How to Perform Data Manipulation Using Pandas?

In any data science project lifecycle, data manipulation is the process of formatting or changing data and making it easier to read, write, and organize. This is an important step in preparing data for analysis.

Every data science model, machine learning model, or AI model requires a dataset to train and operate. However, the data the data science professionals fetch are often messy and not fit for analysis or any other data science processes.

Those datasets can contain incomplete or missing values, incorrect structure, and may have discrepancies like null values in several of its rows and columns. It can even consist of several unnecessary rows and columns that aren’t required for the operation. So, data manipulation is an essential step in the data science project lifecycle that helps with data cleaning and making it suitable for data analysis processes.

Let us explore a bit more and understand how data manipulation is done using Python in data science workflows.

Data Manipulation with Pandas

As mentioned above, data manipulation refers to the process of cleaning data and making it suitable for analysis. Data manipulation makes reading and understanding data easier, and Python is one of the best tools for data analysis.

Pandas is a very powerful data analysis library that simplifies this process by providing all the required features to import, clean, analyze, format, and export data. Most importantly, it is an open-source tool that makes data manipulation, data visualization, and analysis easier with huge community support and customization features.

Different Ways to Use Pandas for Data Manipulation

The first step in using Pandas for data manipulation is to install the Pandas library.

Install Pandas using – pip install pandas

Import Pandas using – import pandas as pd

Now, here are some ways to use Pandas.

• Reading and writing data

  There are various ways to import data. You can use CSV files and even Excel sheets for the same.

  

  After processing data, you can export the dataset to your desired file format using the following commands:

  

• Data Structures

  There are two primary data structures necessary for data manipulation tasks, i.e., Series and DataFrames.

  While a series refers to a one-dimensional labeled array, a DataFrame is a two-dimensional and heterogeneous tabular data structure that somewhat looks like Excel or a Spreadsheet and consists of rows and columns. So, it can perform all kinds of tasks like that in MS Excel.

  Here’s an example to create a Series

  

• Data Exploration

  Data exploration is an important step in the data science workflows that help professionals understand data well. Here’s an example.

  • head() function fetches the data in the first row of the DataFrame, whereas the tail () function returns the last few rows. The default entry size is 5.

    

  • describe() function helps with a statistical summary of the numerical columns. You can use the following command for the same:

    

  • info() functions return an accurate and concise summary of data types, non-null values, and how much memory is used.

    

  • df.shape command helps to get a tuple that contains the DataFrame’s dimensions. Example:

    

• Handling Missing Data

  One important aspect of your data science skills is how properly you address the problem of missing values, as this can help minimize errors and inaccurate conclusions in the data analysis process.

  Here is the command to identify missing values:

  

  To correct this and fill the missing values, the following command is used:

  

• Data Transformation

  Pandas also offer various functions to transform data. Here are a few commands to check out:

  • Applying functions

    

  • Mathematical Operations

    

  • String Operations

    

  • Renaming columns

    

  • Sorting

    

  Along with these, you can use Pandas for various other purposes, including data aggregation and grouping, merging or joining data, working on categorical data, data visualization, etc.

  It is recommended that you enroll in the best data science certifications and courses to fully master the data manipulation techniques with Pandas and enhance your data science skills for a fruitful career.

Conclusion

Pandas has proven to be a great tool for data science professionals that makes data manipulation easier for data analysis process. There are several essential functions in Pandas that make reading, writing, and formatting data easier. So, master core data science skills to properly use them for seamless data science workflows and boost your career.