## Summary of DataFrame' columns

If you need additional information about DataFrame, i.e., memory usage, number of non-null values in addition to the considered in the previous chapter, use the `.info()` method.

# Importing library
import pandas as pd

# Reading csv file
df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/67798cef-5e7c-4fbc-af7d-ae96b4443c0a/audi.csv')
# DataFrame' columns information
print(df.info())

## Numerical columns' summary

For numerical columns you can get the mean, minimal, maximal values, 25%, 50%, 75% quantiles, standart deviation using the `.describe()` method.

# Importing library
import pandas as pd

# Reading csv file
df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/67798cef-5e7c-4fbc-af7d-ae96b4443c0a/audi.csv')
# Numerical columns' summary
print(df.describe())

This course is designed to teach you the very basics of pandas - a must-have  Python library for every data analyst or data scientist (and not only). You will start from defining the basic objects, learn how to read data from files, and how to manipulate and aggregate loaded data.

In the first section of the course you will learn two basic objects provided by pandas - Series and DataFrame. You will learn how to create them and change their indexes or column names.

Often the necessary data are stored in some files. You need to be able to load these data and gain the first insights.

Once you have loaded the data as Series/DataFrame, you need to be able to get certain data based on needs. You will learn how to access rows, columns, and filter observations by conditions.

In the last section of the course, you will get to know how to calculate some aggregate statistics of your data, such as mean, median, sum, product. Also you will learn how to count values and get only unique observations.

Exploring Data [3/3]

Summary of DataFrame' columns

Numerical columns' summary

Awesome!

Exploring Data [3/3]

Summary of DataFrame' columns

Numerical columns' summary