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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