Python:Pandas .unique()

The Pandas .unique() function returns a NumPy array containing all the unique elements in a data series, with no specific order. It operates similarly to NumPy’s .unique(), but can be more efficient for large Series with repeated elements, and it also includes NaN values.

Pandas .unique() Syntax

series.unique()

Parameters:

The .unique() function takes no parameters.

Return value:

Returns a NumPy array containing the unique values from a Pandas Series, in the order they appear.

Example 1: Basic Usage of .unique()

In this example, .unique() is used to return all the unique elements in series:

import pandas as pd

series = pd.Series([3, -1, 5, -1, 2, 1, 3, 2, 1, 5, -2, 1, 2])
unique_elements = series.unique()
print(unique_elements)

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Here is the output:

[ 3 -1  5  2  1 -2]

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Example 2: Using .unique() on a DataFrame Column

In this example, .unique() is used to return all the unique names from the Name column of the df DataFrame:

import pandas as pd

df = pd.DataFrame({
  'Name': ['Alice', 'Bob', 'Alice', 'David', 'Bob'],
  'Age': [25, 30, 25, 40, 30]
})

unique_names = df['Name'].unique()

print(unique_names)

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Here is the output:

['Alice' 'Bob' 'David']

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Codebyte Example: Dealing with Missing Values Using .unique()

This codebyte example shows how .unique() deals with missing values:

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Frequently Asked Questions

1. Does .unique() work on DataFrames directly?

No. .unique() only works on Series. To find unique values in a DataFrame column, you must select the column first:

df['column_name'].unique()

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2. What is the difference between .unique() and .nunique()?

• .unique() returns a NumPy array of the unique values.
• .nunique() returns the count of unique values.

3. What is the difference between .unique() and .drop_duplicates() in Pandas?

• .unique() is used on a single Series and returns a NumPy array of unique values in the order they appear.
• .drop_duplicates() is used on a Series or DataFrame and returns a Pandas object (Series or DataFrame) with duplicate rows or values removed.