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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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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.


SQL Space in Column Names


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| | si2 |year1|68.26| 72.95| | si2 |year2|85.49| 75.8| | si3 |year1|75.08| 79.84| | si3 |year2|54.98| 87.72| | si4 |year1|50.03| 66.85| | si4 |year2|71.26| 69.77| | si5 |year1|52.74| 76.27| | si5 |year2|50.39| 68.58| | si6 |year1|74.86| 60.8| | si6 |year2|58.29| 62.38| | si7 |year1|63.95| 74.51| | si7 |year2|66.69| 56.92| +----------+-----+-------------+

Fix for space in the column name


Suppose the column name "Student ID" contains a space. To prevent errors, you must modify your SQL query.

spark.sql("select `Student ID` as sid from temp_table").show()

Output:

+---+ |sid| +---+ |si1| |si1| |si2| |si2| |si3| |si3| |si4| |si4| |si5| |si5| |si6| |si6| |si7| |si7| +---+


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