Pandas is a Python library used for working with structured and time series data. It provides data structures like Series (1D array) and DataFrame (2D tabular structure) that are built on NumPy arrays for fast and efficient data manipulation. Key features of Pandas include fast DataFrame objects with indexing, loading data from different formats, handling missing data, reshaping/pivoting datasets, slicing/subsetting large datasets, and merging/joining data. The document provides an overview of Pandas, why it is useful, its main data structures (Series and DataFrame), and how to create and use them.

1. Machine Learning
Pandas
-Jyoti Shukla

2. Today’s Agenda
• What is Pandas?
• Why Pandas?
• How to use Pandas?
• Series
• Practical on Series
• DataFrame
• Practical on DataFrame

3. What is Pandas?

4. What is Pandas?
• Panel Data System
• Similar to NumPy, Pandas is also the most widely used python libraries in
machine learning.
• It is a Python package providing fast, flexible, and expressive data
structures designed to make working with structured (tabular,
multidimensional, potentially heterogeneous) and time series data.
• Built on top of Numpy with it’s high performance array-computing
features.

5. Why Pandas?
• Flexible data manipulation capabilities of spreadsheets and relational
databases.
• Sophisticated indexing functionality
• Slice , dice , perform aggregations , select subsets of data.

6. Key Features of Pandas
• Fast and efficient DataFrame object with default and customized indexing.
• Tools for loading data into in-memory data objects from different file
formats.
• Data alignment and integrated handling of missing data.
• Reshaping and pivoting of date sets.
• Label-based slicing, indexing and subsetting of large data sets.
• Columns from a data structure can be deleted or inserted.
• Group by data for aggregation and transformations.
• High performance merging and joining of data.
• Time Series functionality.

8. Pandas deals with the following three data structures −
• Series
• DataFrame
• Panel
These data structures are built on top of Numpy array,
which means they are fast.

9. • Module Required :
import pandas as pd
• Usage is
pd.Series (parameter)
pd.DataFrame (parameter)

10. Dimension & Description
Data Structure Dimvc?,/ensions Description
Series 1 1D labeled homogeneous
array.
Data Frames 2 General 2D labeled, size-
mutable tabular structure
with potentially
heterogeneously typed
columns.
Panel 3 General 3D labeled, size-
mutable

11. Series
• One-dimensional array
• Homogeneous data
• Values of Data Mutable
• Syntax-
pandas.Series( data, index, dtype, copy)
• A series can be created using various inputs like −
Array
Dict
Scalar value or constant

12. DataFrame
• 2 – dimensional tabular data
• Potentially columns are of different types
• Size – Mutable
• Labeled axes (rows and columns)
• Can Perform Arithmetic operations on rows and columns
• A pandas DataFrame can be created using various inputs like −
Lists,dict,Series,Numpy ndarrays
• Syntax-
pandas.DataFrame( data, index, columns, dtype, copy)

14. DataFrame
• Two-dimensional array
import numpy as np
import pandas as pd
arr = np.array([1,2,3,4],[5,6,7,8])
data = pd.DataFrame(arr)

15. Lets explore… practically!!!