https://github.com/vc1492a/datecleanse.py
A Python-based tool that reformats multiple date formats into a single format for later use.
https://github.com/vc1492a/datecleanse.py
Last synced: about 1 month ago
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A Python-based tool that reformats multiple date formats into a single format for later use.
- Host: GitHub
- URL: https://github.com/vc1492a/datecleanse.py
- Owner: vc1492a
- Created: 2016-02-20T01:58:14.000Z (about 9 years ago)
- Default Branch: master
- Last Pushed: 2016-06-12T21:05:12.000Z (almost 9 years ago)
- Last Synced: 2025-01-29T11:11:41.608Z (3 months ago)
- Language: Python
- Homepage:
- Size: 3.32 MB
- Stars: 2
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: readme.md
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README
# dateCleanse.py
dateCleanse.py converts a variety of date formats and to a single, common format: MM/DD/YYYY.
Development pace will coincide with its frequency of use for date cleansing in
different applications, but this tool is ready for use. Written and tested in Python 3.4.3.
## Currently Supported Date Formats
- MM/YYYY
- YYYY/MM
- YYYY-MM
- *YY-MM
- DDDDD (days since 01/01/1900)
- YY/MM
- YY-MM
- YYMM
- YYYY
- M-YY
- M/YY
- YY-M
- MYY
- YYYY-M-DD HH:MM:SS
Note that two-digit years are assumed to occur post year 2000 and that all dates are assumed to occur post 1930.
## Date Formats for Future Release
We do not have any other date formats currently planned for future release. If you'd like to see a different date
format included for future release, please let us know or submit a pull request.
## How-To
Export a single column from your data frame or your vector of dates into the directory *input_data*. If you
are using R, you can export the files to that directory individually as follows:
```r
# include the dates you wish to reformat in the directory titled input_data
setwd("C:/user/folder/input_data")
# repeat the following write command for all the files you want to reformat
write.csv(dates, "name_of_file.csv", row.names=FALSE, quote=FALSE)
```
Your working directory will be different depending on your system configuration. Ensure that row names and quotes are
not included from the R output. Then, simply fire up your favorite Python IDE and hit run.
Or from the command line, type:
```python
python dateCleanse.py
```
The script will ask for the path to the directory which contains the dates to be reformatted. If you placed
your *name_of_file.csv* file in the same directory, simply type:
```python
input_data
```
and hit enter (this step may seem trivial but allows for flexibility if your files are in a different directory).
The script will begin reformatting the date values and show several progress bars. Once the process complete,
cleansed dates are stored in a new file called *newDates.csv*, contained in a new directory titled *output_data*.
## Non-native Dependencies
- tqdm