Performing univariate analysis using a summary table

In univariate analysis, a summary table is very useful for analyzing numerical values within our dataset. In Python, these summary tables present statistics that summarize the central tendency, dispersion, and shape of the distribution of our dataset. The statistics covered include the count of non-empty records, mean, standard deviation, minimum, maximum, 25th percentile, 50th percentile and 75th percentile.

In pandas, the describe method provides these summary tables with all the aforementioned statistics.

Getting ready

We will work with the Amsterdam House Prices Data from Kaggle in this recipe.

You can retrieve all the files from the GitHub repository.

How to do it…

Let’s learn how to create a summary table using the pandas library:

1. Import the pandas and seaborn libraries:

   import pandas as pd

2. Load the .csv into a dataframe using read_csv. Then subset the dataframe to include only the...