Data Cleaning Part I.
Data Cleaning Part I.
This is a multipart series highlighting the processes involved in Cleaning data for Analysis. Data cleaning, refers to the process of identifying and correcting inaccuracies, inconsistencies, and errors in a data set to help improve its readability, quality, reliability and robustness. Data wrangling, also known as data munging, is the process of transforming raw, messy data into a clean, usable format for analysis and decision-making. It involves a range of techniques like cleaning, transforming, and restructuring data to ensure it is reliable, accurate, and consistent. Essentially, data wrangling prepares data for further processing, modeling, and analysis. Benefits of Data Cleaning; includes more accurate decision-making, increased productivity, and improved data-driven insights. In Python, some of the most popular libraries for cleaning data are; Pandas among other libraries like Scikit-learn, Pyjanitor, SciPy, DataPrep, CleanLab, Scrubadub, DataCleaner, CleanPrep and many more. Data cleaning with pandas involves identifying and correcting errors, inconsistencies, and missing values in a dataset to ensure its accuracy and reliability for further analysis.
3 Comments
Henri Samule
Le manque de contrôle d'accès robuste au réseau met en péril la confidentialité des données. Il est essentiel d'implémenter un système de contrôle d'accès basé sur les rôles pour limiter l'accès aux informations sensibles.
ZineZimeDame
Je vous laisse un gentille commentaire.
test
Un nouveau commentaire