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Python pandas ãƒ‘ãƒ•ã‚©ãƒ¼ãƒžãƒ³ã‚¹ç¶æŒã®ãŸã‚ã® 3 ã¤ã® TIPS - StatsFragments
pandas ã§ãã“ãã“å¤§ãã„ãƒ‡ãƒ¼ã‚¿ã‚’æ‰±ã†å ´åˆã€ãã®å‡¦ç†é€Ÿåº¦ãŒæ°—ã«ãªã£ã¦ãã‚‹ã€‚å…¬å¼ãƒ‰ã‚ãƒ¥ãƒ¡ãƒ³ãƒˆã§ã¯ãƒ‘ãƒ•ã‚©ãƒ¼ãƒžãƒ³ã‚¹å‘ä¸Šã®ãŸã‚ã« Cython ã‚„ Numba ã‚’ä½¿ã†æ–¹æ³•ã‚’è¨˜è¼‰ã—ã¦ã„ã‚‹ã€‚ Enhancing Performance â€” pandas 0.16.2 documentation ãŒã€è»½ãè©¦ã—ãŸã„ã ã‘ãªã®ã« ã‚ã–ã‚ã– Cythonã‚„ Numba ã‚’ä½¿ã†ã®ã¯æ‰‹é–“ã ã—ã€ã‹ã¨ã„ã£ã¦ã‚ã¾ã‚Šã«é…ã„ã®ã‚‚å«Œã ã€‚ãã‚“ãªã¨ãã€pandas æœ¬æ¥ã®ãƒ‘ãƒ•ã‚©ãƒ¼ãƒžãƒ³ã‚¹ã‚’ã§ãã‚‹ã ã‘ç¶æŒã™ã‚‹ãŸã‚ã®ãƒã‚¤ãƒ³ãƒˆã‚’æ•´ç†ã—ãŸã„ã€‚ pandas ã«é™ã‚‰ãšã€ãƒ‘ãƒ•ã‚©ãƒ¼ãƒžãƒ³ã‚¹æ”¹å–„ã®éš›ã«ã¯ãƒœãƒˆãƒ«ãƒãƒƒã‚¯ã®ç®‡æ‰€ã«ã‚ˆã£ã¦ã¨ã‚‹ã¹ãå¯¾ç–ã¯ç•°ãªã‚‹ã€‚pandas ã§ã¯é€Ÿåº¦å‘ä¸Š/ã‚¨ãƒƒã‚¸ã‚±ãƒ¼ã‚¹å‡¦ç†ã®ãŸã‚ã« ãƒ‡ãƒ¼ã‚¿ã®åž‹ã‚„æ¡ä»¶ã«ã‚ˆã£ã¦å†…éƒ¨ã§å‡¦ç†ã‚’ç´°ã‹ãåˆ†ã‘ã¦ãŠã‚Šã€å¸¸ã«ã“ã†ã™ã‚Œã°é€Ÿããªã‚‹ï¼ ã¨ã„ã†æ–¹æ³•ã‚’å‡ºã™ã®ã¯é›£ã—ã„ã€‚ä»¥ä¸‹ã¯ã“ã®å‰æã®ã†ãˆã§ã€å†…éƒ¨å®Ÿè£…ã‹ã‚‰ã¿
U1and0 2018/07/27
è¡Œã«å¯¾ã™ã‚‹ãƒ«ãƒ¼ãƒ— / DataFrame.apply ã¯ ä½¿ã‚ãªã„ object åž‹ã¯ä½¿ã‚ãªã„ ãƒ¦ãƒ‹ãƒ¼ã‚¯ã§ãªã„ / ã‚½ãƒ¼ãƒˆã•ã‚Œã¦ã„ãªã„ index ã¯ä½¿ã‚ãªã„

pandas

numpy

python
ãƒªãƒ³ã‚¯
NumPy ã§ãƒ“ãƒ³ã®ä½œæˆã€ãƒ‡ãƒ¼ã‚¿ã¨ãƒ“ãƒ³ã®å¯¾å¿œé–¢ä¿‚ã®å–å¾— - Qiita
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U1and0 2018/06/02
np.digitizeã¨ã¯ã€€histgram, binsã¨ã®é–¢ä¿‚

python

numpy

digitize

hist
ãƒªãƒ³ã‚¯
ã€NumPyã€‘å›žå¸°åˆ†æžã§ç›´ç·šè¿‘ä¼¼ï¼ˆç·šå½¢ãƒ•ã‚£ãƒƒãƒ†ã‚£ãƒ³ã‚°ï¼‰
ç›´ç·šè¿‘ä¼¼ï¼ˆå›žå¸°åˆ†æžï¼‰ Pythonãƒ¢ã‚¸ãƒ¥ãƒ¼ãƒ«NumPyã§ã¯ã€polyfitãƒ¡ã‚½ãƒƒãƒ‰ã§å›žå¸°åˆ†æžãŒã§ãã¾ã™ã€‚ æ›¸å¼ a, b = numpy.polyfit(x, y, 1) â– è¿”ã‚Šå€¤ aï¼šè¿‘ä¼¼ç›´ç·šã®å‚¾ã bï¼šè¿‘ä¼¼ç›´ç·šã®åˆ‡ç‰‡ ã‚½ãƒ¼ã‚¹ã‚³ãƒ¼ãƒ‰ ã‚µãƒ³ãƒ—ãƒ«ãƒ—ãƒã‚°ãƒ©ãƒ ã®ã‚½ãƒ¼ã‚¹ã‚³ãƒ¼ãƒ‰ã§ã™ã€‚ # -*- coding: utf-8 import numpy as np import matplotlib.pyplot as plt def main(): # CSVã®ãƒãƒ¼ãƒ‰ data = np.genfromtxt("nikkei16.csv",delimiter=",", skip_header=1, dtype='float') # 5è¡Œç›®ã‚’æŠ½å‡º(æ—¥çµŒå¹³å‡æ ªä¾¡ã®çµ‚å€¤) f = data[:,4]/1000.0 #ã€€xã®å€¤ã‚’ç”Ÿæˆ x = np.linspace(1, len(f), len(f)) #ã€€ãƒ•
U1and0 2018/05/10
ç·šå½¢ãƒ•ã‚£ãƒƒãƒ†ã‚£ãƒ³ã‚°ã ã£ãŸã‚‰`from scipy.optimize import curve_fit`ã—ãªã„ã§ã‚‚`np.polyfit()`ã§äº‹è¶³ã‚Šã‚‹

fit

numpy

python
ãƒªãƒ³ã‚¯
matplotlibã§3Dã‚°ãƒ©ãƒ•ã‚’æç”»ã™ã‚‹ - white wheelsã®ãƒ¡ãƒ¢
æº–å‚™ ãƒ‡ãƒ¼ã‚¿å‡¦ç†ç”¨ã«numpyã€ãƒ—ãƒãƒƒãƒˆç”¨ã«pyplotã€3æ¬¡å…ƒãªã®ã§mpl_toolkits.mplot3dã‚’ã‚¤ãƒ³ãƒãƒ¼ãƒˆã—ã¾ã™ã€‚ from mpl_toolkits.mplot3d import Axes3D import matplotlib.pyplot as plt import numpy as np æç”»ã™ã‚‹ãƒ‡ãƒ¼ã‚¿ã®ä½œæˆ 3æ¬¡å…ƒã§æç”»ã™ã‚‹ã«ã¯ãƒ¡ãƒƒã‚·ãƒ¥(2æ¬¡å…ƒã®ç¶²ç›®)ã‚’ä½œæˆã™ã‚‹ãŸã‚ã«2æ¬¡å…ƒã®é…åˆ—ã‚’ç”¨æ„ã™ã‚‹å¿…è¦ãŒã‚ã‚Šã¾ã™ã€‚ ã¾ãšarangeãƒ¡ã‚½ãƒƒãƒ‰ã§x,yãã‚Œãžã‚Œã‚’1æ¬¡å…ƒé ˜åŸŸã§åˆ†å‰²ã—ã¾ã™ã€‚ x = np.arange(-3, 3, 0.25) y = np.arange(-3, 3, 0.25) 2æ¬¡å…ƒãƒ¡ãƒƒã‚·ãƒ¥ã‚’ä½œæˆã™ã‚‹ã«ã¯meshgridãƒ¡ã‚½ãƒƒãƒ‰ã‚’åˆ©ç”¨ã—ã¾ã™ã€‚ã“ã®é–¢æ•°ã®æˆ»ã‚Šå€¤ã¯X,Yã«å¯¾å¿œã™ã‚‹è¡Œåˆ—ã§ã€Xã¯è¡Œã«xã®é…åˆ—ã‚’ã€Yã¯åˆ—ã«yã®é…åˆ—ã‚’å…¥ã‚ŒãŸã‚‚ã®ã«ãªã£ã¦ã„ã¾ã™ã€‚ X, Y =
U1and0 2017/01/15
â€œZ = np.sin(X)+ np.cos(Y) fig = plt.figure() ax = Axes3D(fig) ax.plot_wireframe(X,Y,Z) #<---ã“ã“ã§plotâ€

matplotlib

python

numpy
ãƒªãƒ³ã‚¯
Best way to initialize and fill an numpy array?
I want to initialize and fill a numpy array. What is the best way? This works as I expect: >>> import numpy as np >>> np.empty(3) array([ -1.28822975e-231, -1.73060252e-077, 2.23946712e-314]) But this doesn't: >>> np.empty(3).fill(np.nan) >>> Nothing? >>> type(np.empty(3)) <type 'numpy.ndarray'> It seems to me that the np.empty() call is returning the correct type of object, so I don't understand
U1and0 2016/12/05
a = np.empty(3) * np.nan shareimprove this answer edited Mar 14 '14 at 20:45 answered Mar 14 '14 at 19:45 shx2 28.9k54679 Thanks for modify in place hint. That is what was confusing me. â€“ tbc Mar 14 '14 at 20:32 add a comment up vote 23 down vote You could also try: In [79]: np.full(3, np.nan) Out

array

numpy

empty

full

NaN
ãƒªãƒ³ã‚¯
pandasã®ãƒ—ãƒãƒƒãƒˆæ©Ÿèƒ½ã‚’ä½¿ã„Jupyterä¸Šã§ä½œå›³ - Qiita
ç’°å¢ƒ OS X El Capitan 10.11.6 python: 2.7.11 pandas: 0.18.0 matplotlib: 1.5.1 numpy: 1.10.4 IPython: 4.1.2 åˆã‚ã« Pythonã«ã‚ˆã‚‹ä½œå›³ã®ãŠã™ã™ã‚10 Pythonã«ã¯æ§˜ã€…ãªä½œå›³æ–¹æ³•ãŒã‚ã‚Šã€matplotlibã¨ã„ã†ãƒ©ã‚¤ãƒ–ãƒ©ãƒªã‚’ä½¿ã†ã®ãŒåŸºæœ¬ã§ã™ã€‚ãŸã ãã‚Œã¯å°‘ã—é‡Žæš®ã£ãŸã„ã®ã§æ¥½ã«ã‚ªã‚·ãƒ£ãƒ¬ã«æã‘ã‚‹ã‚ˆã†ã«ã™ã‚‹seabornã¨ã„ã†ãƒ©ãƒƒãƒ‘ãƒ¼ãŒã‚ã‚Šã¾ã™ã€‚ã“ã‚Œã«æº€è¶³ã§ããªã‹ã£ãŸã‚‰ã€Bokehã¨ã‹ãŒã„ã„ã®ã‹ã‚‚ã—ã‚Œã¾ã›ã‚“ã€‚ggplotã¯Rã§ä½¿ç”¨ã—ã¦ã„ã‚‹æ–¹ã¯é¦´æŸ“ã¿ã‚„ã™ã„ã‹ã‚‚ã—ã‚Œã¾ã›ã‚“ã€‚ ãŸã ã€ã©ã¡ã‚‰ã«ã—ã‚ãƒ‡ãƒ¼ã‚¿æ•´å½¢ã«å¿…è¦ã¨ãªã‚‹pandasã«ã‚‚matplotlibã®ãƒ©ãƒƒãƒ‘ãƒ¼ã¨ã—ã¦ãƒ—ãƒãƒƒãƒˆæ©Ÿèƒ½ãŒã‚ã‚‹ã®ã§ã€ãã‚Œã‚’ä½¿ã†ã“ã¨ã«ã—ã¾ã™ã€‚ä»¥ä¸‹ã®ã‚µã‚¤ãƒˆã‚ãŸã‚Šã‚’æ‹è¦‹ã—å‹‰å¼·ã—ãŸã®ã§ã™ãŒã€ãƒãƒ¼ã‚¸ãƒ§ãƒ³ã®é•ã„ãªã®ã‹è¡¨è¨˜ã«å¾®å¦™ã«å·®ãŒã‚ã‚Š
U1and0 2016/09/09
pandas

matplotlib

numpy

python
ãƒªãƒ³ã‚¯
NumPy é…åˆ—ã®åŸºç¤Ž â€” æ©Ÿæ¢°å¦ç¿’ã® Python ã¨ã®å‡ºä¼šã„
NumPy é…åˆ—ã®åŸºç¤ŽÂ¶ ã“ã“ã§ã¯ï¼ŒNumPy ã§æœ€ã‚‚é‡è¦ãªã‚¯ãƒ©ã‚¹ã§ã‚ã‚‹ np.ndarray ã«ã¤ã„ã¦ï¼Œ æœ¬ãƒãƒ¥ãƒ¼ãƒˆãƒªã‚¢ãƒ«ã®æ–¹é‡ ã®æ–¹é‡ã«å¾“ã„ï¼Œæœ€ä½Žé™å¿…è¦ãªäºˆå‚™çŸ¥è˜ã«ã¤ã„ã¦èª¬æ˜Žã—ã¾ã™ï¼Ž np.ndarray ã¯ï¼Œ N-d Array ã™ãªã‚ã¡ï¼ŒNæ¬¡å…ƒé…åˆ—ã‚’æ‰±ã†ãŸã‚ã®ã‚¯ãƒ©ã‚¹ã§ã™ï¼Ž NumPy ã‚’ä½¿ã‚ãªã„å ´åˆï¼Œ Python ã§ã¯ã“ã†ã—ãŸNæ¬¡å…ƒé…åˆ—ã‚’è¡¨ç¾ã™ã‚‹ã«ã¯ï¼Œå¤šé‡ã®ãƒªã‚¹ãƒˆãŒåˆ©ç”¨ã•ã‚Œã¾ã™ï¼Ž np.ndarray ã¨å¤šé‡ãƒªã‚¹ãƒˆã«ã¯ä»¥ä¸‹ã®ã‚ˆã†ãªé•ã„ãŒã‚ã‚Šã¾ã™ï¼Ž å¤šé‡ãƒªã‚¹ãƒˆã¯ãƒªãƒ³ã‚¯ã§ã‚»ãƒ«ã‚’çµåˆã—ãŸå½¢å¼ã§ãƒ¡ãƒ¢ãƒªä¸Šã«ä¿æŒã•ã‚Œã¾ã™ãŒï¼Œ np.ndarray ã¯ C ã‚„ Fortran ã®é…åˆ—ã¨åŒæ§˜ã«ãƒ¡ãƒ¢ãƒªã®é€£ç¶šé ˜åŸŸä¸Šã«ä¿æŒã•ã‚Œã¾ã™ï¼Ž ãã®ãŸã‚ï¼Œå¤šé‡ãƒªã‚¹ãƒˆã¯å‹•çš„ã«å¤‰æ›´å¯èƒ½ã§ã™ãŒï¼Œ np.ndarray ã®å½¢çŠ¶å¤‰æ›´ã«ã¯å…¨ä½“ã®å‰Šé™¤ãƒ»å†ç”ŸæˆãŒå¿…è¦ã«ãªã‚Šã¾ã™ï¼Ž å¤šé‡ãƒªã‚¹ãƒˆã¯ãƒªã‚¹ãƒˆå†…ã§ãã®è¦ç´ ã®åž‹ãŒç•°ãªã‚‹ã“ã¨ãŒè¨±
U1and0 2016/08/30
numpy

array

Python
ãƒªãƒ³ã‚¯
Numpyã«ã‚ˆã‚‹ä¹±æ•°ç”Ÿæˆã¾ã¨ã‚ - Qiita
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U1and0 2016/08/21
from numpy.random import * rand() # 0ã€œ1ã®ä¹±æ•°ã‚’1å€‹ç”Ÿæˆ rand(100) # 0ã€œ1ã®ä¹±æ•°ã‚’100å€‹ç”Ÿæˆ rand(10,10) # 0ã€œ1ã®ä¹±æ•°ã§ 10x10 ã®è¡Œåˆ—ã‚’ç”Ÿæˆ

qiita

python

numpy

random
ãƒªãƒ³ã‚¯
Pythonã®æ•°å€¤è¨ˆç®—ãƒ©ã‚¤ãƒ–ãƒ©ãƒª NumPyå…¥é–€
Scientific Computing Tools For Python â€” Numpy NumPy ã¯ Pythonãƒ—ãƒã‚°ãƒ©ãƒŸãƒ³ã‚°è¨€èªžã®æ‹¡å¼µãƒ¢ã‚¸ãƒ¥ãƒ¼ãƒ«ã§ã‚ã‚Šã€å¤§è¦æ¨¡ãªå¤šæ¬¡å…ƒé…åˆ—ã‚„è¡Œåˆ—ã®ã‚µãƒãƒ¼ãƒˆã€ã“ã‚Œã‚‰ã‚’æ“ä½œã™ã‚‹ãŸã‚ã®å¤§è¦æ¨¡ãªé«˜æ°´æº–ã®æ•°å¦é–¢æ•°ãƒ©ã‚¤ãƒ–ãƒ©ãƒªã‚’æä¾›ã™ã‚‹ã€‚(via Wikipedia) ã“ã‚Œã¾ã§çŸ¥è˜ãŒã‚ã„ã¾ã„ã ã£ãŸNumPyã«ã¤ã„ã¦ã€ã‚‚ã†ä¸€åº¦ãŠã•ã‚‰ã„ã—ãŸã„ã¨æ€ã„ã¾ã™ã€‚NumPyã¯SciPyã¨ä½µã›ã¦ç§‘å¦æŠ€è¡“è¨ˆç®—ã§ã‚ˆãåˆ©ç”¨ã•ã‚Œã¦ã„ã¾ã™ã€‚ã¾ãŸã€é«˜é€Ÿã«è¡Œåˆ—æ¼”ç®—ãŒã§ãã‚‹ã®ã§OpenCV(ã‚³ãƒ³ãƒ”ãƒ¥ãƒ¼ã‚¿ãƒ“ã‚¸ãƒ§ãƒ³ãƒ©ã‚¤ãƒ–ãƒ©ãƒª)ã§ã‚‚NumPyã‚’åˆ©ç”¨ã—ãŸPythonã‚¤ãƒ³ã‚¿ãƒ•ã‚§ãƒ¼ã‚¹ãŒæä¾›ã•ã‚Œã‚‹ã‚ˆã†ã«ãªã‚Šã¾ã—ãŸã€‚ OpenCVã®Pythonãƒã‚¤ãƒ³ãƒ‡ã‚£ãƒ³ã‚°ã«ã¤ã„ã¦ã¯åŽ»å¹´ã®ã‚¨ãƒ³ãƒˆãƒªãƒ¼ã§ã‚‚å–ã‚Šä¸Šã’ã¦ã„ã¾ã™ã®ã§å‚è€ƒã¾ã§ã«ã€‚ * ã•ãã‚‰VPSã«OpenCVã‚’ã‚¤ãƒ³ã‚¹ãƒˆãƒ¼ãƒ«ã—ã¦Pythonã‹ã‚‰ä½¿ã† [2017/04/2
U1and0 2016/08/18
python

numpy

arange

linspace
ãƒªãƒ³ã‚¯
iPython Notebookã®--pylab inlineã¯ä½¿ã†ã®ã‚’ã‚„ã‚ã‚ˆã†ã¨ã„ã†è©± - Wolfeyes Bioinformatics beta
TL;DR ipython notebook --pylab inlineã®ã‹ã‚ã‚Šã«ipython notebook --matplotlib inlineã‚’ä½¿ãŠã†ï¼Žã‚‚ã—ãã¯ipythonã®å§‹ã‚ã«%matplotlib inlineã‚’å®Ÿè¡Œã—ã¦ãŠãï¼Ž iPython Notebookã«ã¤ã„ã¦ å‘¨çŸ¥ã®äº‹å®Ÿã ã¨ã¯æ€ã†ãŒï¼ŒiPythonã¯è¶…ä¾¿åˆ©ãªPythonã®ã‚¤ãƒ³ã‚¿ãƒ©ã‚¯ãƒ†ã‚£ãƒ–ã‚·ã‚§ãƒ«ã ï¼Žãã®ä¸€éƒ¨ã¨ã—ã¦iPython Notebookã¨ã„ã†ã®ãŒã‚ã‚Šï¼Œãƒ–ãƒ©ã‚¦ã‚¶ã§ã‚³ãƒ¼ãƒ‰ã‚’å®Ÿè¡Œã§ããŸã‚Šï¼Œå®Ÿè¡Œçµæžœã‚’ãƒŽãƒ¼ãƒˆã¨ã—ã¦ä¿å˜ã—ãŸã‚Šï¼Œmatplotlibãªã©ã§æå†™ã—ãŸã‚°ãƒ©ãƒ•ã‚’ãƒŽãƒ¼ãƒˆã®ä¸ã«ãã®ã¾ã¾è¡¨ç¤ºã—ãŸã‚Šã§ãã‚‹ï¼ŽRã§ã„ã†ã¨ã“ã‚ã®Rstudio+knitrã®ã‚ˆã†ãªï¼Œè§£æžãƒ¬ãƒãƒ¼ãƒˆã‚’ä½œã‚‹ã¨ãã«ã¯é‡å®ã™ã‚‹ãƒ„ãƒ¼ãƒ«ã¨ãªã£ã¦ã„ã‚‹ï¼Ž (http://nbviewer.ipython.org/gist/twiecki/3962843ã‚ˆã‚Š)
U1and0 2016/07/29
ipython notebook --matplotlib inlineã§èµ·å‹•ã™ã‚‹ %matplotlib inlineã‚’iPython Notebookã®å†’é ã§å®Ÿè¡Œã—ã¦ãŠã ã©ã£ã¡ã‹

matplotlib

numpy

Python

ipython
ãƒªãƒ³ã‚¯
Fancy Indexingï¼šé…åˆ—ã®ä¸€éƒ¨ã‚’æ¡ä»¶ã‚’ã¤ã‘ã¦å–ã‚Šå‡ºã™ - ãŠã£ã±ã„ãã‚“ï¼
aã‚’é©å½“ãªNæ¬¡å…ƒé…åˆ—ã¨ã™ã‚‹ã€‚ a[a_1, a_2, a_3, ]ã¨ã™ã‚‹ã¨ã€é…åˆ—ã®1ã¤ã®æˆåˆ†ã‚’å–ã‚Šå‡ºã›ã‚‹ã€‚ a[ãƒªã‚¹ãƒˆ]ã¨ã™ã‚‹ã¨ã€1æ¬¡å…ƒé…åˆ—ã®ãƒªã‚¹ãƒˆç•ªç›®ã®æˆåˆ†ã®ã¿å–ã‚Šå‡ºã—ãŸé…åˆ—ã‚’è¿”ã™ã€‚ (ä¸Šã®ç”¨ã«é…åˆ—ã®ï¼‘ã¤ã®æˆåˆ†ã ã‘å–ã‚Šå‡ºã—ãŸã„æ™‚ã«ã¯tuple(ãƒªã‚¹ãƒˆ)ã®ã‚ˆã†ã«tupleã«ã—ã¦æ¸¡ã›ã°è‰¯ã„ï¼‰ a[ãƒªã‚¹ãƒˆ1,ãƒªã‚¹ãƒˆ2,ãƒªã‚¹ãƒˆ3, ]ã¨ã™ã‚‹ã¨ã€1æ¬¡å…ƒç›®ã‚’ãƒªã‚¹ãƒˆ1ã§2æ¬¡å…ƒç›®ã‚’ãƒªã‚¹ãƒˆ2ã§ã€ã¨ã„ã£ãŸã‚ˆã†ã«å–ã‚Šå‡ºã—ãŸé…åˆ—ã‚’è¿”ã™ã€‚ np.ix_ã‚’ã¤ã‹ã£ã¦a[np.ix_([è¡Œ],[åˆ—])]ã¨ã™ã‚‹ã¨è¡Œåˆ—ã®ä¸€éƒ¨ã®è¡Œã¨åˆ—ã®ã¿å–ã‚Šå‡ºã—ãŸæ–°ã—ã„è¡Œåˆ—ã‚’ä½œã‚Œã‚‹ã€‚ â€»ã“ã“ã¾ã§ã®ãƒªã‚¹ãƒˆã¯é…åˆ—ã«ã—ã¦ã‚‚åŒã˜ã€‚ åŒã˜ã“ã¨ã‚’boolå€¤ã‚’ä½¿ã£ã¦ã€a[[è¡Œã®bool],[åˆ—ã®bool],[3æ¬¡å…ƒç›®ã®bool], ]ã¨ã‚‚å‡ºæ¥ã‚‹(ã“ã‚Œã¯ãƒªã‚¹ãƒˆã«ã™ã‚‹ã¨ã€False=0, True=1ã¨è§£é‡ˆã•ã‚Œã‚‹ã®ã§æ³¨æ„)ã€‚ import numpy as
U1and0 2016/07/28
python

numpy

array
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Random sampling (numpy.random) â€” NumPy v1.15 Manual
Return a sample (or samples) from the â€œstandard normalâ€ distribution.
U1and0 2016/04/29
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python

numpy
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