Pandas has the `count()` function - this function counts *not null* (not None, not np.NaN) cells for each column. Let's take a look at the following example.

data_frame = pd.read_csv(file.csv)
number_values = data_frame.count()

Let's look at how to find the **number** of the *not null* values of a certain column. 

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

Pandas has the `sum()` function - this function returns the sum of the values for each column. This functions works only for numeric columns. Let's take a look at the following example.

data_frame = pd.read_csv(file.csv)
sum = data_frame.sum()

Let's look at how to find the sum of the values in a specific column.

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

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.