What is Python Pandas? Pandas series, Uses & Tutorial

Last updated on July 25th, 2024 at 05:14 am

Pandas is a two-dimensional data frame or structure within the open-source Python library. Elementary components of Pandas are data, rows and columns. Practically, Pandas data frame must be created from available storage like Excel, CSV file or SQL database. 

Python programming language uses Pandas as a software library. The main function of Pandas is the analysis and manipulation of data. Users value Pandas for high-end performance when back-end codes are written in C or Python.

Python Pandas tutorial explicitly teaches how to use Python Pandas. Python Pandas tutorial contains Pandas practice questions to help prospective candidates.

Python Pandas tutorial also contains several “try then yourself” sections and some “frequently asked questions” at the end of each session.  

What is Python Pandas?

Python has evolved in 1991. It rapidly became the most dependable programming language for Data Analysts, Web Designers and machine learning processes. Python is a simple, versatile and easy-to-use language.

Pandas use in Python was introduced by Wes McKinney in 2008. This DataFrame was developed over two key Python libraries – NumPy for mathematical operations and Matplotlib for data visualisation. 

The conclusion becomes easier with Pandas since it cleans data and makes it relevant for analysis. The use of Pandas has become widespread due to the ease of Pandas data structure.

Pandas is considered a flexible and powerful quantitative tool for data manipulation, cleaning, segregation and analysis. Pandas Program in Python may be understood by going through its uses. Pandas use in Python are as follows –

• Pandas data structure is an important use of Pandas. Series and data frames are used to manipulate big data.
• Correlation between two or more columns.
• Detect average, maximum or minimum values.
• Interpolation, cleaning and filtration of data.
• Identification of missing data and handling of non-floating point data.
• Data can be aligned to a set of labels.
• Merges and joins data with system-driven intuition.
• Data inspection and analysis.
• Variables in time series functionality.
• Categorised or graded labelling of axes.
• Pandas has a statistical model functionality.
• Split-apply-combine operations on data sets can be easily performed.
• Statistical analysis in SciPy and machine learning algorithms in Scikit-learn.
• Pandas use in Python makes the system robust, smooth and practical. 

Read more about Python training to learn how Python can be beneficial in reducing the skill gap in the modern workforce. 

Python Pandas tutorial

Learning Pandas has become a key objective for professionals across Engineers, Data Analysts and Scientists. Python Pandas tutorial teaches an aspiring professional all the minute details, one must learn regarding Pandas. Python Pandas tutorial covers stepwise instructions regarding how to use Python Pandas. 

Python Pandas tutorial also encourages students to solve Pandas practice questions to become more confident and conversant in the use of Pandas. Some aspirants also enrol in a data science course which helps them to learn about what is Pandas in Python. 

The topics on how to use Python Pandas as given in the Python Pandas tutorial are as follows:

1. Installation of Pandas 

The process is to install ActivePython as guided in the Python Pandas tutorial.

2. Create/slice a DataFrame in Pandas 

A DataFrame in Pandas is preparing SQL Table or spreadsheet type two dimensional labelled data structure, in the form of columns and rows. 

3. Grouping data in Python Pandas

The grouping function allows parameter-based data splitting into either rows or columns. The steps of this function are stated in the Python Pandas tutorial.

4. Access a row and column in a DataFrame 

A student can use the loc and iloc functions to access both rows and columns in a DataFrame. Practical illustrations with CSV files are available in the Python Pandas tutorial.

5. Delete a row and column in Python 

A student may use the drop function to delete columns and rows in the Python Pandas DataFrame.  

6. Apply function 

This function allows effective manipulation of columns and rows in a DataFrame. A proper guide to this function is available in the Python Pandas tutorial.

7. Import a data set in Python 

A DataFrame object must be created first to import data from a CSV file. It is a good practice to save the file in the same directory as that of the Python code. Python Pandas tutorial help us learn the method in detail.

8. Indexing in Pandas 

The process of indexing a Pandas DataFrame is essentially the identification of subsets of data, like rows, columns or individual cells, from a data frame. The steps are given in the Python Pandas tutorial.

9. Access to an element in the data frame 

An element i.e. a row and a column or multiple rows or columns can be accessed using either iat or at functions. Detailed demonstrations with sample examples are available in the Python Pandas tutorial.

10. Reading CSV and JASON 

The Python Pandas tutorial also covers how to read and understand CSV and JASON files.

11. How to analyse data 

There are quite a few steps which a student must follow to analyse a data set. When the objectives are clear, the data analysis workflow needs to be understood. Data must be obtained and read through the CSV files. 

Data should be cleansed with Python and relevant columns need to be created. Then the data analysis is performed by using Python Pandas. The methods on how to analyse data are given elaborately in the Python Pandas tutorial. 

12. Framing data with Pandas

Python Pandas deal with linear series of data expressed in numbers. However, real-world data comes with other attributes also associated with the numbers. This two-dimensional data structure is known as DataFrame. 

Python Pandas tutorial has enough inputs regarding the understanding of DataFrame.

13. Cleaning data and moving duplicates 

Data cleaning is also known as data cleansing or data scrubbing. It is a method wherein incorrect, incomplete, erroneous or duplicate data in a data set are handled to suit analysis purposes. Data is updated, removed or changed as per requirement. A detailed explanation of the steps is given in the Python Pandas Tutorial. 

14. Cleaning machine learning data sets using Pandas 

A practical data set has all useful information. Columns with irrelevant information should be dropped. Those columns that have data not aligned with the final goal need to be deleted. 

Those columns that have many empty cells also deserve removal. Columns containing non-comparable or non-compatible values also need to be deleted. A proper guide to this step is given in the Python Pandas Tutorial.

15. Correlation and plotting of data using Pandas

First, the right data set must be collected for the correlation matrix. Then, a data frame must be created. Next, correlation can be modelled with Python Pandas, followed by plotting data for graphical representation. A proper guide to this function is available in the Python Pandas tutorial.

Pandas tutorial in Python gives the prospective candidate a detailed insight into all the necessary steps that a prospective candidate needs to know. It also provides information on how to run the Pandas program in Python. Pandas tutorial in Python covers important topics like the Pandas series and operations. 

Pandas tutorial in Python offers both textbook and video formats of learning. Python Pandas tutorial also renders a detailed knowledge of Pandas data structure.

Pandas series

A Pandas series is one of the many data structures. It is a one-dimensional array holding data of the following types – integer, string, float, Python objects etc. Collectively, axes labels are known as indexes.

Series may be created using inputs like an array, scalar value or constant. An empty series may also be created. A user needs to get accustomed to the Pandas program in Python to delve into the series.  

Creation of a Pandas series is done using the following constructors – data, index, type and copy. Data can be any list, dictionary or scalar value. The index should be unique. Dtype refers to series data types while copy is used for copying data.

Pandas series needs to be studied since it is the basics of a DataFrame. DataFrame is a two-dimensional labelled data structure. It consists of rows and columns like a spreadsheet. Python Pandas tutorial coaches a student with both theoretical and practical knowledge of the Pandas series.    

If predetermined indexes are available, they may be utilised to access Pandas series objects. Indexing or subset selection in Pandas is the identification of certain data from a Series object.

Interconversion of series into Data Frame and vice versa is possible. In specific functions, merging of Data Frame with series is also performed. The study of the Pandas series covers a lot of Pandas series attributes and Pandas series methods to perform a variety of functions. 

It is recommended that you always solve a large variety of Pandas practice questions. This will help you to understand the Pandas series and what is Pandas in Python. A solid Python Pandas tutorial will have a good number of exercises and solutions to clear the reader’s doubts. 

Operations

The most important function of data science is to prepare the data for model building, exploration and visualisation. Pandas is an exceptionally useful package in Python with several in-built functions capable of arithmetic, rational and logical operations. Those special symbols that carry out operations on values and variables are known as operators.

There are seven frequently used arithmetic operators and operands in Python Pandas. They are addition, subtraction, division, multiplication, modulus, exponential and floor division.

The rational operators and operands compare a value with another which is greater than, lesser than or equal to it.

The logical operators and operands are generally applied in conditional statements, like true or false. Here, a couple of situations should be satisfied to fulfil an equation. 

Python Pandas tutorial helps a student to be a master of these operators. Practical knowledge of operations will help a student to understand what is Pandas in Python. There are quite a few data operations for the data frame, as follows –

1. Row and column 

Selection of any value can be done by selecting the name of the row and column. Thus the representation is one dimensional and may be considered as a series. 

2. Filter data 

Data may be filtered by using some special data frame functions.

3. Null value functions 

Null values (NaN), as the name suggests, do not contain data for the given item. In Python Pandas, users have the benefit of applying several unique functions for identifying, removing and/or replacing NaN in the data frame. 

4. String operation 

The string function in Pandas helps to deal with missing or NaN values in a data frame.

5. Count values 

This operation supports locating the frequency of items.

6. Plot 

Pandas deploy the plot function to draw the graph of the given data. The reverse 

the function of tabulation can be also performed from a given graph.

Thus, the operation helps users to rationalise data in the first phase and convert the inputs into visual graphs, histograms, pie charts, etc. for easy understanding. All the above-mentioned topics are well covered in the Python Pandas tutorial. 

Wrapping Up

Professional engagements as Data Scientists, Data Analysts and Artificial Intelligence Experts are lucrative in terms of future growth and compensation. A Python Pandas tutorial from a reputed institute will strengthen the learning foundation of the aspirant. 

The Postgraduate Program In Data Science And Analytics by Imarticus will enable prospective candidates to have massive growth right at the beginning of their careers. The duration of this course is 6 months. 

Visit the official website of Imarticus for more details.

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