Pandas: Python Data Analysis Library For Machine Learning

Pandas is a software library written for the Python programming language for data manipulation and analysis. It is used for working with relational or labeled data easily and intuitively.

Key Features of Pandas

Here are the key features:

  1. Pandas library is a fast and efficient DataFrame object with default and customized indexing.
  2. Pandas library helps load the data into in-memory objects from different file formats.
  3. It has functions that deal with Data alignment and integrate the handling of missing data.
  4. Using Pandas, we can reshape and pivot the data sets.
  5. It has Label-based slicing, indexing, and subsetting of more massive datasets.
  6. Pandas can insert or delete Columns from the data structure.
  7. We can use Pandas for data aggregation and transformations.
  8. It gives the High-performance merging and joining of data.
  9. Time Series functionality.

Install Pandas on Mac

Follow this step if you have not installed them previously on your machine.

You can install via PyPI using the following command.

python3 -m pip install --upgrade pandas

To upgrade the version, you can use the following command.

python3 -m pip install --upgrade pandas==0.23.0

Make sure; you install it with proper permission, such as using sudo if you are on Linux or Mac.

Standard Python distribution does not come with the Pandas module. An alternative way is to install NumPy using a popular Python package installer, pip.

If you have installed a software package like Anacondathen Pandas have already been installed.

Pandas Data Structure

Pandas deal with the following two data structures.

  1. DataFrame
  2. Series
The panel is deprecated and will be removed in a future version.
The recommended way to represent these types of 3-dimensional data is via the Panel via a MultiIndex on a DataFrame.to_frame() method.


DataFrames allow you to store and manipulate the tabular data in rows of observations and columns of variables. 

DataFrames in Python are very similar as they come with the Pandas library, and they are defined as two-dimensional labeled data structures with columns of potentially different types.

Features of DataFrame

  1. Potentially columns are of different types
  2. Size – Mutable
  3. Labeled axes (rows and columns)
  4. Can Perform Arithmetic operations on rows and columns

A pandas DataFrame can be created using the following constructor.

pandas.DataFrame( data, index, columns, dtype, copy)


import pandas as pd
import numpy as np

data = [['Krunal', 21],['Rushikesh', 22],['Hardik',30]]
df = pd.DataFrame(data, columns=['Name', 'Enrollment Number'])


DataFrames Data Structure in Pandas

In the above example, we have taken the data: Name and Enrollment Number. For that data, we have used the NumPy library.

Then, we passed that data to the DataFrame and created a tabular data structure.


Series is the one-dimensional labeled array capable of holding data of any data type like integer, string, float, Python objects, etc. The axis labels are collectively called indices. 

Labels need not be unique but must be a hashable type. The object supports integer and label-based indexing and provides various methods for performing operations involving the index.

The syntax of Series in Pandas is the following.

pandas.Series( data, index, dtype, copy)


import pandas as pd

data = [1, 2, 3, 4, 5, 6, 7]
df = pd.Series(data)

Run the file and see the output.

Series in Pandas

That’s it.

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