Basic Data Structures in Pandas

Basic Data Structures in Pandas –

Pandas provides two primary data structures for handling and manipulating data: Series and DataFrame.

1. Series:
   • Series is a one-dimensional labelled array capable of holding any data type.
   • Essentially a column in an Excel spreadsheet or a single column in a database table.
   • It has an associated array of data labels, called an index.
   • Can be created from a list, array, or dictionary.

   Example:

import pandas as pd

data = [1, 3, 5, np.nan, 6, 8]
s = pd.Series(data, name='MySeries')

Output:

0    1.0
1    3.0
2    5.0
3    NaN
4    6.0
5    8.0
Name: MySeries, dtype: float64

2. DataFrame:

• Data Frame is a two-dimensional labeled data structure with columns that can be of different data types.
• Similar to a spreadsheet or SQL table, where each column is a Series.
• Can be created from dictionaries, lists, NumPy arrays, or other DataFrames.

Example:

import pandas as pd

data = {
    'Name': ['Alice', 'Bob', 'Charlie'],
    'Age': [25, 30, 35],
    'City': ['New York', 'San Francisco', 'Los Angeles']
}

df = pd.DataFrame(data)

Output:

   Name  Age           City
0  Alice   25       New York
1    Bob   30  San Francisco
2 Charlie   35    Los Angeles

Index:

• An immutable array that labels the axes of a DataFrame or Series.
• The default index for Series is integer-based, while for DataFrame, it is a sequence of integers.
• Can be explicitly defined or automatically generated.

Example:

import pandas as pd

data = [1, 3, 5, np.nan, 6, 8]
s = pd.Series(data, index=['a', 'b', 'c', 'd', 'e', 'f'])

Output:

a    1.0
b    3.0
c    5.0
d    NaN
e    6.0
f    8.0
dtype: float64

These data structures form the foundation of Pandas, enabling efficient and flexible manipulation and analysis of structured data. Series and DataFrame provide a high-level interface for handling various data types and performing operations like indexing, selection, grouping, merging, and more.