Pyspark Read Xml

XLSX file from your computer, or you can drag and drop a file. 安装Anaconda3 3 Windows下安装 3 Linux下安装(配置window本地环境不需要执行该步骤) 54. This library arises from the needs of a solid Python layer for processing XML Schema based files for MaX (Materials design at the Exascale) European project. How to create a 3D Terrain with Google Maps and height maps in Photoshop - 3D Map Generator Terrain - Duration: 20:32. cmd on Windows). This is actually really easy, but not something spelled out explicitly in the Databricks docs, though it is mentioned in the Spark docs. Installing Hadoop-2. Directly read XML file in Apache Spark using Dataframe API. read_excel(Name. It's meant to be a human-readable and compact solution to represent a complex data structure and facilitate data-interchange between systems. 3, we will introduce improved JSON support based on the new data source API for reading and writing various format using SQL. "How can I import a. You can read data from HDFS (hdfs://), S3 (s3a://), as well as the local file system (file://). conf tcp tid. Databricks provides some nice connectors for reading and writing data to SQL Server. defaultFS라는 name을 가진 property를 하나 추가해주면 한번 PySpark의 기본 예제중 Read Next 깃헙. This article will show you how to read files in csv and json to compute word counts on selected fields. Each version of Spark has several distributions, corresponding with different versions of Hadoop. Unfortunately this is not very readable because take() returns an array and Scala simply prints the array with each element separated by a comma. To work with Hive, we have to instantiate SparkSession with Hive support, including connectivity to a persistent Hive metastore, support for Hive serdes, and Hive user-defined functions if we are using Spark 2. Transform JSON to HTML using standard XSLT stylesheets. "How can I import a. Hive root pom. JSON Explained What is JSON? JSON stands for "JavaScript Object Notation" and is pronounced "Jason" (like in the Friday the 13th movies). If you are reading from a secure S3 bucket be sure to set the following in your spark-defaults. You can also define "spark_options" in pytest. My workflow involves taking lots of json data from S3, transforming it, filtering it, then post processing the filtered output. Sadly, the process of loading files may be long, as Spark needs to infer schema of underlying records by reading them. During this process, it needs two steps where data is first converted from external type to row, and then from row to internal representation using generic RowEncoder. The entry point to programming Spark with the Dataset and DataFrame API. Install/build a compatible version. To use Apache spark we need to convert existing data into parquet format. It will help you to understand, how join works in pyspark. Now, lets get to actually reading in a file. It is because of a library called Py4j that they are able to achieve this. Example SQL Query for SOAP API call using ZappySys XML Driver Here is an example SQL query you can write to call SOAP API. How to create a 3D Terrain with Google Maps and height maps in Photoshop - 3D Map Generator Terrain - Duration: 20:32. 0: Set up Spark, HDFS, Hbase and (maybe) Zookeeper. 0+ with python 3. Apache Spark. Sadly, the process of loading files may be long, as Spark needs to infer schema of underlying records by reading them. After introducing you to the heart of Oracle XML DB, namely the XMLType framework and Oracle XML DB repository, the manual provides a. We then use the take() method to print the first 5 elements of the RDD: raw_data. 3 In here, we just added the XML package to our Spark environment. and will read at least one file in a task on reads. Use Databrick's spark-xml to parse nested xml and create csv files. When referencing missing tags in filter or select statements, exception throws. pyspark read from s3. csv file into pyspark dataframes ?" -- there are many ways to do this; the simplest would be to start up pyspark with Databrick's spark-csv module. tar files are not supported in hdfs , so even if i use the above approach i will have to pull data to local. I am unable to access data from Azure SQL using pyspark. I just got this working after seeing similar issues due to an inability to access the Zookeeper Quorum properly. If XML schema is richer, so contains tags not visible in provided XML records, be aware of exceptions. Specifically, I will show you step-by-step how to:. Install/build a compatible distribution. This manual describes Oracle XML DB, and how you can use it to store, generate, manipulate, manage, and query XML data in the database. Using PySpark, you can work with RDDs in Python programming language also. Both simple and more complex XML data is consumed and the video shows how to run. Read and Write DataFrame from Database using PySpark. Read and Write DataFrame from Database using PySpark Mon 20 March 2017. With Amazon EMR release version 5. Each object can have different data such as text, number, boolean etc. We then use the take() method to print the first 5 elements of the RDD: raw_data. class pyspark. This plugin will allow to specify SPARK_HOME directory in pytest. The output is an AVRO file and a Hive table on the top. In this post "Read and write data to SQL Server from Spark using pyspark", we are going to demonstrate how we can use Apache Spark to read and write data to a SQL Server table. This blog post illustrates an industry scenario there a collaborative involvement of Spark SQL with HDFS, Hive, and other components of the Hadoop ecosystem. PySpark Recipes - PDF eBook Free Download. Example Now, let's take an example program to parse an XML document using SAX. Operations in PySpark DataFrame are lazy in nature but, in case of pandas we get the result as soon as we apply any operation. Thanks for looking into this issue. Hello, I'm trying to get content between xml tag (with some html tag inside) thank to databricks spark xml package in python. To learn the basics of Spark, we recommend reading through the Scala programming guide first; it should be easy to follow even if you don't know Scala. 5 and below. Improved SQL API support to read/write JSON datasets. SQL Server 2019 is the new data platform to solve the challenges of the modern data professional including capabilities and solutions such as: SQL Server Big Data Clusters combining the power of SQL Server, Hadoop, Apache Spark, and Kubernetes to provide an end-to-end data and machine learning platform. Alas, SQL server always seems like it's a special case, so I tend to discount things unless they mention SQL server explicitly. I want to read excel without pd module. when i tried to load the data in pyspark (dataframe) it is showing as corrupted record. Tenny Susanto. Apache Parquet Introduction. 0: Maven; Gradle; SBT; Ivy; Grape; Leiningen; Buildr. A SQLContext can be used create DataFrame, register DataFrame as tables, execute SQL over tables, cache tables, and read parquet files. xml and yarn-site. Jul 8, 2016 2 minute read. PySpark(Python) Structured Streaming Professional Training with HandsOn 3. SparkSession (sparkContext, jsparkSession=None) [source] ¶. example-xml-file-letter. There are different ways of setting up each one of the services (standalone, distributed and pseudo distributed generally) and each use case will require a different one. 4-5) and trying to run Spark Oozie action, however when running a simple PySpark program, getting error: Error: Only local python. Spark is perhaps is in practice extensively, in comparison with Hive in the industry these days. Like most high-level languages, Python includes many methods that belong to the built-in string type. Pyspark – read in avro file April 20, 2015 by datafireball I found a fantastic example in Spark’s example called avro_inputformat. Using PySpark, the following script allows access to the AWS S3 bucket/directory used to exchange data between Spark and Snowflake. The PySpark Shell connects Python APIs with spark core initiating the SparkContext making it so robust. CSV files can be read as DataFrame. 4 minute read About. This article would be a short and sweet guide on how to utilize databricks for XML parsing. Let’s load the Spark shell and see an example:. Problem solved! PySpark Recipes covers Hadoop and its shortcomings. Update: Pyspark RDDs are still useful, but the world is moving toward DataFrames. This is how I got HBASE read/write support in Pyspark 2. If we are using earlier Spark versions, we have to use HiveContext which is. Spark SQL APIs can read data from any relational data source which supports JDBC driver. Interacting with HBase from PySpark. Millions of people use XMind to clarify thinking, manage complex information, run brainstorming and get work organized. This tool parses xml files automatically (independently of their structure), and explodes their arrays if needed, and inserts them in a new HiveQL table, to make this data accesible for data analysis. OK, I Understand. Yes, I did investigate from my side and tried with them. ElementTree. Python provides a comprehensive XML package which provides different APIs to parse XML. More details can be found in the python interpreter documentation, since matplotlib support is identical. Using PySpark, the following script allows access to the AWS S3 bucket/directory used to exchange data between Spark and Snowflake. The keys are strings and the values are the JSON types. ini and thus to make "pyspark" importable in your tests which are executed by pytest. In this tutorial, you learn how to create a dataframe from a csv file, and how to run interactive Spark SQL queries against an Apache Spark cluster in Azure HDInsight. For more information, see Apache Zeppelin. Python is no good here - you might as well drop into Scala for this one. Keep in mind that you will be paying more for larger and more. Any advice? Let me know; I can post the script here. The few differences between Pandas and PySpark DataFrame are: Operation on Pyspark DataFrame run parallel on different nodes in cluster but, in case of pandas it is not possible. There is a Spark XML library. It will help you to understand, how join works in pyspark. In our next tutorial, we shall learn to Read multiple text files to single RDD. not HDFS or S3 or other file systems). Hive root pom. More details can be found in the python interpreter documentation, since matplotlib support is identical. The keys are strings and the values are the JSON types. XML to JSON. With this, Spark can actually can achieve the performance of hand written code. There are a lot of opportunities to work on projects that mimic real-life scenarios as well as to create a powerful machine learning model with the help of different libraries. Reading Excel Files. you can copy the source data in HDFS and after that launch the Pyspark with spark XML package as mentioned below :. The read method will read in all the data into one text string. Dataframes in Pyspark can be created in multiple ways: Data can be loaded in through a CSV, JSON, XML or a Parquet file. baahu June 24, 2017 No Comments on Spark: Read Xml files using XmlInputFormat Tweet There would be instances where in we are given a huge xml which contains smaller xmls and we need to extract the same for further processing. We can read data from excel to R and write data from R back to Excel file using the readxl package in R. Directly reading in Apache Spark using DataFrame API : You can find the source XML data here. Apache PySpark (Python) Professional Training (Core Saprk and Fundamentals) 2. With Skip-gram we want to predict a window of words given a single word. Apply the solution directly in your own code. The data is in raw xml format which is ingested into the oracle data store. Menu Parse XML with PySpark in Databricks 25 February 2019. DStreams is the basic abstraction in Spark Streaming. Reading of XML file data of order requests. There is a Spark XML library. JSON is a very common way to store data. In Step 1, choose an. toInt i: Int = 1 As you can see, I just cast the string "1" to an Int object using the toInt method, which is available to any String. In this post "Read and write data to SQL Server from Spark using pyspark", we are going to demonstrate how we can use Apache Spark to read and write data to a SQL Server table. PySpark(Python) Structured Streaming Professional Training with HandsOn 3. tar files are not supported in hdfs , so even if i use the above approach i will have to pull data to local. PySpark recipes¶ DSS lets you write recipes using Spark in Python, using the PySpark API. Read SQL Server table to DataFrame using Spark SQL JDBC connector - pyspark. and will read at least one file in a task on reads. You want to open a plain-text file in Scala and process the lines in that file. It also provides an optimized API that can read the data from the various data source containing different files formats. header: when set to true, the first line of files are used to name columns and are not included in data. We will be using a general purpose instance for running spark. If you are looking for PySpark, I would still recommend reading through this article as it would give you an Idea on Parquet usage. It is unique in that it combines the speed and XML feature completeness of these libraries with the simplicity of a native Python API, mostly compatible but superior to the well-known ElementTree API. There have been a lot of different curators of this collection and everyone has their own way of entering data into the file. The read method will read in all the data into one text string. Databricks Certified Developer Apache Spark 2. format("com. class pyspark. Description. Requirement You have two table named as A and B. fit(featurizedImages) The result is an end-to-end pipeline that you can use to read, preprocess and classify images in scalable fashion. I have created HDInsight spark cluster and using Jupyter notebook. df = spark. Hi Naveen, the input is set of xml files in a given path. JSON is a very common way to store data. Let's load the Spark shell and see an example:. The operating system is CentOS 6. Specifically, I will show you step-by-step how to:. Here we have taken the FIFA World Cup Players Dataset. But, I cannot find any example code about how to do this. Databricks is powered by Apache® Spark™, which can read from Amazon S3, MySQL, HDFS, Cassandra, etc. With Spark's DataFrame support, you can use pyspark to READ and WRITE from Phoenix tables. 1, “How to open and read a text file in Scala. Note that without coalesce, Spark will keep each XML file as a separate partition which makes it less. Directly reading in Apache Spark using DataFrame API : You can find the source XML data here. ini and thus to make "pyspark" importable in your tests which are executed by pytest. Load a regular Jupyter Notebook and load PySpark using findSpark package. Apache HBase is typically queried either with its low-level API (scans, gets, and puts) or with a SQL syntax using Apache Phoenix. Perhaps the cleanest, most succinct solution to write a String to a File is through the use of the FileWriter. To work with Hive, we have to instantiate SparkSession with Hive support, including connectivity to a persistent Hive metastore, support for Hive serdes, and Hive user-defined functions if we are using Spark 2. Message view « Date » · « Thread » Top « Date » · « Thread » From: Michael Armbrust Subject: Re: org. XMind is the most professional and popular mind mapping tool. To read a directory of CSV files, specify a directory. xml에는 다음과 같이 fs. pyspark --packages com. Example: Load a DataFrame. PySpark Examples #5: Discretized Streams (DStreams) This is the fourth blog post which I share sample scripts of my presentation about "Apache Spark with Python". October 15, 2015 How To Parse and Convert JSON to CSV using Python May 20, 2016 How To Parse and Convert XML to CSV using Python November 3, 2015 Use JSPDF for Exporting Data HTML as PDF in 5 Easy Steps July 29, 2015 How To Manage SSH Keys Using Ansible August 26, 2015 How To Write Spark Applications in Python. Tenny Susanto. What is WholeStageCodeGen first? Its basically a hand written code type Code gen designed based on Thomas Neumann's seminal VLDB 2011 paper. First, here are some notes about prerequisites when you are running PySpark with yarn-cluster mode on a multi-node cluster: When a Spark job is submitted, the Spark code checks for the PYSPARK_ARCHIVES_PATH environment variable. PySpark SQL. The PySpark Shell connects Python APIs with spark core initiating the SparkContext making it so robust. All of PySpark’s library dependencies, including Py4J, are bundled with PySpark and automatically imported. Spark SQL APIs can read data from any relational data source which supports JDBC driver. But sometimes you want to execute a stored procedure or a simple statement. Support only files less than 2GB in size. Introduction. (Sample code to create the above spreadsheet. An XML Schema validator and decoder. Hive root pom. textFile() method, with the help of Java and Python examples. Send back the response of every order request as defined by business rules in xml file format. How to write into and read from a TFRecords file in TensorFlow. toJavaRDD(). Provides a dialog to set all options for the conversion. Hello, I am new to HDInsights (HDP-2. This packages implements a CSV data source for Apache Spark. xml and placed in the. We also need the python json module for parsing the inbound twitter data. Built-In String Methods. Just don't do it. Using PySpark, you can work with RDDs in Python programming language also. Message view « Date » · « Thread » Top « Date » · « Thread » From: Michael Armbrust Subject: Re: org. Just in case you need a little more explaining, keep reading. Jul 8, 2016 2 minute read. Spark supports two different way for streaming: Discretized Streams (DStreams) and Structured Streaming. Read a JSON file with the Microsoft PROSE Code Accelerator SDK. In this post, you will learn how to save a large amount of data (images) into a single TFRecords format file and load it batch-wise to train your network in tensorflow. Requirement You have two table named as A and B. python spark hadoop pyspark. This library arises from the needs of a solid Python layer for processing XML Schema based files for MaX (Materials design at the Exascale) European project. It is better to go with Python UDF:. The sample folder contains some sample Spline enabled Spark jobs. xml 그리고 yarn-site. element is an element instance. key or any of the methods outlined in the aws-sdk documentation Working with AWS credentials In order to work with the newer s3a. Docs for (spark-kotlin) will arrive here ASAP. Support only files less than 2GB in size. NOTE 2: I know there is another function called toDF() that can convert RDD to dataframe but wuth that too I have the same issue as how to pass the unknown columns. Millions of people use XMind to clarify thinking, manage complex information, run brainstorming and get work organized. In this blog, we will show how Structured Streaming can be leveraged to consume and transform complex data streams from Apache Kafka. You can make beautiful data-driven, interactive and collaborative documents with SQL, Scala and more. 前言最近在研究pyspark,用到的主要是pyspark的sql模块和ml模块。既然用到sql模块,便免不了要涉及dataframe。至于dataframe的基本操作,大家可以自行百度或者必应,很容易 博文 来自: bra_ve的博客. Python - Opening and changing large text files. xml만 있으면 됩니다. In this book, we will guide you through the latest incarnation of Apache Spark using Python. 下面的示例代码默认 HBase 中的行键、列族名、列名和值都是字符串转成的 byte 数组: read_hbase_pyspark. The lxml XML toolkit is a Pythonic binding for the C libraries libxml2 and libxslt. ini to customize pyspark. To work with Hive, we have to instantiate SparkSession with Hive support, including connectivity to a persistent Hive metastore, support for Hive serdes, and Hive user-defined functions if we are using Spark 2. Browse other questions tagged xml apache-spark dataframe pyspark apache-spark-xml or ask your. More details can be found in the python interpreter documentation, since matplotlib support is identical. It can also be used to resolve relative paths. The architecture of Spark, PySpark, and RDD are presented. xml and placed in the. Apache Spark. JSON is a very common way to store data. This is just a simple case, but the XML could be much more complex and you would need to include more and more changes. Assuming you've pip-installed pyspark, to start an ad-hoc interactive session, save the first code block to, say,. xml telling it to point only at the internal. For every row custom function is applied of the dataframe. take(5) To explore the other methods an RDD object has access to, check out the PySpark documentation. Preserve attribute and namespace information on converting XML to JSON. You can read data from HDFS (hdfs://), S3 (s3a://), as well as the local file system (file://). Example: Load a DataFrame. Apache Zeppelin is: A web-based notebook that enables interactive data analytics. The comma is known as the delimiter, it may be another character such as a semicolon. fs, or Spark APIs or use the /dbfs/ml folder described in Local file APIs for deep learning. header: when set to true, the first line of files are used to name columns and are not included in data. I have a 6 nodes cluster with Hortonworks HDP 2. There are a lot of opportunities to work on projects that mimic real-life scenarios as well as to create a powerful machine learning model with the help of different libraries. In this tutorial, you learn how to create a dataframe from a csv file, and how to run interactive Spark SQL queries against an Apache Spark cluster in Azure HDInsight. Pyspark | Linear regression with Advanced Feature Dataset using Apache MLlib Ames Housing Data: The Ames Housing dataset was compiled by Dean De Cock for use in data science education and expanded version of the often-cited Boston Housing dataset. October 15, 2015 How To Parse and Convert JSON to CSV using Python May 20, 2016 How To Parse and Convert XML to CSV using Python November 3, 2015 Use JSPDF for Exporting Data HTML as PDF in 5 Easy Steps July 29, 2015 How To Manage SSH Keys Using Ansible August 26, 2015 How To Write Spark Applications in Python. Hello, I am new to HDInsights (HDP-2. However, it has various disadvantages which I have listed below, e. For a small dataset, it is feasible to compute pairwise similarities or distances for all data instances, but for a large dataset, it is impossible. Thanks for looking into this issue. Western Australia - Bywong, Jerrawangala, Mandorah, Kingsthorpe, Lake Frome, Fingal, Canadian, Amelup, Rochdale, Redwater, Duncan, Gladstone, Saint-Leonard, Torbay. Requirement You have two table named as A and B. A SQLContext can be used create DataFrame, register DataFrame as tables, execute SQL over tables, cache tables, and read parquet files. Browse other questions tagged xml apache-spark dataframe pyspark apache-spark-xml or ask your. It is because of a library called Py4j that they are able to achieve this. Apache PySpark (Python) Professional Training (Core Saprk and Fundamentals) 2. /python/run-tests. By file-like object, we refer to objects with a read() method, such as a file handler (e. Our company just use snowflake to process data. Being new to using PySpark, I am wondering if there is any better way to write the. Locality sensitive search is often used in searching for similar objects in a large dataset. xlsx) sparkDF = sqlContext. There have been a lot of different curators of this collection and everyone has their own way of entering data into the file. I had this issue reading gpg-encrypted files. The code that we'll write will work with both types of file formats -. The RDD object raw_data closely resembles a List of String objects, one object for each line in the dataset. PySpark(Python) Structured Streaming Professional Training with HandsOn 3. Therefore, roundtrip in reading and writing XML files has the same structure but writing a DataFrame read from other sources is possible to have a different structure. Dataframes in Pyspark can be created in multiple ways: Data can be loaded in through a CSV, JSON, XML or a Parquet file. Building a Kafka and Spark Streaming pipeline - Part I Posted by Thomas Vincent on September 25, 2016 Many companies across a multitude of industries are currently maintaining data pipelines used to ingest and analyze large data streams. path: location of files. Spark SQL APIs can read data from any relational data source which supports JDBC driver. What is WholeStageCodeGen first? Its basically a hand written code type Code gen designed based on Thomas Neumann's seminal VLDB 2011 paper. pyspark --packages com. read pyspark collect_set or collect_list with groupby groupby columns collect_list Apply multiple functions to multiple groupby columns. You can do this using globbing. 7) The setting of PutFile is given below. AnalysisException: u'path OBFUSCATED_PATH_THAT_I_CLEANED_BEFORE_SUBMIT already exists. Here are a few examples of parsing nested data structures in JSON using Spark DataFrames (examples here done with Spark 1. Both simple and more complex XML data is consumed and the video shows how to run. CSV format to convert the Excel Spreadsheet to a CSV file. UDF is particularly useful when writing Pyspark codes. Hive root pom. • Developed Pyspark code to read data from Hive, group the fields and generate XML files • Enhanced the Pyspark code to write the generated XML files to a directory to zip them to CDAs. xml 그리고 yarn-site. /pyspark_init. To read a directory of CSV files, specify a directory. 7 and Python 3. Python provides a comprehensive XML package which provides different APIs to parse XML. Reading of XML file data of order requests. orient: string, Indication of expected JSON string format. Yet in the hive-site. But sometimes you want to execute a stored procedure or a simple statement. xlsx extension. I just got this working after seeing similar issues due to an inability to access the Zookeeper Quorum properly. Apache Spark. pyspark read from s3. You can do this using globbing. apache-spark,pyspark. Read from Redshift and S3 with Spark (Pyspark) on EC2. Note: There is a new version for this artifact. 安装Anaconda3 3 Windows下安装 3 Linux下安装(配置window本地环境不需要执行该步骤) 54. Actually here the vectors are not native SQL types so there will be performance overhead one way or another. The proof of concept we ran was on a very simple requirement, taking inbound files from. Databricks Data Import How-To Guide Databricks is an integrated workspace that lets you go from ingest to production, using a variety of data sources. If XML schema is richer, so contains tags not visible in provided XML records, be aware of exceptions. read the data from the hive table using Spark. Converting H5 into Spark RDD with Pyspark. Directly reading in Apache Spark using DataFrame API : You can find the source XML data here. If the file is too large, it can crash the executor. 0 then you can follow the following steps:. " Back to top Problem. Read on O'Reilly Online Learning with a 10-day trial Start Analyzing neuroimaging data with PySpark and Thunder Parsing XML Documents with Scala’s XML. The sample folder contains some sample Spline enabled Spark jobs. It can be very easy to use Spark to convert XML to Parquet and then query and analyse the output data. g how to create DataFrame from an RDD, List, Seq, TXT, CSV, JSON, XML files, Database e. OK, I Understand. This packages implements a CSV data source for Apache Spark. The fact-checkers, whose work is more and more important for those who prefer facts over lies, police the line between fact and falsehood on a day-to-day basis, and do a great job. Today, my small contribution is to pass along a very good overview that reflects on one of Trump’s favorite overarching falsehoods. Namely: Trump describes an America in which everything was going down the tubes under  Obama, which is why we needed Trump to make America great again. And he claims that this project has come to fruition, with America setting records for prosperity under his leadership and guidance. “Obama bad; Trump good” is pretty much his analysis in all areas and measurement of U.S. activity, especially economically. Even if this were true, it would reflect poorly on Trump’s character, but it has the added problem of being false, a big lie made up of many small ones. Personally, I don’t assume that all economic measurements directly reflect the leadership of whoever occupies the Oval Office, nor am I smart enough to figure out what causes what in the economy. But the idea that presidents get the credit or the blame for the economy during their tenure is a political fact of life. Trump, in his adorable, immodest mendacity, not only claims credit for everything good that happens in the economy, but tells people, literally and specifically, that they have to vote for him even if they hate him, because without his guidance, their 401(k) accounts “will go down the tubes.” That would be offensive even if it were true, but it is utterly false. The stock market has been on a 10-year run of steady gains that began in 2009, the year Barack Obama was inaugurated. But why would anyone care about that? It’s only an unarguable, stubborn fact. Still, speaking of facts, there are so many measurements and indicators of how the economy is doing, that those not committed to an honest investigation can find evidence for whatever they want to believe. Trump and his most committed followers want to believe that everything was terrible under Barack Obama and great under Trump. That’s baloney. Anyone who believes that believes something false. And a series of charts and graphs published Monday in the Washington Post and explained by Economics Correspondent Heather Long provides the data that tells the tale. The details are complicated. Click through to the link above and you’ll learn much. But the overview is pretty simply this: The U.S. economy had a major meltdown in the last year of the George W. Bush presidency. Again, I’m not smart enough to know how much of this was Bush’s “fault.” But he had been in office for six years when the trouble started. So, if it’s ever reasonable to hold a president accountable for the performance of the economy, the timeline is bad for Bush. GDP growth went negative. Job growth fell sharply and then went negative. Median household income shrank. The Dow Jones Industrial Average dropped by more than 5,000 points! U.S. manufacturing output plunged, as did average home values, as did average hourly wages, as did measures of consumer confidence and most other indicators of economic health. (Backup for that is contained in the Post piece I linked to above.) Barack Obama inherited that mess of falling numbers, which continued during his first year in office, 2009, as he put in place policies designed to turn it around. By 2010, Obama’s second year, pretty much all of the negative numbers had turned positive. By the time Obama was up for reelection in 2012, all of them were headed in the right direction, which is certainly among the reasons voters gave him a second term by a solid (not landslide) margin. Basically, all of those good numbers continued throughout the second Obama term. The U.S. GDP, probably the single best measure of how the economy is doing, grew by 2.9 percent in 2015, which was Obama’s seventh year in office and was the best GDP growth number since before the crash of the late Bush years. GDP growth slowed to 1.6 percent in 2016, which may have been among the indicators that supported Trump’s campaign-year argument that everything was going to hell and only he could fix it. During the first year of Trump, GDP growth grew to 2.4 percent, which is decent but not great and anyway, a reasonable person would acknowledge that — to the degree that economic performance is to the credit or blame of the president — the performance in the first year of a new president is a mixture of the old and new policies. In Trump’s second year, 2018, the GDP grew 2.9 percent, equaling Obama’s best year, and so far in 2019, the growth rate has fallen to 2.1 percent, a mediocre number and a decline for which Trump presumably accepts no responsibility and blames either Nancy Pelosi, Ilhan Omar or, if he can swing it, Barack Obama. I suppose it’s natural for a president to want to take credit for everything good that happens on his (or someday her) watch, but not the blame for anything bad. Trump is more blatant about this than most. If we judge by his bad but remarkably steady approval ratings (today, according to the average maintained by 538.com, it’s 41.9 approval/ 53.7 disapproval) the pretty-good economy is not winning him new supporters, nor is his constant exaggeration of his accomplishments costing him many old ones). I already offered it above, but the full Washington Post workup of these numbers, and commentary/explanation by economics correspondent Heather Long, are here. On a related matter, if you care about what used to be called fiscal conservatism, which is the belief that federal debt and deficit matter, here’s a New York Times analysis, based on Congressional Budget Office data, suggesting that the annual budget deficit (that’s the amount the government borrows every year reflecting that amount by which federal spending exceeds revenues) which fell steadily during the Obama years, from a peak of $1.4 trillion at the beginning of the Obama administration, to $585 billion in 2016 (Obama’s last year in office), will be back up to $960 billion this fiscal year, and back over $1 trillion in 2020. (Here’s the New York Times piece detailing those numbers.) Trump is currently floating various tax cuts for the rich and the poor that will presumably worsen those projections, if passed. As the Times piece reported: