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Techniques of Data Wrangling
What is Data Wrangling?
Data Wrangling, also known as data munging, is the process of cleaning, structuring, and enriching raw data into a desired format for better decision making in less time.It involves various steps to ensure that data is accurate, consistent, and usable.
Steps in Data Wrangling
Data Collection: Gathering raw data from various sources such as databases, APIs, and web scraping.
Data Cleaning: Handling missing values, removing duplicates, and correcting errors to ensure data quality.
Data Structuring: Organizing data into a format suitable for analysis, such as converting data types and creating new features.
Data Enrichment: Enhancing data by adding additional information from other sources.
Data Validation: Ensuring the data is accurate and reliable by checking for inconsistencies and errors.
Data Analysis: Analyzing the cleaned and structured data to extract insights and inform decision-making.
Common Data Wrangling Techniques
Handling Missing Values: Techniques include imputation (filling missing values with a substitute) or removing incomplete rows/columns.
Removing Duplicates: Identifying and removing duplicate records to ensure data quality.
Data Transformation: Converting data types, normalizing data, and aggregating data.
Feature Engineering: Creating new features from existing data to improve model performance.
Example
A dataset may contain missing values in some columns.By using data wrangling techniques, you can fill these missing values with the mean or median of the column or remove the rows with missing values entirely.
Quiz
1. What is data wrangling?
- a) The process of creating data
- b) The process of cleaning and transforming raw data
- c) The process of deleting data
- d) The process of visualizing data
2. rue or False: Data wrangling is also known as data munging.
- a) True
- b) False
3. Which technique is used to handle missing data?
- a) Ignoring it
- b) Filling it with the mean or median
- c) Deleting the entire dataset
- d) Changing it to text
4. What is the purpose of data normalization?
- a) To convert data into a common format
- b) To delete duplicate entries
- c) To create visualizations
- d) To write reports
5. Which tool is NOT commonly used for data wrangling?
- a) Excel
- b) Python
- c) R
- d) Photoshop