Pandas DataFrame Filtering: Using the Isin Method

1. Introduction

In this tutorial, you will learn how to use the isin method to filter data based on multiple values in Pandas, a popular Python library for data analysis.

Filtering data is a common task when working with dataframes, which are two-dimensional data structures that store data in rows and columns. You may want to select a subset of data that meets certain criteria, such as values that match a list of options, values that fall within a range, or values that satisfy a condition.

One way to filter data is to use the boolean indexing technique, which involves creating a boolean mask that indicates which rows or columns to keep or drop. For example, you can use the == operator to compare a column with a single value and get a boolean mask that is True for the rows that match the value and False for the ones that do not.

# Import pandas
import pandas as pd

# Create a dataframe
df = pd.DataFrame({'name': ['Alice', 'Bob', 'Charlie', 'David', 'Eve'],
                   'age': [25, 30, 35, 40, 45],
                   'gender': ['F', 'M', 'M', 'M', 'F'],
                   'city': ['New York', 'Los Angeles', 'Chicago', 'Houston', 'Phoenix']})

# Display the dataframe
df

# Create a boolean mask for the gender column
mask = df['gender'] == 'F'

# Display the mask
mask

However, what if you want to filter data based on multiple values, such as a list of options? For example, what if you want to select the rows where the city is either New York or Chicago? Using the == operator with a list will not work, as it will raise an error.

# Try to create a boolean mask for the city column with a list
mask = df['city'] == ['New York', 'Chicago']

# Display the error
ValueError: Lengths must match to compare

This is where the isin method comes in handy. The isin method is a dataframe or series method that checks whether each element in a column or row is contained in a given list, set, or series, and returns a boolean mask. You can use the isin method to filter data based on multiple values easily and efficiently.

In this tutorial, you will learn how to:

• Use the isin method with a single column
• Use the isin method with multiple columns
• Combine the isin method with other filtering methods

By the end of this tutorial, you will be able to perform pandas dataframe filtering with the isin method and filtering with multiple values like a pro.

Are you ready? Let’s get started!

2. What is the isin method and why use it?

The isin method is a dataframe or series method that checks whether each element in a column or row is contained in a given list, set, or series, and returns a boolean mask. You can use the isin method to filter data based on multiple values easily and efficiently.

For example, suppose you have a dataframe that contains information about different fruits, such as their name, color, and price. You want to select the rows where the fruit name is either apple or banana. You can use the isin method to create a boolean mask that is True for the rows that match the list of values and False for the ones that do not.

# Import pandas
import pandas as pd

# Create a dataframe
df = pd.DataFrame({'name': ['apple', 'banana', 'cherry', 'durian', 'elderberry'],
                   'color': ['red', 'yellow', 'red', 'green', 'purple'],
                   'price': [1.2, 0.8, 2.5, 3.0, 4.0]})

# Display the dataframe
df

# Create a boolean mask for the name column with the isin method
mask = df['name'].isin(['apple', 'banana'])

# Display the mask
mask

The isin method is useful for pandas dataframe filtering because it allows you to filter data based on multiple values without having to use multiple logical operators, such as | (or) or & (and). Using multiple logical operators can make your code more complex and less readable, especially if you have a long list of values to filter by.

The isin method also works with different types of values, such as strings, numbers, or booleans. You can also use different types of containers, such as lists, sets, or series, to pass the values to the isin method. However, you should be careful about the order and the duplicates of the values, as they may affect the result of the isin method.

In the next section, you will learn how to use the isin method with a single column and see some examples of filtering with multiple values in Pandas.

3. How to use the isin method with a single column

In this section, you will learn how to use the isin method with a single column and see some examples of filtering with multiple values in Pandas. You will use the same dataframe that you created in the previous section, which contains information about different fruits, such as their name, color, and price.

To use the isin method with a single column, you need to follow these steps:

1. Select the column that you want to filter by using the dot notation or the bracket notation. For example, df['name'] or df.name will select the name column.
2. Call the isin method on the selected column and pass a list, set, or series of values that you want to filter by. For example, df['name'].isin(['apple', 'banana']) will create a boolean mask that is True for the rows where the name is either apple or banana.
3. Use the boolean mask to filter the dataframe by passing it inside the brackets. For example, df[df['name'].isin(['apple', 'banana'])] will return a subset of the dataframe that contains only the rows where the name is either apple or banana.

Let’s see some examples of using the isin method with a single column.

# Filter the dataframe by the name column with the isin method
df[df['name'].isin(['apple', 'banana'])]

You can see that the isin method returns a subset of the dataframe that contains only the rows where the name is either apple or banana. You can also use a set or a series instead of a list to pass the values to the isin method.

# Use a set of values to filter the dataframe by the color column with the isin method
df[df['color'].isin({'red', 'green'})]

# Use a series of values to filter the dataframe by the price column with the isin method
df[df['price'].isin(pd.Series([1.2, 2.5, 4.0]))]

You can also use the ~ operator to invert the boolean mask and filter the dataframe by the values that are not in the list, set, or series. For example, if you want to select the rows where the name is not apple or banana, you can use the following code:

# Filter the dataframe by the name column with the isin method and invert the mask
df[~df['name'].isin(['apple', 'banana'])]

As you can see, using the isin method with a single column is a simple and effective way to perform pandas dataframe filtering based on multiple values. However, what if you want to filter data based on multiple columns and values? In the next section, you will learn how to use the isin method with multiple columns and see some examples of more complex filtering scenarios.

4. How to use the isin method with multiple columns

In this section, you will learn how to use the isin method with multiple columns and see some examples of more complex filtering scenarios in Pandas. You will use the same dataframe that you created in the previous section, which contains information about different fruits, such as their name, color, and price.

To use the isin method with multiple columns, you need to follow these steps:

1. Create a dictionary that maps each column name to a list, set, or series of values that you want to filter by. For example, {'name': ['apple', 'banana'], 'color': ['red', 'green']} will create a dictionary that specifies the values for the name and color columns.
2. Call the isin method on the dataframe and pass the dictionary as the argument. For example, df.isin({'name': ['apple', 'banana'], 'color': ['red', 'green']}) will create a boolean mask that is True for the elements that match the values in the dictionary and False for the ones that do not.
3. Use the boolean mask to filter the dataframe by passing it inside the brackets. For example, df[df.isin({'name': ['apple', 'banana'], 'color': ['red', 'green']})] will return a subset of the dataframe that contains only the elements that match the values in the dictionary.

Let’s see some examples of using the isin method with multiple columns.

# Filter the dataframe by the name and color columns with the isin method
df[df.isin({'name': ['apple', 'banana'], 'color': ['red', 'green']})]

You can see that the isin method returns a subset of the dataframe that contains only the elements that match the values in the dictionary. However, you may notice that the result also contains many NaN values, which represent missing data. This is because the isin method does not check for the combination of values across multiple columns, but rather for the individual values in each column. Therefore, if an element does not match any of the values in the dictionary for its column, it will be replaced with NaN.

If you want to filter the dataframe by the combination of values across multiple columns, you need to use the all or any methods along with the isin method. The all method will return a boolean mask that is True for the rows where all the elements match the values in the dictionary, and False otherwise. The any method will return a boolean mask that is True for the rows where at least one element matches the values in the dictionary, and False otherwise.

For example, if you want to select the rows where the name is either apple or banana and the color is either red or green, you can use the following code:

# Filter the dataframe by the combination of values across the name and color columns with the isin and all methods
df[df.isin({'name': ['apple', 'banana'], 'color': ['red', 'green']}).all(axis=1)]

As you can see, using the isin method with multiple columns allows you to perform more complex pandas dataframe filtering based on multiple values. However, what if you want to combine the isin method with other filtering methods, such as boolean indexing or query? In the next section, you will learn how to combine the isin method with other filtering methods and see some examples of more advanced filtering scenarios.

5. How to combine the isin method with other filtering methods

In this section, you will learn how to combine the isin method with other filtering methods, such as boolean indexing or query, and see some examples of more advanced filtering scenarios in Pandas. You will use the same dataframe that you created in the previous section, which contains information about different fruits, such as their name, color, and price.

To combine the isin method with other filtering methods, you need to follow these steps:

1. Create a boolean mask with the isin method as you learned in the previous sections. For example, mask = df['name'].isin(['apple', 'banana']) will create a boolean mask for the name column.
2. Create another boolean mask with the boolean indexing or query method. For example, mask2 = df['price'] > 1 will create a boolean mask for the price column with the boolean indexing method. Alternatively, you can use the query method to create the same mask, such as mask2 = df.query('price > 1').
3. Combine the two boolean masks with the logical operators, such as | (or) or & (and). For example, mask3 = mask | mask2 will create a boolean mask that is True for the rows where either the name is apple or banana or the price is greater than 1.
4. Use the combined boolean mask to filter the dataframe by passing it inside the brackets. For example, df[mask3] will return a subset of the dataframe that satisfies the combined condition.

Let’s see some examples of combining the isin method with other filtering methods.

# Filter the dataframe by the name column with the isin method and the price column with the boolean indexing method
mask = df['name'].isin(['apple', 'banana'])
mask2 = df['price'] > 1
mask3 = mask & mask2
df[mask3]

You can see that the result contains only the row where the name is apple and the price is greater than 1. You can also use the query method instead of the boolean indexing method to create the second mask, such as:

# Filter the dataframe by the name column with the isin method and the price column with the query method
mask = df['name'].isin(['apple', 'banana'])
mask2 = df.query('price > 1')
mask3 = mask & mask2
df[mask3]

You can also use the ~ operator to invert the masks and filter the dataframe by the opposite conditions. For example, if you want to select the rows where the name is not apple or banana and the price is less than or equal to 1, you can use the following code:

# Filter the dataframe by the name column with the isin method and the price column with the boolean indexing method and invert the masks
mask = ~df['name'].isin(['apple', 'banana'])
mask2 = ~df['price'] > 1
mask3 = mask & mask2
df[mask3]

As you can see, combining the isin method with other filtering methods allows you to perform more advanced pandas dataframe filtering based on multiple values and conditions. In the next and final section, you will learn how to write a conclusion for your tutorial and summarize the main points that you covered.

6. Conclusion

Congratulations! You have reached the end of this tutorial on Pandas DataFrame Filtering: Using the Isin Method. You have learned how to use the isin method to filter data based on multiple values in Pandas, a popular Python library for data analysis. You have also learned how to use the isin method with single and multiple columns, and how to combine the isin method with other filtering methods, such as boolean indexing or query.

By following this tutorial, you have gained valuable skills that will help you perform pandas dataframe filtering with the isin method and filtering with multiple values in various scenarios. You have also learned how to write a clear and structured tutorial that provides practical instructions on how to solve a specific problem.

Here are the main points that you covered in this tutorial:

• The isin method is a dataframe or series method that checks whether each element in a column or row is contained in a given list, set, or series, and returns a boolean mask.
• You can use the isin method to filter data based on multiple values easily and efficiently, without having to use multiple logical operators.
• You can use the isin method with a single column by selecting the column, calling the isin method with a list, set, or series of values, and using the boolean mask to filter the dataframe.
• You can use the isin method with multiple columns by creating a dictionary that maps each column name to a list, set, or series of values, calling the isin method with the dictionary, and using the boolean mask to filter the dataframe.
• You can combine the isin method with other filtering methods, such as boolean indexing or query, by creating another boolean mask with the other method, combining the two masks with the logical operators, and using the combined mask to filter the dataframe.

We hope you enjoyed this tutorial and found it useful. If you have any questions or feedback, please feel free to leave a comment below. Thank you for reading and happy coding!