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Python Top Libraries You Need to Create ML Model

Creating a Model of Machine Learning in Python, you need two libraries. One is 'NUMPY' and the other one is 'PANDA'.

For this project, we are using Python Libraries to Create a Model.
What Are Key Libraries You Need I have explained in the below steps. You need Two.
NUMPY - It has the capabilities of CalculationsPANDA - It has the capabilities of Data processing. To Build a model of Machine learning you need the right kind of data. So, to use data for your project, the Data should be refined. Else, it will not give accurate results. Data AnalysisData Pre-processing How to Import Libraries in Pythonimportnumpy as np # linear algebra
importpandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)

How to Check NUMPY/Pandas installed After '.' you need to give double underscore on both the sides of version. 
How Many Types of Data You Need You need two types of data. One is data to build a model and the other one is data you need to test the model. Data to build…

Spark SQL Query how to write it in Ten steps

Spark SQL example
Spark SQL example
The post tells how to write SQL query in Spark and explained in ten steps.This example demonstrates how to use sqlContext.sql to create and load two tables and select rows from the tables into two DataFrames.

The next steps use the DataFrame API to filter the rows for salaries greater than 150,000 from one of the tables and shows the resulting DataFrame. Then the two DataFrames are joined to create a third DataFrame. Finally the new DataFrame is saved to a Hive table.

1. At the command line, copy the Hue sample_07 and sample_08 CSV files to HDFS:
$ hdfs dfs -put HUE_HOME/apps/beeswax/data/sample_07.csv /user/hdfs
$ hdfs dfs -put HUE_HOME/apps/beeswax/data/sample_08.csv /user/hdfs

where HUE_HOME defaultsto /opt/cloudera/parcels/CDH/lib/hue (parcel installation) or /usr/lib/hue
(package installation).

2. Start spark-shell:
$ spark-shell

3. Create Hive tables sample_07 and sample_08:

scala> sqlContext.sql("CREATE TABLE sample_07 (code string,description string,total_emp
scala> sqlContext.sql("CREATE TABLE sample_08 (code string,description string,total_emp

Also Read: Learn SparkSQL by your own with little money

4. In Beeline, show the Hive tables:
[0: jdbc:hive2://> show tables;
| tab_name |
16 | Spark Guide
Developing Spark Applications
| sample_07 |
| sample_08 |

Also read: The role of Spark in Hadoop eco system

5. Load the data in the CSV files into the tables:
scala> sqlContext.sql("LOAD DATA INPATH '/user/hdfs/sample_07.csv' OVERWRITE INTO TABLE
scala> sqlContext.sql("LOAD DATA INPATH '/user/hdfs/sample_08.csv' OVERWRITE INTO TABLE

6. Create DataFrames containing the contents of the sample_07 and sample_08 tables:
scala> val df_07 = sqlContext.sql("SELECT * from sample_07")
scala> val df_08 = sqlContext.sql("SELECT * from sample_08")

Apache Spark
7. Show all rows in df_07 with salary greater than 150,000:
scala> df_07.filter(df_07("salary") > 150000).show()
The output should be:
| code| description|total_emp|salary|
|11-1011| Chief executives| 299160|151370|
|29-1022|Oral and maxillof...| 5040|178440|
|29-1023| Orthodontists| 5350|185340|
|29-1024| Prosthodontists| 380|169360|
|29-1061| Anesthesiologists| 31030|192780|
|29-1062|Family and genera...| 113250|153640|
|29-1063| Internists, general| 46260|167270|
|29-1064|Obstetricians and...| 21340|183600|
|29-1067| Surgeons| 50260|191410|
|29-1069|Physicians and su...| 237400|155150|

8.Create the DataFrame df_09 by joining df_07 and df_08, retaining only the code and description columns.
scala> val df_09 = df_07.join(df_08, df_07("code") ===

The new DataFrame looks like:
| code| description|
|00-0000| All Occupations|
|11-0000|Management occupa...|
|11-1011| Chief executives|
|11-1021|General and opera...|
|11-1031| Legislators|
|11-2011|Advertising and p...|
|11-2021| Marketing managers|
|11-2022| Sales managers|
|11-2031|Public relations ...|
|11-3011|Administrative se...|
|11-3021|Computer and info...|
|11-3031| Financial managers|
|11-3041|Compensation and ...|
|11-3042|Training and deve...|
|11-3049|Human resources m...|
|11-3051|Industrial produc...|
|11-3061| Purchasing managers|
|11-3071|Transportation, s...|
|11-9011|Farm, ranch, and ...|

9. Save DataFrame df_09 as the Hive table sample_09:
scala> df_09.write.saveAsTable("sample_09")

10. In Beeline, show the Hive tables:
[0: jdbc:hive2://> show tables;
| tab_name |
| sample_07 |
| sample_08 |
| sample_09 |


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Hyperledger Fabric Real Interview Questions Read Today

I am practicing Hyperledger. This is one of the top listed blockchains. This architecture follows R3 Corda specifications. Sharing the interview questions with you that I have prepared for my interview.

Though Ethereum leads in the real-time applications. The latest Hyperledger version is now ready for production applications. It has now become stable for production applications.
The Hyperledger now backed by IBM. But, it is still an open source. These interview questions help you to read quickly. The below set of interview questions help you like a tutorial on Hyperledger fabric. Hyperledger Fabric Interview Questions1). What are Nodes?
In Hyperledger the communication entities are called Nodes.

2). What are the three different types of Nodes?
- Client Node
- Peer Node
- Order Node
The Client node initiates transactions. The peer node commits the transaction. The order node guarantees the delivery.

3). What is Channel?
A channel in Hyperledger is the subnet of the main blockchain. You c…