Filtering
Explore filtering techniques using pandas to select specific data rows or columns based on conditions. Understand methods like boolean indexing, loc, query, and isin to clean and manage datasets effectively, improving data quality and analysis efficiency.
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Introduction
Filtering refers to the process of finding specific items in a list or a table. This process lets us choose only rows or columns that meet certain conditions. For example, if we have a table of students and their grades, we can use filtering to select only the students who got an A grade. Or, we can use filtering to select only the students’ names and grades, not their ages.
Reasons to perform filtering
There are many reasons why we perform filtering using pandas. Here are a few reasons why filtering is crucial when working with data, along with a few examples for each point:
- We can use it for data cleaning: Filtering can identify and remove invalid or missing values from our data, improving the quality and accuracy of our analysis. For example, we might filter a dataset to remove rows with null values or outliers.
- We can improve efficiency: Filtering can be faster and more efficient than
