.info() and .describe() methods | Python (2024)

FAQs

What does info() and describe() do? ›

The . info() method allows us to learn the shape of object types of our data. The . describe() method gives us summary statistics for numerical columns in our DataFrame.

The info() method prints information about the DataFrame. The information contains the number of columns, column labels, column data types, memory usage, range index, and the number of cells in each column (non-null values).

describe() The describe() method is used for calculating some statistical data like percentile, mean and std of the numerical values of the Series or DataFrame. It analyzes both numeric and object series and also the DataFrame column sets of mixed data types.

The describe() method returns description of the data in the DataFrame. If the DataFrame contains numerical data, the description contains these information for each column: count - The number of not-empty values. mean - The average (mean) value. std - The standard deviation.

Comparison with df.

df.info() - Provides metadata like index, columns, data types. df. describe() - Provides summary statistics like mean, quartiles, count etc.

For numeric data, the result's index will include count , mean , std , min , max as well as lower, 50 and upper percentiles. By default the lower percentile is 25 and the upper percentile is 75 . The 50 percentile is the same as the median.

describe() is used to generate descriptive statistics of the data in a Pandas DataFrame or Series. It summarizes central tendency and dispersion of the dataset.

Method 3: Get Function Name in Python using __qualname__ attribute. The __qualname__ gives more complete information than __name__ and therefore can be more helpful in debugging. To extract the name from any object or class, you can also use its __qualname__ attribute.

It is an important and widely used method of Python. This Method prints the information or summary of the dataframe. It prints the various information of the Dataframe such as index type, dtype, columns, non-values, and memory usage. It gives a quick overview of the dataset.

Python methods are functions that are associated with an object or a class in Python programming. They are used to perform specific tasks or operations on the data stored within objects or classes.

Why do we use () in Python? ›

Generally, parentheses () are used for grouping expressions, defining functions or passing arguments, and defining tuples. Square brackets [] are used for creating lists, indexing elements and slicing snippets.

Descriptive statistics with describe() function

Descriptive or summary statistics in python – pandas, can be obtained by using the describe() function. The describe() function gives us the count , mean , standard deviation(std) , minimum , Q1(25%) , median(50%) , Q3(75%) , IQR(Q3 - Q1) and maximum values.

Methods are the specific tools and procedures you use to collect and analyze data (for example, experiments, surveys, and statistical tests). In shorter scientific papers, where the aim is to report the findings of a specific study, you might simply describe what you did in a methods section.

Describing a method involves providing a clear and systematic explanation of how something is done or achieved.

describe() is used to generate descriptive statistics of the data in a Pandas DataFrame or Series. It summarizes central tendency and dispersion of the dataset.

As a result, the describe() method returns a summarized statistics of the series. It varies depending on the type of input series object. For numerical series, the describe() method analyzes basic statistics like count, mean, std, min, max, and quantile (25%, 50%, and 75%).

The function dataframe. isnull(). sum(). sum() returns the number of missing values in the dataset.