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python interview questions :171
Dictionary of types?
- Since type names don't have to be unique across modules, you must use the type (classObject) itself.
- Luckily, types will automatically have a non-trivial hash value:
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python interview questions :172
What is the purpose of the `//` operator in python?
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Integer division vs. float division:
python interview questions :173
What Is The Command To Debug A Python Program?
- The following command helps run a Python program in debug mode.
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python interview questions :174
How Do You Monitor The Code Flow Of A Program In Python?
- In Python, we can use <sys> module’s <settrace()> method to setup trace hooks and monitor the functions inside a program.
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- You need to define a trace callback method and pass it to the <settrace()> method. The callback should specify three arguments as shown below.
python interview questions :175
Why And When Do You Use Generators In Python?
- A generator in Python is a function which returns an iterable object. We can iterate on the generator object using the <yield> keyword. But we can only do that once because their values don’t persist in memory, they get the values on the fly.
- Generators give us the ability to hold the execution of a function or a step as long as we want to keep it. However, here are a few examples where it is beneficial to use generators.
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- We can replace loops with generators for efficiently calculating results involving large data sets.
- Generators are useful when we don’t want all the results and wish to hold back for some time.
- Instead of using a callback function, we can replace it with a generator. We can write a loop inside the function doing the same thing as the callback and turns it into a generator.
python interview questions :176
What Does The <Yield> Keyword Do In Python?
- The <yield> keyword can turn any function into a generator. It works like a standard return keyword. But it’ll always return a generator object. Also, a function can have multiple calls to the <yield> keyword.
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python interview questions :177
How To Convert A List Into Other Data Types?
- Sometimes, we don’t use lists as is. Instead, we have to convert them to other types.
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Turn A List Into A String.
- We can use the <”.join()> method which combines all elements into one and returns as a string.
Turn A List Into A Tuple.
- Call Python’s <tuple()> function for converting a list into a tuple. This function takes the list as its argument. But remember, we can’t change the list after turning it into a tuple because it becomes immutable.
Turn A List Into A Set.
- Converting a list to a set poses two side-effects.
- Set doesn’t allow duplicate entries, so the conversion will remove any such item if found.
- A set is an ordered collection, so the order of list items would also change.
- However, we can use the <set()> function to convert a list to a set.
Turn A List Into A Dictionary.
- In a dictionary, each item represents a key-value pair. So converting a list isn’t as straight forward as it were for other data types.
- However, we can achieve the conversion by breaking the list into a set of pairs and then call the <zip()> function to return them as tuples.
- Passing the tuples into the <dict()> function would finally turn them into a dictionary.
python interview questions :178
How Do You Count The Occurrences Of Each Item Present In The List Without Explicitly Mentioning Them?
- Unlike sets, lists can have items with same values. In Python, the list has a <count()> function which returns the occurrences of a particular item.
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- Count The Occurrences Of An Individual Item.
- Count The Occurrences Of Each Item In The List.
- We’ll use the list comprehension along with the <count()> method. It’ll print the frequency of each of the items.
python interview questions :179
What Is NumPy And How Is It Better Than A List In Python?
- NumPy is a Python package for scientific computing which can deal with large data sizes. It includes a powerful N-dimensional array object and a set of advanced functions.
- Also, the NumPy arrays are superior to the built-in lists. There are a no. of reasons for this.
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- NumPy arrays are more compact than lists.
- Reading and writing items is faster with NumPy.
- Using NumPy is more convenient than to the standard list.
- NumPy arrays are more efficient as they augment the functionality of lists in Python.
python interview questions :180
What Are Different Ways To Create An Empty NumPy Array In Python?
- There are two methods which we can apply to create empty NumPy arrays.
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- The First Method To Create An Empty Array.
- The Second Method To Create An Empty Array.