Pandas offers the `count()` function, which counts all non-null cells (neither None nor np.NaN) for each column. Consider the following example:
```python
data_frame = pd.read_csv(file.csv)
number_of_cells = data_frame.count()
```

To find the **count** of non-null values in a specific column, use:
```python
data_frame = pd.read_csv(file.csv)
number_of_cells = data_frame['name of the column'].count()
```

Pandas also provides the `sum()` function. This function calculates the sum of values for each column, but it only works with numeric columns. Check out the following example:
```python
data_frame = pd.read_csv(file.csv)
total = data_frame.sum()
```

To find the sum of values in a particular column:
```python
data_frame = pd.read_csv(file.csv)
total = data_frame['name of the column'].sum()
```

Pandas is an extremely user-friendly library for data analysis. It's also designed to handle large datasets, using data structures like DataFrame and Series. This makes it an invaluable tool for Data Science. In this guide, you'll get acquainted with a range of statistical functions, including how to find correlations, modes, medians, and maximum and minimum values within a dataset. You'll also learn how to handle missing values and manipulate specific values, as well as how to remove them.

In this section, we'll explore the fundamentals of Series and DataFrame structures. You'll also learn about the distinctions between these two types of structures.

Data can be sourced in various formats, such as CSV, JSON, SQL, HTML, and more. With Pandas, you're not limited to a single format — you can work with data across a multitude of file types. In this chapter, we'll specifically focus on the CSV and TXT formats.

Here, you'll learn how to process raw data by removing extraneous information and managing null values in a dataset.

sum() and count()

Tarefa

Pandas First Steps

sum() and count()

Tarefa