Pyspark Withcolumn Multiple Columns

MEMORY_ONLY_SER): """Sets the storage level to persist its values across operations after the first time it is computed. public DatasetwithColumn(String colName, Column col) Step by step process to add new column to Dataset To add a new column to Dataset in Apache Spark 1. I will focus on manipulating RDD in PySpark by applying operations (Transformation and Actions). I start from exploring database using some query languages, especially investigate on aggregated information. def when (self, condition, value): """ Evaluates a list of conditions and returns one of multiple possible class:`Column` expression. What I need now is if TableA's Column1 matches TableB's Column1 then take the data from TableB Column2, and put it in a new column called Column2 in TableA. In the third part, the PySpark application was ported to Scala Spark and unit tested. So their size is limited by your server memory, and you will process them with the power of a single server. It will vary. How a column is split into multiple pandas. In Spark SQL dataframes also we can replicate same functionality by using WHEN clause multiple times, once for each conditional check. types as T def my_func (col): do stuff to column here return transformed_value # if we assume that my_func returns a string my_udf = F. Adding two columns to existing DataFrame using withColumn. withColumn() method. DataFrame A distributed collection of data grouped into named columns. com/questions/36251004/pyspark-aggregation-on-mutiple-columns. There are multiple ways of generating SEQUENCE numbers however I find zipWithIndex as the best one in terms of simplicity and performance combined. It is not possible to add a column based on the data from an another table. In the first part, we saw how to retrieve, sort and filter data using Spark RDDs, DataFrames and SparkSQL. Dear All, I am trying to run FPGrowth from MLLib on my transactional data. I'm not a huge fan of this. The Scala foldLeft method can be used to iterate over a data structure and perform multiple operations on a Spark DataFrame. Some of the columns are single values, and others are lists. Type coercions implemented in parser are somewhat limited and in some cases unobvious. Below example creates a “fname” column from “name. I need to concatenate two columns in a dataframe. You can access the standard functions using the to "num" column scala> df. functions import udf, array from pyspark. In this post I am going to describe with example code as to how we can add a new column to an existing DataFrame using withColumn() function of DataFrame. IllegalArgumentException: 'Data type ArrayType(DoubleType,true) is not supported. Import all needed package Few objects/classes will be used in the article. Moving from our Traditional ETL tools like Pentaho or Talend which I'm using too, I came across Spark(pySpark). PySpark Cheat Sheet: Spark in Python This PySpark cheat sheet with code samples covers the basics like initializing Spark in Python, loading data, sorting, and repartitioning. In particular, it will cover the use of PySpark within Qubole's environment to explore your data, transform the data into meaningful features, build a Random Forest Regression model, and utilize the model to predict your next month's sales numbers. Spark lets you spread data and computations over clusters with multiple nodes (think of each node as a separate computer). They are extracted from open source Python projects. 03/15/2017; 31 minutes to read +6; In this article. types import * from pyspark. spark / python / pyspark / sql / column. Partition by multiple columns. Here we have taken the FIFA World Cup Players Dataset. functions as F import pyspark. It provides APIs in Java, Python, or Scala. How do I install pyspark for use in standalone scripts? PySpark: withColumn() with two conditions and three outcomes; How do I do multiple CASE WHEN conditions using SQL Server 2008? Apache Spark: How to use pyspark with Python 3; How do I read a parquet in PySpark written from Spark?. Predicting Song Listens Using Apache Spark. In this case, we can use when() to create a column when the outcome of a conditional is true. It's origin goes back to 2009, and the main reasons why it has gained so much importance in the past recent years are due to changes in enconomic factors that underline computer applications and hardware. Spark DataFrame expand on a lot of these concepts, allowing you to transfer that knowledge easily by understanding the simple syntax of Spark DataFrames. In the third part, the PySpark application was ported to Scala Spark and unit tested. Spark also offers us a way to define our own Transformer and Estimatorcomponents if the ones provided aren't enough. unpersist() withColumn (colName, col) ¶ Adds a column or replaces the existing column that has the same name. On Spark, you can very flexible write scripts for data processing. pyspark union dataframe (2) I have a dataframe which has one row, and several columns. Data Wrangling-Pyspark: Dataframe Row & Columns. pyspark spark-sql function column no space left on device Question by Rozmin Daya · Mar 17, 2016 at 04:37 AM · I have a dataframe for which I want to update a large number of columns using a UDF. What am I going to learn from this PySpark Tutorial? This spark and python tutorial will help you understand how to use Python API bindings i. withColumn (colName, col)¶. from pyspark. I am attempting to create a binary column which will be defined by the value of the tot_amt column. def when (self, condition, value): """ Evaluates a list of conditions and returns one of multiple possible class:`Column` expression. com 準備 サンプルデータは iris 。 今回は HDFS に csv を置き、そこから読み取って DataFrame を作成する。. Pyspark is a powerful framework for large scale data analysis. I know that if I were to operate on a single string I'd just use the split() method in python: "1x1". groupBy on Spark Data frame. We will show two ways of appending the new column, the first one being the naïve way and the second one the Spark way. We could have also used withColumnRenamed() to replace an existing column after the transformation. I can create new columns in Spark using. There are several ways to achieve this. It is estimated to account for 70 to 80% of total time taken for model development. Posted by Easy Programming at. withColumn() method to add a new column to a Spark dataframe. The goal of this post is to present an overview of some exploratory data analysis methods for machine learning and other applications in PySpark and Spark SQL. Data exploration and modeling with Spark. I need to concatenate two columns in a dataframe. withColumn, column expression can reference only the columns from a given data frame. collect_list('names')) will give me values for country & names attribute & for names attribute it will give column header as collect. id: Data frame identifier. This blog post will show how to chain Spark SQL functions so you can avoid messy nested function calls that are hard to read. pyspark spark-sql function column no space left on device Question by Rozmin Daya · Mar 17, 2016 at 04:37 AM · I have a dataframe for which I want to update a large number of columns using a UDF. PySpark: Appending columns to DataFrame when DataFrame. In both PySpark and pandas, df dot column…will give you the list of the column names. com DataCamp Learn Python for Data Science Interactively. Solution Assume the name of hive table is "transact_tbl" and it has one column named as "connections", and values in connections column are comma separated and total two commas. Hi Brian, You shouldn't need to use exlode, that will create a new row for each value in the array. In Spark SQL dataframes also we can replicate same functionality by using WHEN clause multiple times, once for each conditional check. How is it possible to replace all the numeric values of the. Its advantage is that its a columnar storage format, which means that each column is stored separately, rather than each row (think about when you open up a CSV, you generally read it by rows. com 準備 サンプルデータは iris 。 今回は HDFS に csv を置き、そこから読み取って DataFrame を作成する。. def when (self, condition, value): """ Evaluates a list of conditions and returns one of multiple possible result expressions. Row A row of data in a DataFrame. This is mainly useful when creating small DataFrames for unit tests. On Spark, you can very flexible write scripts for data processing. I can write a function something like. All of this is submitted as one message per data set and ends up in the payload column in Spark, in some binary format. pyspark unit test. withColumn() methods. In many Spark applications, there are common use cases in which columns derived from one or more existing columns in a DataFrame are appended during the data preparation or data transformation stages. The reason max isn't working for your dataframe is because it is trying to find the max for that column for every row in you dataframe and not just the max in the array. Adding two columns to existing DataFrame using withColumn. Pyspark DataFrames Example 1: FIFA World Cup Dataset. functions are. This is a quick and easy solution if you have a file with 1k or fewer rows (about 1MB) and do not want to explode beyond 20k rows. In this post I am going to describe with example code as to how we can add a new column to an existing DataFrame using withColumn() function of DataFrame. When you use DataFrame. As you would remember, a RDD (Resilient Distributed Database) is a collection of elements, that can be divided across multiple nodes in a cluster to run parallel processing. Some of the columns are single values, and others are lists. We could have also used withColumnRenamed() to replace an existing column after the transformation. Recommend:pyspark - How to exclude multiple columns in Spark dataframe in Python. I need to concatenate two columns in a dataframe. Managing Spark dataframes in Python. This function actually does only one thing which is calling df = pd. Column // The target type triggers the implicit conversion to Column scala> val idCol: Column = $ "id" idCol: org. DataFrame A distributed collection of data grouped into named columns. Overview For SQL developers that are familiar with SCD and merge statements, you may wonder how to implement the same in big data platforms, considering database or storages in Hadoop are not designed/optimised for record level updates and inserts. What your are trying to achieve here is simply not supported. You may need to add new columns in the existing SPARK dataframe as per the requirement. Pyspark is a powerful framework for large scale data analysis. How do I install pyspark for use in standalone scripts? PySpark: withColumn() with two conditions and three outcomes; How do I do multiple CASE WHEN conditions using SQL Server 2008? Apache Spark: How to use pyspark with Python 3; How do I read a parquet in PySpark written from Spark?. Select Multiple Values from Same Column; one sql statement and split into separate columns 2 Convert column from string of numbers to a subtotal on select statment. csv/ year=2019/ month=01/ day=01/ Country=CN/ part…. Spark dataframe split one column into multiple columns using split function April 23, 2018 adarsh 4d Comments Lets say we have dataset as below and we want to split a single column into multiple columns using withcolumn and split functions of dataframe. The add-in can be downloaded here. withColumn() methods. withColumn, column expression can reference only the columns from a given data frame. 如果已经启动了一个连接 mysql 数据库的 pyspark, 再重新启动一个时会报错, 这个时候就要把之前的启动的杀掉: ps aux | grep 'spark'. Its advantage is that its a columnar storage format, which means that each column is stored separately, rather than each row (think about when you open up a CSV, you generally read it by rows. I have yet found a convenient way to create multiple columns at once without chaining multiple. unpersist() withColumn (colName, col) ¶ Adds a column or replaces the existing column that has the same name. com/questions/36251004/pyspark-aggregation-on-mutiple-columns. As a generic example, say I want to return a new column called "code" that returns a code based on the value of "Amt". Server not enabled. Apache arises as a new engine and programming model for data analytics. #Three parameters have to be passed through approxQuantile function #1. Some of the columns are single values, and others are lists. Apache Spark and Python for Big Data and Machine Learning. What Spark adds to existing frameworks like Hadoop are the ability to add multiple map and reduce tasks to a single workflow. In the upcoming 1. While you can get away with quite a bit when writing SQL - which is all too familiar to most of us now, the transition into other languages (from a BI background) requires a bit more. When using multiple columns in the orderBy of a WindowSpec the order by seems to work only for the first column. Here's a weird behavior where RDD. firstname” and drops the “name” column. Using GROUP BY on Multiple Columns. Each function can be stringed together to do more complex tasks. Performing operations on multiple columns in a PySpark DataFrame. Hi Brian, You shouldn't need to use exlode, that will create a new row for each value in the array. 1 (one) first highlighted chunk. I can create new columns in Spark using. Pyspark DataFrames Example 1: FIFA World Cup Dataset. functions as F import pyspark. Adding Multiple Columns to Spark DataFrames Jan 8, 2017 I have been using spark's dataframe API for quite sometime and often I would want to add many columns to a dataframe(for ex : Creating more features from existing features for a machine learning model) and find it hard to write many withColumn statements. SQLContext Main entry point for DataFrame and SQL functionality. zip or DataFrame. withColumn ('testColumn', F. PySpark is a Spark Python API that exposes the Spark programming model to Python - With it, you can speed up analytic applications. spark-issues mailing list archives: November 2017 (SPARK-22641) Pyspark UDF relying on column added with withColumn after distinct [Updated] (SPARK-22641. Adding two columns to existing DataFrame using withColumn. In addition to above points, Pandas and Pyspark DataFrame have some basic differences like columns selection, filtering, adding the columns, etc. Select Multiple Values from Same Column; one sql statement and split into separate columns 2 Convert column from string of numbers to a subtotal on select statment. It is not possible to add a column based on the data from an another table. Here's a weird behavior where RDD. This allows Google to show you relevant ads, Amazon to recommend relevant products, and Netflix to recommend movies that you might like. SQLContext Main entry point for DataFrame and SQL functionality. Moving from our Traditional ETL tools like Pentaho or Talend which I'm using too, I came across Spark(pySpark). Managing Spark dataframes in Python. firstname” and drops the “name” column. import pyspark. To decode this JSON message to DataFrame column we have one reliable function provided by spark, That is nothing but From_json(). sql package). What Spark adds to existing frameworks like Hadoop are the ability to add multiple map and reduce tasks to a single workflow. As you might have noticed type of timestamp column is explicitly forced to be a TimestampType. Obviously the imputed columns all end with _imputed. This is the fifth tutorial on the Spark RDDs Vs DataFrames vs SparkSQL blog post series. It's important to understand that this type coercion is performed in JSON parser, and it has nothing to do with DataFrame's type casting functionality. What your are trying to achieve here is simply not supported. I'm using pyspark, loading a large csv file into a dataframe with spark-csv, and as a pre-processing step I need to apply a variety of operations to the data available in one of the columns (that contains a json string). col – the name of the numerical column #2. # Provide the min, count, and avg and groupBy the location column. I have general analytics routine in my recent project. Although it is a useful tool for building machine learning pipelines, I find it difficult and frustrating to integrate scikit-learn with pandas DataFrames, especially in production code. UserDefinedFunction (my_func, T. Matrix which is not a type defined in pyspark. com 準備 サンプルデータは iris 。 今回は HDFS に csv を置き、そこから読み取って DataFrame を作成する。. unpersist() withColumn (colName, col) ¶ Adds a column or replaces the existing column that has the same name. r m x p toggle line displays. withColumn() method. What is difference between class and interface in C#; Mongoose. What about integer type? Two different strategies. You may need to add new columns in the existing SPARK dataframe as per the requirement. Because of the easy-to-use API, you can easily develop pyspark programs if you are familiar with Python programming. 0 (zero) top of page. What am I going to learn from this PySpark Tutorial? This spark and python tutorial will help you understand how to use Python API bindings i. I have written a post on how to set up an Linux image with Spark installed. From the output, we can see that column salaries by function collect_list has the same values in a window. First lets create a udf_wrapper decorator to keep the code concise from pyspark. Partition by multiple columns. When you pass a column object, you can perform operations like addition or subtraction on the column to change the data contained in it, much like inside. types as T def my_func (col): do stuff to column here return transformed_value # if we assume that my_func returns a string my_udf = F. 3 kB each and 1. I need to concatenate two columns in a dataframe. withColumn ( "tmp" , F. select ( "tmp. It also supports Scala, but Python and Java are new. Because of the easy-to-use API, you can easily develop pyspark programs if you are familiar with Python programming. It is not possible to add a column based on the data from an another table. A data analyst gives a tutorial on how to use the Python language in conjunction with Apache Spark, known as PySpark, in order to perform big data operations. I guess this is where Spark is headed to since handling multiple variables at a time is a much more common scenario than one column at a time. pyspark unit test. The example creates the data like this: #data = [["a",. Python For Data Science Cheat Sheet PySpark - SQL Basics Learn Python for data science Interactively at www. These arguments can either be the column name as a string (one for each column) or a column object (using the df. First lets create a udf_wrapper decorator to keep the code concise from pyspark. Apache Spark and Python for Big Data and Machine Learning. I would like to add several columns to a spark (actually pyspark) dataframe , these columns all being functions of several input columns in the df. If :func:`Column. withColumn() method to add a new column to a Spark dataframe. How to sort a dataframe by multiple column(s) 885. 5, with more than 100 built-in functions introduced in Spark 1. GROUP BY on Spark Data frame is used to aggregation on Data Frame data. In this post, I will show how to set up a Python environment to run Python. Using iterators to apply the same operation on multiple columns is vital for maintaining a DRY codebase. It's difficult to reproduce because it's nondeterministic, doesn't occur in local mode, and requires ≥2 workers. UDF is particularly useful when writing Pyspark codes. I would like to add this column to the above data. This first article focuses on the streaming and present the use case. Casting a variable. Some of the columns are single values, and others are lists. GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. unpersist() withColumn (colName, col) ¶ Adds a column or replaces the existing column that has the same name. In general, the numeric elements have different values. One of the most common uses of big data is to predict what users want. com/questions/46829276/spark-dataframe-aggregate-and-groupby-multiple-columns-while-retaining-order. functions are. How a column is split into multiple pandas. I know that if I were to operate on a single string I'd just use the split() method in python: "1x1". col – the name of the numerical column #2. Pyspark Apply Function To Multiple Columns The above fold() function should be working if there is no gap between the STARTING/STOP blocks. functions import col from pyspark. This new column can be initialized with a default value or you can assign some dynamic value to it depending on some logical conditions. withColumn cannot be used. Managing Spark dataframes in Python. Ensure the cluster has the Spark server enabled with spark. withColumn ('testColumn', F. select ( "tmp. Below example creates a “fname” column from “name. Column = id Beside using the implicits conversions, you can create columns using col and column functions. Writing an UDF for withColumn in PySpark. with value spark new multiple from constant columns column another python apache-spark dataframe pyspark spark-dataframe apache-spark-sql Add new keys to a dictionary? How to sort a dataframe by multiple column(s)?. PySpark is a Spark Python API that exposes the Spark programming model to Python - With it, you can speed up analytic applications. using an UDF that uses two existing columns as input and then applying a. types import StringType We're importing array because we're going to compare two values in an array we pass, with value 1 being the value in our DataFrame's homeFinalRuns column, and value 2 being awayFinalRuns. Update: Pyspark RDDs are still useful, but the world is moving toward DataFrames. In this post, I will show how to set up a Python environment to run Python. Apache Spark and Python for Big Data and Machine Learning. Just import them all here for simplicity. Using iterators to apply the same operation on multiple columns is vital for maintaining a DRY codebase. All the types supported by PySpark can be found here. otherwise` is not invoked, None is returned for unmatched conditions. column `pyspark. The issue is DataFrame. I know I can hard code 4 column names as pass in the UDF but in this case it will vary so I would like to know how to get it done? Here are two examples in the first one we have two columns to add and in the second one we have three columns to add. Spark – Add new column to Dataset A new column could be added to an existing Dataset using Dataset. ←Home Building Scikit-Learn Pipelines With Pandas DataFrames April 16, 2018 I've used scikit-learn for a number of years now. Some of the columns are single values, and others are lists. Python PySpark script to join 3 dataframes and produce a horizontal bar chart plus summary detail - python_barh_chart_gglot. At start thought it's simple but now Im lost. GROUP BY on Spark Data frame is used to aggregation on Data Frame data. withColumn("len uses two functions that accept a Column and return. In this post, I will show how to set up a Python environment to run Python. #Three parameters have to be passed through approxQuantile function #1. GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. withColumn(). We use the built-in functions and the withColumn() API to add new columns. Another simpler way is to use Spark SQL to frame a SQL query to cast the columns. 03/15/2017; 31 minutes to read +6; In this article. Import everything Create Function Make it a UDF Call this UDF Key notes: 1) we need to carefully define the return result types. Column // The target type triggers the implicit conversion to Column scala> val idCol: Column = $ "id" idCol: org. They are extracted from open source Python projects. I visited the Department of Atmospheric and Oceanic Sciences at the University of Wisconsin-Madison for two days and had a lot of fun discussing atmospheric (and machine learning) research with the scientists there. I have to use withColumn once, then return the column value as Array[String], then using explode, create two more columns. probabilities – a list of quantile probabilities Each number must belong to [0, 1]. In this case, we can use when() to create a column when the outcome of a conditional is true. https://stackoverflow. In many Spark applications, there are common use cases in which columns derived from one or more existing columns in a DataFrame are appended during the data preparation or data transformation stages. Type coercions implemented in parser are somewhat limited and in some cases unobvious. I recorded a video to help them promote it, but I also learned a lot in the process, relating to how databases can be used in Spark. com DataCamp Learn Python for Data Science Interactively. 0: WithColumn using UDF on two columns and then filter: Invalid PythonUDF. This is a quick and easy solution if you have a file with 1k or fewer rows (about 1MB) and do not want to explode beyond 20k rows. I was trying to do it using the built-in withColumn and when functions:. One of the most common operation in any DATA Analytics environment is to generate sequences. Column A column expression in a DataFrame. All the types supported by PySpark can be found here. Introduction to DataFrames - Scala We use the built-in functions and the withColumn() API to add new columns. com 準備 サンプルデータは iris 。 今回は HDFS に csv を置き、そこから読み取って DataFrame を作成する。. If the functionality exists in the available built-in functions, using these will perform better. ←Home Building Scikit-Learn Pipelines With Pandas DataFrames April 16, 2018 I've used scikit-learn for a number of years now. Here is the output from the previous sample code. The code above will create a dataframe with 10 rows and 3 columns. There are 2 scenarios: The content of the new column is derived from the values of the existing column The new…. with value spark new multiple from constant columns column another python apache-spark dataframe pyspark spark-dataframe apache-spark-sql Add new keys to a dictionary? How to sort a dataframe by multiple column(s)?. The syntax of withColumn() is provided below. I am facing an issue here that I have a dataframe with 2 columns, "ID" and "Amount". I want to use the first table as lookup to create a new column in second table. Spark – Add new column to Dataset A new column could be added to an existing Dataset using Dataset. Just upload your file and pick which columns you want exploded. collect_list('names')) will give me values for country & names attribute & for names attribute it will give column header as collect. For example, we can implement a partition strategy like the following: data/ example. IllegalArgumentException: 'Data type ArrayType(DoubleType,true) is not supported. withColumn(). I am attempting to create a binary column which will be defined by the value of the tot_amt column. Is there any chance to do it. Column A column expression in a DataFrame. withColumn, column expression can reference only the columns from a given data frame. Apache arises as a new engine and programming model for data analytics. It's origin goes back to 2009, and the main reasons why it has gained so much importance in the past recent years are due to changes in enconomic factors that underline computer applications and hardware. This allows Google to show you relevant ads, Amazon to recommend relevant products, and Netflix to recommend movies that you might like. types as T def my_func (col): do stuff to column here return transformed_value # if we assume that my_func returns a string my_udf = F. All the types supported by PySpark can be found here. In particular, it will cover the use of PySpark within Qubole's environment to explore your data, transform the data into meaningful features, build a Random Forest Regression model, and utilize the model to predict your next month's sales numbers. After working with Databricks and PySpark for a while now, its clear there needs to be as much best practice defined upfront as possible when coding notebooks. Hi Brian, You shouldn't need to use exlode, that will create a new row for each value in the array. pyspark union dataframe (2) I have a dataframe which has one row, and several columns. How is it possible to replace all the numeric values of the. One of the most common operation in any DATA Analytics environment is to generate sequences. using an UDF that uses two existing columns as input and then applying a. The Scala foldLeft method can be used to iterate over a data structure and perform multiple operations on a Spark DataFrame. Select Multiple Values from Same Column; one sql statement and split into separate columns 2 Convert column from string of numbers to a subtotal on select statment. For example, we can implement a partition strategy like the following: data/ example. Endnotes In this article, I have introduced you to some of the most common operations on DataFrame in Apache Spark. I have yet found a convenient way to create multiple columns at once without chaining multiple. Here is the output from the previous sample code. DataFrame A distributed collection of data grouped into named columns. withColumn ( "tmp" , F. PySpark's when() functions kind of like SQL's WHERE clause (remember, we've imported this the from pyspark. There are multiple ways of generating SEQUENCE numbers however I find zipWithIndex as the best one in terms of simplicity and performance combined. One of the most common uses of big data is to predict what users want. I have yet found a convenient way to create multiple columns at once without chaining multiple. Here’s a small gotcha — because Spark UDF doesn’t convert integers to floats, unlike Python function which works for both integers and floats, a Spark UDF will return a column of NULLs if the input data type doesn’t match the output data type, as in the following example. You can vote up the examples you like or vote down the ones you don't like. All of this is submitted as one message per data set and ends up in the payload column in Spark, in some binary format. Because of the easy-to-use API, you can easily develop pyspark programs if you are familiar with Python programming. The first parameter we pass into when() is the conditional (or multiple conditionals, if you want). Row A row of data in a DataFrame. Multi-Column Key and Value - Reduce a Tuple in Spark Posted on February 12, 2015 by admin In many tutorials key-value is typically a pair of single scalar values, for example ('Apple', 7). withColumn cannot be used here since the matrix needs to be of the type pyspark. Introduction: The Big Data Problem. Introduction to PySpark 24 minute read What is Spark, anyway? Spark is a platform for cluster computing. 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: