معرفی شرکت ها

تبلیغات ما

مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.

مشاهده بیشتر

تبلیغات ما

مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.

مشاهده بیشتر

تبلیغات ما

مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.

مشاهده بیشتر

تبلیغات ما

مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.

مشاهده بیشتر

تبلیغات ما

مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.

مشاهده بیشتر

توضیحات

Create random samples from CSV file

ویژگی	مقدار
سیستم عامل	-
نام فایل	csvsample-0.1.4
نام	csvsample
نسخه کتابخانه	0.1.4
نگهدارنده	[]
ایمیل نگهدارنده	[]
نویسنده	Alan Kang
ایمیل نویسنده	jania902@gmail.com
آدرس صفحه اصلی	https://github.com/akngs/csvsample
آدرس اینترنتی	https://pypi.org/project/csvsample/
مجوز	-

## csvsample ``csvsample`` extracts some rows from CSV file to create randomly sampled CSV. ### Features * The size of original file does not need to be specified beforehand. It means that the sampling process can be applied to data stream with unknown size such as system logs, no matter how large the amount of data is. * All methods accepts optional ``seed`` value. The same data set with the same sampling rate always yields exactly the same result, which is good for reproducibility. ### Install You can install ``csvsample`` via ``pip``: pip install csvsample ### API ``csvsample.sample()`` is the main API: csvsample.sample(lines, sampling_method, **kwargs) ``lines`` can be any ``iterable`` containing valid CSV rows including header row. ``sampling_method`` should be one of followings: * ``random`` * ``hash`` * ``reservoir`` ``random`` sampling method performs random sampling using pseudo random number generator: import csvsample with open('input.csv', 'r') as i: with open('output.csv', 'w') as o: o.writelines(csvsample.sample(i, 'random', sample_rate=0.1)) ``hash`` sampling method performs hash-based sampling using extremely-fast hash function. Let's say that instead of saving all users' log, you want to randomly select 10% of users and only save logs of those selected users. Simple random sampling won't work. You can use hash-based sampling. "Consistent" nature of the algorithm guarantees that any user ID selected once will always be selected again: sampled = csvsample.sample(lines, 'hash', sample_size=0.1, col='user_id') ``reservoir`` sampling method performs reservoir sampling. Let's say that you have an URL of 100GB csv file. Since you don't have enough disk space, you just want to save small portion of sample which is representative and unbiased. [reservoir sampling](https://en.wikipedia.org/wiki/Reservoir_sampling) method allows you to acquire random sample without saving entire data first: sampled = csvsample.sample(lines, 'reservoir', sample_size=1000) Now ``sampled`` variable contains exactly 1,000 randomly selected lines. ### Helpers There are some convenience helpers: * ``csvsample.sample_url(url, sampling_method, **kwargs)`` read CSV from given ``url``. You can specify character set encoding via ``encoding`` keyword argument. (default: ``utf-8``) ``csvsample.sample()`` and other helpers return a generator containing sampled CSV rows including header row. The generator contains special function ``to_buf()`` which converts itself into ``io.StringIO`` instance so that you can pass the sampled CSV to other libraries such as Pandas: import csvsample import pandas as pd sampled = csvsample.sample_url(url, 'random', sample_rate=0.1) df = pd.read_csv(sampled.to_buf()) ### Command-line interface ``csvsample`` also provides command-line interface. Following URL contains a CSV file from [DataHub](https://datahub.io/): > curl -sL https://bit.ly/2ItnHvK | head region,year,population WORLD,1950,2536274.721 WORLD,1951,2583816.786 WORLD,1952,2630584.384 WORLD,1953,2677230.358 A number of rows including header is 18019: > curl -sL https://bit.ly/2ItnHvK | wc -l 18019 Let's make 10% of random sample: > curl -sL https://bit.ly/2ItnHvK | csvsample random 0.1 > sample.csv > wc -l sample.csv 1777 sample.csv > head -n 5 sample.csv region,year,population WORLD,1952,2630584.384 WORLD,1972,3851545.181 WORLD,1977,4229201.257 WORLD,1988,5148556.956 You may use reservoir sampling method to obtain exact number of rows: > curl -sL https://bit.ly/2ItnHvK | csvsample reservoir 100 > sample.csv > wc -l sample.csv 100 sample.csv

نیازمندی

مقدار	نام
-	fire
-	xxhash
xtr	check-manifest;
xtr	pytest;

نحوه نصب

نصب پکیج whl csvsample-0.1.4:

pip install csvsample-0.1.4.whl

نصب پکیج tar.gz csvsample-0.1.4:

pip install csvsample-0.1.4.tar.gz