In Pandas, the Series object provides several attributes and methods. We can access them directly to get the information about the Series object.
Table Of Contents
- Name of the Pandas Series object
- Get all values of the Series as a list
- Get count of number of elements in Series
- Check if Series is empty or not
- Get first N elements of Pandas Series
- Get last N elements of Pandas Series
- Get the count of non NaN values
Name of the Series object
A Series object contains the labeled values and it is like a single column of Excel file. Similar to column in Excel, it can also has a name associated with it. Let’s create a Pandas series object with name,
import pandas as pd # Create a Series object from a list users = pd.Series( ['Mark', 'Rita', 'Vicki', 'Justin', 'John', 'Michal'], index = ['a', 'b', 'c', 'd', 'e', 'f'], name = "Students") # Display the Pandas Series print(users)
Output:
a Mark b Rita c Vicki d Justin e John f Michal Name: Students, dtype: object
In the last line of the output, just before the data type, it printed the name of the Series too. We can access the name of the Series object using the name property of the Series. For example,
# Display the name attribute of the Series Object print(users.name)
Output:
Students
We can also change the name of the existing Series object using name property. For example,
users.name = 'Users' # Display the Pandas Series print(users)
Output:
a Mark b Rita c Vicki d Justin e John f Michal Name: Users, dtype: object
We changed the name of the Series object to ‘Users’ from ‘Students’.
Get all values of the Series as a list
A Series is a one dimensional labeled array. It means, each value has a label associated with it. But what if we want to get all the values only? In Pandas, the Series provides a property values, it returns a NumPy array containing all the values of Series. For example,
import pandas as pd # Create a Series object from a list users = pd.Series( ['Mark', 'Rita', 'Vicki', 'Justin', 'John', 'Michal'], index = ['a', 'b', 'c', 'd', 'e', 'f'], name = "Students") # Display the Pandas Series print(users) # Display All values of the Series print(users.values) # Display type of the object returned by values attribute print(type(users.values))
Output:
a Mark b Rita c Vicki d Justin e John f Michal Name: Users, dtype: object ['Mark' 'Rita' 'Vicki' 'Justin' 'John' 'Michal'] <class 'numpy.ndarray'>
We can pass it to the list() function to get all values of a Series object as a list. For example,
print(list(users.values))
Output:
['Mark', 'Rita', 'Vicki', 'Justin', 'John', 'Michal']
Get count of number of elements in Series
In Pandas, the Series object provides a property size, which returns the count of number of elements on the Series. For example,
# Get the count of elements in Series print(users.size)
Output:
6
It returned the size of the Pandas series.
Check if Series is empty or not
In Pandas, the Series object provides a property empty, which returns True if Series is empty, otherwise returns False. Let’s see an example for this,
import pandas as pd # Create a Series object from a list users = pd.Series( ['Mark', 'Rita', 'Vicki', 'Justin', 'John', 'Michal'], index = ['a', 'b', 'c', 'd', 'e', 'f'], name = "Students") # check if series is empty or not print(users.empty)
Output:
False
As series was not empty, therefore it returned False. Let’s create an empty Series object and check if it is empty or not. For example,
import pandas as pd # Create Empty Series users = pd.Series(dtype=int) # check if series is empty or not print(users.empty)
Output:
True
Get first N elements of Pandas Series
In Pandas, the Series object provides a function head(N). It returns the first n values of the Series object. For example,
import pandas as pd # Create a Series object from a list users = pd.Series( ['Mark', 'Rita', 'Vicki', 'Justin', 'John', 'Michal'], index = ['a', 'b', 'c', 'd', 'e', 'f'], name = "Students") # Get first 3 elements of series subset = users.head(3) # Display the Subset of Series print(subset)
Output:
a Mark b Rita c Vicki Name: Students, dtype: object
It returned first three values of the Series object. If n is not provided then by default it returns the first 5 values from the Series object.
Get last N elements of Pandas Series
In Pandas, the Series object provides a function tail(N). It returns the last n values of the Series object. For example,
import pandas as pd # Create a Series object from a list users = pd.Series( ['Mark', 'Rita', 'Vicki', 'Justin', 'John', 'Michal'], index = ['a', 'b', 'c', 'd', 'e', 'f'], name = "Students") # Get last 3 elements of series subset = users.tail(3) # Display the Subset of Series print(subset)
Output:
d Justin e John f Michal Name: Students, dtype: object
It returned last three values of the Series object. If n is not provided then by default it returns the last 5 values from the Series object.
Get the count of non NaN values
In Pandas, the Series object provides a function count(). It returns the count of non NaN values in the Series object. For example,
import pandas as pd import numpy as np # Create a Series object from a list users = pd.Series(['Mark', np.NaN, 'Vicki', 'Justin', np.NaN, 'Michal']) # Get count of non NaN values in Pandas Series count = users.count() print(count)
Output:
4
Therefore in total 6 values in the Series object but non nan values are only 4.
Summary:
We learned about basic properties and methods of the Pandas Series object.
Pandas Tutorials -Learn Data Analysis with Python
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Pandas Tutorial Part #1 - Introduction to Data Analysis with Python
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Pandas Tutorial Part #2 - Basics of Pandas Series
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Pandas Tutorial Part #3 - Get & Set Series values
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Pandas Tutorial Part #4 - Attributes & methods of Pandas Series
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Pandas Tutorial Part #5 - Add or Remove Pandas Series elements
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Pandas Tutorial Part #6 - Introduction to DataFrame
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Pandas Tutorial Part #7 - DataFrame.loc[] - Select Rows / Columns by Indexing
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Pandas Tutorial Part #8 - DataFrame.iloc[] - Select Rows / Columns by Label Names
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Pandas Tutorial Part #9 - Filter DataFrame Rows
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Pandas Tutorial Part #10 - Add/Remove DataFrame Rows & Columns
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Pandas Tutorial Part #11 - DataFrame attributes & methods
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Pandas Tutorial Part #12 - Handling Missing Data or NaN values
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Pandas Tutorial Part #13 - Iterate over Rows & Columns of DataFrame
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Pandas Tutorial Part #14 - Sorting DataFrame by Rows or Columns
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Pandas Tutorial Part #15 - Merging or Concatenating DataFrames
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Pandas Tutorial Part #16 - DataFrame GroupBy explained with examples
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