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Step-by-Step Guide to Reading Different Files in Python

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 In the world of data science, automation, and general programming, working with files is unavoidable. Whether you’re dealing with CSV reports, JSON APIs, Excel sheets, or text logs, Python provides rich and easy-to-use libraries for reading different file formats. In this guide, we’ll explore how to read different files in Python , with code examples and best practices. 1. Reading Text Files ( .txt ) Text files are the simplest form of files. Python’s built-in open() function handles them effortlessly. Example: # Open and read a text file with open ( "sample.txt" , "r" ) as file: content = file.read() print (content) Explanation: "r" mode means read . with open() automatically closes the file when done. Best Practice: Always use with to handle files to avoid memory leaks. 2. Reading CSV Files ( .csv ) CSV files are widely used for storing tabular data. Python has a built-in csv module and a powerful pandas library. Using cs...
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AWS CLI PySpark a Beginner's Comprehensive Guide

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AWS (Amazon Web Services) and PySpark are separate technologies, but they can be used together for certain purposes. Let me provide you with a beginner's guide for both AWS and PySpark separately. AWS (Amazon Web Services): Amazon Web Services (AWS) is a cloud computing platform that offers a wide range of services for computing power, storage, databases, machine learning, analytics, and more. 1. Create an AWS Account: Go to the AWS homepage. Click on "Create an AWS Account" and follow the instructions. 2. Set Up AWS CLI: Install the AWS Command Line Interface (AWS CLI) on your local machine. Configure it with your AWS credentials using AWS configure. 3. Explore AWS Services: AWS provides a variety of services. Familiarize yourself with core services like EC2 (Elastic Compute Cloud), S3 (Simple Storage Service), and IAM (Identity and Access Management). PySpark: PySpark is the Python API for Apache Spark, a fast and general-purpose cluster computing system. It allows you ...
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How to Handle Spaces in PySpark Dataframe Column

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In PySpark, you can employ SQL queries by importing your CSV file data to a DataFrame. However, you might face problems when dealing with spaces in column names of the DataFrame. Fortunately, there is a solution available to resolve this issue. Reading CSV file to Dataframe Here is the PySpark code for reading CSV files and writing to a DataFrame. #initiate session spark = SparkSession.builder \ .appName("PySpark Tutorial") \ .getOrCreate() #Read CSV file to df dataframe data_path = '/content/Test1.csv' df = spark.read.csv(data_path, header=True, inferSchema=True) #Create a Temporary view for the DataFrame df2.createOrReplaceTempView("temp_table") #Read data from the temporary view spark.sql("select * from temp_table").show() Output --------+-----+---------------+---+ |Student| Year|Semester1|Semester2| | ID | | Marks | Marks | +----------+-----+---------------+ | si1 |year1|62.08| 62.4| | si1 |year2|75.94| 76.75| | si...
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