Data cleaning and preprocessing are critical steps in preparing data for analysis. In this article, we will explore various techniques using Pandas to handle common data issues.

Handling Missing Values:

Missing values can significantly impact data analysis, and Pandas provides useful functions to deal with them. For example, we can use the dropna() function to remove rows or columns with missing values. Alternatively, we can fill missing values using fillna() by providing a specific value or using interpolation techniques.

Removing Duplicates:

Duplicate data can adversely affect analysis results. To remove duplicates, we can use the drop_duplicates() function in Pandas. We can specify the subset of columns to consider for duplicates and choose to keep the first occurrence or the last occurrence.

Dealing with Outliers:

Outliers can skew statistics and impact the overall analysis. Pandas allows us to detect and handle outliers using various methods such as Z-score, percentiles, or using domain-specific knowledge.