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Big Data Analytics with Hadoop 3

You're reading from   Big Data Analytics with Hadoop 3 Build highly effective analytics solutions to gain valuable insight into your big data

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Product type Paperback
Published in May 2018
Publisher Packt
ISBN-13 9781788628846
Length 482 pages
Edition 1st Edition
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Author (1):
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Sridhar Alla Sridhar Alla
Author Profile Icon Sridhar Alla
Sridhar Alla
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Table of Contents (13) Chapters Close

Preface 1. Introduction to Hadoop FREE CHAPTER 2. Overview of Big Data Analytics 3. Big Data Processing with MapReduce 4. Scientific Computing and Big Data Analysis with Python and Hadoop 5. Statistical Big Data Computing with R and Hadoop 6. Batch Analytics with Apache Spark 7. Real-Time Analytics with Apache Spark 8. Batch Analytics with Apache Flink 9. Stream Processing with Apache Flink 10. Visualizing Big Data 11. Introduction to Cloud Computing 12. Using Amazon Web Services

Data analysis

Download OnlineRetail.csv from the link provided with the book. Then, you can load the file using Pandas.

The following is a simple way of reading a local file using Pandas:

import pandas as pd
path = '/Users/sridharalla/Documents/OnlineRetail.csv'
df = pd.read_csv(path)

However, since we are analyzing data in a Hadoop cluster, we should be using hdfs not a local system. The following is an example of how the hdfs file can be loaded into a pandas DataFrame:

import pandas as pd
from hdfs import InsecureClient
client_hdfs = InsecureClient('http://localhost:9870')
with client_hdfs.read('/user/normal/OnlineRetail.csv', encoding = 'utf-8') as reader:
df = pd.read_csv(reader,index_col=0)

The following is what the following line of code does:

df.head(3)

You will get the following result:

Basically, it displays the top three entries...

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