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. Introduction
If you are spending more time in wrestling with file formats and data cleanup than you are analyzing data, you are not alone. Most data professionals spend 60-80 % of their time wasting over and over-working tasks that are more difficult and away from important tasks.
In this article, I have collected some useful scripts below to make boring easier but facilitating the necessary tasks in the general datawork flu.
🔗 🔗 Link from the code on the Gut Hub
. 1. Data Quality Checker
A thing of pain.: Opening a new dataset is often very much. Are there values there? Types of duplicate strange data? After discovering data issues after hours of analysis, you eliminate the same research code repeatedly.
What does the script do: A simple script script for processing the data frames and producing a comprehensive data quality report with information about missing values, copies, outlaiers and more. Then saves everything in a text file that you can refer to as needed.
How does it work: The script usually examines the quality of the data quality-based, missing values, inaccurate data types-using the built-in methods, calculates the percentage and statistics, then makes everything in a clear report. It uses an intercortal range (IQR) method to detect outline, which works reliably in different data distribution.
⏩ ⏩ Get Data Quality Checker Script
. 2. Smart file integration
A thing of pain.: Your data is in CSV files, Excel Sheets, and JSON exports that are scattered in folders. Manually attaching them means to open every file, test column alignment, copy paste, and break nothing. Yes, and a matching column is enough to ruin everything.
What does the script do: Regardless of format (CSV, Excel, JSON), automatically searches and connects all data files in the folder. The column handles the similarities beautifully and tracks which data comes from which source file.
How does it work: The script operates through a directory, identifies supported file types, uses a pandas reader for each form, and connects everything using a strong integrative logic of pandas. It contains a source column so that you can always detect data its origin.
⏩ ⏩ Get Smart File Integration Script
. 3. Datastate profiler
A thing of pain.: Write dozens of research code lines to understand a new dataset: describe()For, for, for,. value_counts()Connected Matrix, Price analysis disappears. When you finish discovering, you may have forgotten what you were trying to analyze.
What does the script do: In seconds produce a complete datastate profile, which includes summary statistics, concrete heat map, category malfunction, and memory correction tips. Creates helpful concepts for documents and reporting.
How does it work: The script separates numeric and dutoconing columns, applies appropriate methods of analysis for all types, produces visuals using marine boron and metaphotleb, and also provides viable correction recommendations based on data patterns.
⏩ ⏩ Get a Dataset Profile Script
. 4. Data version manager
A thing of pain.: You make changes to your datastas, realizing that something has gone wrong, and it doesn’t have it back. Or you need to show a client how the data looks like last week, but you’re overwriting the same file. Version control for data is often difficult. Tools tools are available to simplify the data version control. But simple scripts are, well, easy and efficient.
What does the script do: Automatically saves the time stamps version of your data frames with statements, tracks the file to detect the changes, and allows you to return to any previous version immediately. Cleaning tools are included to manage storage space.
How does it work: The script forms a structural backup system with metadata logging. It uses MD5 hashning to detect the actual changes (avoiding duplicate saving), maintains a CSV log of all versions with time stamp and detail, and provides easy ways to list and maintenance of any previous version.
⏩ ⏩ Get the data version manager script
. 5. Multi -format data exporter
A thing of pain.: Different people want data in different forms. Analysts probably want a clean spreadsheet with a formed header. The giant team needs JSON with metad data. Database Admin wants Sqlite. You manually create each format with various settings and formatting rules.
What does the script do: Export your processed data to multiple professional formats simultaneously. Formated Excel files with multiple sheets, Medata formed JSON, clean CSV files, and SQLITE databases with appropriate schemes.
How does it work: The script uses format -related correction techniques: Excel files receive styled headers and auto -sized columns, JSON exports include metadata and appropriate data type information, CSV files are cleared, and SQ late database contains full documents for metabases.
⏩ ⏩ Get Multi Format Exporter Scrupted
. Wrap
I hope you have this script helpful. We have covered five practical scripts that handle most of the data work parts.
- Data Quality Checker Scan Details for Lost Values, Cops and Out Layers
- Smart file integration connects CSV, Excel, and JSON files from any folder
- Datastate profile produces quick statistics, concepts and concepts
- Data version manager secures and tracks changes to your datases with easy rollback
- Multi -format exporter creates professional Excel, JSON, CSV, and SQLite output simultaneously
Each script is dealt with by obstruction of a specific workflower and can be used independently or together. You can add maximum functionality as needed to improve the need!
The best part? You can immediately start using any of these scripts. Choose the point of solving your biggest current pain point, try it on sample dataset, then decide whether it is helpful or not. Happy coding!
Pray Ca Is a developer and technical author from India. She likes to work at the crossroads of mathematics, programming, data science, and content creation. The fields of interest and expertise include dupas, data science, and natural language processing. She enjoys reading, writing, coding and coffee! Currently, they are working with the developer community to learn and share their knowledge with the developer community by writing a lesson, how to guide, feed and more. The above resources review and coding also engages lessons.