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This article introduces the knowledge of "what are the tips for writing Python". In the operation of practical cases, many people will encounter such a dilemma. Then let the editor lead you to learn how to deal with these situations. I hope you can read it carefully and be able to achieve something!

1. Reverse string

Although it may seem like a basic operation, using char loops to reverse strings can be tedious. Fortunately, Python includes a simple built-in operation to perform this task accurately, simply by accessing the index on the string::-1.

A = "! dlrow olleH" backward = a [::-1]

2. Dims as a variable

In most languages, you need to iterate through values or access dark spots by location in order to put an array into a set of variables, as follows:

Firstdim = array [1]

However, there is a better and faster way in Python. To change a column of values to a variable, you can simply set the variable name to an array of the same length as the array:

Array = [5, 10, 15, 20] five, ten, fift, twent = array

3. Next () iteration of the generator

In most normal situations in programming, you can access an index and use a counter to get the location number, which will be just a value added to:

Array1 = [5,10,15,20] array2 = (x * * 2 for x in range (10)) counter = 0for I in array1:# This code wouldn't work because 'i'is not in array2. # I = array2 [I] I = array2 [counter] # ^ ^ This code would because we areaccessing the position of i

We can also replace it with next (). Next uses an iterator that stores the current location in memory and iterates over the list in the background:

G = (x * * 2 for x in range (10)) print (next (g)) print (next (g))

4. Intelligent unpacking

Iteratively extracting values can be time-consuming, and there are several good ways to extract lists in Python. One of them is *, which populates the unassigned values and adds them to the new list under the variable name.

A, * b, c = [1, 2, 3, 4, 5]

5. Enumerate

I don't know. Enumerating is not very good. Enumerating indexes that can get some values in a list is especially useful when using arrays instead of data frames in data science:

For iMagol w in enumerate (array): print (iMagne w)

6. Named slicing

In Python, splitting lists is very simple, and all kinds of excellent tools can do it. The good thing is that it can also name lists, which is especially useful for linear algebra in Python:

A = [0,1,2,3,4,5] LASTTHREE = slice (- 3, None) slice (- 3, None, None) print (a [lastest])

7. Itertools

If you study Python deeply, you must be familiar with itertools. Itertools is a module in the standard library that can constantly solve iterative problems. It not only makes it much easier to write complex loops, but also makes the code simpler and faster. There are hundreds of examples of using Itertools. Take a look at one of them:

C = [[1,2], [3,4], [5,6]] # Let's convert this matrix to a 1 dimensional list. Import itertools as it newlist = list (it.chain.from_iterable (c))

8. Group adjacent list

In for loops, it's certainly easy to group adjacent loops, especially with zip (), but this is certainly not the best way. To make it easier to do this, you can write a lambda expression in zip that groups adjacent lists as follows:

A = [1,2,3,4,5,6] group_adjacent = lambda a, k: zip (* ([iter (a)] * k) group_adjacent (a, 3) [(1,2,3), (4,5,6)] group_adjacent (a, 2) [(1,2), (3,4), (5,6)] group_adjacent (a, 1)

9. Counter

The collection is also a good standard library in the module. Here we introduce the counters in the collection. Using counters, you can easily get a count of a list. This is useful for getting the total number of values in the data, the empty count of the data, and viewing the unique values of the data.

"Why not just use Pandas?" Using Pandas to achieve this is undoubtedly much more difficult, and this is just another dependency that needs to be added to the virtual environment when deploying the algorithm. In addition, the counter type in Python has many features that the Pandas family does not have, which makes it more useful in some cases.

A = collections.Counter ([1,1,2,3,3,3,3,4,5,6,7]) A Counter ({3: 4,1: 2 Counter 2: 2,4: 1,5: 1,6: 1,7: 1}) A.most_common (1) [(3,4)] A.most_common (3) [(3,4), (1,2), (2jin2)]

10. Get out of the team

As shown below, queuing can make the code very neat:

Import collections Q = collections.deque () Q.append (1) Q.appendleft (2) Q.extend ([3,4]) Q.extendleft ([5,6]) Q.pop () Q.popleft () Q.rotate (3) Q.rotate (- 3) print (Q)

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