How to Retrieve Distinct Row Values from a DataFrame in Pandas?

Retrieving Distinct Row Values from a DataFrame

In this situation, we aim to extract rows from a DataFrame based on unique values in a particular column, let's denote it as COL2.

To accomplish this task, we introduce the drop_duplicates function. It allows us to eliminate duplicate rows by specifying the columns we want to check for duplicate values.

Preserving First Occurrence:

For instance, if we want to keep only the first occurrence of each distinct COL2 value, we can utilize:

<code class="python">df = df.drop_duplicates('COL2')</code>

Alternatively, we can write:

<code class="python">df = df.drop_duplicates('COL2', keep='first')</code>

This retains the first row for each unique value in COL2.

Maintaining Last Occurrence:

If instead we wish to preserve the last occurrence of distinct values, we modify the keep parameter to 'last':

<code class="python">df = df.drop_duplicates('COL2', keep='last')</code>

Removing All Duplicates:

To remove all duplicate rows, including those with identical values in COL2, we set keep to False:

<code class="python">df = df.drop_duplicates('COL2', keep=False)</code>

By following these techniques, you can efficiently eliminate duplicate rows based on distinct values in the specified column, ensuring that your DataFrame contains only unique data.

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