¿Cómo contar valores en una columna de DataFrame?

How to count values in a DataFrame column?

To count the number of occurences in e.g. a column in a dataframe you can use Pandas value_counts() method. For example, if you type df[‘condition’]. value_counts() you will get the frequency of each unique value in the column “condition”.

value_counts() Method: Count Unique Occurrences of Values in a Column. In pandas, for a column in a DataFrame, we can use the value_counts() method to easily count the unique occurences of values. There’s additional interesting analyis we can do with value_counts() too.

Count Values in Pandas Dataframe

1. Syntax: DataFrame.count(axis=0, level=None, numeric_only=False)
2. Parameters:
3. Returns: It returns count of non-null values and if level is used it returns dataframe.

This will apply a column based aggregation function (in this case value_counts) to each of the columns. This short little line of code will give you the output you want. apply – count frequency in every column. If you set axis=1, you get frequency in every row fillna (0) – make output more fancy.

Examples of how to count the occurrence of an element in a pandas data frame column: Lets take for example the file ‘default of credit card clients Data Set” that can be downloaded here

The first example show how to apply Pandas method value_counts on multiple columns of a Dataframe ot once by using pandas.DataFrame.apply. This solution is working well for small to medium sized DataFrames. The syntax is simple – the first one is for the whole DataFrame: While the second example is only for a few columns from a given DataFrame:

When counting the occurence of distinct values, it gives you new information about the data set. Furthermore, when you count occurances among multiple columns it can show relationships between columns that you would not see simply by looking at the raw numbers.