Gain insights into Pandas' powerful tools for data analysis. Learn about Series, DataFrames, and optimize performance for data operations, filtering, and file uploads in data science and machine learning.

Pandas is a very popular Python library that provides powerful, flexible and high-performance tools to process data.

If you’re looking to get into data science, machine learning, or you just want to brush up on your analytics skills, then look no further. In this course, you’ll learn how to process and analyze data with Pandas. 

You’ll start out by covering the basic data structures in Pandas: Series and dataframes. In the latter half of the course, you’ll learn how to perform operations on different data structures, and how to optimize performance for file uploads, data filtering, and more.

Data Analysis & Processing with Pandas

Removing duplicate rows from a dataset is a very common task during data cleaning. Sometimes the row is dropped when the whole row is a duplicate. In other cases, we only drop those rows based on selected columns.

pandas provides a powerful and handy function, `drop_duplicates`. Let's see how to use it.

# Check if there are duplicate rows in the DataFrame

Before you drop the duplicate rows, you may want to know if a row or only selected columns are duplicated. The `duplicated()` function can tell you if the values of a column are duplicated or not. This function could be applied to the index, series, and Data Frame.

- To find out if the whole row is duplicated.
  ```
  df.duplicated()
  ```
- Find out if some selected columns are duplicated.
  ```
  df.duplicated(subset=["col1", "col2"])
  ```

In this lesson, we learn how to remove duplicate rows from a DataFrame object.

Introduction

Basic Operations on DataFrames

Other topics

Improve performance

Drop Duplicate Row

Check if there are duplicate rows in the DataFrame