Pandas: 5 Ways to Handle Unique Values in DataFrame Columns
Data analysis using Pandas often involves identifying unique values in a DataFrame. Several methods exist to achieve this, as discovered through a recent search.
The Pandas library offers multiple ways to retrieve unique values from a DataFrame column. The drop_duplicates() method removes duplicates, returning a DataFrame with only distinct values.
The set() function can also be used to obtain unique values, converting the column into a set and automatically eliminating duplicates. For a count of each unique value's occurrence, the value_counts() method is useful, returning the result as a Series. The unique() method provides a NumPy array of unique values, preserving their initial order. Lastly, the nunique() method simply counts the number of unique values in a column.
In summary, Pandas offers several methods to handle unique values in a DataFrame column, including drop_duplicates(), set(), value_counts(), unique(), and nunique(). Each serves a different purpose in data analysis and manipulation.
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