PySpark, released by Apache Spark community, is basically a Python API for supporting Python with Spark. By utilizing PySpark, you can work and integrate with RDD easily in Python. The library Py4j helps to achieve this feature.<\/p>\n
There are several features of PySpark framework:<\/p>\n
1. Faster processing than other frameworks.<\/p>\n
2. Real-time computations and low latency due to in-memory processing.<\/p>\n
3. Polyglot, which means compatible with several languages like Java, Python, Scala and R.<\/p>\n
4. Powerful caching and efficient disk persistence.<\/p>\n
5. Deployment can be performed by Hadoop through Yarn.<\/p>\n
Audience for PySpark Tutorial<\/span><\/h3>\n\n- The professionals who are aspiring to make a career in programming language and also those who want to perform real-time processing through framework can go for this PySpark tutorial. <\/span><\/li>\n
- Also,\u00a0those who want to\u00a0learn PySpark along with its\u00a0several modules, as well as submodules, must go for this PySpark tutorial.<\/span><\/li>\n<\/ul>\n
Prerequisites to PySpark<\/span><\/h3>\nWe assume that before learning PySpark, the readers already have basic knowledge about the programming language as well as frameworks. Also,\u00a0it is recommended to have a sound knowledge of Spark,\u00a0Hadoop, Scala Programming Language,\u00a0HDFS as well as Python.<\/span><\/p>\nFactors about PySpark API<\/span><\/h3>\nThere are a few key differences between the Python and Scala APIs which we will discuss in this PySpark Tutorial:<\/span><\/p>\n\n- Since Python is dynamically typed, therefore PySpark RDDs can easily hold objects of multiple types.<\/span><\/li>\n
- PySpark doesn’t support some API calls, like lookup and non-text input files. However, this feature will be added in future releases.<\/span><\/li>\n<\/ul>\n
Although RDDs support the same methods as their Scala counterparts in PySpark but takes Python functions<\/strong> and returns Python collection types as a result. Using Python\u2019s lambda syntax, short functions can be passed to RDD methods.<\/span><\/p>\nlogData = sc.textFile(logFile).cache()\r\nerrors = logData.filter(lambda line: \"ERROR\" in line)<\/pre>\nBasically, the<\/span>\u00a0functions in PySpark which are defined with the def keyword;\u00a0 can\u00a0be passed easily. And, this is very beneficial for longer functions that cannot be shown using the lambda:<\/span>\u00a0<\/span><\/p>\ndef is_error(line):\r\n return \"ERROR\" in line\r\nerrors = logData.filter(is_error)<\/pre>\nMoreover, in enclosing scopes functions can access objects, however, to those objects modifications within RDD methods will not be propagated back:<\/p>\n
error_keywords = [\"Exception\", \"Error\"]\r\ndef is_error(line):\r\n return any(keyword in line for keyword in error_keywords)\r\nerrors = logData.filter(is_error)<\/pre>\nAlso, to launch an interactive shell, PySpark fully supports interactive use, so simply run .\/bin\/pyspark.<\/p>\n
Installing and Configuring PySpark<\/span><\/h3>\nBasically, we require Python 2.6 or higher for PySpark. Moreover, by using a standard CPython interpreter in order to support Python modules<\/strong> that use C extensions, we can execute PySpark applications. <\/span><\/p>\nIn addition, PySpark requires python to be available on the system PATH and use it to run programs by default. However, by setting the PYSPARK_PYTHON environment variable in conf\/spark-env.sh (or .cmd on Windows), an alternate Python executable may be specified.<\/span><\/p>\nMoreover, including Py4J, all of PySpark\u2019s library dependencies, are in a bundle with PySpark.<\/span><\/p>\nFurther, using the bin\/pyspark script, Standalone PySpark applications\u00a0must run. Also,\u00a0using the settings in conf\/spark-env.sh or .cmd, it automatically configures the Java as well as Python environment. And, while it comes to the bin\/pyspark package, the script automatically adds to the PYTHONPATH.<\/span><\/p>\nInteractive Use of PySpark<\/span><\/h3>\nTo run PySpark applications, the bin\/pyspark script launches a Python interpreter. At first build Spark, then launch it directly from the command line without any options, to use PySpark interactively:<\/span><\/p>\n$ sbt\/sbt assembly\r\n$ .\/bin\/pyspark<\/pre>\nAlso, to explore data interactively we can use the Python shell and moreover it is a simple way to learn the API:<\/span><\/p>\nwords = sc.textFile(\"\/usr\/share\/dict\/words\")\r\nwords.filter(lambda w: w.startswith(\"spar\")).take(5)\r\n[u'spar', u'sparable', u'sparada', u'sparadrap', u'sparagrass']\r\nhelp(pyspark) # Show all pyspark functions<\/pre>\nHowever, the bin\/pyspark shell creates SparkContext that runs applications locally on a single core, by default. Further, <\/span>set the MASTER environment variable, in order\u00a0<\/span>to connect to a non-local cluster, or also to use multiple cores. <\/span><\/p>\nFor example:<\/strong><\/p>\nIf we want to use the bin\/pyspark shell along with the standalone Spark cluster<\/strong>:<\/span><\/p>\n$ MASTER=spark:\/\/IP:PORT .\/bin\/pyspark<\/pre>\nOr, to use four cores on the local machine:<\/span>
\n<\/b><\/p>\n$ MASTER=local[4] .\/bin\/pyspark<\/pre>\nIPython<\/span><\/h3>\nMoreover, we can easily launch PySpark in IPython by following this PySpark tutorial. Here, IPython refers to an enhanced Python interpreter. Basically, with IPython 1.0.0, PySpark can easily work. Hence, set the IPYTHON variable to 1\u00a0at the time of running bin\/pyspark, to use IPython:<\/span><\/p>\n$ IPYTHON=1 .\/bin\/pyspark<\/pre>\nIn addition, by setting IPYTHON_OPTS, we can customize the of its command. <\/span><\/p>\nFor example:<\/strong><\/p>\nIn order to launch the IPython Notebook by using PyLab graphing support:<\/span><\/p>\n$ IPYTHON_OPTS=\"notebook --pylab inline\" .\/bin\/pyspark<\/pre>\nMoreover, if we set the MASTER environment variable, IPython also works on a cluster or on multiple cores.<\/span><\/p>\nStandalone Programs<\/span><\/h3>\nBy creating a SparkContext in our script and by running the script using bin\/pyspark, we can use PySpark from standalone Python scripts<\/strong>. <\/span><\/p>\nSo, using the Python API (PySpark), we will see how to write a standalone application.<\/span><\/p>\nFor example<\/strong><\/p>\nHere we are creating a simple Spark application, SimpleApp1.py:<\/span><\/p>\n\"\"\"SimpleApp1.py\"\"\"\r\nfrom pyspark import SparkContext\r\nlogFile = \"$YOUR_SPARK_HOME\/README.md\" # Should be some file on your system\r\nsc = SparkContext(\"local\", \"Simple App1\")\r\nlogData = sc.textFile(logFile).cache()\r\nnumAs = logData.filter(lambda s: 'a' in s).count()\r\nnumBs = logData.filter(lambda s: 'b' in s).count()\r\nprint \"Lines with a: %i, lines with b: %i\" % (numAs, numBs)<\/pre>\n
Prerequisites to PySpark<\/span><\/h3>\nWe assume that before learning PySpark, the readers already have basic knowledge about the programming language as well as frameworks. Also,\u00a0it is recommended to have a sound knowledge of Spark,\u00a0Hadoop, Scala Programming Language,\u00a0HDFS as well as Python.<\/span><\/p>\nFactors about PySpark API<\/span><\/h3>\nThere are a few key differences between the Python and Scala APIs which we will discuss in this PySpark Tutorial:<\/span><\/p>\n\n- Since Python is dynamically typed, therefore PySpark RDDs can easily hold objects of multiple types.<\/span><\/li>\n
- PySpark doesn’t support some API calls, like lookup and non-text input files. However, this feature will be added in future releases.<\/span><\/li>\n<\/ul>\n
Although RDDs support the same methods as their Scala counterparts in PySpark but takes Python functions<\/strong> and returns Python collection types as a result. Using Python\u2019s lambda syntax, short functions can be passed to RDD methods.<\/span><\/p>\nlogData = sc.textFile(logFile).cache()\r\nerrors = logData.filter(lambda line: \"ERROR\" in line)<\/pre>\nBasically, the<\/span>\u00a0functions in PySpark which are defined with the def keyword;\u00a0 can\u00a0be passed easily. And, this is very beneficial for longer functions that cannot be shown using the lambda:<\/span>\u00a0<\/span><\/p>\ndef is_error(line):\r\n return \"ERROR\" in line\r\nerrors = logData.filter(is_error)<\/pre>\nMoreover, in enclosing scopes functions can access objects, however, to those objects modifications within RDD methods will not be propagated back:<\/p>\n
error_keywords = [\"Exception\", \"Error\"]\r\ndef is_error(line):\r\n return any(keyword in line for keyword in error_keywords)\r\nerrors = logData.filter(is_error)<\/pre>\nAlso, to launch an interactive shell, PySpark fully supports interactive use, so simply run .\/bin\/pyspark.<\/p>\n
Installing and Configuring PySpark<\/span><\/h3>\nBasically, we require Python 2.6 or higher for PySpark. Moreover, by using a standard CPython interpreter in order to support Python modules<\/strong> that use C extensions, we can execute PySpark applications. <\/span><\/p>\nIn addition, PySpark requires python to be available on the system PATH and use it to run programs by default. However, by setting the PYSPARK_PYTHON environment variable in conf\/spark-env.sh (or .cmd on Windows), an alternate Python executable may be specified.<\/span><\/p>\nMoreover, including Py4J, all of PySpark\u2019s library dependencies, are in a bundle with PySpark.<\/span><\/p>\nFurther, using the bin\/pyspark script, Standalone PySpark applications\u00a0must run. Also,\u00a0using the settings in conf\/spark-env.sh or .cmd, it automatically configures the Java as well as Python environment. And, while it comes to the bin\/pyspark package, the script automatically adds to the PYTHONPATH.<\/span><\/p>\nInteractive Use of PySpark<\/span><\/h3>\nTo run PySpark applications, the bin\/pyspark script launches a Python interpreter. At first build Spark, then launch it directly from the command line without any options, to use PySpark interactively:<\/span><\/p>\n$ sbt\/sbt assembly\r\n$ .\/bin\/pyspark<\/pre>\nAlso, to explore data interactively we can use the Python shell and moreover it is a simple way to learn the API:<\/span><\/p>\nwords = sc.textFile(\"\/usr\/share\/dict\/words\")\r\nwords.filter(lambda w: w.startswith(\"spar\")).take(5)\r\n[u'spar', u'sparable', u'sparada', u'sparadrap', u'sparagrass']\r\nhelp(pyspark) # Show all pyspark functions<\/pre>\nHowever, the bin\/pyspark shell creates SparkContext that runs applications locally on a single core, by default. Further, <\/span>set the MASTER environment variable, in order\u00a0<\/span>to connect to a non-local cluster, or also to use multiple cores. <\/span><\/p>\nFor example:<\/strong><\/p>\nIf we want to use the bin\/pyspark shell along with the standalone Spark cluster<\/strong>:<\/span><\/p>\n$ MASTER=spark:\/\/IP:PORT .\/bin\/pyspark<\/pre>\nOr, to use four cores on the local machine:<\/span>
\n<\/b><\/p>\n$ MASTER=local[4] .\/bin\/pyspark<\/pre>\nIPython<\/span><\/h3>\nMoreover, we can easily launch PySpark in IPython by following this PySpark tutorial. Here, IPython refers to an enhanced Python interpreter. Basically, with IPython 1.0.0, PySpark can easily work. Hence, set the IPYTHON variable to 1\u00a0at the time of running bin\/pyspark, to use IPython:<\/span><\/p>\n$ IPYTHON=1 .\/bin\/pyspark<\/pre>\nIn addition, by setting IPYTHON_OPTS, we can customize the of its command. <\/span><\/p>\nFor example:<\/strong><\/p>\nIn order to launch the IPython Notebook by using PyLab graphing support:<\/span><\/p>\n$ IPYTHON_OPTS=\"notebook --pylab inline\" .\/bin\/pyspark<\/pre>\nMoreover, if we set the MASTER environment variable, IPython also works on a cluster or on multiple cores.<\/span><\/p>\nStandalone Programs<\/span><\/h3>\nBy creating a SparkContext in our script and by running the script using bin\/pyspark, we can use PySpark from standalone Python scripts<\/strong>. <\/span><\/p>\nSo, using the Python API (PySpark), we will see how to write a standalone application.<\/span><\/p>\nFor example<\/strong><\/p>\nHere we are creating a simple Spark application, SimpleApp1.py:<\/span><\/p>\n\"\"\"SimpleApp1.py\"\"\"\r\nfrom pyspark import SparkContext\r\nlogFile = \"$YOUR_SPARK_HOME\/README.md\" # Should be some file on your system\r\nsc = SparkContext(\"local\", \"Simple App1\")\r\nlogData = sc.textFile(logFile).cache()\r\nnumAs = logData.filter(lambda s: 'a' in s).count()\r\nnumBs = logData.filter(lambda s: 'b' in s).count()\r\nprint \"Lines with a: %i, lines with b: %i\" % (numAs, numBs)<\/pre>\n
Factors about PySpark API<\/span><\/h3>\nThere are a few key differences between the Python and Scala APIs which we will discuss in this PySpark Tutorial:<\/span><\/p>\n\n- Since Python is dynamically typed, therefore PySpark RDDs can easily hold objects of multiple types.<\/span><\/li>\n
- PySpark doesn’t support some API calls, like lookup and non-text input files. However, this feature will be added in future releases.<\/span><\/li>\n<\/ul>\n
Although RDDs support the same methods as their Scala counterparts in PySpark but takes Python functions<\/strong> and returns Python collection types as a result. Using Python\u2019s lambda syntax, short functions can be passed to RDD methods.<\/span><\/p>\nlogData = sc.textFile(logFile).cache()\r\nerrors = logData.filter(lambda line: \"ERROR\" in line)<\/pre>\nBasically, the<\/span>\u00a0functions in PySpark which are defined with the def keyword;\u00a0 can\u00a0be passed easily. And, this is very beneficial for longer functions that cannot be shown using the lambda:<\/span>\u00a0<\/span><\/p>\ndef is_error(line):\r\n return \"ERROR\" in line\r\nerrors = logData.filter(is_error)<\/pre>\nMoreover, in enclosing scopes functions can access objects, however, to those objects modifications within RDD methods will not be propagated back:<\/p>\n
error_keywords = [\"Exception\", \"Error\"]\r\ndef is_error(line):\r\n return any(keyword in line for keyword in error_keywords)\r\nerrors = logData.filter(is_error)<\/pre>\nAlso, to launch an interactive shell, PySpark fully supports interactive use, so simply run .\/bin\/pyspark.<\/p>\n
Installing and Configuring PySpark<\/span><\/h3>\nBasically, we require Python 2.6 or higher for PySpark. Moreover, by using a standard CPython interpreter in order to support Python modules<\/strong> that use C extensions, we can execute PySpark applications. <\/span><\/p>\nIn addition, PySpark requires python to be available on the system PATH and use it to run programs by default. However, by setting the PYSPARK_PYTHON environment variable in conf\/spark-env.sh (or .cmd on Windows), an alternate Python executable may be specified.<\/span><\/p>\nMoreover, including Py4J, all of PySpark\u2019s library dependencies, are in a bundle with PySpark.<\/span><\/p>\nFurther, using the bin\/pyspark script, Standalone PySpark applications\u00a0must run. Also,\u00a0using the settings in conf\/spark-env.sh or .cmd, it automatically configures the Java as well as Python environment. And, while it comes to the bin\/pyspark package, the script automatically adds to the PYTHONPATH.<\/span><\/p>\nInteractive Use of PySpark<\/span><\/h3>\nTo run PySpark applications, the bin\/pyspark script launches a Python interpreter. At first build Spark, then launch it directly from the command line without any options, to use PySpark interactively:<\/span><\/p>\n$ sbt\/sbt assembly\r\n$ .\/bin\/pyspark<\/pre>\nAlso, to explore data interactively we can use the Python shell and moreover it is a simple way to learn the API:<\/span><\/p>\nwords = sc.textFile(\"\/usr\/share\/dict\/words\")\r\nwords.filter(lambda w: w.startswith(\"spar\")).take(5)\r\n[u'spar', u'sparable', u'sparada', u'sparadrap', u'sparagrass']\r\nhelp(pyspark) # Show all pyspark functions<\/pre>\nHowever, the bin\/pyspark shell creates SparkContext that runs applications locally on a single core, by default. Further, <\/span>set the MASTER environment variable, in order\u00a0<\/span>to connect to a non-local cluster, or also to use multiple cores. <\/span><\/p>\nFor example:<\/strong><\/p>\nIf we want to use the bin\/pyspark shell along with the standalone Spark cluster<\/strong>:<\/span><\/p>\n$ MASTER=spark:\/\/IP:PORT .\/bin\/pyspark<\/pre>\nOr, to use four cores on the local machine:<\/span>
\n<\/b><\/p>\n$ MASTER=local[4] .\/bin\/pyspark<\/pre>\nIPython<\/span><\/h3>\nMoreover, we can easily launch PySpark in IPython by following this PySpark tutorial. Here, IPython refers to an enhanced Python interpreter. Basically, with IPython 1.0.0, PySpark can easily work. Hence, set the IPYTHON variable to 1\u00a0at the time of running bin\/pyspark, to use IPython:<\/span><\/p>\n$ IPYTHON=1 .\/bin\/pyspark<\/pre>\nIn addition, by setting IPYTHON_OPTS, we can customize the of its command. <\/span><\/p>\nFor example:<\/strong><\/p>\nIn order to launch the IPython Notebook by using PyLab graphing support:<\/span><\/p>\n$ IPYTHON_OPTS=\"notebook --pylab inline\" .\/bin\/pyspark<\/pre>\nMoreover, if we set the MASTER environment variable, IPython also works on a cluster or on multiple cores.<\/span><\/p>\nStandalone Programs<\/span><\/h3>\nBy creating a SparkContext in our script and by running the script using bin\/pyspark, we can use PySpark from standalone Python scripts<\/strong>. <\/span><\/p>\nSo, using the Python API (PySpark), we will see how to write a standalone application.<\/span><\/p>\nFor example<\/strong><\/p>\nHere we are creating a simple Spark application, SimpleApp1.py:<\/span><\/p>\n\"\"\"SimpleApp1.py\"\"\"\r\nfrom pyspark import SparkContext\r\nlogFile = \"$YOUR_SPARK_HOME\/README.md\" # Should be some file on your system\r\nsc = SparkContext(\"local\", \"Simple App1\")\r\nlogData = sc.textFile(logFile).cache()\r\nnumAs = logData.filter(lambda s: 'a' in s).count()\r\nnumBs = logData.filter(lambda s: 'b' in s).count()\r\nprint \"Lines with a: %i, lines with b: %i\" % (numAs, numBs)<\/pre>\n
- \n
- Since Python is dynamically typed, therefore PySpark RDDs can easily hold objects of multiple types.<\/span><\/li>\n
- PySpark doesn’t support some API calls, like lookup and non-text input files. However, this feature will be added in future releases.<\/span><\/li>\n<\/ul>\n
Although RDDs support the same methods as their Scala counterparts in PySpark but takes Python functions<\/strong> and returns Python collection types as a result. Using Python\u2019s lambda syntax, short functions can be passed to RDD methods.<\/span><\/p>\n
logData = sc.textFile(logFile).cache()\r\nerrors = logData.filter(lambda line: \"ERROR\" in line)<\/pre>\n
Basically, the<\/span>\u00a0functions in PySpark which are defined with the def keyword;\u00a0 can\u00a0be passed easily. And, this is very beneficial for longer functions that cannot be shown using the lambda:<\/span>\u00a0<\/span><\/p>\n
def is_error(line):\r\n return \"ERROR\" in line\r\nerrors = logData.filter(is_error)<\/pre>\n
Moreover, in enclosing scopes functions can access objects, however, to those objects modifications within RDD methods will not be propagated back:<\/p>\n
error_keywords = [\"Exception\", \"Error\"]\r\ndef is_error(line):\r\n return any(keyword in line for keyword in error_keywords)\r\nerrors = logData.filter(is_error)<\/pre>\n
Also, to launch an interactive shell, PySpark fully supports interactive use, so simply run .\/bin\/pyspark.<\/p>\n
Installing and Configuring PySpark<\/span><\/h3>\n
Basically, we require Python 2.6 or higher for PySpark. Moreover, by using a standard CPython interpreter in order to support Python modules<\/strong> that use C extensions, we can execute PySpark applications. <\/span><\/p>\n
In addition, PySpark requires python to be available on the system PATH and use it to run programs by default. However, by setting the PYSPARK_PYTHON environment variable in conf\/spark-env.sh (or .cmd on Windows), an alternate Python executable may be specified.<\/span><\/p>\n
Moreover, including Py4J, all of PySpark\u2019s library dependencies, are in a bundle with PySpark.<\/span><\/p>\n
Further, using the bin\/pyspark script, Standalone PySpark applications\u00a0must run. Also,\u00a0using the settings in conf\/spark-env.sh or .cmd, it automatically configures the Java as well as Python environment. And, while it comes to the bin\/pyspark package, the script automatically adds to the PYTHONPATH.<\/span><\/p>\n
Interactive Use of PySpark<\/span><\/h3>\n
To run PySpark applications, the bin\/pyspark script launches a Python interpreter. At first build Spark, then launch it directly from the command line without any options, to use PySpark interactively:<\/span><\/p>\n
$ sbt\/sbt assembly\r\n$ .\/bin\/pyspark<\/pre>\n
Also, to explore data interactively we can use the Python shell and moreover it is a simple way to learn the API:<\/span><\/p>\n
words = sc.textFile(\"\/usr\/share\/dict\/words\")\r\nwords.filter(lambda w: w.startswith(\"spar\")).take(5)\r\n[u'spar', u'sparable', u'sparada', u'sparadrap', u'sparagrass']\r\nhelp(pyspark) # Show all pyspark functions<\/pre>\n
However, the bin\/pyspark shell creates SparkContext that runs applications locally on a single core, by default. Further, <\/span>set the MASTER environment variable, in order\u00a0<\/span>to connect to a non-local cluster, or also to use multiple cores. <\/span><\/p>\n
For example:<\/strong><\/p>\n
If we want to use the bin\/pyspark shell along with the standalone Spark cluster<\/strong>:<\/span><\/p>\n
$ MASTER=spark:\/\/IP:PORT .\/bin\/pyspark<\/pre>\n
Or, to use four cores on the local machine:<\/span>
\n<\/b><\/p>\n$ MASTER=local[4] .\/bin\/pyspark<\/pre>\n
IPython<\/span><\/h3>\n
Moreover, we can easily launch PySpark in IPython by following this PySpark tutorial. Here, IPython refers to an enhanced Python interpreter. Basically, with IPython 1.0.0, PySpark can easily work. Hence, set the IPYTHON variable to 1\u00a0at the time of running bin\/pyspark, to use IPython:<\/span><\/p>\n
$ IPYTHON=1 .\/bin\/pyspark<\/pre>\n
In addition, by setting IPYTHON_OPTS, we can customize the of its command. <\/span><\/p>\n
For example:<\/strong><\/p>\n
In order to launch the IPython Notebook by using PyLab graphing support:<\/span><\/p>\n
$ IPYTHON_OPTS=\"notebook --pylab inline\" .\/bin\/pyspark<\/pre>\n
Moreover, if we set the MASTER environment variable, IPython also works on a cluster or on multiple cores.<\/span><\/p>\n
Standalone Programs<\/span><\/h3>\n
By creating a SparkContext in our script and by running the script using bin\/pyspark, we can use PySpark from standalone Python scripts<\/strong>. <\/span><\/p>\n
So, using the Python API (PySpark), we will see how to write a standalone application.<\/span><\/p>\n
For example<\/strong><\/p>\n
Here we are creating a simple Spark application, SimpleApp1.py:<\/span><\/p>\n
\"\"\"SimpleApp1.py\"\"\"\r\nfrom pyspark import SparkContext\r\nlogFile = \"$YOUR_SPARK_HOME\/README.md\" # Should be some file on your system\r\nsc = SparkContext(\"local\", \"Simple App1\")\r\nlogData = sc.textFile(logFile).cache()\r\nnumAs = logData.filter(lambda s: 'a' in s).count()\r\nnumBs = logData.filter(lambda s: 'b' in s).count()\r\nprint \"Lines with a: %i, lines with b: %i\" % (numAs, numBs)<\/pre>\n
- PySpark doesn’t support some API calls, like lookup and non-text input files. However, this feature will be added in future releases.<\/span><\/li>\n<\/ul>\n
![\"PySpark](\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/06\/Python-vs-Scala-01.jpg\")
<\/a><\/p>\n