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empty Return a new array of given shape and type. np.empty((3,3)) >>> array([[-1., 0., 5.], [12., 18., 24.], [35., 60., inf]]) where Return elements depending on condition. ar=np.arange(1,10) np.where(ar>5) >>> (array([5, 6, 7, 8]),) allclose Returns True if two arrays are element-wise equal. np.allclose([2,3],[2,3],equal_nan=True) >>> True equal_nan : Whether to compare NaN’s as equal. If True,..

ndarray.ndim Number of array dimensions. c=np.array([ [[1,2,3],[3,4,4]], [[5,6,6],[7,8,8]] ]) print(c.ndim) print(c.shape) >>> 3 (2, 2, 3) ravel It returned 1-d array. np.array([[1,2,3],[4,5,6]]).ravel() >>> array([1, 2, 3, 4, 5, 6]) Same as below. np.array([[1,2,3],[4,5,6]]).reshape(-1) >>> array([1, 2, 3, 4, 5, 6]) permutation It returns the permutated range. np.random.permutation(10) >>> arra..

re Regular Expression. prodlist >>> 0 (직) 데친고사리 1kg(냉장} 1 (직) 깐도라지채 1kg(냉장} 2 콩나물 박스 4kg(상 곱슬이) 3 *삼색수제비 1kg(동성 냉동) 4 *)자숙바지락살 350g(냉동) ptn = "\([^)]+\)}" prodlist = [re.sub(ptn, "", str).strip() for str in prodlist] prodlist >>> ['데친고사리 1kg(냉장}', '깐도라지채 1kg(냉장}', '콩나물 박스 4kg', '*삼색수제비 1kg', '*)자숙바지락살 350g'] prodlist = [ re.sub("([0-9]+)?(k)?g", "", str).strip() for str in prodlist ] prodlist >>..

Counter Remove duplicates and return the frequency.Counter('mississippi')>>>Counter({'m': 1, 'i': 4, 's': 4, 'p': 2})Counter(list('mississippi'))>>>Counter({'m': 1, 'i': 4, 's': 4, 'p': 2}) most_common most_common(n)Return a list of the n most common elements and their counts.Counter('abracadabra').most_common(3)>>>[('a', 5), ('b', 2), ('r', 2)] FreqDist Frequency Di..

Normalize JSON data into a flat table. data=[{'id':1, 'name':{'first':'Coleen', 'last':'Volk'}}, {'name':{'given':'Mose','family':'Regner'}}, {'id':2, 'name':'Faye Raker'}] pd.json_normalize(data) >>> idname.firstname.lastname.givenname.familyname 01.0ColeenVolkNaNNaNNaN 1NaNNaNNaNMoseRegnerNaN 22.0NaNNaNNaNNaNFaye Raker data=[{'state':'florida', 'shortname':'FL', 'info':{'governor':'rick scott'..