Pandas `Series.filter()`

method is used to return the subset of values from Series that satisfies the condition. The `filter()`

is applied with help of the index labels or on the values themselves. We can filter or subset the values of the pandas series using various functions.

In this article, I will explain `filter()`

syntax, parameters, and how to filter the values or rows from the Pandas Series and also filtering values by using where(), isin(), loc[], and `lambda`

functions.

**Key Points –**

- The
`filter()`

function in Pandas Series allows for the extraction of elements based on user-defined conditions. - By utilizing the
`filter()`

function, users can perform targeted data manipulation tasks, efficiently isolating and working with subsets of the Series that meet desired criteria without the need for extensive manual filtering or iteration. - The
`filter()`

function facilitates the creation of subsets from a larger Series, aiding in data analysis tasks where isolating specific data points is essential. - By using the
`filter()`

function, analysts and data scientists can efficiently explore and analyze data by focusing on subsets that are relevant to their research or problem-solving objectives. - The
`filter()`

function in Pandas Series is used to select and filter data based on specific criteria, allowing users to focus on relevant information.

## Quick Examples of Series filter() Method

Below are quick examples of the Pandas Series filter() method.

```
# Quick examples of series filter() function
# Example 1: use Series.filter() function
# To filter a pandas series
ser2 = ser.filter(regex = '. .')
# Example 2: filter() index by labels
ser2 = ser.filter(items = ['Spark', 'Python'])
# Example 3 : use loc[] and lambda
# To filter a pandas series
ser2 = ser.loc[lambda x : x == 23000]
# Example 4: use loc[] property & OR condition
ser2 = ser.loc[lambda x : (x 28000)]
# Example 5: use where() function to filter series
ser2 = ser.where(ser < 25000).dropna()
# Example 6: use isin() function to filter series
ser2 = ser[ser.isin([23000,28000])]
```

## Syntax of Series.filter() Function

Following is the syntax of the create Series.filter() function.

```
# Syntax of Series.filter() function
Series.filter(items=None, like=None, regex=None, axis=None)
```

### Parameter of filter()

Following are the parameters of the filter().

`items`

– A list of labels to filter on the specified axis.`like`

– A string that is used to filter labels based on a substring match.`regex`

– A regular expression (regex) to filter labels based on pattern matching.`axis`

– {0 or ‘index’, 1 or ‘columns’, None}, default None. When not specified it used columns. The axis along which the filtering will be applied. By default, it is set to`None`

, which means the filtering is done on the index.

### Return Value of filter()

It returns the value of the filter() the same type as the input object.

## Create Pandas Series

Pandas Series is a fundamental data structure in the Pandas library, representing a one-dimensional array with labeled indices. It’s versatile, capable of storing various data types including strings, integers, floats, and even other Python objects. Accessing elements within a Series is intuitive, as you can use the corresponding default indices or the labels themselves for retrieval.

`Note`

: Series data structure is the same as the NumPy array data structure but only one difference that is arrays indices are integers and start with 0, whereas in series, the index can be anything even strings. The labels do not need to be unique but they must be of hashable type.

To run some examples of the Pandas series `filter()`

method, let’s create Pandas series using list.

```
import pandas as pd
# Create the Series
ser = pd.Series([20000,25000,23000,28000,55000,23000])
# Create the Index
index = ['Java','Spark','PySpark','Pandas','python NumPy','Python']
# Set the index
ser.index = index
print(ser)
# Output:
# Java 20000
# Spark 25000
# PySpark 23000
# Pandas 28000
# python NumPy 55000
# Python 23000
# dtype: int64
```

## Use Series.filter() Function To Filter a Pandas Series

By using `Series.filter()`

function you can filter the Series by index labels or by values. When you use index labels to files you can use regular expressions by using `“regex”`

. The following example filters values from the given series object whose index label name has a space.

```
# Use Series.filter() function to filter a pandas series
ser2 = ser.filter(regex = '. .')
print(ser2)
# Output:
# python NumPy 55000
# dtype: int64
```

## Filter Series by Index Labels

By default `pandas.Series.filter()`

select the indexes by labels you specified using` item`

, `like`

, and `regex`

parameters. The following example filters series with the list of index labels `Spark`

and `Python`

.

```
# Filter() index by labels
ser2 = ser.filter(items = ['Spark', 'Python'])
print(ser2)
# Output:
# Spark 25000
# Python 23000
# dtype: int64
```

## Use loc[] & Lambda to Filter a Pandas Series

Alternatively, you can also use filter a Pandas Series using `loc[]`

along with a lambda function to retrieve values equal to 23000.

```
# Use loc[] and lambda to filter a pandas series
ser2 = ser.loc[lambda x : x == 23000]
print(ser2)
# Output:
# PySpark 23000
# Python 23000
# dtype: int64
```

When applying logical OR conditions, it’s important to use the bitwise OR operator `|`

instead of the `or`

keyword. In this program, `(ser < 23000)|(ser > 28000)`

create a boolean mask where it’s True for values less than 23000 or greater than 28000. Then, `ser.loc[]`

filters the Series based on this boolean mask, returning only the elements that satisfy the condition.

```
# Use loc[] property & OR condition
ser2 = ser.loc[lambda x : (x < 23000 or x > 28000)]
print(ser2)
# Output:
# Java 20000
# python NumPy 55000
# dtype: int64
```

## Use where() Function To Filter Series

Similarly, we can also use `where()`

function to filter a series by values using expressions. Using the `where()`

function to filter a Series (`ser`

) where the values are less than 25000 and then dropping the NaN values using `dropna()`

.

```
# Use where() function to filter series
ser2 = ser.where(ser < 25000).dropna()
print(ser2)
# Output:
# Pandas 28000.0
# python NumPy 55000.0
# dtype: float64
```

The above program will produce a Series (`ser2`

) where values greater than or equal to 25000 will be replaced with NaN, and values less than 25000 will be retained. The `dropna()`

method is optional in this case since `where()`

already handles the NaN values, and you can choose whether to include it based on your specific requirements.

## Use isin() Function To Filter Series

By use `isin()`

function is used to get the values from the series that are present in the list of values.

```
# Use isin() function to filter series
ser2 = ser[ser.isin([23000,28000])]
print(ser2)
# Output:
# PySpark 23000
# Pandas 28000
# Python 23000
# dtype: int64
```

In the above example, `ser.isin([23000, 30000])`

creates a boolean mask indicating whether each element in the original Series (`ser`

) is in the specified list `[23000, 28000]`

. The boolean mask is then used to filter the original Series using boolean indexing, resulting in `ser2`

containing only the elements that match the specified values.

## Complete Example

```
import pandas as pd
# Create the Series
ser = pd.Series([20000,25000,23000,28000,55000,23000])
# Create the Index
index_ = ['Java','Spark','PySpark','Pandas','python NumPy','Python']
# Set the index
ser.index = index_
print(ser)
# Use Series.filter() function to filter a pandas series
ser2 = ser.filter(regex = '. .')
print(ser2)
# Filter() index by labels
ser2 = ser.filter(items = ['Spark', 'Python'])
print(ser2)
# Use loc[] and lambda to filter a pandas series
ser2 = ser.loc[lambda x : x == 23000]
print(ser2)
# Use loc[] property & OR condition
ser2 = ser.loc[lambda x : (x 28000)]
print(ser2)
# Use where() function to filter series
ser2 = ser.where(ser < 25000).dropna()
print(ser2)
# Use isin() function to filter series
ser2 = ser[ser.isin([23000,28000])]
print(ser2)
```

## FAQ on Series filter() Method

**How does the filter() function work in Pandas Series?**

The function works by creating a boolean mask based on the specified condition. It then applies this mask to the Series, retaining only the elements that satisfy the condition.

**Can the filter() function be applied to both the index and values of a Series?**

The `filter()`

function can be applied to both the index and values of a Pandas Series. By default, it filters based on the index, but users can specify the `axis`

parameter to filter along the columns (axis=1).

**What is the typical use case for the filter() function in Pandas Series?**

The `filter()`

function is commonly used in data analysis tasks where there is a need to extract subsets of data based on specific conditions, enabling users to focus on relevant information and perform targeted analysis on a Pandas Series.

**Are the filtering conditions in filter() mutually exclusive?**

The filtering conditions in `filter()`

are mutually exclusive. Users can choose to use either `items`

, `like`

, or `regex`

to specify the filtering criteria, and only one of them should be used at a time.

**How does the filter() function compare to other filtering methods in Pandas, such as boolean indexing?**

The `filter()`

function is a versatile tool for filtering data, offering a more structured and parameterized approach compared to boolean indexing. While boolean indexing is powerful, the `filter()`

function provides additional options for label-based filtering, substring matching, and regular expression filtering. The choice between them depends on the specific requirements of the analysis.

### Conclusion

In this article, I have explained how to filter the Pandas Series by using `filter()`

, `where()`

, `isin()`

, and `loc[]`

with `lambda`

function with examples.

Happy Learning !!

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