Paula  Hall

Paula Hall

1619517660

Syntax Gotchas Writing PySpark When Knowing Pandas

If you have some basic knowledge in data analysis with Python Pandas and are curious about PySpark and don’t know where to start, tag along.

Python Pandas encouraged us to leave excel tables behind and to look at data from a coder perspective instead. Data sets became bigger and bigger, turned from data bases to data files and into data lakes. Some smart minds from Apache blessed us with the Scala based framework Spark to process the bigger amounts in a reasonable time. Since Python is the go to language for data science nowadays, there was a Python API available soon that’s called PySpark.

For a while now I am trying to conquer this Spark interface with its non-pythonic syntax that everybody in the big data world praises. It took me a few attempts and it’s still work in progress. However in this post I want to show you, who is also starting learning PySpark, how to replicate the same analysis you would otherwise do with Pandas.

The data analysis example we are going to look at you can find in the book “Python for Data Analysis” by Wes McKinney. In that analysis, the aim is to find out the top ranked movies from the MovieLens 1M data set, which is acquired and maintained by the GroupLens Research project from the University of Minnesota.

As a coding framework I used Kaggle, since it comes with the convenience of notebooks that have the basic data science modules installed and are ready to go with two clicks.

The complete analysis and the Pyspark code you can also find in this Kaggle notebookand the Pandas code inthis one. We won’t replicate the same analysis here, but instead focus on the syntax differences when handling Pandas and Pyspark dataframes. I will always show the Pandas code first following with the PySpark equivalent.

The basic functions that we need for this analysis are:

  • Loading data from csv format
  • Combining datasets from different tables
  • Extracting information

#python #data-science #pyspark #introduction-to-pyspark #pandas-dataframe

What is GEEK

Buddha Community

Syntax Gotchas Writing PySpark When Knowing Pandas
Paula  Hall

Paula Hall

1619517660

Syntax Gotchas Writing PySpark When Knowing Pandas

If you have some basic knowledge in data analysis with Python Pandas and are curious about PySpark and don’t know where to start, tag along.

Python Pandas encouraged us to leave excel tables behind and to look at data from a coder perspective instead. Data sets became bigger and bigger, turned from data bases to data files and into data lakes. Some smart minds from Apache blessed us with the Scala based framework Spark to process the bigger amounts in a reasonable time. Since Python is the go to language for data science nowadays, there was a Python API available soon that’s called PySpark.

For a while now I am trying to conquer this Spark interface with its non-pythonic syntax that everybody in the big data world praises. It took me a few attempts and it’s still work in progress. However in this post I want to show you, who is also starting learning PySpark, how to replicate the same analysis you would otherwise do with Pandas.

The data analysis example we are going to look at you can find in the book “Python for Data Analysis” by Wes McKinney. In that analysis, the aim is to find out the top ranked movies from the MovieLens 1M data set, which is acquired and maintained by the GroupLens Research project from the University of Minnesota.

As a coding framework I used Kaggle, since it comes with the convenience of notebooks that have the basic data science modules installed and are ready to go with two clicks.

The complete analysis and the Pyspark code you can also find in this Kaggle notebookand the Pandas code inthis one. We won’t replicate the same analysis here, but instead focus on the syntax differences when handling Pandas and Pyspark dataframes. I will always show the Pandas code first following with the PySpark equivalent.

The basic functions that we need for this analysis are:

  • Loading data from csv format
  • Combining datasets from different tables
  • Extracting information

#python #data-science #pyspark #introduction-to-pyspark #pandas-dataframe

Pandas Data Processing Tasks Translated to PySpark

We live in the era of big data. With the growth of the internet, data is rapidly growing in a huge amount and with high variability. Big data processing could give you a headache because it naturally takes a lot of running time. Apache Spark (or Spark) is one of the popular tools to process big data.

Spark is a unified analytics engine for large-scale data processing. With Spark, we can perform data processing quickly and distribute processing tasks across multiple computers. People use Spark because it is deployable in popular programming languages such as Python, Scala, Java, R and SQL. It also has a stack of libraries that support streaming data, machine learning and graph processing.

One of the Spark interfaces is PySpark that allows you to write Spark applications using Python APIs. PySpark supports Spark’s features such as Spark SQL, DataFrame, Streaming, MLlib (Machine Learning) and Spark Core. As someone working a lot with Pandas, I found that PySpark can do what I do there. However, the implementation is quite different. I will list some of the data processing tasks I usually perform in Pandas and translate them to PySpark.

#pandas #big-data-analytics #data-engineering #pyspark #spark #pandas data processing tasks translated to pyspark

Reading and Writing Data in Pandas

In my last post, I mentioned summarizing and computing descriptive statistics  using the Pandas library. To work with data in Pandas, it is necessary to load the data set first. Reading the data set is one of the important stages of data analysis. In this post, I will talk about reading and writing data.

Before starting the topic, our Medium page includes posts on data science, artificial intelligence, machine learning, and deep learning. Please don’t forget to follow us on Medium 🌱 to see these posts and the latest posts.

Let’s get started.

#python-pandas-tutorial #pandas-read #pandas #python-pandas

Reading and Writing Data in Pandas

In my last post, I mentioned summarizing and computing descriptive statistics  using the Pandas library. To work with data in Pandas, it is necessary to load the data set first. Reading the data set is one of the important stages of data analysis. In this post, I will talk about reading and writing data.

Before starting the topic, our Medium page includes posts on data science, artificial intelligence, machine learning, and deep learning. Please don’t forget to follow us on Medium 🌱 to see these posts and the latest posts.

Let’s get started.

#python-pandas-tutorial #pandas-read #pandas #python-pandas

Udit Vashisht

1586702221

Python Pandas Objects - Pandas Series and Pandas Dataframe

In this post, we will learn about pandas’ data structures/objects. Pandas provide two type of data structures:-

Pandas Series

Pandas Series is a one dimensional indexed data, which can hold datatypes like integer, string, boolean, float, python object etc. A Pandas Series can hold only one data type at a time. The axis label of the data is called the index of the series. The labels need not to be unique but must be a hashable type. The index of the series can be integer, string and even time-series data. In general, Pandas Series is nothing but a column of an excel sheet with row index being the index of the series.

Pandas Dataframe

Pandas dataframe is a primary data structure of pandas. Pandas dataframe is a two-dimensional size mutable array with both flexible row indices and flexible column names. In general, it is just like an excel sheet or SQL table. It can also be seen as a python’s dict-like container for series objects.

#python #python-pandas #pandas-dataframe #pandas-series #pandas-tutorial