Zipped (compressed) files take up less storage space and can be transferred to other computers more quickly than uncompressed files. In Windows, you work with zipped files and folders in the same way that you work with uncompressed files and folders. Combine several files into a single zipped folder to more easily share a group of files.
Press and hold (or right-click) the file or folder, select (or point to) Send to, and then select Compressed (zipped) folder.A new zipped folder with the same name is created in the same location. To rename it, press and hold (or right-click) the folder, select Rename, and then type the new name.
If you add encrypted files to a zipped folder, they'll be unencrypted when they're unzipped, which might result in unintentional disclosure of personal or sensitive information. For that reason, we recommend that you avoid zipping encrypted files.
Some types of files, like JPEG images, are already highly compressed. If you zip several JPEG pictures into a folder, the total size of the folder will be about the same as the original collection of pictures.
If you need to share a large file or multiple files, a compressed file takes up less storage space and can be sent faster because of its smaller file size. Learn how to compress or uncompress files on your iPhone or iPod touch.
If you selected one file, a ZIP file with the same filename saves to that folder. If you selected multiple files, a ZIP archive named Archive.zip saves to that folder. To change its name, touch and hold on the ZIP archive, then select Rename.
If you need to share a large file or multiple files, a compressed file takes up less storage space and can be sent faster because of its smaller file size. Learn how to compress or uncompress files on your iPad.
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The process above will create the zip file in exactly the same directory as the original file. You can rename it by right-clicking the folder, then select \"rename\", then type the new name. You can add additional files to the zip folder by dragging and dropping them. You can forward/attach that zip file as necessary.
If you compress a single item, the compressed file will have the name of the original file. If you compress multiple files, the compressed file will be titled Archive.zip. If you already have an Archive.zip folder, a numbering sequence will begin for your files (i.e. Archive 2.zip).
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Returns an iterator of tuples, where the i-th tuple contains the i-th element from each of the argument sequences or iterables. The iterator stops when the shortest input iterable is exhausted. With a single iterable argument, it returns an iterator of 1-tuples. With no arguments, it returns an empty iterator. (Source)
Here, you use zip(numbers, letters) to create an iterator that produces tuples of the form (x, y). In this case, the x values are taken from numbers and the y values are taken from letters. Notice how the Python zip() function returns an iterator. To retrieve the final list object, you need to use list() to consume the iterator.
In these cases, the number of elements that zip() puts out will be equal to the length of the shortest iterable. The remaining elements in any longer iterables will be totally ignored by zip(), as you can see here:
Since 5 is the length of the first (and shortest) range() object, zip() outputs a list of five tuples. There are still 95 unmatched elements from the second range() object. These are all ignored by zip() since there are no more elements from the first range() object to complete the pairs.
If trailing or unmatched values are important to you, then you can use itertools.zip_longest() instead of zip(). With this function, the missing values will be replaced with whatever you pass to the fillvalue argument (defaults to None). The iteration will continue until the longest iterable is exhausted:
Here, you use itertools.zip_longest() to yield five tuples with elements from letters, numbers, and longest. The iteration only stops when longest is exhausted. The missing elements from numbers and letters are filled with a question mark , which is what you specified with fillvalue.
In Python 3, however, zip() returns an iterator. This object yields tuples on demand and can be traversed only once. The iteration ends with a StopIteration exception once the shortest input iterable is exhausted. If you supply no arguments to zip(), then the function returns an empty iterator:
Here, your call to zip() returns an iterator. The first iteration is truncated at C, and the second one results in a StopIteration exception. In Python 3, you can also emulate the Python 2 behavior of zip() by wrapping the returned iterator in a call to list(). This will run through the iterator and return a list of tuples.
If you regularly use Python 2, then note that using zip() with long input iterables can unintentionally consume a lot of memory. In these situations, consider using itertools.izip(*iterables) instead. This function creates an iterator that aggregates elements from each of the iterables. It produces the same effect as zip() in Python 3:
In this example, you call itertools.izip() to create an iterator. When you consume the returned iterator with list(), you get a list of tuples, just as if you were using zip() in Python 3. The iteration stops when the shortest input iterable is exhausted.
Here, you iterate through the series of tuples returned by zip() and unpack the elements into l and n. When you combine zip(), for loops, and tuple unpacking, you can get a useful and Pythonic idiom for traversing two or more iterables at once.
In Python 3.6 and beyond, dictionaries are ordered collections, meaning they keep their elements in the same order in which they were introduced. If you take advantage of this feature, then you can use the Python zip() function to iterate through multiple dictionaries in a safe and coherent way:
Here, you iterate through dict_one and dict_two in parallel. In this case, zip() generates tuples with the items from both dictionaries. Then, you can unpack each tuple and gain access to the items of both dictionaries at the same time.
Leodanis is an industrial engineer who loves Python and software development. He's a self-taught Python developer with 6+ years of experience. He's an avid technical writer with a growing number of articles published on Real Python and other sites.
For the first time in our history, the United States is raising a generation of children who may live sicker and shorter lives than their parents. In 2020 and again in 2021, we witnessed the steepest plunge in life expectancy since World War II, largely fueled by the coronavirus pandemic. Reversing this trend will of course depend on healthy choices by each of us. But not everyone in America has the same opportunities to be healthy. We know that the drivers of inequitable social, economic, built, and physical conditions within and across place and race can dramatically reduce opportunities for better health and well-being.
In 2018, the National Center for Health Statistics released first-of-its-kind neighborhood-level data on life expectancy through the U.S. Small-Area Life Expectancy Estimates Project (USALEEP), in partnership with RWJF and the National Association for Public Health Statistics and Information Systems (NAPHSIS). The data revealed how life expectancy estimates vary greatly even at the census tract level, from block to block. At this time, this data will not be updated.
A Culture of Health is rooted in equity and provides every individual with a fair and just opportunity to thrive, no matter who they are, where they live, or how much money they have. Browse the resources below to learn more about what shapes our health, and how to take action.
Arias E, Escobedo LA, Kennedy J, Fu C, Cisewski J. U.S. Small-area Life Expectancy Estimates Project: Methodology and Results Summary. National Center for Health Statistics. Vital Health Statistics 2 (181). 2018.
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