Types of Attributes and Methods of Series
A Series in the Pandas library is a one-dimensional labeled array, essential for dealing with data in Python. Let's look into some key functions of a Pandas Series: axes, dtype, empty, ndim, size, values, head, and tail, along with examples and their expected outputs.
Basic Series Functionality
1. axes: This returns the list of the labels of the series.
# Import Pandas
import pandas as pd
# Create a series
s = pd.Series([1,2,3,4,5], index = ['a','b','c','d','e'])
# Show the axes
print(s.axes)
The output will be: [Index(['a', 'b', 'c', 'd', 'e'], dtype='object')]
2. dtype: This returns the data type of the object in the Series.
# Show the data type
print(s.dtype)
The output will be: int64
3. empty: This checks if the series is empty. It returns True if the series is empty, and False otherwise.
# Check if the series is empty
print(s.empty)
The output will be: False
4. ndim: This returns the number of dimensions of the underlying data. For a Series, this is always 1.
# Show the number of dimensions
print(s.ndim)
The output will be: 1
5. size: This returns the number of elements in the underlying data.
# Show the size
print(s.size)
The output will be: 5
6. values: This returns the Series as ndarray or ndarray-like depending on the dtype.
# Show the values
print(s.values)
The output will be: [1 2 3 4 5]
7. head(n): This returns the first 'n' rows of the series. If 'n' is not specified, it returns the first 5 rows.
# Show the first 3 rows
print(s.head(3))
# output
# a 1
# b 2
# c 3
# dtype: int64
8. tail(n): This returns the last 'n' rows of the series. If 'n' is not specified, it returns the last 5 rows.
# Show the last 2 rows
print(s.tail(2))
# output
# d 4
# e 5
# dtype: int64
These are some of the basic functionalities provided by the Series object in Pandas. By mastering these, you'll have a strong foundation for manipulating and analyzing data efficiently in Python using Pandas.