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PyTorch is a popular open-source machine-learning library that has recently gained immense popularity among data scientists and researchers. With its easy-to-use interface, dynamic computational graph, and rich ecosystem of tools and resources, PyTorch has made deep learning accessible to a wider audience than ever before.

However, like any other technology, PyTorch is not immune to common mistakes that can affect the accuracy and effectiveness of the models. Understanding these mistakes and how to avoid them is crucial for building high-quality models that can solve complex problems.

In this blogpost, we will explore some of the most common mistakes made by PyTorch users and provide practical tips on avoiding them. We will cover a range of topics, including data preparation, model building, training, and evaluation, to give you a complete understanding of the common pitfalls in PyTorch.

Learning Objectives

By the end of this guide, you will:

Understand the common mistakes made by PyTorch users

Learn practical tips on how to avoid these mistakes

Be able to build high-quality PyTorch models that are accurate and effective

This article was published as a part of the Data Science Blogathon.

Table of Contents What is PyTorch?

PyTorch is a Python-based open-source machine learning library widely used for building deep learning models. PyTorch was developed by Facebook’s AI Research team and is known for its flexibility, ease of use, and dynamic computational graph, allowing on-the-fly adjustments to the model architecture during runtime.

PyTorch supports a range of applications, from computer vision and natural language processing to deep reinforcement learning, and provides a range of pre-built modules and functions that can be used to build complex models easily.

Common Mistakes in PyTorch

While PyTorch is a powerful tool for deep learning, users make several common mistakes that can affect the accuracy and effectiveness of the models. These mistakes include:

Not Setting the Device for the Model and Data

Not Initializing the Weights of the Model

Not Turning Off Gradient Computation for Non-Trainable Parameters

Not Using the Correct Loss Function

Not Using Early Stopping

Not Monitoring the Gradient Magnitude

Not Saving and Loading the Model

Not Using Data Augmentation

In the following sections, we will dive deeper into each of these mistakes and provide practical tips on avoiding them.

1. Not Setting the Device for the Model and Data

One of the most common mistakes when using PyTorch is forgetting to set the device for the model and data. PyTorch provides support for both CPU and GPU computing, and it is important to set the correct device to ensure optimal performance. PyTorch will run on the CPU by default, but you can easily switch to the GPU by setting the device to “cuda” if a GPU is available.

model = data =

It is important to note that if you have a GPU, using it can significantly speed up the training process. However, you may need to switch back to the CPU if you do not have a GPU or are running on a GPU with limited memory. In addition, some models may be too large to fit in GPU memory, so you will also need to run on the CPU.

2. Not Initializing the Weights of the Model

Another common mistake is forgetting to initialize the weights of the model. In PyTorch, you can initialize the weights of a model using the nn.init module, which provides a variety of weight initialization methods. It is important to initialize the weights properly to ensure that the model trains well and converges to a good solution. For example, you can use the nn.init.xavier_uniform_ method to initialize the weights with a uniform distribution scaled by the square root of the number of inputs:

for name, param in model.named_parameters(): if "weight" in name: nn.init.xavier_uniform_(param)

It is also important to note that different initialization methods may work better for different types of models and tasks. For example, the nn.init.kaiming_normal_ method may work better for ReLU activation functions during the nn.init.xavier_uniform_ method may work better for sigmoid activation functions.

3. Not Turning Off Gradient Computation for Non-Trainable Parameters

When training a neural network, it is important to set the requires_grad attribute of the parameters to False for any parameters that should not be updated during training. If this attribute is not set correctly, PyTorch will continue to compute gradients for these parameters, which can lead to a slow training process and unexpected results.

for name, param in model.named_parameters(): if name.startswith("fc"): param.requires_grad = False

In addition to turning off gradient computation for non-trainable parameters, it is also important to freeze the parameters of pre-trained models if you use transfer learning. Freezing the parameters of a pre-trained model can help prevent overfitting and ensure that the pre-trained features are not changed during training. To freeze the parameters of a model, you can set the requires_grad attribute of the model to False:

for param in model.parameters(): param.requires_grad = False 4. Not Using the Correct Loss Function

Another common mistake is using the wrong loss function for the task. PyTorch provides various loss functions, such as classification and regression. Choosing the correct loss function for your task is important to ensure that the model trains correctly.

A common mistake when training a neural network is using the wrong loss function. The loss function is used to measure the difference between the predicted output and the actual output of the model, and it is an important part of the training process. In PyTorch, you can choose from various loss functions, including mean squared error, cross-entropy, and others. Choosing the correct loss function is important based on the task you are trying to perform.

For example, if you are training a binary classification model, you should use the binary cross-entropy loss, which is defined as follows:

loss_fn = nn.BCELoss()

If you are training a multi-class classification model, you should use the cross-entropy loss, which is defined as follows:

loss_fn = nn.CrossEntropyLoss() 5. Not Using Early Stopping

Early stopping is a technique used to prevent overfitting in neural networks. The idea is to stop training the model when the validation loss increases, which indicates that the model is starting to overfit the training data. In PyTorch, you can implement early stopping by monitoring the validation loss and using a loop to stop training when the validation loss increases.

best_val_loss = float("inf") for epoch in range(num_epochs): train_loss = train(model, train_data, loss_fn, optimizer) val_loss = evaluate(model, val_data, loss_fn) if val_loss < best_val_loss: best_val_loss = val_loss else: break 6. Not Monitoring the Gradient Magnitude

Gradient magnitude is an important indicator of the training process and can help you identify issues with your model or training process. If the gradient magnitude is too large, it can indicate that the model is exploding, while if the gradient magnitude is too small, it can indicate that the model is vanishing. In PyTorch, you can monitor the gradient magnitude by computing the mean and standard deviation of the gradients for each parameter in the model.

for name, param in model.named_parameters(): if chúng tôi is not None: print(name, param.grad.mean(), param.grad.std()) 7. Not Saving and Loading the Model

Finally, another common mistake is forgetting to save and load the model. It is important to save the model periodically during and after training to resume training or use the trained model for inference later. In PyTorch, you can load a model using the and torch.load functions, respectively., "") model = MyModel() model.load_state_dict(torch.load("")) 8. Not Using Data Augmentation

Data augmentation is a technique that involves transforming the input data to generate new and diverse examples. This can be useful for increasing the training set’s size, improving the model’s robustness, and reducing the risk of overfitting.

To avoid this mistake, it is recommended to use data augmentation whenever possible to increase the size and diversity of the training set. PyTorch provides a range of data augmentation functions, such as random cropping, flipping, and color jittering, which can be applied to the input data using torchvision.


By following these best practices and avoiding these common mistakes, you can ensure that your PyTorch models are well-designed, optimized, and working effectively. Whether a beginner or an experienced practitioner, these tips will help you write better PyTorch code and achieve better results with your models.

The key takeaways from this article are:

Always set the device for the model and data. This ensures that your code runs on the appropriate hardware (e.g., CPU or GPU).

Don’t forget to initialize the weights of your model. Failure to do so can lead to suboptimal performance or even convergence failure during training.

Be mindful of non-trainable parameters and whether or not gradient computation is necessary for them. Turning off gradient computation for non-trainable parameters can improve the speed and efficiency of your training.

Choose the correct loss function for your task. Different loss functions are suited for different problems (e.g., classification vs. regression).

Use early stopping to prevent overfitting. Early stopping involves stopping the training process once the model’s performance on the validation set starts to degrade.

Monitor the gradient magnitude during training to ensure it doesn’t become too large or too small. This can help prevent issues such as exploding or vanishing gradients.

Save and load your model at appropriate checkpoints. This can allow you to resume training from a saved checkpoint or deploy your trained model in production.

Consider using data augmentation techniques to increase the size of your training set and improve the generalization performance of your model.

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Looping In Bash: A Comprehensive Guide

Bash is a versatile command-line shell and scripting language that is widely used on Unix-based systems. One of the most important features of Bash is its ability to perform looping operations. In this article, we’ll explore the basics of looping in Bash and provide examples of how to use them in real-world scenarios.

What is Looping?

Looping is a programming concept that allows us to execute a block of code repeatedly until a specific condition is met. In Bash, there are several ways to perform looping operations, including for, while, until, and select loops.

For Loop

The for loop is a common looping construct that allows us to iterate over a range of values. The syntax for a for loop in Bash is as follows:

for variable in list do # Statements to be executed done

Here, variable is a user-defined variable that takes on each value in list during each iteration of the loop. The statements to be executed are enclosed within the do and done keywords.

For example, let’s say we want to print the numbers from 1 to 5. We can use a for loop to achieve this as follows:

for i in {1..5} do echo $i done

This will output:

1 2 3 4 5

In addition to iterating over a range of values, the for loop can also iterate over the elements of an array. For example:

fruits=("apple" "banana" "orange") for fruit in "${fruits[@]}" do echo $fruit done

This will output:

apple banana orange While Loop

The while loop is another common looping construct in Bash. It allows us to execute a block of code repeatedly while a certain condition is true. The syntax for a while loop is as follows:

while [ condition ] do # Statements to be executed done

Here, condition is a test that is evaluated before each iteration of the loop. If the condition is true, the statements within the loop are executed. This process continues until the condition is false.

For example, let’s say we want to print the numbers from 1 to 5 using a while loop. We can do this as follows:

i=1 while [ $i -le 5 ] do echo $i ((i++)) done

This will output:

1 2 3 4 5

In this example, the condition [ $i -le 5 ] tests whether i is less than or equal to 5. If the condition is true, the statements within the loop are executed. The ((i++)) statement increments the value of i by 1 after each iteration.

Until Loop

The until loop is similar to the while loop, except that it continues to execute a block of code until a certain condition is true. The syntax for an until loop is as follows:

until [ condition ] do # Statements to be executed done

Here, condition is a test that is evaluated before each iteration of the loop. If the condition is false, the statements within the loop are executed. This process continues until the condition is true.

For example, let’s say we want to print the numbers from 1 to 5 using an until loop. We can do this as follows:

i=1 until [ $i -gt 5 ] do echo $i ((i++)) done

This will output:

1 2 3 4 5

In this example, the condition [ $i -gt 5 ] tests whether i is greater than 5. If the condition is false, the statements within the loop are executed. The ((i++)) statement increments the value of i by 1 after each iteration.

Select Loop

The select loop is a specialized loop that allows us to present a menu of options to the user and prompt them for a selection. The syntax for a select loop is as follows:

select variable in list do # Statements to be executed done

Here, variable is a user-defined variable that is set to the selected value from list. The statements within the loop are executed after the user makes a selection.

For example, let’s say we want to present a menu of fruits to the user and prompt them for a selection. We can do this as follows:

PS3="Select a fruit: " select fruit in "apple" "banana" "orange" do echo "You selected $fruit" break done

This will output:

1) apple 2) banana 3) orange Select a fruit: 2 You selected banana

In this example, the PS3 variable is set to the prompt that will be displayed to the user. The select statement presents the options to the user and waits for a selection. Once the user makes a selection, the statements within the loop are executed. The break statement is used to exit the loop once a selection has been made.


Looping is an essential concept in programming, and Bash provides several constructs that allow us to perform looping operations. The for, while, until, and select loops are all powerful tools for iterating over ranges of values, testing conditions, and presenting menus to users. By mastering these constructs, you can become a more efficient and effective Bash programmer.

How To Use Koldbold Ai: A Comprehensive Guide

See More : Best Free AI Logo Generator: Create Stunning Logos Effortlessly

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Koldbold UI is a new user interface introduced in Koldbold AI. By making a simple setting change before deployment, users can access the United version of Koldbold UI. This interface provides a seamless and intuitive experience, making it easier for writers to interact with the AI and enhance their writing process.

Collaboration Mode in Koldbold AI allows you to treat the text area as a collaborative space between you and the AI. It writes in a predictive manner, harnessing its imagination to generate creative and unique content. This mode fosters a dynamic partnership between the writer and the AI, resulting in truly innovative writing outcomes.

Also Read : Chai AI Website: A Destination for Conversations with AI

If you are specifically interested in writing novels, Koldbold AI offers a solution through soft prompts. Soft prompts can be trained for free using the Easy Softprompt Tuner. To optimize Koldbold AI for novels, you need to provide a folder with UTF-8 text files as your source data. By utilizing soft prompts, you can enhance the AI’s ability to generate novel-like narratives and storylines.

Koldbold AI is an exceptional AI-assisted writing tool that empowers writers to unlock their creativity and produce captivating content. Whether you choose Koldbold AI Colab, Koldbold AI Client, Koldbold UI, or Collaboration Mode, you’ll have access to cutting-edge AI capabilities that enhance your writing process. Additionally, by leveraging soft prompts, you can optimize Koldbold AI specifically for novel writing.

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A Comprehensive Guide To Apache Spark Rdd And Pyspark

This article was published as a part of the Data Science Blogathon


Hadoop is widely used in the industry to examine large data volumes. The reason for this is that the Hadoop framework is based on a basic programming model (MapReduce), which allows for a scalable, flexible, fault-tolerant, and cost-effective computing solution.


Apache Spark is an innovative cluster computing platform that is optimized for speed. It is based on Hadoop MapReduce and extends the MapReduce architecture to be used efficiently for a wider range of calculations, such as interactive queries and stream processing. Spark’s key feature is in-memory cluster computing, which boosts an application’s processing speed.

Components of Apache Spark Apache Spark Core by Apache Apache Spark SQL

Spark SQL is a component built on top of Spark Core that introduces SchemaRDD, a new data abstraction that supports structured and semi-structured data.

Listed below are the four libraries of Spark SQL.

DataFrame API

Interpreter & Optimizer

SQL Service

Data Source API

Streaming Spark

To execute streaming analytics, Spark Streaming makes use of Spark Core’s quick scheduling functionality. It ingests data in mini-batches and transforms it using RDD (Resilient Distributed Datasets) transformations. DStream is the most basic stream unit, which comprises a sequence of RDDs (Resilient Distributed Datasets) that process real-time data.

MLlib (Machine Learning Library):

MLlib is a collection of machine learning libraries. Because of the distributed memory-based Spark architecture, Spark MLlib is called for distributed machine learning framework. It is done by the MLlib developers against the Alternating Least Squares (ALS) implementations.


GraphX is a Spark-based distributed graph processing framework. It provides an API for defining graph computing that uses the Pregel abstraction API to model user-defined graphs. For this abstraction, it also provides an efficient runtime.

Installation of Apache spark:

We’ll need to go through a few steps to get started with Apache Spark and the PySpark library. If you’ve done nothing like this before, it can be a little perplexing, but don’t fear. We’ll make it happen.

Installation Prerequisites:

One of the prerequisites for installing Spark is the installation of Java. The initial steps in getting Apache Spark and PySpark fully operational are to make sure we have everything we need. Java 8, Python 3, and the ability to chúng tôi files are all required.

Let’s look at what Java version you have installed on your desktop computer. If you’re using Windows, open the Command Prompt by going to Start, typing cmd, then pressing Enter. Type the following command there:

$java -version

Followed by the command;

$javac -version

If you don’t already have Java and python installed on your computer, install them from the below link before moving on to the next step.

Download and set up path

1) Verifying Scala and spark installation:

For Linus-Based System:

If we need to install Spark into a Linux-based system. The following steps show how to install Apache Spark.

We need to install a tar file from the Download Scala. Follow the command for extracting the Scala tar file.

$ tar xvf scala-2.11.6.tgz

Scala software files:

To move the Scala software files to the directory (/usr/local/scala), use the commands below.

$ su – Password: # mv scala-2.11.6 /usr/local/scala # exit

Set PATH for Scala;

The command to set PATH for Scala:

$ export PATH = $PATH:/usr/local/scala/bin

Scala Installation Verification:

It’s a good idea to double-check everything after installation. To check if Scala is installed, run the following command.

$scala -version Scala installation in windows:

Open a command prompt and type cd to go to the bin directory of the installed Scala, as seen below.

This is the scala shell, where we may type programs and view the results directly in the shell. The command below can check the Scala version.

Downloading Apache Spark

Visit the following link to get the most recent version of Spark( Download Spark). We’ll be using the spark-1.3.1-bin-hadoop2.6 version for this guide. We can find the Spark tar file in the download folder after you’ve downloaded it.

Extract the downloaded file into that folder. The chúng tôi file for the underlying Hadoop version that Spark will use is the next thing you need to add.

the command is for extracting the spark tar file is:

$ tar xvf spark-1.3.1-bin-hadoop2.6.tgz

Moving files from the Spark:

The instructions below will move the Spark software files to the directory (/usr/local/spark).

$ su – Password: # mv spark-1.3.1-bin-hadoop2.6 /usr/local/spark # exit

Setting up the environment for Spark:

In the /.bashrc file, add the following line. It entails setting the PATH variable to the location of the spark program files.

$export PATH=$PATH:/usr/local/spark/bin

Command for sourcing the ~/.bashrc file :

$ source ~/.bashrc

Spark Installation verification:

Write the following command for opening the Spark shell.

$spark-shell Apache Spark launch

Let us now launch our Spark to view it in all of its magnificence. To run Spark, open a new command prompt and type spark-shell. Spark will be up and running in a new window.

What exactly is Apache spark?

Apache Spark is a data processing framework that can handle enormous data sets quickly and distribute processing duties across many computers, either on its own or with other distributed computing tools.


PySpark is a combination of Apache Spark and Python. It is an excellent language for performing large-scale exploratory data analysis, machine learning pipelines, and data platform ETLs. PySpark is an excellent language to learn if you’re already familiar with Python and libraries like Pandas. It’ll help you construct more scalable analytics and pipelines. This post shows how to get started with PySpark and execute typical tasks.

Pyspark Environment:

There are a few different ways to get started with Spark:


You can create your cluster using bare metal or virtual computers. For this option, Apache Ambari is a valuable project, but it’s not my preferred method for getting up and running quickly.

Most cloud providers have Spark clusters:

AWS offers EMR and Google Cloud Platform has DataProc. DataProc is a faster way to an interactive environment than self-hosting.

Spark solutions are available from companies such as Databricks and Cloudera, making it simple to get started with Spark.

It’s simple to get started with a Spark cluster and notebook environment in this Data Bricks Community Edition environment. With the Spark 2.4 runtime and Python 3, I built a cluster. For the Pandas UDFs feature, you’ll need at least Spark version 2.3 to run the code.

How to import apache spark in the notebook?

To use PySpark in your Jupyter notebook, simply run the following command to install the PySpark pip package:

pip install pyspark

The above command can also use Kaggle as we will, you can just type “pip install pyspark” and Apache Spark will be installed and ready to use.

Python will work with Apache Spark because it is on your system’s PATH. If you wish to use something like Google Colab, run the following block of code, which will automatically set up Apache Spark:

!tar xf spark-3.0.3-bin-hadoop2.7.tgz !pip install -q findspark import os os.environ[“JAVA_HOME”] = “/usr/lib/jvm/java-8-openjdk-amd64” os.environ[“SPARK_HOME”] = “/content/spark-3.0.3-bin-hadoop2.7” import findspark findspark.init()

Apache Spark Dataframes

The Spark data frame is the most important data type in PySpark. This object functions similarly to data frames in R and Pandas and can be thought of as a table dispersed throughout a cluster. If you wish to use PySpark for distributed computation, you’ll need to work with Spark data frames rather than conventional Python data types.

Operations in PySpark are postponed until they require a result in the pipeline. You can define actions for importing a data set from S3 and performing a variety of transformations to the data frame, for example, but we will not do it right away from these operations. Instead, a graph of transformations is maintained, and when the data is needed, we do the transformations as a single pipeline operation when writing the results back to S3. This method avoids storing the entire data frame in memory and allows for more efficient processing across a cluster of devices. They fetched everything into memory with Pandas data frames, and we apply every operation to pandas.

Apache Spark Web UI–Spark Execution

To monitor the progress of your Spark/PySpark application, resource consumption of Spark cluster, and Spark configurations, Apache Spark provides a set of Web UI/User Interfaces (Jobs, Stages, Tasks, Storage, Environment, Executors, and SQL).

These user interfaces are useful for better understanding how Spark runs the Spark/PySpark Jobs. Your application code is a set of instructions that tells the driver to perform a Spark Job and then lets the driver decide how to do so using executors.

Transformations are the instructions given to the driver, and action if causes the transformation to take place. Here, we’re reading chúng tôi file and checking the DataFrame’s count. Let’s have a look at how Spark UI renders an application.

By default, Spark includes an API for reading delimiter files, such as comma, pipe, and tab-separated files, as well as many options for handling with and without headers, double quotes, data types, and so on.

We separated spark UI into the below tabs.

Spark Jobs







RDD Programming with Apache spark

Consider the example of a word count, which counts each word in a document. Consider the following text as input, which is saved in a home directory as an chúng tôi file.

chúng tôi − input file.

“Watch your thoughts; they become words. Watch your words; they become actions. Watch your actions; they become habits. Watch your habits; they become character. Watch your character; it becomes your destiny.”

Create RDD in Apache spark:

Let us create a simple RDD from the text file. Use the following command to create a simple RDD.

Word count Transformation:

The goal is to count the number of words in a file. Create a flat map (flatMap(line ⇒ line.split(“ ”)). to separate each line into words.

We execute the word count logic using the following command. Because this is not an action, but a transformation (pointing to a new RDD or telling Spark what to do with the data), there will be no output once you run it.

Current RDD:

If you want to know what the current RDD is while working with the RDD, use the following command. For debugging, it will display a description of the current RDD and its dependencies.

Persistence of Transformations:

You can use the persist() or cache() methods on an RDD to mark it as persistent. It will be stored in memory on the nodes the first time it is computed in an action. To save the intermediate transformations in memory, run the command below.

Applying the Action:

Performing an action, such as storing all transformations, produces a text file. The absolute path of the output folder is passed as a string argument to the saveAsTextFile(” “) method. To save the output to a text file, use the command below. The ‘output’ folder is in the current location in the following example.

Examining the Results:

To get to your home directory, open another terminal (where a spark is executed in the other terminal). To check the output directory, use the instructions below.

The following command is used to see output from Part-00000 files.


(watch,3) (are,2) (habits,1) (as,8) (beautiful,2) (they, 7) (look,1)

The following command is used to see output from Part-00001 files.

Output 1:

(walk, 1) (or, 1) (talk, 1) (only, 1) (love, 1) (care, 1) (share, 1)

(1) Create Data Frame:

(‘Michael’,’Rose’,”,’2000-05-19′,’M’,4000) ) val columns = Seq(“firstname”,”middlename”,”lastname”,”dob”,”gender”,”salary”) df = spark.createDataFrame(data), schema = columns).toDF(columns:_*) f.split(“,”) })

Apache Spark RDD Operations

Transformations based on RDDs–Transformations are lazy operations that yield another RDD instead of updating an RDD.

RDD actions are operations that cause RDD values to be computed and returned.

Spark transformations RDD yields another RDD, and transformations are lazy, which means they don’t run until action on RDD is called FlatMap, map, reduceByKey, filter, sortByKey, and return new RDD instead of updating the current RDD are some RDD transformations.

How to load data in Apache Spark?

Map() —

The map() transformation is used to do complex operations, such as adding a column, changing a column, and so on. The output of map transformations always has the same amount of records as the input.

In our word count example, we add a new column with the value 1 for each word; the RDD returns PairRDDFunctions, which contain key-value pairs, with a word of type String as the key and 1 of type Int as the value. I’ve defined the rdd3 variable with type.

flatMap() —

After applying the function, the flatMap() transformation flattens the RDD and returns a new RDD. In the example below, it splits each record in an RDD by space first, then flattens it. Each record in the resulting RDD has a single word.


Filtering records in an RDD are done with the filter() transformation. We are filtering all words that begin with the letter “a”.


sortByKey() is a function that allows you to sort your data by key.

It sorts RDD elements by key using the sortByKey() transformation. we use the map transformation to change RDD[(String,Int)] to RDD[(Int, String]) and then use sortByKey to sort on an integer value. Finally, foreach with println statements returns every words in RDD as a key-value pair, as well as their count.

//Print rdd6 result to console rdd6.foreach(println)

reduceByKey() :

reduceByKey() combines the values of each key with the function supplied. It decreases the word string in our case by using the sum function on value. Our RDD yielded a list of unique terms and their counts.

Apache Spark RDD Actions

We’ll stick with our word count example for now; foreach() action is used to manage accumulators, write to a database table, or access external data sources, but foreachPartiton() is more efficient since it allows you to conduct heavy initializations per partition. On our word count example, let’s look at some more action procedures.

max–This function is used to return the max record.

println(“Max Record : “+datMax._1 + “,”+ datMax._2)

fold–This function aggregates the elements of each partition, and then the results for all of the partitions.

val sum = acc+v sum

$ Output: fold: 20

reduce–This function is used to decrease the records to single, we can use this to count or sum.

println(“dataReduce Record : “+totalWordCount._1)

Collect–Returns an array of all data from RDD. When working with large RDDs with millions or billions of records, be cautious about using this method because the driver may run out of memory.

println(“Key:”+ f._1 +”, Value:”+f._2) })

saveAsTextFile–we can use saveAsTestFile action to write the RDD to a text file.

What is Pyspark RDD?

The PySpark RDD (Resilient Distributed Dataset) is a core data structure in PySpark that is a fault-tolerant, immutable distributed collection of items, which means you can’t change it after you’ve created it. RDD divides each dataset into logical partitions that can be computed on separate cluster nodes.

PySpark is the Python API for Apache Spark, an open-source distributed computing framework used for big data processing and analytics. It allows developers to write Spark applications using Python, leveraging the power and scalability of Spark’s distributed computing capabilities. PySpark provides a high-level interface for working with distributed datasets, enabling tasks like data manipulation, querying, and machine learning. It seamlessly integrates with other Python libraries and offers a familiar programming experience for Python developers. PySpark supports parallel processing, fault tolerance, and in-memory caching, making it well-suited for handling large-scale data processing tasks in a distributed computing environment.

How to read CSV or JSON files into DataFrame

Using csv(“path”) or format(“csv”).load(“path”) we can read a CSV file into a PySpark DataFrame of DataFrameReader. These methods take a file path to read from as an input. You can specify data sources by their fully qualified names when using the format(“CSV”) method. However, for built-in sources, you can simply use their short names (CSV, JSON, parquet, JDBC, text e.t.c).

df ="org.apache.spark.sql.csv") .load("/tmp/resources/zipcodes.csv") df.printSchema()

Loading a CSV file in PySpark is a little more difficult. Because there is no local storage in a distributed environment, a distributed file system such as HDFS, Databricks file store (DBFS), or S3 must give the file’s path.

When I use PySpark, I usually work with data stored in S3. Many databases provide an unload to S3 feature, and you can also move files from your local workstation to S3 via the AWS dashboard. I’ll be using the Databricks file system (DBFS) for this article, which gives paths in the manner of /FileStore. The first step is to upload the CSV file that you want to work with.

file_location = "/FileStore/tables/game_skater_stats.csv"df ="csv").option("inferSchema", True).option("header", True).load(file_location)display(df)

The next snippet shows how to save the data frame from a previous snippet as a parquet file on DBFS, then reload the data frame from the parquet file.'/FileStore/parquet/game_skater_stats', format='parquet')df ="/FileStore/parquet/game_skater_stats") display(df) How to 

Write PySpark DataFrame to CSV file?

df.write.option("header",True) .csv("/tmp/spark_output/zipcodes")

Writing Data:

It’s not a good idea to write data to local storage while using PySpark, just like it’s not a good idea to read data with Spark. You should instead use a distributed file system like S3 or HDFS. If you’re going to use Spark to process the findings, parquet is a decent format to save data frames in.'/FileStore/parquet/game_stats',format='parquet')

Create a data frame:

To generate a DataFrame from a list, we’ll need the data, so let’s get started by creating the data and columns we’ll need.

columns = ["language","count"] data = [("Java", "20000"), ("Python", "100000"), ("c#", "3000")]

The toDF() method of PySpark RDD is used to construct a DataFrame from an existing RDD. Because RDD lacks columns, the DataFrame is generated with the default column names “_1” and “_2” to represent the two columns we have.

columns = ["language","users_count"] dfFromRDD1 = rdd.toDF(columns) dfFromRDD1.printSchema() Convert PySpark RDD to DataFrame

The RDD’s toDF() function is used in PySpark to convert RDD to DataFrame. We’d have to change RDD to DataFrame because DataFrame has more benefits than RDD. For example, DataFrame is a distributed collection of data arranged into named columns that give optimization and efficiency gains, comparable to database tables.

from chúng tôi import SparkSession spark = SparkSession.builder.appName('PySpark create using parallelize()').getOrCreate() dept = [("Finance",10),("Marketing",20),("Sales",30),("IT",40)] rdd = spark.sparkContext.parallelize(dept)

To begin, pass a Python list object to the sparkContext.parallelize() function to generate an RDD.

When you construct an RDD in PySpark, this collection will be parallelized if you have data in a list, which means you have a collection of data in the PySpark driver’s memory.

deptColumns = ["dept_name","dept_id"] df2 = rdd.toDF(deptColumns) df2.printSchema() Convert PySpark DataFrame to Pandas

A function toPandas() can convert a PySpark DataFrame to a Python Pandas DataFrame (). A function toPandas() can convert a PySpark DataFrame to a Python Pandas DataFrame (). PySpark works on several machines, whereas pandas run on a single node. If you’re working on a Machine Learning application with massive datasets, PySpark is much faster than pandas at processing operations.

First, we have to create data frames in PySpark.

import pyspark from chúng tôi import SparkSession spark = SparkSession.builder.appName('Pyspark data frames to pandas').getOrCreate() data = [("James","","Smith","36636","M",60000), ("Michael","Rose","","40288","M",70000), columns = ["first_name","middle_name","last_name","dob","gender","salary"] pysparkDF = spark.createDataFrame(data = data, schema = columns) pysparkDF.printSchema()

toPandas() collects all records in the PySpark DataFrame and sends them to the driver software; it should only be used on a tiny fraction of the data. When using a larger dataset, the application crashes because of a memory problem.

pandasDF = pysparkDF.toPandas() print(pandasDF) Most commonly used PySpark functions

PySpark show() :

PySpark DataFrame show() displays the contents of a DataFrame in a Table Row and Column Format. The column values are truncated at 20 characters by default, and only 20 rows are displayed.

from chúng tôi import SparkSession spark = SparkSession.builder.appName('pyspark show()').getOrCreate() columns = ["Seqno","Quote"] data = [("1", "Be the change that you wish to see in the world"), ("2", "Everyone thinks of changing the world, but no one thinks of changing himself."), df = spark.createDataFrame(data,columns)

Let’s look at how to display the complete contents of the Quote column, which are truncated at 20 characters.

Pyspark Filter():

If you’re coming from a SQL background, you can use the where() clause instead of the filter() method to filter the rows from an RDD/DataFrame depending on the specified condition or SQL expression.

from pyspark.sql.types import StructType,StructField from pyspark.sql.types import StringType, IntegerType, ArrayType data = [ (("James","","Smith"),["Java","Scala","C++"],"OH","M"), (("Anna","Rose",""),["Spark","Java","C++"],"NY","F"), (("Julia","","Williams"),["CSharp","VB"],"OH","F"), ] schema = StructType([ StructField('name', StructType([ StructField('firstname', StringType(), True), StructField('middlename', StringType(), True), StructField('lastname', StringType(), True) ])), StructField('languages', ArrayType(StringType()), True), StructField('state', StringType(), True), StructField('gender', StringType(), True) ]) df = spark.createDataFrame(data = data, schema = schema) df.printSchema()

To filter the rows from a DataFrame, use Column with the condition. You can express complex conditions by referring to column names with dfObject.colname.

df.filter(df.state == "OH").show(truncate=False)

PySpark map():

PySpark map (map()) is an RDD transformation that applies the transformation function (lambda) to each RDD/DataFrame element and returns a new RDD.

from chúng tôi import SparkSession spark = SparkSession.builder.master("local[1]") .appName("pyspark map()").getOrCreate() data = ["Project","Gutenberg’s","Alice’s","Adventures", "in","Wonderland","Project","Gutenberg’s","Adventures", "in","Wonderland","Project","Gutenberg’s"] rdd=spark.sparkContext.parallelize(data)

RDD map() transformations are used to do sophisticated operations, such as adding a column, changing a column, converting data, and so on. The output of map transformations always has the same amount of records as the input. x: (x,1)) for element in rdd2.collect(): print(element)

PySpark Select():

PySpark select() is a transformation function that returns a new DataFrame with the selected columns. It may pick single, multiple, column by index, all columns from a list, and nested columns from a DataFrame.

import pyspark from chúng tôi import SparkSession spark = SparkSession.builder.appName('Pyspark Select()').getOrCreate() data = [("James","Smith","USA","CA"), ("Michael","Rose","USA","NY") ] columns = ["firstname","lastname","country","state"] df = spark.createDataFrame(data = data, schema = columns)

By giving the column names to the select() function, you can choose a single or several columns from the DataFrame. This produces a new DataFrame with the selected columns because DataFrame is immutable. The Dataframe contents are displayed using the show() function."name").show(truncate=False)

PySpark Join():

PySpark Join is used to join two DataFrames together, and by chaining them together, you can join several DataFrames. It supports all fundamental SQL join types, including INNER, LEFT OUTER, RIGHT OUTER, LEFT ANTI, LEFT SEMI, CROSS, and SELF JOIN.

emp = [(1,"Smith",-1,"2024","10","M",3000), (2,"Rose",1,"2010","20","M",4000), (3,"Williams",1,"2010","10","M",1000), (4,"Jones",2,"2005","10","F",2000), ] empColumns = ["emp_id","name","superior_emp_id","year_joined", "emp_dept_id","gender","salary"] empDF1 = spark.createDataFrame(data=emp, schema = empColumns) empDF1.printSchema() dept = [("Finance",10), ("Marketing",20), ("Sales",30), ("IT",40) ] deptColumns = ["dept_name","dept_id"] deptDF1 = spark.createDataFrame(data=dept, schema = deptColumns) deptDF1.printSchema()

Inner join is PySpark’s default and most commonly used join. This connects two datasets based on key columns, with rows from both datasets being deleted if the keys don’t match (emp & dept).

empDF.join(deptDF,empDF.emp_dept_id == deptDF.dept_id,"inner") .show(truncate=False) Frequently Asked Questions

Big Data engineers are to identify patterns in large data sets and design algorithms to make raw data more relevant to businesses. This IT position causes a diverse range of technical abilities, including a thorough understanding of SQL database design and several programming languages.

Skillsets and responsibilities for big data engineers:

Analytical abilities

 Data visualization abilities

Knowledge of business domains and big data tools.

Programming abilities

Problem-solving abilities.

Data mining Techniques

About Myself:

This is Lavanya from Chennai. I am a passionate writer and enthusiastic content maker. The most intractable problems always thrill me. I am currently pursuing my B.E., in Computer Engineering and have a strong interest in the fields of data engineering, machine learning, data science, and artificial intelligence, and I am constantly looking for ways to integrate these fields with other disciplines such as science and chemistry to further my analysis goals.


I hope you found this blog post interesting! You should now be familiar with the Apache spark and Pyspark RDD operations and functions, as well as scopes of big data. In this article, we glanced at how to install and use the spark framework using python and how it may help you know about some of the RDD functions in the spark environment.


If you have questions about Spark RDD Operations, please contact us. I will gladly assist you in resolving them.

The media shown in this article is not owned by Analytics Vidhya and are used at the Author’s discretion.


Comprehensive Guide To Good Product Backlog

What is a Product Backlog?

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The product undergoes continuous changes during its evolution, and the process persists until the project reaches completion and the stakeholder or owner receives the delivered product. In other words, it is dynamic and changes the product development process. The backlog assessment makes the product reasonable, useful, and flaw-free as long as the product exists. It can be agile or scrum per the project’s requirement and the product owner.


The backlog bridges the gap between the product owner and the development team. The product owner and the development team open up on a common communication platform to accomplish the product. The product owner must prioritize work in this backlog at any time per the feedback collected from the customers/clients and the new requirements. The development team then starts working according to the product backlog the owner provided. When the two product owners and the development team work are in sync, it helps boost focus and team morale. The changes should be kept minimum once the work is in progress to create fewer disruptions in the functioning of the development team.

Benefit/Merit Key Points to Remember

There are two important cornerstones for product backlog development: roadmap and requirement. The roadmap, also known as a draft or outline, outlines the entire project and breaks it into several smaller segments known as epics. Each epic can encompass various user stories. The product owner organizes the user stories into a list, facilitating the development team’s work by making it more efficient and less time-consuming. The product owner then prioritizes the epic and can choose to deliver the complete epic. Several factors may affect the prioritization of the epic during product development, such as:

Urgency raised by the client/customer.

Implementation process.

The urgency of collecting feedback and jumping onto the next iteration.

Synchronization between the work items.

Template/Contents of a Product Backlog

Difference Between a Simple Task List and a Product Backlog

It is incremental in nature and dynamic, which means that the upcoming requirements are being added to the initial version of the backlog.

This values the customer’s feedback.

The client or the customer suggests improvements; thus, constant updates are added.

The items in the backlog are organized and prioritized per the customer’s needs.

It grows rapidly and documents the agile or scrum to-do list.

It contains no low-level tasks as the documentation becomes large and difficult to manage.


Maintaining this backlog is essential as continuous updates and improvements grow very rapidly. The product owner is responsible for regularly reviewing and maintaining the product to ensure this backlog is well organized and updated. The product owner must regularize these backlogs before moving to the next epic/iteration or phase. Suppose the backlog is not defined and prioritized before the next work plan meeting. In that case, it may lead to aborting the next phase creating chaos and confusion. Thus, it becomes necessary for the product owner to review the product every day or every alternate day.

Maintaining this becomes necessary to avoid confusion regarding the next task. If the team fails to organize the items in the backlog effectively before the next phase, it could lead to the subsequent stage’s cancellation.

Adhering to the Backlog task is also important to complete the current task. If other tasks and projects create disturbances or too many new items appear on the product, addressing those items on the backlog becomes necessary.

Dedicate one part of the backlog to the new improvements and ideas and the other to the bugs detected in the created product eliminating the hustles and dilemmas. To simplify this backlog, assign an age limit to the ideas and scrap the ideas beyond that age limit.

Thus maintaining and reviewing it regularly will help you make the unmanageable colossus product into a manageable and structured outline/guideline. A lean and managed backlog will accelerate and catalyze product development, implement innovation, and achieve higher customer satisfaction. This will help you to deliver the best-of-class service possible.

Recommended Articles

This has been a guide to Product Backlog. Here we discuss the purpose, merit, key points, and difference between a simple task list and a product backlog. You can also go through our other suggested articles to learn more –

What Does A Chief Analytics Officer Do? Read This Comprehensive Guide

blog / Leadership What Does a Chief Analytics Officer do? Read This Comprehensive Guide

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Businesses rely on data and analytics to make data-driven strategies, understand customers’ behavior, make informed decisions, and augment their revenue. According to a Statista report, the total amount of data creation is anticipated to surpass 180 zettabytes by 2025. If you’re wondering what that has to do with a Chief Analytics Officer (CAO), then the answer is, everything! This exponential rate at which data is being created has prompted companies to hire CAOs to head their data analytics operations. So, what does a chief analytics officer do? To put it succinctly, a CAO is a C-Suite executive who is responsible for transforming data into meaningful insights, developing data-based infrastructure, and collaborating with the company’s C-Suite. This article delves deeper into the roles and responsibilities of a chief analytics officer, right from the qualifications needed to the salary they pull in.

What Skills Does a Chief Analytics Officer Need?

A CAO is responsible for leading the entire data analytics operations of an organization, from creating and maintaining data warehouses to capturing and analyzing data. As a chief analytics officer, then, a certain skill set is a must to shoulder this kind of responsibility with aplomb.

Leadership Skills

Chief analytics officers are responsible for heading the company’s data operations network. Being an effective leader, therefore, goes with the territory. You need leadership skills to guide data analysis teams in their daily responsibilities and to enable them to achieve goals and ensure positive business outcomes.  

Technology Know-How

Technology knowledge includes the understanding of analytics and data science. With its basic understanding, a CAO can make informed decisions, ask relevant questions to C-Suite peers, and encourage and motivate their team to employ technology in more efficient ways. 

Communication Skills

Good communication is the bedrock of a successful leader and a CAO is no different. They need to effectively and clearly communicate with team members and the company’s C-Suite to provide necessary and accurate information related to data analysis. Any miscommunication may lead to wrong decisions and impact business growth, not to mention lead to misunderstandings.

Data Analysis

With data analysis skills, a CAO can gather data, process it, and derive actionable insights from it. This knowledge will also ensure that a chief analytics officer can guide their team well and offer solutions in case of any roadblocks.

What Does a Chief Analytics Officer do?

The priority that an organization places on data and analytics can be understood from the presence of a CAO among its C-Suite. A chief analytics officer’s job includes the following:

Supervising and monitoring data analytics and data science operations 

Determining new business opportunities based on data

Establishing the primary objectives of businesses 

Collaborating with the C-Suite to develop data insights 

Collecting and analyzing data to derive actionable insights from it

ALSO READ: Why a Chief Analytics Officer Program is What You Need Right Now

A Chief Data Officer vs a Chief Analytics Officer

The jobs of both a chief analytics officer and that of a chief data officer revolve around data and analytics. This can sometimes make it difficult to distinguish between the two positions. However, there are some basic differences between the two.

A Chief Data Officer (CDO) is responsible for managing and developing data strategies to meet business objectives. A CAO, on the other hand, focuses on data analysis to meet the organization’s needs by focusing on business, operational, and customer analytics. Additionally, a CDO is considered a C-level technology role. On the other hand, a CAO is considered a C-level business role. 

How to Become a Chief Analytics Officer Step 1. Get the Relevant Education

To become a qualified CAO, you need a Master’s and having a PhD is a huge plus in data-related areas such as Data Science, Statistics, Information Management Systems, or Analytics. You can also opt for pursuing a postgraduate program in business analytics and business intelligence. 

Step 2. Earn Required Experience

You need to have a minimum of 10 years’ work experience as a senior data analyst or senior data science professional. Furthermore, experience with developing data science strategies while maintaining and monitoring data flow across various departments (including operations, products, marketing, and customer services) in an organization can be beneficial.

Step 3. Enhance Your Expertise

Apart from developing the skills needed for the job, you need to possess people management and project management skills, strategic thinking, and business knowledge. 

Salary Expectations for a Chief Analytics Officer

A CAO’s salary depends on a variety of factors, including experience, location, and industry. According to Glassdoor, the average salary that a person in this position can expect is $255,795 per year in the U.S. 

Skill up with Emeritus  

Now that you know the answer to the ‘what does a chief analytics officer do’ question, you know the job is specialized, challenging, and important in this data-centered world. It also calls for you to develop leadership skills and lead the team from the front. Emeritus’ online leadership courses are exactly what aspirants to this role will need. Developed in collaboration with top universities, these programs help hone leadership skills with guidance from industry experts. Enroll in the course and set your career on the path to growth. 

By Riku Ghosh

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