Data Wrangling Techniques In Python:

 Data wrangling, also known as data munging, is cleaning, transforming, and preparing raw data into a format suitable for analysis. Python offers powerful libraries and tools for data wrangling, making it a popular choice among data scientists and analysts. In this short blog post, we'll explore some essential data-wrangling techniques in Python.

Why Data Wrangling Matters:

Data is rarely clean and ready for analysis straight from its source. Data wrangling is crucial because:

1. Data Quality: It ensures data accuracy, completeness, and consistency, improving the quality of analysis and decision-making.

2. Compatibility: Data from different sources often require alignment, transformation, and integration to work together seamlessly.

3. Analysis Readiness: Wrangling prepares data for downstream tasks such as visualization, modeling, and statistical analysis.

Key Data Wrangling Techniques in Python:

Data Cleaning:

On average, data analysts spend around one-quarter of their time cleaning data. Why? You need clean data for data mapping and analysis, so accuracy is essential. You can use Python or Apache to clean data quickly and accurately. For instance, Apache Kudu can process and analyze large datasets. The process of cleaning data usually involves:

• Standardizing the data.
• Deleting duplicate or missing values.
• Removing outliers.
  

When you standardize data, you ensure all the labels and values are formatted similarly. For example, let's say some data are percentages and others are fractions. Converting the fractions into percentages would standardize the dataset.

Data Transformation: 

Perform transformations such as scaling, encoding categorical variables, and creating new features to prepare data for analysis and modeling. The first step in data wrangling is learning about your data. This helps you organize data for later analysis. To familiarize yourself with the data, you should perform an exploratory data analysis (EDA). EDA gives you data insights like a dataset's structure and any patterns and trends. It can also highlight incomplete or missing values.

Data Aggregation: 

Aggregate data by grouping, summarizing, and calculating statistics (e.g., mean, median, count) using Pandas' group by function.

Data enrichment is an optional step since it depends on whether your dataset contains enough information. You will need to enrich data if:

• There are gaps in the dataset.
• You don't have enough data to achieve statistical significance.

Data Merging and Joining:

 Merge and join datasets based on common keys or indices using Pandas' merge and join functions.

Many business leaders overlook the importance of data wrangling as there's often little to show for it. So it's important to emphasize the benefits of data wrangling, such as:

• Ensuring datasets are complete and usable.
• Understanding complex datasets and their business implications.
• Getting the data ready for automation and machine learning tools.
• Ensuring you can easily compare and reuse data throughout the business.
• Guaranteeing the quality of the data and later analyses.

Data Reshaping:

 Reshape data between wide and long formats using functions like melt, pivot, stack, and unstack in  Pandas.

Once your data are clean and rich, you need to make sure they are accurate. In other words, you need to ensure your data are:

• High quality.
• Consistent.
• Accurate.
• Secure.
• Authentic.

CONCLUSION:

Data wrangling is a crucial skill for data analysts to have. It ensures the data are usable, understandable, and ready to analyze. It's also vital if you want to use the data for machine learning and other automated processes.

Good data wranglers must be able to piece together data from a variety of sources. They must also be able to clean them, standardize them, enrich them, and confirm their accuracy. After all, you rarely find raw data in a usable format. Most importantly, though, data wranglers need to understand the business context of the data. So, set clear goals - and get wrangling

COMPILED BY: NEERAJ KHATRI