Data wrangling, also known as data munging, is the process of cleaning and transforming raw data into a format that is more suitable for analysis and visualization. This involves a combination of tasks such as identifying and correcting errors in the data, filling in missing values, and converting data into a consistent format.

One common example of data wrangling is dealing with missing values in a dataset. For instance, imagine you have a dataset containing information about customers, including their name, address, and age. However, some of the entries in the age column are missing, which can make it difficult to perform analyses that involve age. To fix this, you could use a variety of techniques to impute the missing values, such as replacing the missing entries with the average age of the other customers, or using a machine learning algorithm to predict the missing values based on the other information in the dataset.

Another example of data wrangling is converting data from different sources into a consistent format. For instance, you might have data from two different sources that both contain information about customers, but the data is stored in different formats. One source might store the data in a CSV file, while the other uses a SQL database. To use the data together in your analysis, you would need to convert the data into a common format, such as a Pandas DataFrame in Python. This would involve tasks such as extracting the relevant data from each source, cleaning and formatting the data, and then combining the two datasets into a single data structure.

Overall, data wrangling is an important step in the data analysis process, as it ensures that the data is clean, consistent, and ready for further analysis and visualization. It can be a time-consuming and tedious task, but it is crucial for obtaining accurate and reliable insights from your data.