When working with large and complex datasets, it’s essential to get an overview of the data to understand its characteristics and identify any patterns or trends. In Python, the Pandas library provides several methods to get descriptive statistics of a DataFrame.

The describe() method is one of the most commonly used methods to get descriptive statistics of a DataFrame. It returns a summary of the key statistics such as the mean, median, standard deviation, minimum, and maximum values for each numeric column. This method provides a quick and easy way to get an overview of the data and identify any outliers or skewness in the data.

Another useful method for getting descriptive statistics is the value_counts() method. This method returns the frequency of each unique value in a column. It’s useful for understanding the distribution of categorical variables, such as the number of observations in each category or class.

Another way of getting statistics is by using the agg() method. With this method, you can pass one or several aggregation functions as argument to apply to one or several columns. This way you can get more detailed statistics on your data.

You can also use the info() method to get a summary of the DataFrame’s columns, including the data type, number of non-null values, and memory usage. This method provides a quick and easy way to check the data types of columns, the number of missing values, and the overall structure of the DataFrame.

In addition, there are other methods such as mean(), median(), std(), min(), max(), var(), quantile() that allow you to obtain the specific statistics for one column or for the entire dataframe.

In summary, Pandas provides several methods for getting descriptive statistics of a DataFrame. The describe() method provides a summary of key statistics, the value_counts() method returns the frequency of each unique value in a column, agg() method allow to apply aggregation functions, info() method provides information of the Dataframe, while mean(), median(), std(), min(), max(), var(), quantile() are other ways to get specific statistics of one column or entire dataframe. It’s important to choose the most appropriate method for your use case and explore the data to get an in-depth understanding of its characteristics.