## DataFrame size

To get the dimensionality of DataFrame (i.e., number of rows and columns), use the `.shape` attribute. It will return a tuple (immutable list-like structure) with 2 values: the first one is the number of rows, the second one is the number of columns.

# 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' dimensionality
print(df.shape)

## Values' types

Before aggregating and visualizing data, you need to understand are these data have appropriate formats. For example, you may face the situation when prices will be represented in text form - this will make impossible to aggregate it. To get the columns values' types, use the `.dtypes` attribute.

# 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')
# Columns values' types
print(df.dtypes)

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 [2/3]

DataFrame size

Values' types

Awesome!

Exploring Data [2/3]

DataFrame size

Values' types