A Pandas Series is a one-dimensional labeled array capable of holding any data type. It is similar to a column in a DataFrame and can be created from a list, dictionary, or scalar value.

Example 1: Creating a Series from a List

A Series can be easily created from a Python list using the Pandas library.


import pandas as pd

data = [1, 2, 3, 4, 5]
series = pd.Series(data)
print(series)

Console Output:

0 1 1 2 2 3 3 4 4 5 dtype: int64

Example 2: Creating a Series from a Dictionary

You can also create a Series from a dictionary, where the keys become the index.


import pandas as pd

data = {'a': 10, 'b': 20, 'c': 30}
series = pd.Series(data)
print(series)

Console Output:

a 10 b 20 c 30 dtype: int64

Example 3: Accessing Data from a Series

Accessing data from a Series is similar to accessing data from a list or dictionary.


import pandas as pd

data = [10, 20, 30, 40, 50]
series = pd.Series(data)
print(series[2])  # Access by index

Console Output:

Example 4: Performing Operations on Series

Pandas Series supports vectorized operations, which means you can perform operations on the entire Series.


import pandas as pd

data = [1, 2, 3, 4, 5]
series = pd.Series(data)
print(series * 2)  # Multiply each element by 2

Console Output:

0 2 1 4 2 6 3 8 4 10 dtype: int64

Example 5: Handling Missing Data in Series

Pandas Series can handle missing data using NaN (Not a Number) values.


import pandas as pd
import numpy as np

data = [1, 2, np.nan, 4, 5]
series = pd.Series(data)
print(series)

Console Output:

0 1.0 1 2.0 2 NaN 3 4.0 4 5.0 dtype: float64