Often dataframes have missing values (exactly `None`, `np.NaN`). And when we work with dataframes, we need to be able to detect this missing data. To do so, pandas has some special functions. 

The first function is `isna()`. This function returns a boolean dataframe. Accordingly, a **True** value means a missed value in the dataframe, and a **False** value means a *not* missed value.

Let's look at an example below. Please run this code to see the resulting dataframe with **True** and **False** values for better understanding.

import pandas as pd
import numpy as np

dataset = {'animal': [np.NaN, 'Dog', np.NaN, 'Cat','Parrot', None],
'name': ['Dolly', None, 'Erin', 'Kelly', None, 'Odie']}
animal = pd.DataFrame(dataset)
# Find missing values
missing_value = animal.isna()
print(missing_value)

The second function is `isnull()`. This function performs the same as the previous one. There is no difference between them. Let's look at how we can use it. Please run this code as well to see whether you will have the same resulting dataframe.

import pandas as pd
import numpy as np

dataset = {'animal': [np.NaN, 'Dog', np.NaN, 'Cat','Parrot', None],
'name': ['Dolly', None, 'Erin', 'Kelly', None, 'Odie']}
animal = pd.DataFrame(dataset)
# Find missing values
missing_value = animal.isnull()
print(missing_value)

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.