Pandas – Analyzing DataFrames

Pandas – Analyzing DataFrames

Viewing the Data

One of the most used method for getting a quick overview of the DataFrame, is the head() method.

The head() method returns the headers and a specified number of rows, starting from the top.

Example

Get a quick overview by printing the first 10 rows of the DataFrame:

Try it Yourself »

In our examples we will be using a CSV file called ‘data.csv’.

Download data.csv, or open data.csv in your browser.

Note: if the number of rows is not specified, the head() method will return the top 5 rows.

Example

Print the first 5 rows of the DataFrame:

Try it Yourself »

There is also a tail() method for viewing the last rows of the DataFrame.

The tail() method returns the headers and a specified number of rows, starting from the bottom.

Example

Print the last 5 rows of the DataFrame:

Try it Yourself »


w3schoolsCERTIFIED.2022

Get Certified!

Complete the Pandas modules, do the exercises, take the exam, and you will become w3schools certified!

$10 ENROLL


Info About the Data

The DataFrames object has a method called info(), that gives you more information about the data set.

Example

Print information about the data:

Result

Try it Yourself »

Result Explained

The result tells us there are 169 rows and 4 columns:

And the name of each column, with the data type:

Null Values

The info() method also tells us how many Non-Null values there are present in each column, and in our data set it seems like there are 164 of 169 Non-Null values in the “Calories” column.

Which means that there are 5 rows with no value at all, in the “Calories” column, for whatever reason.

Empty values, or Null values, can be bad when analyzing data, and you should consider removing rows with empty values. This is a step towards what is called cleaning data, and you will learn more about that in the next chapters.

ArmenianEnglish