Pandas Series loc[] Method

Pandas Series.loc[] function is used to access a group of rows and columns by labels or a boolean array in the given Series object. We can select some values from the given Pandas Series object based on the labels using loc[] function. In this article, I will explain Series.loc[] to select Series object values by index labels.

Key Points –

• Pandas Series loc[] method allows for label-based indexing, enabling access to data based on the index labels.
• It provides a powerful way to retrieve specific elements or slices of data from a Pandas Series using label-based indexing.
• loc[] function supports various inputs including single labels, lists of labels, or label slices to fetch desired data.
• It is essential for selecting specific rows or columns from a Pandas Series based on their index labels.
• Proper usage of loc[] function ensures efficient data retrieval and manipulation in Pandas Series, contributing to streamlined data analysis workflows.

Quick Examples of Series loc[] Function

If you’re in a hurry, here are some quick examples of the Pandas Series loc[] method.

# Quick examples of series loc[] function

# Examples 1: Use Series.loc[] function 
# To selected labels
ser2 = ser.loc[['Pandas', 'Spark', 'Python']]

# Examples 2: Select Rows Between two Index Labels
# Includes both Spark and Python rows
ser2 = ser.loc['Spark':'Python']

# Examples 3: Select Alternate rows by indeces
ser2 = ser.loc['Spark':'Pandas':2]

# Examples 4: Use loc[] and lambda
ser2 = ser.loc[lambda x : x == 28000]

# Examples 5: Use loc[] property & OR condition
ser2 = ser.loc[lambda x : (x  28000)]

Usage of Pandas Series loc[] Method

The loc[] method in a Pandas Series is primarily used for label-based indexing, allowing you to access, filter, or modify data by the label of the index. It is useful when you want to select rows or values based on their labels.

To run some examples of the pandas series loc[] method, let’s generate a Pandas Series.

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

Use Series.loc[] to Select Values from Series

You can use the Pandas Series.loc[] function to get or select the single value by Index or multiple values by index by specifying the index labels (in a list) from the Series object.

# Use Series.loc[] function to selected labels
ser2 = ser.loc[['Pandas', 'Spark', 'Python']]
print(ser2)

Yields below output.

# Output:
Pandas    28000
Spark     25000
Python    23000
dtype: int64

Select Rows Between Two Index Labels

loc[] also supports selecting rows or values between two index labels by range in pandas Series. For instance, all items between starting and ending index. The below example selects rows between Spark and Python.

# Select Rows Between two Index Labels
# Includes both Spark and Python rows
ser2 = ser.loc['Spark':'Python']
print(ser2)

Yields below output.

# Output:
Spark           25000
PySpark         23000
Pandas          28000
python NumPy    55000
Python          23000
dtype: int64

Select Alternate Rows By Indices

Similarly, by using ranges you can also select every alternate row from Series. In order to do so, you need to provide the third value as a step.

# Select Alternate rows by indeces
ser2 = ser.loc['Spark':'Pandas':2]
print(ser2)

Yields below output.

# Output:
Spark     25000
Pandas    28000
dtype: int64

Use Series.loc[] and Lambda

You can also use Pandas Series.loc[] along with lambda function. The following example returns values from a series where values are equal to 28000.

# Use loc[] and lambda
ser2 = ser.loc[lambda x : x == 28000]
print(ser2)

Yields below output.

# Output:
Pandas    28000
Oracle    28000
dtype: int64

Use Series.loc[] & OR Condition

You can also apply a “OR” condition with the “loc[]” property. The following example returns the values that are less than 25000 or values greater than 28000. For examples.

# Use loc[] property & OR condition
ser2 = ser.loc[lambda x : (x  28000)]
print(ser2))

Yields below output.

# Output:
Java            20000
PySpark         23000
python NumPy    55000
Python          23000
dtype: int64

Complete Examples of Pandas Series loc

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

# Use Series.loc[] function to selected labels
ser2 = ser.loc[['Pandas', 'Spark', 'Python']]
print(ser2)

# Select Rows Between two Index Labels
# Includes both Spark and Python rows
ser2 = ser.loc['Spark':'Python']
print(ser2)

# Select Alternate rows by indeces
ser2 = ser.loc['Spark':'Pandas':2]
print(ser2)

# Use loc[] and lambda
ser2 = ser.loc[lambda x : x == 28000]
print(ser2)

# Use loc[] property & OR condition
ser2 = ser.loc[lambda x : (x  28000)]
print(ser2)

FAQ on Pandas Series loc[] Method

Conclusion

In this article, you have learned about pandas Series loc property. Series.loc[] is label-based to select some values from the given Series object in pandas with examples.

Happy Learning !!

Related Articles

• Convert Pandas DataFrame to Series
• Remove NaN From Pandas Series
• Pandas Series where() Function
• Pandas Series filter() Function
• Pandas Series.clip() Function
• Pandas Series any() Function
• Pandas Series.quantile() Function
• Use pandas.to_numeric() Function 
• Pandas Series Drop duplicates() Function
• How to create Pandas Series in Python?
• Pandas loc[] attribute multiple conditions
• Pandas iloc[] Usage with Examples
• Pandas Difference Between loc[] vs iloc[]
• Convert Pandas Series of Lists to One Series
• Series.reindex() – Change the Index Order in Pandas Series

References

• https://pandas.pydata.org/docs/reference/api/pandas.Series.is_monotonic_increasing.html