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You're reading from  Big Data Analytics with Hadoop 3

Product typeBook
Published inMay 2018
PublisherPackt
ISBN-139781788628846
Edition1st Edition
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Sridhar Alla
Sridhar Alla
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Sridhar Alla

Sridhar?Alla?is the co-founder and CTO of Blue Whale Consulting and is expert at helping companies (big and small) define their vision for systems and capabilities that will allow them to establish a strategic execution plan to deal with the ever-growing data collected to support analytics and product teams. He has very experienced at dealing with all aspects of data collection, security, governance, and processing as part of end-to-end big data analytics and machine learning initiatives (including predictive modeling, deep learning, and ML automation). Sridhar?is a published book author and an avid presenter at numerous conferences, including Strata, Hadoop World, and Spark Summit.? He also has several patents filed with the US PTO on large-scale computing and distributed systems.? He has over 18 years' experience writing code in Scala, Java, C, C++, Python, R, and Go, and has extensive hands-on knowledge of Spark, Flink, TensorFlow, Keras, Hadoop, Cassandra, HBase, MongoDB, Riak, Redis, Zeppelin, Mesos, Docker, Kafka, ElasticSearch, Solr, H2O, machine learning, text analytics, distributed computing, and high-performance computing. Sridhar lives with his wife and daughter in New Jersey and in his spare time loves blogging and coaching organizations on next-generation advancements in technology and their alignment with business goals.
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Using Python to visualize data


Python provides many extensive capabilities of analysis of big data as well as the plotting and visualization of data.

Note

Analyzing and Visualizing Big Data using Python is covered in Chapter 4, Scientific Computing and Big Data Analysis with Python and Hadoop.

Here is one such example of using Python, involving a single column:

d8 = pd.DataFrame(df, columns=['Quantity'])[0:100]
d8.plot()

Here, only the first 100 elements are selected to make the graph less crowded and illustrate the example better.

Now, you'll have:

Suppose that you want multiple columns to show up. Look at the following code:

d8 = pd.DataFrame(df, columns=['Quantity', 'UnitPrice'])[0:100]
d8.plot()

Just remember that it will not plot qualitative data columns such as Description but only things that can be graphed, such as Quantity and UnitPrice.

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Big Data Analytics with Hadoop 3
Published in: May 2018Publisher: PacktISBN-13: 9781788628846

Author (1)

author image
Sridhar Alla

Sridhar?Alla?is the co-founder and CTO of Blue Whale Consulting and is expert at helping companies (big and small) define their vision for systems and capabilities that will allow them to establish a strategic execution plan to deal with the ever-growing data collected to support analytics and product teams. He has very experienced at dealing with all aspects of data collection, security, governance, and processing as part of end-to-end big data analytics and machine learning initiatives (including predictive modeling, deep learning, and ML automation). Sridhar?is a published book author and an avid presenter at numerous conferences, including Strata, Hadoop World, and Spark Summit.? He also has several patents filed with the US PTO on large-scale computing and distributed systems.? He has over 18 years' experience writing code in Scala, Java, C, C++, Python, R, and Go, and has extensive hands-on knowledge of Spark, Flink, TensorFlow, Keras, Hadoop, Cassandra, HBase, MongoDB, Riak, Redis, Zeppelin, Mesos, Docker, Kafka, ElasticSearch, Solr, H2O, machine learning, text analytics, distributed computing, and high-performance computing. Sridhar lives with his wife and daughter in New Jersey and in his spare time loves blogging and coaching organizations on next-generation advancements in technology and their alignment with business goals.
Read more about Sridhar Alla