- Python Pandas - Home
- Python Pandas - Introduction
- Python Pandas - Environment Setup
- Python Pandas - Basics
- Python Pandas - Introduction to Data Structures
- Python Pandas - Index Objects
- Python Pandas - Panel
- Python Pandas - Basic Functionality
- Python Pandas - Indexing & Selecting Data
- Python Pandas - Series
- Python Pandas - Series
- Python Pandas - Slicing a Series Object
- Python Pandas - Attributes of a Series Object
- Python Pandas - Arithmetic Operations on Series Object
- Python Pandas - Converting Series to Other Objects
- Python Pandas - DataFrame
- Python Pandas - DataFrame
- Python Pandas - Accessing DataFrame
- Python Pandas - Slicing a DataFrame Object
- Python Pandas - Modifying DataFrame
- Python Pandas - Removing Rows from a DataFrame
- Python Pandas - Arithmetic Operations on DataFrame
- Python Pandas - IO Tools
- Python Pandas - IO Tools
- Python Pandas - Working with CSV Format
- Python Pandas - Reading & Writing JSON Files
- Python Pandas - Reading Data from an Excel File
- Python Pandas - Writing Data to Excel Files
- Python Pandas - Working with HTML Data
- Python Pandas - Clipboard
- Python Pandas - Working with HDF5 Format
- Python Pandas - Comparison with SQL
- Python Pandas - Data Handling
- Python Pandas - Sorting
- Python Pandas - Reindexing
- Python Pandas - Iteration
- Python Pandas - Concatenation
- Python Pandas - Statistical Functions
- Python Pandas - Descriptive Statistics
- Python Pandas - Working with Text Data
- Python Pandas - Function Application
- Python Pandas - Options & Customization
- Python Pandas - Window Functions
- Python Pandas - Aggregations
- Python Pandas - Merging/Joining
- Python Pandas - MultiIndex
- Python Pandas - Basics of MultiIndex
- Python Pandas - Indexing with MultiIndex
- Python Pandas - Advanced Reindexing with MultiIndex
- Python Pandas - Renaming MultiIndex Labels
- Python Pandas - Sorting a MultiIndex
- Python Pandas - Binary Operations
- Python Pandas - Binary Comparison Operations
- Python Pandas - Boolean Indexing
- Python Pandas - Boolean Masking
- Python Pandas - Data Reshaping & Pivoting
- Python Pandas - Pivoting
- Python Pandas - Stacking & Unstacking
- Python Pandas - Melting
- Python Pandas - Computing Dummy Variables
- Python Pandas - Categorical Data
- Python Pandas - Categorical Data
- Python Pandas - Ordering & Sorting Categorical Data
- Python Pandas - Comparing Categorical Data
- Python Pandas - Handling Missing Data
- Python Pandas - Missing Data
- Python Pandas - Filling Missing Data
- Python Pandas - Interpolation of Missing Values
- Python Pandas - Dropping Missing Data
- Python Pandas - Calculations with Missing Data
- Python Pandas - Handling Duplicates
- Python Pandas - Duplicated Data
- Python Pandas - Counting & Retrieving Unique Elements
- Python Pandas - Duplicated Labels
- Python Pandas - Grouping & Aggregation
- Python Pandas - GroupBy
- Python Pandas - Time-series Data
- Python Pandas - Date Functionality
- Python Pandas - Timedelta
- Python Pandas - Sparse Data Structures
- Python Pandas - Sparse Data
- Python Pandas - Visualization
- Python Pandas - Visualization
- Python Pandas - Additional Concepts
- Python Pandas - Caveats & Gotchas
Python Pandas - Attributes of a Series Object
Pandas Series is one of the primary data structures, provides a convenient way to handle and manipulate one-dimensional data. It looks similar to a single column in a spreadsheet or a single column in a database table.
Series object attributes are tools that help you get information about series object and its data. Pandas provides multiple attributes to understand and manipulate the data in a Series. In this tutorial you will learn about Pandas Series attributes.
Data Information
These attributes provide information about the data in the Series −
| Sr.No. | Methods & Description |
|---|---|
| 1 |
dtype Returns the data type of the underlying data. |
| 2 |
dtypes Returns the data type of the underlying data. |
| 3 |
nbytes Returns the number of bytes in the underlying data. |
| 4 |
ndim Returns the number of dimensions of the underlying data, which is always 1 for a Series. |
| 5 |
shape Returns a tuple representing the shape of the underlying data. |
| 6 |
size Returns the number of elements in the underlying data. |
| 7 |
values Returns the Series as an ndarray or ndarray-like object depending on the data type. |
Data Access
These attributes help in accessing data within the Series −
| Sr.No. | Methods & Description |
|---|---|
| 1 |
at Accesses a single value using a row/column label pair. |
| 2 |
iat Accesses a single value by integer position. |
| 3 |
loc Accesses a group of rows and columns by labels or a boolean array. |
Data Properties
These attributes provide properties and metadata about the Series −
| Sr.No. | Methods & Description |
|---|---|
| 1 |
empty Indicates whether the Series or DataFrame is empty. |
| 2 |
flags Gets the properties associated with the Pandas object. |
| 3 |
hasnans Returns True if there are any NaN values. |
| 4 |
index Returns the index (axis labels) of the Series. |
| 5 |
is_monotonic_decreasing Returns True if the values are monotonically decreasing. |
| 6 |
is_monotonic_increasing Returns True if the values are monotonically increasing. |
| 7 |
is_unique Returns True if all values are unique. |
| 8 |
name Returns the name of the Series. |
Other
This category includes attributes that perform a variety of other operations −
| Sr.No. | Methods & Description |
|---|---|
| 1 |
array Provides the underlying data of the Series as an ExtensionArray. |
| 2 |
attrs Returns a dictionary of global attributes of the dataset. |
| 3 |
axes Returns a list of the row axis labels. |
| 4 |
T Returns the transpose of the Series, which is essentially the same as the original Series. |