Diff function in python

Pandas: как найти разницу между двумя строками

Вы можете использовать функцию DataFrame.diff() , чтобы найти разницу между двумя строками в кадре данных pandas.

Эта функция использует следующий синтаксис:

DataFrame.diff (периоды = 1, ось = 0)

• периоды: количество предыдущих строк для расчета разницы.
• ось: найти разницу в строках (0) или столбцах (1).

В следующих примерах показано, как использовать эту функцию на практике.

Пример 1: найти разницу между каждой предыдущей строкой

Предположим, у нас есть следующие Pandas DataFrame:

import pandas as pd #create DataFrame df = pd.DataFrame() #view DataFrame df period sales returns 0 1 12 2 1 2 14 2 2 3 15 3 3 4 15 3 4 5 18 5 5 6 20 4 6 7 19 4 7 8 24 6 

Следующий код показывает, как найти разницу между каждой текущей строкой в DataFrame и предыдущей строкой:

#add new column to represent sales differences between each row df['sales_diff'] = df['sales']. diff () #view DataFrame df period sales returns sales_diff 0 1 12 2 NaN 1 2 14 2 2.0 2 3 15 3 1.0 3 4 15 3 0.0 4 5 18 5 3.0 5 6 20 4 2.0 6 7 19 4 -1.0 7 8 24 6 5.0 

Обратите внимание, что мы также можем найти разницу между несколькими предыдущими строками. Например, следующий код показывает, как найти разницу между каждой текущей строкой и строкой, возникшей тремя строками ранее:

#add new column to represent sales differences between current row and 3 rows earlier df['sales_diff'] = df['sales']. diff (periods= 3 ) #view DataFrame df period sales returns sales_diff 0 1 12 2 NaN 1 2 14 2 NaN 2 3 15 3 NaN 3 4 15 3 3.0 4 5 18 5 4.0 5 6 20 4 5.0 6 7 19 4 4.0 7 8 24 6 6.0 

Пример 2. Поиск разницы по условию

Мы также можем отфильтровать DataFrame, чтобы показать строки, в которых разница между текущей строкой и предыдущей строкой меньше или больше некоторого значения.

Например, следующий код возвращает только те строки, в которых значение в текущей строке меньше значения в предыдущей строке:

import pandas as pd #create DataFrame df = pd.DataFrame() #find difference between each current row and the previous row df['sales_diff'] = df['sales']. diff () #filter for rows where difference is less than zero df = df[df['sales_diff']< 0 ] #view DataFrame df period sales returns sales_diff 3 4 13 3 -2.0 6 7 19 4 -1.0 

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Функция numpy.diff() в Python

Функция numpy.diff() в Python вычисляет n-ю дискретную разницу по заданной оси входного массива.

Синтаксис

Аргументы

Функция np.diff() принимает в качестве параметра пять аргументов:

1. a: это массив, для которого разница находится с помощью функции np.diff().
2. n: количество раз, когда массив дифференцируется, передается в качестве аргумента n. По умолчанию установлено значение 1.
3. axis: это ось, по которой рассчитывается разница: справа налево или слева направо. По умолчанию установлено значение -1. Однако мы также можем иметь ось как 0.
4. prepend: значения, добавляемые в начало перед выполнением функции diff().
5. append: значения, добавляемые в конце перед выполнением функции diff().

Возвращаемое значение

Функция np.diff() возвращает массив. Этот массив состоит из значений, которые различаются между двумя числами в массиве.

Пример 1

В этой программе мы импортировали библиотеку numpy и создали массив numpy с помощью функции np.array(). Затем мы передали этот массив в функцию np.diff(). Следовательно, мы передали значение для a.

Функция выполняет такие операции, как diff_arr[i] = arr[i+1] — arr[i].

Следовательно, первый индекс будет хранить разницу между первыми двумя элементами, 10 – 5; поэтому первый элемент равен 5. Затем во втором индексе diff_arr значение сохраняется как 7 – 10 равно -3. Точно так же эта функция вычисляет все элементы массива.

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difflib — Helpers for computing deltas¶

This module provides classes and functions for comparing sequences. It can be used for example, for comparing files, and can produce information about file differences in various formats, including HTML and context and unified diffs. For comparing directories and files, see also, the filecmp module.

class difflib. SequenceMatcher

This is a flexible class for comparing pairs of sequences of any type, so long as the sequence elements are hashable . The basic algorithm predates, and is a little fancier than, an algorithm published in the late 1980’s by Ratcliff and Obershelp under the hyperbolic name “gestalt pattern matching.” The idea is to find the longest contiguous matching subsequence that contains no “junk” elements; these “junk” elements are ones that are uninteresting in some sense, such as blank lines or whitespace. (Handling junk is an extension to the Ratcliff and Obershelp algorithm.) The same idea is then applied recursively to the pieces of the sequences to the left and to the right of the matching subsequence. This does not yield minimal edit sequences, but does tend to yield matches that “look right” to people.

Timing: The basic Ratcliff-Obershelp algorithm is cubic time in the worst case and quadratic time in the expected case. SequenceMatcher is quadratic time for the worst case and has expected-case behavior dependent in a complicated way on how many elements the sequences have in common; best case time is linear.

Automatic junk heuristic: SequenceMatcher supports a heuristic that automatically treats certain sequence items as junk. The heuristic counts how many times each individual item appears in the sequence. If an item’s duplicates (after the first one) account for more than 1% of the sequence and the sequence is at least 200 items long, this item is marked as “popular” and is treated as junk for the purpose of sequence matching. This heuristic can be turned off by setting the autojunk argument to False when creating the SequenceMatcher .

New in version 3.2: The autojunk parameter.

This is a class for comparing sequences of lines of text, and producing human-readable differences or deltas. Differ uses SequenceMatcher both to compare sequences of lines, and to compare sequences of characters within similar (near-matching) lines.

Each line of a Differ delta begins with a two-letter code:

line unique to sequence 1

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numpy.diff#

Calculate the n-th discrete difference along the given axis.

The first difference is given by out[i] = a[i+1] - a[i] along the given axis, higher differences are calculated by using diff recursively.

Parameters : a array_like

n int, optional

The number of times values are differenced. If zero, the input is returned as-is.

axis int, optional

The axis along which the difference is taken, default is the last axis.

prepend, append array_like, optional

Values 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.

The 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 datetime64 , which results in a timedelta64 output array.

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) 

>>> 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]') 

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Diff() function python – Python NumPy diff() Function

Diff() function python: The diff() function of the numpy module is used for calculating the nth discrete difference along the specified axis.

The first difference is given by out[i] = a[i+1] – a[i] along the specified axis, higher differences are calculated recursively by this function.

numpy.diff(a, n=1, axis=-1, prepend=, append=)

a: This is required. It is an array (array-like) given as input.

n: This is optional. It indicates the number of times values are differenced. If the value is 0, the input is returned unchanged.

axis: This is optional. It indicates the axis along which the difference is calculated; the last axis is the default.

prepend, append: This is optional. Before conducting the difference, give the values to prepend or append to along the axis. Scalar values are expanded to arrays of length 1 in the axis direction and the shape of the input array in all other axes. Otherwise, the dimension and shape must be identical with “a” except along the axis.

Return Value:

Numpy diff: An array with n-th discrete difference along the specified axis of “a” is returned. The output has the same shape as a, except along the axis where the dimension is smaller by n.

NumPy diff() Function in Python

• Import NumPy module using the import keyword.
• Pass some random list as an argument to the array() function to create an array.
• Store it in a variable.
• Print the above-given array.
• Get the first difference array by passing the given array and 1 as arguments the diff() function.
• To get the second difference array we apply the diff() function for the above result and pass second argument as 1.
• We can directly get the second difference array by passing 2 as the second arguments to the diff() function.
• Print the first difference array.
• Print the second difference array from the first difference array.
• Print the second difference array directly.
• The Exit of the Program

Below is the implementation:

# Import NumPy module using the import keyword. import numpy as np # Pass some random list as an argument to the array() function to # create an array. # Store it in a variable. gvn_arry = np.array([5, 10, 12, 4, 25]) # Print the above given array. print("The above given array is:") print(gvn_arry) # Get the first difference array by passing the given array and 1 as # argument the diff() function. fst_diffrnce = np.diff(gvn_arry, 1) # To get the second difference array we apply the diff() function for the # above result and pass second argument as 1 scnd_diffrnce = np.diff(fst_diffrnce, 1) # We can directly get the second difference array by passing 2 as second argument to # the diff() function direct_scnddiff = np.diff(gvn_arry, 2) # Print the first difference array print("The first difference array:", fst_diffrnce) # Print the second difference array from first difference array print("The second difference array from first difference array:", scnd_diffrnce) # Print the second difference array directly print("The second difference array directly :", direct_scnddiff)

The above given array is: [ 5 10 12 4 25] The first difference array: [ 5 2 -8 21] The second difference array from first difference array: [ -3 -10 29] The second difference array directly : [ -3 -10 29]

• Import NumPy module using the import keyword.
• Pass some random list as an argument to the array() function to create an array.
• Store it in a variable.
• Print the above-given array.
• Get the first difference array along axis=0 by passing the given array, 1 and axis=0 as arguments the diff() function.
• To get the first difference array along axis=1, apply the diff() function by # passing the given array, 1 and axis=1 as the arguments to it.
• Print the first difference array along axis= 0.
• Print the first difference array along axis= 1.
• The Exit of the Program.

Below is the implementation:

# Import NumPy module using the import keyword. import numpy as np # Pass some random list as an argument to the array() function to # create an array. # Store it in a variable. gvn_arry = np.array([[2, 5, 8], [1, 3, 2], [4, 6, 5]]) # Print the above given array. print("The above given array is:") print(gvn_arry) # Get the first difference array along axis=0 by passing the given array, 1 and axis=0 as # arguments the diff() function. fst_diffrnce_0 = np.diff(gvn_arry, 1, axis=0) # To get the first difference array along axis=1, apply the diff() function by # passing the given array, 1 and axis=1 as the arguments to it. fst_diffrnce_1 = np.diff(gvn_arry, 1, axis=1) # Print the first difference array along axis= 0 print("The first difference array along axis= 0:\n", fst_diffrnce_0) # Print the first difference array along axis= 1 print("The first difference array along axis= 1:\n", fst_diffrnce_1)

The above given array is: [[2 5 8] [1 3 2] [4 6 5]] The first difference array along axis= 0: [[-1 -2 -6] [ 3 3 3]] The first difference array along axis= 1: [[ 3 3] [ 2 -1] [ 2 -1]]

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