Apr 15, 2025 · In this article, we’ll explore the Series.value_counts() function in Pandas which helps you quickly count the frequency of unique values in a Series. The value_counts() method returns a Series containing the count of unique values in the original Series.

pandas.DataFrame.value_counts# DataFrame. value_counts (subset = None, normalize = False, sort = True, ascending = False, dropna = True) [source] # Return a Series containing the frequency of each distinct row in the Dataframe. Parameters: subset label or list of labels, optional. Columns to use when counting unique combinations. normalize bool ...

Nov 19, 2024 · To count occurrences of values in a Pandas DataFrame, use the value_counts () method. This function helps analyze the frequency of values within a specific column or the entire Pandas DataFrame. Let’s start by setting up a sample DataFrame to demonstrate these methods. The DataFrame contains four columns: ‘name’, ‘subjects’, ‘marks’ and ‘age’.

Apr 3, 2019 · value_counts() aggregates the data and counts each unique value. You can achieve the same by using groupby which is a more broad function to aggregate data in pandas. count() simply returns the number of non NaN/Null values in column (series) you apply it on.

Aug 10, 2021 · You can use the value_counts() function to count the frequency of unique values in a pandas Series. This function uses the following basic syntax: my_series. value_counts ()

Jan 19, 2024 · Use the unique(), value_counts(), and nunique() methods on Series. nunique() is also available as a method on DataFrame. This article begins by explaining the basic usage of each method, then shows how to get unique values and their counts, and more. To count values that meet certain conditions, refer to the following article.

Apr 10, 2025 · In this article, we will learn various methods to count values in a Pandas DataFrame. We will be using below dataframe to learn about various methods: Output: 1. Counting Unique Values in a Column. To count the unique values in a specific column of a DataFrame you can use the nunique() method.

To get the counts only for specific columns: where df is the name of your dataframe and 'a' and 'b' are the columns for which you want to count the values. The solution that selects all categorical columns and makes a dataframe with all value counts at once: df[cat_cols] .melt(var_name='column', value_name='value') .value_counts()) counts.

The value_counts() method in Pandas is used to count the number of occurrences of each unique value in a Series. Example import pandas as pd # create a Series data = pd.Series(['apple', 'banana', 'apple', 'orange', 'banana', 'apple'])

Return a Series containing counts of unique values. The resulting object will be in descending order so that the first element is the most frequently-occurring element. Excludes NA values by default. If True then the object returned will contain the relative frequencies of the unique values. Sort by frequencies when True.