Pandas has a very useful `mean()` function that looks for the mean of all values in each column. Let's look at how to use it.

data_frame = pd.read_csv(file.csv)
mean_values = data_frame.mean()

Let's look at how to find the mean value of a certain column.

data_frame = pd.read_csv(file.csv)
mean_values = data_frame['name of the column'].mean()

Pandas has another function, `mode()`, which returns the value that appears most often in each column. Take a look at the example.

data_frame = pd.read_csv(file.csv)
mode_values = data_frame.mode()

Let's look at how to find the mode of a certain column.

data_frame = pd.read_csv(file.csv)
mode_values = data_frame['name of the column'].mode()

There is also a `describe()` function in pandas, which is shown in the following code as an example.

data_frame = pd.read_csv(file.csv)
important_metrics = data_frame.describe()

This function includes the following metrics from the data:
 - total number of lines;
 - average or mean value;
 - standard deviation;
 - the smallest (min) and the largest (max) values in the dataset;
 - 25th, 50th, and 75th percentiles.

In this section, we will introduce the basics of the series and dataframe  data structures. We will also learn the difference between these structures.

Raw data comes in many forms: CSV, JSON, SQL, HTML, and so on. With pandas, we can work with data in a variety of formats. In this chapter, we will take a closer look at the CSV and TXT formats.

In this section, we will learn how to work with raw data, how to remove unwanted data, and how to work with null values in a dataset.