University:
University of California, San Diego
Course:
CSE 100/101 | Data Structures and Algorithms
Academic year:

2023
Views:

220

Pages:

3
Author:

GregoryB

Introduction to Python's Pandas library for reading and saving data Every industry, including business, science, and engineering, depends heavily on data analysis. It necessitates mining vast quantities of unprocessed data for insightful information. We must acquire and manipulate data in an accessible format in order to accomplish this. You can do it thanks to the potent pandas library for Python. This article will offer a thorough tutorial on using Python's pandas package to read and save data. Knowing the Fundamentals of Data Acquisition Data acquisition is the process of reading and writing data from numerous sources into a notebook. When reading data with Python's pandas package, format and file location are two crucial considerations. The format is the method of encoding the data. Typically, different encoding techniques can be identified by the file name's suffix, such as CSV, JSON, XLSX, HDF, etc. The path identifies the location of the data's storage, which could be on your computer or online over the internet. Data Reading with Pandas In this post, an example used automobile dataset that was retrieved from a website will be used. The information was presented as rows of data points with many characteristics separated by commas when the URL address was typed into a web browser. This implies that the data is in CSV, or comma-separated values, format. A pandas 'read_CSV'data frame can be easily read in from files with columns that are separated by commas using the pandas technique. There are only three easy steps involved in reading data in Pandas: - Bring in Pandas - Make the file path a variable - Use the 'read_CSV' data import procedure 'read_CSV', however, presumes that the data has a header. We must tell the data to not assign headers if 'read_CSV' does not have one, as is the case with our dataset of used cars, by setting 'header' to 'None'. Data Analysis Using Pandas It is crucial to check the data frame after reading the dataset to make sure everything went as planned. We can print the top 'n' rows of the data frame using the 'head' technique or the bottom 'n' rows using the 'tail' method to better understand the data. In our example, the dataset was properly read, but because we set header to "None," pandas automatically set the column "header" as a list of integers. We can give the columns names that make sense so that the data is simpler to work with. In our example, we added the column names to the data frame by setting 'df.columns' equal to a list of headers after locating them in a different file online. Pandas data storage You might want to export your data frame to a new CSV file at some time after working with it. The file path and file name you wish to write to can be specified using the 'to_CSV' method to do this. For instance, you can use the syntax "df.to_CSV" to save the data frame "df" as "automobile.CSV" to your PC.