30 Dec Pandas Series – Attributes and Methods

Posted at 17:36h in Pandas by

The Series in Pandas is a one-dimensional array that uses the Series() method to create a Series, but it also uses different built-in attributes and methods for basic functionalities. In this lesson, let us see such attributes and methods in Python Pandas for Series:

  • dtype: Return the dtype.
  • ndim: Return the Number of dimensions
  • size: Return the number of elements.
  • name: Return the name of the Series.
  • hasnans: Returns True if NaNs are in the series.
  • index: The index of the series
  • head(): Return the first n rows.
  • tail(): Return the last n rows.
  • info(): Display the Summary of the series

Before moving further, we’ve prepared a video tutorial to understand the Attributes and Methods of Pandas Series:

Let us understand the Pandas Series attributes and methods one by one:

dtype

The pandas.series.dtype is used to return the datatype of the Series.

Let us now see an example to implement the type attribute in Python Pandas:

import pandas as pd

# Data to be stored in the Pandas Series
data = [10, 20, 40, 80, 100]

# Create a Series using the Series() method
s = pd.Series(data)

# Display the Series
print("Series: \n", s)

# Datatype
print("\nSeries Datatype: ", s.dtype)

Output

Series: 
0     10
1     20
2     40
3     80
4    100
dtype: int64

Series Datatype:  int64

ndim

The pandas.series.ndim is used to return the number of dimensions of the Series.

Let us now see an example to implement the ndim attribute in Python Pandas

import pandas as pd

# Data to be stored in the Pandas Series
data = [10, 20, 40, 80, 100]

# Create a Series using the Series() method
s = pd.Series(data)

# Display the Series
print("Series: \n", s)

# Dimensions
print("\nSeries Dimensions: ", s.ndim)

Output

Series: 
0     10
1     20
2     40
3     80
4    100
dtype: int64

Series Dimensions:  1

size

The pandas.series.size is used to return the number of elements in the Pandas Series.

Let us now see an example to implement the size attribute in Python Pandas:

import pandas as pd

# Data to be stored in the Pandas Series
data = [10, 20, 40, 80, 100]

# Create a Series using the Series() method
s = pd.Series(data)

# Display the Series
print("Series: \n", s)

# Return the number of elements in the Series
print("\nSeries Size: ", s.size)

Output

Series: 
0     10
1     20
2     40
3     80
4    100
dtype: int64

Series Size:  5

name

The pandas.series.name is used to return the name of the Series in Pandas.

Let us now see an example to implement the name attribute in Python Pandas:

import pandas as pd

# Data to be stored in the Pandas Series
data = [10, 20, 40, 80, 100]

# Create a Series using the Series() method
# We have set the Series name using the name attribute
s = pd.Series(data, name ="MyNumberSeries")

# Display the Series
print("Series: \n", s)

# Return the name of the Series
print("\nSeries Name: ", s.name)

Output

Series: 
0     10
1     20
2     40
3     80
4    100
Name: MyNumberSeries, dtype: int64

Series Name:  MyNumberSeries

hasnans

The pandas.series.hasnans attribute returns True if NaNs are in the Pandas Series.

Let us now see an example to implement the hasnans attribute in Python Pandas:

import pandas as pd
import numpy as np

# Data to be stored in the Pandas Series
data = [10, 20, 40, 80, 100, np.NaN]

# Create a Series using the Series() method
s = pd.Series(data)

# Display the Series
print("Series: \n", s)

# Check whether the Series has NaNs
print("\nDoes the Series has NaN? ", s.hasnans)

Output

Series: 
0     10.0
1     20.0
2     40.0
3     80.0
4    100.0
5      NaN
dtype: float64

Does the Series has NaN?  True

index

The pandas.series.index attribute is used to display the index of the Pandas Series.

Let us now see an example to implement the index attribute in Python Pandas:

import pandas as pd

# Data to be stored in the Pandas Series
data = [10, 20, 40, 80, 100]

# Create a Series using the Series() method
s = pd.Series(data, index=["RowA", "RowB", "RowC", "RowD", "RowE"])

# Display the Series
print("Series (with custom index labels): \n", s)

# Return the index of the Series
print("\nSeries Index: ", s.index)

Output

Series (with custom index labels): 
RowA     10
RowB     20
RowC     40
RowD     80
RowE    100
dtype: int64

Series Index:  Index(['RowA', 'RowB', 'RowC', 'RowD', 'RowE'], dtype='object')

head()

The pandas.series.head() method is used to return the first n rows of the Pandas Series.

Let us now see an example to implement the head() method in Python Pandas:

import pandas as pd

# Data to be stored in the Pandas Series
data = [10, 20, 40, 80, 100, 200, 300, 500]

# Create a Series using the Series() method
s = pd.Series(data, index=["RowA", "RowB", "RowC", "RowD", "RowE", "RowF", "RowG", "RowH"])

# Display the Series
print("Series (with custom index labels): \n", s)

# Return the first n rows. 
# The 5 is default for n
print("\nThe first 5 rows of the series:\n", s.head())

Output

Series (with custom index labels): 
RowA     10
RowB     20
RowC     40
RowD     80
RowE    100
RowF    200
RowG    300
RowH    500
dtype: int64

The first 5 rows of the series:
RowA     10
RowB     20
RowC     40
RowD     80
RowE    100
dtype: int64

tail()

The pandas.series.tail() method is used to return the last n rows of the Pandas Series.

Let us now see an example to implement the tail() method in Python Pandas:

import pandas as pd

# Data to be stored in the Pandas Series
data = [10, 20, 40, 80, 100, 200, 300, 500]

# Create a Series using the Series() method
s = pd.Series(data, index=["RowA", "RowB", "RowC", "RowD", "RowE", "RowF", "RowG", "RowH"])

# Display the Series
print("Series (with custom index labels): \n", s)

# Return the last n rows.
# The 5 is default for n
print("\nThe last 5 rows of the series:\n", s.tail())

Output

Series (with custom index labels): 
RowA     10
RowB     20
RowC     40
RowD     80
RowE    100
RowF    200
RowG    300
RowH    500
dtype: int64

The last 5 rows of the series:
RowD     80
RowE    100
RowF    200
RowG    300
RowH    500
dtype: int64

info()

The pandas.series.info() method is used to display the Summary of the Pandas Series.

Let us now see an example to implement the info() method in Python Pandas:

import pandas as pd

# Data to be stored in the Pandas Series
data = [10, 20, 40, 80, 100, 200, 300, 500]

# Create a Series using the Series() method
s = pd.Series(data, index=["RowA", "RowB", "RowC", "RowD", "RowE", "RowF", "RowG", "RowH"])

# Display the Series
print("Series (with custom index labels): \n", s)

# Return the summary of the series
print("\nSeries Summary:\n", s.info())

Output

Series (with custom index labels): 
RowA     10
RowB     20
RowC     40
RowD     80
RowE    100
RowF    200
RowG    300
RowH    500
dtype: int64
<class 'pandas.core.series.Series'>
Index: 8 entries, RowA to RowH
Series name: None
Non-Null Count  Dtype
--------------  -----
8 non-null      int64
dtypes: int64(1)
memory usage: 128.0+ bytes

In this lesson, we saw what functionalities in the form of attributes and methods are present in Python Pandas Series.

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