e4.\n <\/p>\n5.1 \ub370\uc774\ud130 \uc218\uc9d1<\/h3>\n
# \uc608\uc2dc \ub370\uc774\ud130 \uc218\uc9d1 (\uc77c\uc77c \uc885\uac00)\ndates = pd.date_range('2023-01-01', periods=100)\nprices = pd.Series([100 + (x * 2) + (np.random.randint(-5, 5)) for x in range(100)], index=dates)\n\n# \uac00\uaca9 \ub370\uc774\ud130 \uc2dc\uac01\ud654\nprices.plot(title='Price Data', xlabel='Date', ylabel='Price')\nplt.show()<\/code><\/pre>\n5.2 \uc774\ub3d9 \ud3c9\uade0 \uacc4\uc0b0<\/h3>\n
# \uc774\ub3d9 \ud3c9\uade0 \uacc4\uc0b0 (\ub2e8\uae30: 5\uc77c, \uc7a5\uae30: 20\uc77c)\nshort_mavg = prices.rolling(window=5).mean()\nlong_mavg = prices.rolling(window=20).mean()\n\n# \uc774\ub3d9 \ud3c9\uade0 \uc2dc\uac01\ud654\nplt.figure(figsize=(12, 6))\nplt.plot(prices, label='Price', color='blue')\nplt.plot(short_mavg, label='5-Day MA', color='red')\nplt.plot(long_mavg, label='20-Day MA', color='green')\nplt.title('Price with Moving Averages')\nplt.xlabel('Date')\nplt.ylabel('Price')\nplt.legend()\nplt.show()<\/code><\/pre>\n5.3 \ub9e4\ub9e4 \uc2e0\ud638 \uc0dd\uc131<\/h3>\n
# \ub9e4\ub9e4 \uc2e0\ud638 \uc0dd\uc131\nsignals = pd.Series(index=prices.index, data=0) # \uc2e0\ud638 \ucd08\uae30\ud654\n\n# \ub9e4\ub9e4 \uc804\ub7b5 \uc870\uac74: \ub2e8\uae30 \uc774\ub3d9 \ud3c9\uade0\uc774 \uc7a5\uae30 \uc774\ub3d9 \ud3c9\uade0\uc744 \uc0c1\ud5a5\ub3cc\ud30c\nsignals[short_mavg > long_mavg] = 1 # \ub9e4\uc218 \uc2e0\ud638\nsignals[short_mavg < long_mavg] = -1 # \ub9e4\ub3c4 \uc2e0\ud638\n\n# \ub9e4\ub9e4 \uc2e0\ud638 \uc2dc\uac01\ud654\nplt.figure(figsize=(12, 6))\nplt.plot(prices, label='Price', color='blue')\nplt.plot(short_mavg, label='5-Day MA', color='red')\nplt.plot(long_mavg, label='20-Day MA', color='green')\nplt.scatter(signals[signals == 1].index, prices[signals == 1], marker='^', color='g', label='Buy Signal', s=100)\nplt.scatter(signals[signals == -1].index, prices[signals == -1], marker='v', color='r', label='Sell Signal', s=100)\nplt.title('Trading Signals')\nplt.xlabel('Date')\nplt.ylabel('Price')\nplt.legend()\nplt.show()<\/code><\/pre>\n6. \uacb0\ub860<\/h2>\n
\n pandas Series\ub294 \ub370\uc774\ud130\uc758 \ubd84\uc11d\uacfc \uc870\uc791\uc5d0 \uc788\uc5b4\uc11c \ub9e4\uc6b0 \uc720\uc6a9\ud55c \ub3c4\uad6c\uc774\uba70, \uc790\ub3d9\ub9e4\ub9e4 \uc804\ub7b5\uc744 \uad6c\uc131\ud558\ub294 \ub370 \ud544\uc218\uc801\uc778 \uc694\uc18c\uc785\ub2c8\ub2e4.
\n \uc55e\uc73c\ub85c \ub354 \ubcf5\uc7a1\ud55c \uc804\ub7b5\uc774\ub098 \uba38\uc2e0\ub7ec\ub2dd\uc744 \uc774\uc6a9\ud55c \uc790\ub3d9\ub9e4\ub9e4 \uc2dc\uc2a4\ud15c\uc744 \uc5f0\uad6c\ud558\ub294 \ub370\uc5d0 \ud070 \ub3c4\uc6c0\uc774 \ub420 \uac83\uc785\ub2c8\ub2e4.\n <\/p>\n