How to Count Distinct Values of a Pandas Dataframe Column?
Let’s see How to Count Distinct Values of a Pandas Dataframe Column?
Consider a tabular structure as given below which has to be created as Dataframe. The columns are height, weight and age. The records of 8 students form the rows.
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First step is to create the Dataframe for the above tabulation. Look at the code snippet below.
Method 1: Using for loop.
The Dataframe has been created and one can hard coded using for loop and count the number of unique values in a specific column. For example In the above table, if one wishes to count the number of unique values in the column height. The idea is to use a variable cnt for storing the count and a list visited that has the previously visited values. Then for loop that iterates through the ‘height’ column and for each value, it checks whether the same value has already been visited in the visited list. If the value was not visited previously, then the count is incremented by 1.
Below is the implementation:
No.of.unique values : 5 unique values : [165, 164, 158, 167, 160]
But this method is not so efficient when the Dataframe grows in size and contains thousands of rows and columns. To give an efficient there are three methods available which are listed below:
Method 2: Using unique().
The unique method takes a 1-D array or Series as an input and returns a list of unique items in it. The return value is a NumPy array and the contents in it based on the input passed. If indices are supplied as input, then the return value will also be the indices of the unique value.
No.of.unique values : 5
Method 3: Using Dataframe.nunique().
This method returns the count of unique values in the specified axis. The syntax is :
Syntax: Dataframe.nunique (axis=0/1, dropna=True/False)
No.of.unique values in each column : height 5 weight 4 age 4 dtype: int64
To get the number of unique values in a specified column:
No.of.unique values in height column : 5
Method 3: Using Series.value_counts().
This method returns the count of all unique values in the specified column.
Syntax: Series.value_counts(normalize=False, sort=True, ascending=False, bins=None, dropna=True)
No.of.unique values : 5