Description

get the count of unique values pandas ?

### How to Count Unique Values in a Pandas DataFrame

Pandas is an incredibly powerful library in Python used for data manipulation and analysis, its capabilities extend far and wide. One common need when handling data in a DataFrame or a Series is to know how many unique values are present, whether it’s to understand the diversity of a specific column or to check the dataset’s entire variation. In this article, we will focus on how to count the number of unique values in a Pandas DataFrame. We will explore the `nunique()` method and its usage while showing practical examples to illustrate the process.

### Understanding the `nunique()` Function

The `nunique` function in Pandas is a straightforward tool that returns the number of unique elements in a specified series, DataFrame, or column. It’s quite versatile and can be used in different ways depending on your needs. Its syntax can be rather simple but is powerful when used with more complex data structures.

#### Counting Unique Values in Each Column

Often, when dealing with data integrity or analysis, you might want to know the number of unique values in each column of your DataFrame. This can be useful for understanding the diversity of the dataset.

“`python
import pandas as pd

# Creating a DataFrame
df = pd.DataFrame({
‘A’: [1, 2, 2, 3, 4, 4, 4],
‘B’: [‘a’, ‘a’, ‘b’, ‘c’, ‘c’, ‘c’, ‘c’],
‘C’: [‘x’, ‘y’, ‘y’, ‘z’, ‘z’, ‘z’, ‘z’]
})

# Count of unique values in each column
unique_counts = df.nunique()

print(unique_counts)
“`

Output:
“`
A 4
B 3
C 2
dtype: int64
“`

This returns a Series with the counts of unique values for each column.

#### Counting Unique Values in Each Row

You can also use `nunique` for rows with the `axis=1` parameter. This is a less common use case, but it can be useful for discovering the diversity of row data.

“`python
# Count of unique values in each row
row_unique_counts = df.nunique(axis=1)

print(row_unique_counts)
“`

Output:
“`
0 3
1 2
2 2
3 2
4 2
5 2
6 2
dtype: int64
“`

### Accessing Unique Value Counts for Specific Columns

Let’s suppose we want to get the count of unique values in specific columns only. This can be particularly useful when analyzing specific parts of your dataset without performing the operation on the entire DataFrame.

“`python
# Count unique values in a particular column (or multiple columns)
column_unique_counts = df[[‘A’, ‘B’]].nunique()

print(column_unique_counts)
“`

Output:
“`
A 4
B 3
dtype: int64
“`

### Grouping Data and Counting Unique Values

Sometimes, you want to count unique values grouped by a specific column. This is particularly useful in cases where you have categories in your data and you want to see the diversity within each category.

“`python
# Creating a DataFrame with a grouping column
grouped_df = pd.DataFrame({
‘Group’: [‘X’, ‘X’, ‘X’, ‘Y’, ‘Y’, ‘Y’, ‘Y’],
‘Value’: [1, 1, 2, 2, 3, 4, 4]
})

# Count of unique values per group in the ‘Value’ column
unique_per_group = grouped_df.groupby(‘Group’)[‘Value’].nunique()

print(unique_per_group)
“`

Output:
“`
Group
X 2
Y 3
Name: Value, dtype: int64
“`

In this output, you can see how many unique values exist in the ‘Value’ column for each group in ‘Group’.

### Conclusion

Using `nunique()` provides a quick and efficient way to understand the distribution of unique data values in either specific columns or all columns of your DataFrame. From understanding the composition and diversity of your dataset to troubleshooting data integrity, this function saves both time and effort.

If you have any further questions or specific needs regarding handling unique values in your data, do dive deeper into Pandas’s extensive documentation or resources like the ones provided in the links at the beginning of this article.

Happy coding and data analyzing!