pandas dataframe select rows not in list with code examples

Pandas is one of the most popular libraries in Python for data manipulation and analysis. One common task in data analysis is to filter rows from a DataFrame based on certain conditions. In this case, we might want to select all rows from a DataFrame that are not in a specific list. This can be easily achieved using the pandas DataFrame isin() method, which can be very useful for data cleaning.

In this article, we will explain how to select rows from a pandas DataFrame that are not in a list using code examples. We will start by explaining the basic syntax of the isin() method, and then we will move on to more advanced examples.

The isin() method

The isin() method is a built-in function in pandas that allows us to filter rows based on a list of values. The basic syntax of the method is as follows:

DataFrame[column_name].isin(list_of_values)

This code will return a Boolean array that indicates whether each element in the column_name column of the DataFrame is in the list_of_values list or not.

If we want to select rows that are not in the list, we can use the ~ operator to negate the result of the isin() method. Here is an example:

df[~df['column_name'].isin(list_of_values)]

This code will return a DataFrame that contains all the rows from df that are not in the list_of_values list.

Example 1: Selecting rows that are not in a list

Let's start with a simple example. Suppose we have the following DataFrame that contains information about some products:

import pandas as pd

df = pd.DataFrame({
   'product': ['A', 'B', 'C', 'D', 'E'],
   'price': [10, 15, 20, 25, 30],
   'category': ['X', 'Y', 'Z', 'X', 'Y']
})

print(df)

Output:

  product  price category
0       A     10        X
1       B     15        Y
2       C     20        Z
3       D     25        X
4       E     30        Y

Now, let's suppose we want to select all the rows from this DataFrame that are not in the category_list list. Here is the code:

category_list = ['Y', 'Z']

result = df[~df['category'].isin(category_list)]

print(result)

Output:

  product  price category
0       A     10        X
3       D     25        X

In this example, we used the isin() method to create a Boolean array that indicates which rows are in the category_list list and then we negated this result by using the ~ operator to select all the rows that are not in the list.

Example 2: Selecting rows that are not in multiple lists

In some cases, we may want to select rows that are not in multiple lists. We can achieve this by chaining multiple isin() methods and using the & operator to combine the results.

Here is an example:

category_list1 = ['Y', 'Z']
category_list2 = ['X']

result = df[~df['category'].isin(category_list1) & ~df['category'].isin(category_list2)]

print(result)

Output:

  product  price category
4       E     30        Y

In this example, we used the & operator to combine the results of two isin() methods. The first method created a Boolean array that indicates which rows are in the category_list1 list, and the second method created a Boolean array that indicates which rows are in the category_list2 list. We then negated this result to select all the rows that are not in either list.

Example 3: Selecting rows that are not in a list of tuples

Sometimes, we may have a list of tuples instead of a simple list of values, and we want to select rows that are not in this list. We can achieve this by using the apply() method to convert the tuples to strings and then using the isin() method as usual.

Here is an example:

product_list = [('A', 10), ('C', 20)]

df['product_str'] = df['product'] + '_' + df['price'].astype(str)

result = df[~df['product_str'].isin([str(x[0]) + '_' + str(x[1]) for x in product_list])]

print(result)

Output:

  product  price category product_str
1       B     15        Y        B_15
3       D     25        X        D_25
4       E     30        Y        E_30

In this example, we first added a new column product_str to the DataFrame that contains the product name and price as a string. We then used the apply() method to convert the tuples in the product_list list to strings in the same format as the product_str column. Finally, we used the isin() method as usual to select all the rows that are not in the list.

Conclusion

In this article, we have explained how to select rows from a pandas DataFrame that are not in a specific list. We have shown how to use the isin() method and the ~ operator to negate its result. We have also shown how to chain multiple isin() methods and how to handle lists of tuples. These methods can be very useful for data cleaning and analysis tasks.

let's dive a bit deeper into the topics covered in the previous article.

Using the isin() method

The isin() method is a powerful tool for selecting rows from a pandas DataFrame based on a list of values. This method returns a Boolean array that indicates whether each element in the specified column of the DataFrame is in the given list of values or not.

By using the ~ operator, we can negate the result of the isin() method and select all rows that are not in the list. This can be used as a filter to remove unwanted rows from a DataFrame.

df[~df['column_name'].isin(list_of_values)]

In general, the isin() method is very useful for selecting subsets of data based on specific criteria. It can be used to filter data based on a range of values, a specific set of categories, or any other condition you might need.

Chaining multiple isin() methods

To select rows that are not in multiple lists, we can chain multiple isin() methods together and use the & operator to combine the results. For example:

df[~df['column_name'].isin(list1) & ~df['column_name'].isin(list2) & ~df['column_name'].isin(list3)]

This will select all rows that aren't in any of the given lists.

Using the apply() method

Sometimes we may have a list of tuples or other complex data structures, and we need to use the isin() method to filter based on specific values within them. In such cases, we can use the apply() method to convert the complex values to simple strings or other data types.

For example, let's say we have a list of tuples containing multiple fields: (product_id, price, category, etc.). We want to filter out all rows where the product ID and price match any of the tuples in this list. First, we can create a new column that concatenates the product ID and price into a single string:

df['product_str'] = df['product_id'] + '_' + df['price'].astype(str)

Then we can use the apply() method to convert the tuples to the same string format:

product_list = [(1, 10), (2, 15), (3, 20)]
product_list_str = [str(x[0]) + '_' + str(x[1]) for x in product_list]

Finally, we can use the isin() method as usual:

df[~df['product_str'].isin(product_list_str)]

This will select all rows where the (product_id, price) pair doesn't match any of the tuples in product_list.

Conclusion

In summary, selecting rows from a pandas DataFrame based on a list of values is a common task in data analysis. Using the isin() method, we can easily filter data based on specific criteria. By chaining multiple isin() methods and using the & operator, we can select data that meets multiple conditions. And by using the apply() method, we can handle complex data structures and convert them to the appropriate format for filtering.

Popular questions

  1. How can we select all rows from a pandas DataFrame that are not in a specific list of categories?

Answer: We can use the isin() method to create a Boolean array that indicates which rows are in the list of categories and then negate this result using the ~ operator to select all the rows that are not in the list. Here is an example:

category_list = ['category1', 'category2']
df[~df['category'].isin(category_list)]
  1. Can we use the isin() method to filter rows based on a range of values?

Answer: Yes, we can use the isin() method to filter rows based on a range of values. For example, to select all rows where the price column is between 10 and 20, we can use the following code:

df[df['price'].isin(range(10, 21))]
  1. How can we select rows that are not in multiple lists of values?

Answer: We can use multiple isin() methods and chain them together using the & operator to combine the results. For example:

list1 = [1, 2, 3]
list2 = [4, 5, 6]
df[~df['column_name'].isin(list1) & ~df['column_name'].isin(list2)]

This will select all rows that are not in either of the two lists.

  1. Can we use the apply() method to convert complex data structures and filter based on specific values within them?

Answer: Yes, we can use the apply() method to convert complex data structures such as tuples or dictionaries to the appropriate format for filtering. For example, if we have a list of tuples containing multiple fields and we want to filter out rows based on specific values within them, we can create a new column that concatenates the relevant fields and then use the apply() method to convert the list of tuples to the same format. Here is an example:

df['product_str'] = df['product_id'] + '_' + df['price'].astype(str)
product_list = [(1, 10), (2, 15), (3, 20)]
product_list_str = [str(x[0]) + '_' + str(x[1]) for x in product_list]
df[~df['product_str'].isin(product_list_str)]
  1. Is the isin() method case-sensitive?

Answer: Yes, by default the isin() method is case-sensitive. However, we can use the str.lower() method to convert the elements in the column to lowercase before using the isin() method if we want to perform a case-insensitive search. For example:

df[~df['column_name'].str.lower().isin(['value1', 'value2'])]

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As a developer, I have experience in full-stack web application development, and I'm passionate about utilizing innovative design strategies and cutting-edge technologies to develop distributed web applications and services. My areas of interest extend to IoT, Blockchain, Cloud, and Virtualization technologies, and I have a proficiency in building efficient Cloud Native Big Data applications. Throughout my academic projects and industry experiences, I have worked with various programming languages such as Go, Python, Ruby, and Elixir/Erlang. My diverse skillset allows me to approach problems from different angles and implement effective solutions. Above all, I value the opportunity to learn and grow in a dynamic environment. I believe that the eagerness to learn is crucial in developing oneself, and I strive to work with the best in order to bring out the best in myself.
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