Working with data often means sifting through a large set of numbers, names, or categories to focus on what matters. In pandas, that’s where filtering by column values comes in. It lets you narrow your DataFrame to include just the rows you want—nothing more, nothing less. Whether you’re cleaning up a dataset, selecting a group for analysis, or preparing data for visualization, filtering is a regular task. Thankfully, pandas make it pretty smooth.

Let’s go over some of the practical ways to filter rows based on what’s in a column. You’ll see how to use conditions, match values, apply functions, and more.

Ways to Filter Pandas DataFrame by Column Values

Using Simple Conditions

Perhaps the most straightforward way to filter is through the use of conditions. If you've used pandas in the past, you've likely already encountered this technique. It's popular because it simply works, and it's easy to understand.

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import pandas as pd

df = pd.DataFrame({

'Name': ['Alice', 'Bob', 'Charlie', 'David'],

'Age': [25, 30, 35, 40]

})

# Filter rows where Age is greater than 30

filtered_df = df[df['Age'] > 30]

This returns you all the rows in which the 'Age' column has a value greater than 30. You can replace other comparison operators as well—less than, equals, not equals, etc. The same principle can be used with strings:

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# Names that are not 'Bob'

df[df['Name'] != 'Bob']

These are basic conditionals, and you can chain them using logical operators if you need more than one condition.

2. Filtering with Multiple Conditions

If you wish to apply more than one filter, you'll have to use & for AND, or for OR, and enclose each condition in parentheses. pandas does not recognize regular Python's and or or within DataFrame filtering.

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# Age over 30 AND name starts with 'D'

df[(df['Age'] > 30) & (df['Name'].str.startswith('D'))]

This makes it easy to layer on multiple filters without writing a loop or separate functions. Just be sure to use parentheses, or pandas will throw an error.

If you’re working with many filters, or they change often, you might find it cleaner to build them in separate lines and combine later:

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condition1 = df['Age'] > 30

condition2 = df['Name'].str.contains('a')

filtered_df = df[condition1 & condition2]

This approach can make your code easier to follow and tweak.

3. Checking for Specific Values with isin()

Sometimes you're not filtering with a condition like “greater than” or “equal to”—you just want to pull out a specific list of values. That’s where isin() comes in handy.

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# Filter for names that are either Alice or Charlie

df[df['Name'].isin(['Alice', 'Charlie'])]

This is a common case when working with categorical data or filtering based on labels. You can use ~ before the statement to do the opposite:

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# Exclude Alice and Charlie

df[~df['Name'].isin(['Alice', 'Charlie'])]

It’s compact, readable, and works well with long lists of values.

4. Filtering Using Custom Functions

There are times when your filtering logic can’t be neatly expressed with a single condition or value list. You may want to apply more flexible checks, and for that, .apply() or .map() comes in.

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# Define a custom function

def age_group(age):

return age >= 30 and age <= 35

# Filter using apply

df[df['Age'].apply(age_group)]

You can also use lambdas to keep things shorter:

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df[df['Name'].apply(lambda x: x.startswith('C'))]

For columns that contain complex data or when your filter logic needs a few lines to explain, writing a function and using apply() can save you from making a mess with chained conditions.

5. Filtering with the Query Method

query() gives you a way to filter using a string expression. It’s often cleaner, especially if your column names are simple.

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# Using query for readability

df.query('Age > 30 and Name.str.startswith("D")', engine='python')

This syntax is easier to write and looks more like SQL. It’s especially useful when dealing with multi-line filters or working with notebooks where readability matters. Just be careful if your column names have spaces—you’ll need to use backticks around those names in your query.

6. Filtering Rows That Contain or Match a String

Text columns often need to be filtered based on whether they include a keyword or match a pattern. pandas makes this flexible through string methods. Just add .str before the method, and it'll work on the whole column.

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# Names containing the letter 'a'

df[df['Name'].str.contains('a')]

# Names that end with 'e'

df[df['Name'].str.endswith('e')]

For pattern matching, you can include a regex. If you're working with structured text data—names, codes, IDs—this becomes useful very quickly.

7. Filtering by Index Instead of Column

Even though filtering usually works on column values, sometimes it’s simpler to filter by index—especially if your index carries useful information like dates, labels, or categories.

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# Set Name as index and filter by index

df_indexed = df.set_index('Name')

df_indexed.loc[['Alice', 'David']]

This works well when your index is already aligned with what you want to filter by. It’s less common in early data stages, but helpful later on when your data structure changes.

8. Combining Filters into Reusable Functions

Once you start repeating certain filters across different datasets or projects, it makes sense to turn them into reusable functions. This doesn’t just save time—it also keeps your workflow consistent.

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def filter_by_age(df, min_age, max_age):

return df[(df['Age'] >= min_age) & (df['Age'] <= max_age)]

# Use the function

filtered = filter_by_age(df, 28, 36)

This kind of structure is useful when sharing code with others or cleaning up long notebooks. You don’t have to remember or rewrite filters every time.

Wrapping Up

Filtering rows in a pandas DataFrame by column values is one of those things that seems simple, but quickly grows into many variations depending on what kind of data you’re working with. Conditions, lists of values, custom logic, and even string patterns all come into play. Whether you’re preparing data for charts, slicing it for analysis, or just narrowing things down to see what's going on, these techniques are the backbone of that process. If you're familiar with a few of them, switching between methods depending on the situation becomes second nature.