Descriptive statistics for categorical variables python

How do you describe a categorical variable in Python?
A categorical variable takes on a limited, and usually fixed, number of possible values ( categories ; levels in R).
Examples are gender, social class, blood type, country affiliation, observation time or rating via Likert scales..
How to get statistical summary for categorical variables in Python?
Proportions are often used to summarize categorical data and can be calculated by dividing individual frequencies by the total number of responses.
In Python/pandas, df['column_name']. value_counts(normalize=True) will ignore missing data and divide the frequency of each category by the total in any category..
How to get statistical summary of categorical data in Python?
Proportions are often used to summarize categorical data and can be calculated by dividing individual frequencies by the total number of responses.
In Python/pandas, df['column_name']. value_counts(normalize=True) will ignore missing data and divide the frequency of each category by the total in any category..
What statistics do you use for categorical data?
The basic statistics available for categorical variables are counts and percentages.
Number of cases in each cell of the table or number of responses for multiple response sets.
If weighting is in effect, this value is the weighted count..
A categorical variable takes on a limited, and usually fixed, number of possible values ( categories ; levels in R).
Examples are gender, social class, blood type, country affiliation, observation time or rating via Likert scales.
Categorical variables represent types of data which may be divided into groups.
Examples of categorical variables are race, sex, age group, and educational level.
Proportions are often used to summarize categorical data and can be calculated by dividing individual frequencies by the total number of responses.
In Python/pandas, df['column_name']. value_counts(normalize=True) will ignore missing data and divide the frequency of each category by the total in any category.

By default, the describe() function in pandas calculates descriptive statistics for all numeric variables in a DataFrame. This method will calculate count, unique, top and freq for each categorical variable in a DataFrame. This method will calculate count, unique, top and freq for every variable in a DataFrame.

Categorical variables Using both the describe() and value_counts() methods are useful since they compliment each other with the information returned. The describe() method says that "Female" occurs more than "Male" but one can see that is not the case since they both occur an equal amount.

Example 2: Calculate Categorical Descriptive Statistics For All Variables

We can use the following syntax to calculate count, unique, top and freqfor every variable in the DataFrame: The output shows count, unique

Additional Resources

The following tutorials explain how to perform other common operations in pandas: Pandas: How to Use describe() by Group Pandas: How to Use describe()

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Descriptive statistics for categorical variables python

How do you describe a categorical variable in Python?

How to get statistical summary for categorical variables in Python?

How to get statistical summary of categorical data in Python?

What statistics do you use for categorical data?

Example 2: Calculate Categorical Descriptive Statistics For All Variables

Additional Resources