Python is a great language for doing data analysis, primarily because of the fantastic ecosystem of data-centric Python packages. Pandas is one of those packages and makes importing and analyzing data much easier.
Pandas DataFrame.nunique Syntax
Pandas dataframe.nunique() function returns a Series with a number of distinct observations over the requested axis. If we set the value of the axis to 0, then it finds the total number of unique observations over the index axis. If we set the value of the axis to 1, then it finds the total number of unique observations over the column axis. It also provides the feature to exclude the NaN values from the count of unique numbers.
Syntax: DataFrame.nunique(axis=0, dropna=True)
Parameters:
- axis : {0 or ‘index’, 1 or ‘columns’}, default 0
- dropna : Don’t include NaN in the counts.
Returns : nunique : Series
Pandas DataFrame nunique() Method
Example 1: Use nunique() function to find the number of unique values over the column axis.
Python3
# importing pandas as pdimport pandas as pd# Creating the first dataframedf = pd.DataFrame({"A":[14, 4, 5, 4, 1], "B":[5, 2, 54, 3, 2], "C":[20, 20, 7, 3, 8], "D":[14, 3, 6, 2, 6]})# Print the dataframedf |
Output:
Let’s use the dataframe.nunique() function to find the unique values across the column axis.
Python3
# find unique valuesdf.nunique(axis=1) |
Output:
As we can see in the output, the function prints the total no. of unique values in each row.
Example 2: Use nunique() function to find the number of unique values over the index axis in a Dataframe. The Dataframe contains NaN values.
Python3
# importing pandas as pdimport pandas as pd# Creating the first dataframedf = pd.DataFrame({"A": ["Sandy", "alex", "brook", "kelly", np.nan], "B": [np.nan, "olivia", "olivia", "", "amanda"], "C": [20 + 5j, 20 + 5j, 7, None, 8], "D": [14.8, 3, None, 6, 6]})# apply the nunique() functiondf.nunique(axis=0, dropna=True) |
Output:
The function is treating the empty string as a unique value in column 2.
