Pandas Series.filter() function 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.
1. Quick Examples of Series filter() Function
If you are in hurry below are some quick examples of the Pandas Series filter() function.
# Below are a quick example
# 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])]
2. Syntax of Series.filter() Function
Following is the syntax of create Series.filter() function.
# Syntax of Series.filter() function
Series.filter(items=None, like=None, regex=None, axis=None)
2.1 Parameter of filter()
Following are the parameters of the filter().
items– Takes a list of axis labels that you wanted to filter.like– Takes the axis string label that you wanted to filter.regex– regular expression.axis– {0 or ‘index’, 1 or ‘columns’, None}, default None. When not specified it used columns.
2.2 Return Value of filter()
It returns the value of the filter() the same type as the input object.
3. Create Pandas Series
Pandas Series is a one-dimensional, Index-labeled data structure that is available only in the Pandas library. It can store all the datatypes such as strings, integers, float, and other python objects. We can access each element in the Series with the help of corresponding default indices.
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.
Now, let’s create pandas series using list of values.
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)
Yields below output.
# Output
Java 20000
Spark 25000
PySpark 23000
Pandas 28000
python NumPy 55000
Python 23000
dtype: int64
4. 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)
Yields below output.
# Output
python NumPy 55000
dtype: int64
5. 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)
Yields below output.
# Output
Spark 25000
Python 23000
dtype: int64
6. Use loc[] & Lambda to Filter a Pandas Series
You can also filter the Pandas Series using Series.loc[] along with lambda function. The following example returns values from a series where values are equal to 23000.
# Use loc[] and lambda to filter a pandas series
ser2 = ser.loc[lambda x : x == 23000]
print(ser2)
Yields below output.
# Output
PySpark 23000
Python 23000
dtype: int64
Alternatively, you can also apply an “OR” condition with the “loc[]” property. The following example filters values that are less than 23000 or values greater than 28000. For examples.
# Use loc[] property & OR condition
ser2 = ser.loc[lambda x : (x < 23000 or x > 28000)]
print(ser2)
Yields below output.
# Output
Java 20000
python NumPy 55000
dtype: int64
7. Use where() Function To Filter Series
We can also use where() function to filter a series by values using expressions.
# Use where() function to filter series
ser2 = ser.where(ser < 25000).dropna()
print(ser2)
Yields below output.
# Output
Pandas 28000.0
python NumPy 55000.0
dtype: float64
8. 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)
Yields below output.
# Output
PySpark 23000
Pandas 28000
Python 23000
dtype: int64
9. Complete Example For Series filter() Function
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)
10. Conclusion
In this article, you have learned the how to filter the Pandas Series by using filter(), where(), isin(), and loc[] with lambda function by using examples.
Happy Learning !!
Related Articles
- How to create Pandas Series in Python?
- Convert Pandas Series of Lists to One Series
- Remove NaN From Pandas Series
- How to Combine Two Series into pandas DataFrame
- Pandas GroupBy Multiple Columns Explained
- Pandas Aggregate Functions with Examples
- Series.reindex() – Change the Index Order in Pandas Series
- pandas DataFrame.sort_index() – Sort by Index
