Discovering Duplicates in Pandas
In data analysis, duplicate rows can skew results and lead to inaccurate insights. These are rows that appear more than once in your dataset, often due to errors in data collection or entry.
Let’s explore how to detect and remove duplicates using Pandas.
- Identifying Duplicate Rows
- If you're scanning through your dataset and notice that certain rows (e.g., row 11 and 12) look identical, they’re likely duplicates.
- Pandas provides a simple way to detect these using the duplicated() method. This method returns a Boolean Series True for each row that is a duplicate of a previous one, and False otherwise.
Program
# Check for duplicate rows
print(df.duplicated())
This output can help you quickly spot which rows are repeated.
Removing Duplicate Rows
Once you've identified the duplicates, you can easily remove them using the drop_duplicates() method.
Program
# Remove all duplicate rows from the DataFrame
df.drop_duplicates(inplace=True)
This command will keep the first occurrence of each duplicated row and remove the rest. The inplace=True argument ensures the changes are applied directly to the original DataFrame.
