Cara menggunakan python difflib differ example
Selisih (Difference)Selisih diskrit berarti mengurangkan dua elemen array secara berurutan. Misalnya: untuk array [1, 2, 3, 4], selisih diskritnya menjadi [2-1, 3-2, 4-3] = [1, 1, 1] Untuk mencari selisih diskrit, gunakan fungsi diff(). Contoh: import numpy as np arr = np.array([10, 15, 25, 5]) newarr = np.diff(arr) print(newarr) Mengembalikan : Operasi ini dapat dilakukan berulang kali dengan memberikan parameter n. Misalnya: pada array [1, 2, 3, 4], selisih diskrit dimana n = 2 akan menjadi [2-1, 3-2, 4-3] = [1, 1, 1], karena n = 2, maka kita akan melakukan operasi ini sekali lagi, dengan hasil baru: [1-1, 1-1] = [0, 0] Contoh: import numpy as np arr = np.array([10, 15, 25, 5]) newarr = np.diff(arr, n=2) print(newarr) Mengembalikan : Selisih (Difference)Selisih diskrit berarti mengurangkan dua elemen array secara berurutan. Misalnya: untuk array [1, 2, 3, 4], selisih diskritnya menjadi [2-1, 3-2, 4-3] = [1, 1, 1] Untuk mencari selisih diskrit, gunakan fungsi diff(). Contoh: import numpy as np arr = np.array([10, 15, 25, 5]) newarr = np.diff(arr) print(newarr) Mengembalikan : Operasi ini dapat dilakukan berulang kali dengan memberikan parameter n. Misalnya: pada array [1, 2, 3, 4], selisih diskrit dimana n = 2 akan menjadi [2-1, 3-2, 4-3] = [1, 1, 1], karena n = 2, maka kita akan melakukan operasi ini sekali lagi, dengan hasil baru: [1-1, 1-1] = [0, 0] Contoh: import numpy as np arr = np.array([10, 15, 25, 5]) newarr = np.diff(arr, n=2) print(newarr) Mengembalikan : View Discussion Improve Article Save Article View Discussion Improve Article Save Article numpy.diff(arr[, n[, axis]]) function is used when we calculate the n-th order discrete difference along the given axis. The first order difference is given by out[i] = arr[i+1] – arr[i] along the given axis. If we have to calculate higher differences, we are using diff recursively.
Code #1 : Python3
Output: Input array : [1 3 4 7 9] First order difference : [2 1 3 2] Second order difference : [-1 2 -1] Third order difference : [ 3 -3] Python3
Output: Input array : [[1 2 3 5] [4 6 7 9]] Difference with axis 0 : [[3 4 4 4]] Difference with axis 1 : [[1 1 2] [2 1 2]] Calculate the n-th discrete difference along the given axis. The first difference is given by Input array nint, optionalThe number of times values are differenced. If zero, the input is returned as-is. axisint, optionalThe axis along which the difference is taken, default is the last axis. prepend, appendarray_like, optionalValues to prepend or append to a along axis prior to performing the difference. Scalar values are expanded to arrays with length 1 in the direction of axis and the shape of the input array in along all other axes. Otherwise the dimension and shape must match a except along axis. New in version 1.16.0. Returns diffndarrayThe n-th differences. The shape of the output is the same as a except along axis where the dimension is smaller by n. The type of the output is the same as the type of the
difference between any two elements of a. This is the same as the type of a in most cases. A notable exception is Notes Type is preserved for boolean arrays, so the result will contain False when consecutive elements are the same and True when they differ. For unsigned integer arrays, the results will also be unsigned. This should not be surprising, as the result is consistent with calculating the difference directly: >>> u8_arr = np.array([1, 0], dtype=np.uint8) >>> np.diff(u8_arr) array([255], dtype=uint8) >>> u8_arr[1,...] - u8_arr[0,...] 255 If this is not desirable, then the array should be cast to a larger integer type first: >>> i16_arr = u8_arr.astype(np.int16) >>> np.diff(i16_arr) array([-1], dtype=int16) Examples >>> x = np.array([1, 2, 4, 7, 0]) >>> np.diff(x) array([ 1, 2, 3, -7]) >>> np.diff(x, n=2) array([ 1, 1, -10]) >>> x = np.array([[1, 3, 6, 10], [0, 5, 6, 8]]) >>> np.diff(x) array([[2, 3, 4], [5, 1, 2]]) >>> np.diff(x, axis=0) array([[-1, 2, 0, -2]]) >>> x = np.arange('1066-10-13', '1066-10-16', dtype=np.datetime64) >>> np.diff(x) array([1, 1], dtype='timedelta64[D]') |