There are several aggregate functions in `pandas`. One of them is `.count()` method, which counts number of non-null observation for each column.

# 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')
# Count number of non-null rows
print(df.count())

This method is popular for counting number of rows under specific condition. For example, you may count number of cars manufactured after 2015. This time you don't need to see the number of observation in each column, so you can choose particular one.

# 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')
# Count number of cars manufactured after 2015
print(df.loc[df.year > 2015, 'model'].count())

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.

Number of Observations