Jamison  Fisher

Jamison Fisher

1646953740

Cylon: Scalable, Distributed Memory, Parallel Runtime with A Pandas

Cylon

Cylon is a fast, scalable distributed memory data parallel library for processing structured data. Cylon implements a set of relational operators to process data. While ”Core Cylon” is implemented using system level C/C++, multiple language interfaces (Python and Java ) are provided to seamlessly integrate with existing applications, enabling both data and AI/ML engineers to invoke data processing operators in a familiar programming language. By default it works with MPI for distributing the applications.

Internally Cylon uses Apache Arrow to represent the data in a column format.

Getting Started

We can use Conda to install PyCylon. At the moment Cylon only works on Linux Systems. The Conda binaries need Ubuntu 16.04 or higher.

conda create -n cylon-0.4.0 -c cylondata pycylon python=3.7
conda activate cylon-0.4.0

Now lets run our first Cylon application inside the Conda environment. The following code creates two DataFrames and joins them.

from pycylon import DataFrame, CylonEnv
from pycylon.net import MPIConfig

df1 = DataFrame([[1, 2, 3], [2, 3, 4]])
df2 = DataFrame([[1, 1, 1], [2, 3, 4]])

# local merge
df3 = df1.merge(right=df2, on=[0, 1])
print("Local Merge")
print(df3)

Now lets run a parallel version of this program. Here if we create n processes (parallelism), n instances of the program will run. They will each load two DataFrames in their memory and do a distributed join among the DataFrames. The results will be created in the parallel processes as well.

from pycylon import DataFrame, CylonEnv
from pycylon.net import MPIConfig
import random

# distributed join
env = CylonEnv(config=MPIConfig())

df1 = DataFrame([random.sample(range(10*env.rank, 15*(env.rank+1)), 5),
                 random.sample(range(10*env.rank, 15*(env.rank+1)), 5)])
df2 = DataFrame([random.sample(range(10*env.rank, 15*(env.rank+1)), 5),
                 random.sample(range(10*env.rank, 15*(env.rank+1)), 5)])
df2.set_index([0], inplace=True)
print("Distributed Join")
df3 = df1.join(other=df2, on=[0], env=env)
print(df3)

You can run the above program in the Conda environment by using the following command. It uses mpirun command with 2 parallel processes.

mpirun -np 2 python <name of your python file>

Compiling Cylon

Refer to the documentation on how to compile Cylon

Compiling on Linux

Download Details:
Author: cylondata
Source Code: https://github.com/cylondata/cylon
License: Apache-2.0 License

#pandas 

What is GEEK

Buddha Community

Cylon: Scalable, Distributed Memory, Parallel Runtime with A Pandas
Paula  Hall

Paula Hall

1623396211

Making Pandas fast with Dask parallel computing

So you, my dear Python enthusiast, have been learning Pandas and Matplotlib for a while and have written a super cool code to analyze your data and visualize it. You are ready to run your script that reads a huge file and all of a sudden your laptop starts making un ugly noise and burning like hell. Sounds familiar?

Well, I have got a couple of good news for you: this issue doesn’t need to happen anymore and you no, you don’t need to upgrade your laptop or your server.

Introducing Dask:

Dask is a flexible library for parallel computing with Python. It provides multi-core and distributed parallel execution on larger-than-memory datasets. It figures out how to break up large computations and route parts of them efficiently onto distributed hardware.

A massive cluster is not always the right choice

Today’s laptops and workstations are surprisingly powerful and, if used correctly, can handle datasets and computations for which we previously depended on clusters. A modern laptop has a multi-core CPU, 32GB of RAM, and flash-based hard drives that can stream through data several times faster than HDDs or SSDs of even a year or two ago.

As a result, Dask can empower analysts to manipulate 100GB+ datasets on their laptop or 1TB+ datasets on a workstation without bothering with the cluster at all.

The project has been a massive plus for the Python machine learning Ecosystem because it democratizes big data analysis. Not only can you save money on bigger servers, but also it copies the Pandas API so you can run your Panda script changing very few lines of code.

#making pandas fast with dask parallel computing #dask parallel computing #pandas #pandas fast #dask #dask parallel

Kasey  Turcotte

Kasey Turcotte

1623927960

Pandas DataFrame vs. Spark DataFrame: When Parallel Computing Matters

With Performance Comparison Analysis and Guided Example of Animated 3D Wireframe Plot

Python is famous for its vast selection of libraries and resources from the open-source community. As a Data Analyst/Engineer/Scientist, one might be familiar with popular packages such as NumpyPandasScikit-learnKeras, and TensorFlow. Together these modules help us extract value out of data and propels the field of analytics. As data continue to become larger and more complex, one other element to consider is a framework dedicated to processing Big Data, such as Apache Spark. In this article, I will demonstrate the capabilities of distributed/cluster computing and present a comparison between the Pandas DataFrame and Spark DataFrame. My hope is to provide more conviction on choosing the right implementation.

Pandas DataFrame

Pandas has become very popular for its ease of use. It utilizes DataFrames to present data in tabular format like a spreadsheet with rows and columns. Importantly, it has very intuitive methods to perform common analytical tasks and a relatively flat learning curve. It loads all of the data into memory on a single machine (one node) for rapid execution. While the Pandas DataFrame has proven to be tremendously powerful in manipulating data, it does have its limits. With data growing at an exponentially rate, complex data processing becomes expensive to handle and causes performance degradation. These operations require parallelization and distributed computing, which the Pandas DataFrame does not support.

Introducing Cluster/Distribution Computing and Spark DataFrame

Apache Spark is an open-source cluster computing framework. With cluster computing, data processing is distributed and performed in parallel by multiple nodes. This is recognized as the MapReduce framework because the division of labor can usually be characterized by sets of the mapshuffle, and reduce operations found in functional programming. Spark’s implementation of cluster computing is unique because processes 1) are executed in-memory and 2) build up a query plan which does not execute until necessary (known as lazy execution). Although Spark’s cluster computing framework has a broad range of utility, we only look at the Spark DataFrame for the purpose of this article. Similar to those found in Pandas, the Spark DataFrame has intuitive APIs, making it easy to implement.

#pandas dataframe vs. spark dataframe: when parallel computing matters #pandas #pandas dataframe #pandas dataframe vs. spark dataframe #spark #when parallel computing matters

Jamison  Fisher

Jamison Fisher

1646953740

Cylon: Scalable, Distributed Memory, Parallel Runtime with A Pandas

Cylon

Cylon is a fast, scalable distributed memory data parallel library for processing structured data. Cylon implements a set of relational operators to process data. While ”Core Cylon” is implemented using system level C/C++, multiple language interfaces (Python and Java ) are provided to seamlessly integrate with existing applications, enabling both data and AI/ML engineers to invoke data processing operators in a familiar programming language. By default it works with MPI for distributing the applications.

Internally Cylon uses Apache Arrow to represent the data in a column format.

Getting Started

We can use Conda to install PyCylon. At the moment Cylon only works on Linux Systems. The Conda binaries need Ubuntu 16.04 or higher.

conda create -n cylon-0.4.0 -c cylondata pycylon python=3.7
conda activate cylon-0.4.0

Now lets run our first Cylon application inside the Conda environment. The following code creates two DataFrames and joins them.

from pycylon import DataFrame, CylonEnv
from pycylon.net import MPIConfig

df1 = DataFrame([[1, 2, 3], [2, 3, 4]])
df2 = DataFrame([[1, 1, 1], [2, 3, 4]])

# local merge
df3 = df1.merge(right=df2, on=[0, 1])
print("Local Merge")
print(df3)

Now lets run a parallel version of this program. Here if we create n processes (parallelism), n instances of the program will run. They will each load two DataFrames in their memory and do a distributed join among the DataFrames. The results will be created in the parallel processes as well.

from pycylon import DataFrame, CylonEnv
from pycylon.net import MPIConfig
import random

# distributed join
env = CylonEnv(config=MPIConfig())

df1 = DataFrame([random.sample(range(10*env.rank, 15*(env.rank+1)), 5),
                 random.sample(range(10*env.rank, 15*(env.rank+1)), 5)])
df2 = DataFrame([random.sample(range(10*env.rank, 15*(env.rank+1)), 5),
                 random.sample(range(10*env.rank, 15*(env.rank+1)), 5)])
df2.set_index([0], inplace=True)
print("Distributed Join")
df3 = df1.join(other=df2, on=[0], env=env)
print(df3)

You can run the above program in the Conda environment by using the following command. It uses mpirun command with 2 parallel processes.

mpirun -np 2 python <name of your python file>

Compiling Cylon

Refer to the documentation on how to compile Cylon

Compiling on Linux

Download Details:
Author: cylondata
Source Code: https://github.com/cylondata/cylon
License: Apache-2.0 License

#pandas 

Kasey  Turcotte

Kasey Turcotte

1623142193

Optimize Pandas Memory Usage for Large Datasets

Make effective use of data types to prevent crashing of memory

Pandas is a popular Python package for data science, as it offers powerful, expressive, and flexible data structures for data explorations and visualization. But when it comes to handling large-sized datasets, it fails, as it cannot process larger than memory data.

Pandas offer a vast list of API for data explorations and visualization, which makes it more popular among the data scientist community. Dask, modin, Vaex are some of the open-source packages that can scale up the performance of Pandas library and handle large-sized datasets.

When the size of the dataset is comparatively larger than memory using such libraries is preferred, but when dataset size comparatively equal or smaller to memory size, we can optimize the memory usage while reading the dataset. In this article, we will discuss how to optimize memory usage while loading the dataset using pandas.read_csv(),**pandas.read_excel() orpandas.read_excel()**functions.

#machine-learning #education #pandas #optimize pandas memory usage for large datasets #pandas memory #datasets

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