Pyspark Dataframe Select Rows By Value

I want to access values of a particular column from a data sets that I've read from a csv file. One external, one managed - If I query them via Impala or Hive I can see the data. explode() splits multiple entries in a column into multiple rows: from pyspark. Often times new features designed via…. In lesson 01, we read a CSV into a python Pandas DataFrame. Column A column expression in a DataFrame. merge is a generic function whose principal method is for data frames: the default method coerces its arguments to data frames and calls the "data. It includes operatio ns such as "selecting" rows, columns, and cells by name or by number, filtering out rows, etc. Let us filter our gapminder dataframe whose year column is not equal to 2002. def registerFunction (self, name, f, returnType = StringType ()): """Registers a python function (including lambda function) as a UDF so it can be used in SQL statements. To use groupBy(). Python Pandas : How to drop rows in DataFrame by index labels; Pandas : Sort a DataFrame based on column names or row index labels using Dataframe. Here is a version I wrote to do the job. generating a datamart). DataFrameNaFunctions Methods for handling missing data (null values). Row UDF vs Vectorized UDF Ser/Deser Overhead Removed 32. Select rows from a Pandas DataFrame based on values in a column. To return the first n rows use DataFrame. Selecting specific rows that meet the desired criteria Use the DataFrame ‘ix’ method to remove specific rows or to select only the rows that meet a certain criteria. At the end of the PySpark tutorial, you will learn to use spark python together to perform basic data analysis operations. Now that you have created the data DataFrame, you can quickly access the data using standard Spark commands such as take(). When you execute this code notice the output now has a. If you are looking for PySpark, I would still recommend reading through this article as it would give you an Idea on SQL schema usage. sql import Row from pyspark. Personally I would go with Python UDF and wouldn’t bother with anything else: Vectors are not native SQL types so there will be performance overhead one way or another. Think of relational database tables: DataFrames are very similar and allow you to do similar operations on them: slice data: select subset of rows or columns based on conditions (filters) sort data by one or more columns; aggregate data and compute summary statistics. select A SparkSession can be used create DataFrame, register DataFrame as tables, dfomitting rows. PySpark can be a bit difficult to get up and running on your machine. Incipient Analyst A budding analyst tries to share a few of the codes so as to reduce duplication of efforts across the industry # In order to run the Random. Select rows whose column value is in an iterable array:. Row: DataFrame数据的行; pyspark. Hi, I have a data frame with following values: Name,address,age. std_id = dpt_data. : , SparkSession import pyspark. Consider a pyspark dataframe consisting of 'null' elements and numeric elements. alias ("d")) display (explodedDF) explode() accepts a column name to "explode" (we only had one column in our DataFrame, so this should be easy to follow). The names of the key column(s) must be the same in each table. Because this is a SQL notebook, the next few commands use the %python magic command. How to filter out rows based on missing values in a column? To filter out the rows of pandas dataframe that has missing values in Last_Namecolumn, we will first find the index of the column with non null values with pandas notnull() function. The volume of unstructured text in existence is growing dramatically, and Spark is an excellent tool for analyzing this type of data. Select rows from a DataFrame based on values in a column in pandas ; Updating a dataframe column in spark ; Add column sum as new column in PySpark dataframe ; PySpark DataFrames-way to enumerate without converting to Pandas? How to add a constant column in a Spark DataFrame?. Now, in this post, we will see how to create a dataframe by constructing complex schema using StructType. sql("select * from table_name"). Combine the results into a new DataFrame. So you have to pull the right element from the original data. functions import explode explodedDF = df. dtypes like in pandas or just df. Dropping rows and columns in pandas dataframe. In this blog, I will share how to work with Spark and Cassandra using DataFrame. Like the other two methods we've covered so far, dropduplicates() also accepts the subset argument: df = df. In Spark, dataframe is actually a wrapper around RDDs, the basic data structure in Spark. 5, you are provided with numbers of date processing functions and you can use these functions in your case. No errors - If I try to create a Dataframe out of them, no errors. If values is a Series, that's the index. a frame corresponding to the current row return a new value to for each row by an aggregate/window function Can use SQL grammar or DataFrame API. I plan to do this by maintaining a list of all rows elements and mapping it to individual row values. Using a default value instead of 'null' is a common practice, and as a Spark's struct field can be nullable, it applies to DataFrames too. I have a data frame in pyspark with more than 300 columns. Unit 08 Lab 1: Spark (PySpark) is a DataFrame method to display the first 5 rows from the data frame. Is there a command to reorder the column value in PySpark as required. The describe() function performs summary statistics calculations on all numeric columns, and returns them as a DataFrame. A dataframe in Spark is similar to a SQL table, an R dataframe, or a pandas dataframe. The easiest way to create a DataFrame visualization in Databricks is to call display(). Because this is a SQL notebook, the next few commands use the %python magic command. : , SparkSession import pyspark. In this case first and the last row. Spark supports multiple programming languages as the frontends, Scala, Python, R, and other JVM languages. # withColumn + UDF | must receive Column objects in the udf. Spark has moved to a dataframe API since version 2. Selecting Rows. Dataframe basics for PySpark. We are going to load this data, which is in a CSV format, into a DataFrame and then we. Spark from version 1. By default the data frames are merged on the columns with names they both have, but separate specifications of the columns can be given by by. The first is the second DataFrame that we want to join with the first one. Here is a version I wrote to do the job. I plan to do this by maintaining a list of all rows elements and mapping it to individual row values. Once you've performed the GroupBy operation you can use an aggregate function off that data. Tables are equivalent to Apache Spark DataFrames. How to transpose a pyspark dataframe? column header and all list values should be column values Remove first column from dataframe dt2 = dt. There will be a new column added to the dataframe with Boolean values ,we can apply filter to get only those are true. select (explode ("data"). Since RDD is more OOP and functional structure, it is not very friendly to the people like SQL, pandas or R. Unmatched right tables records will be NULL. sql import DataFrame # Length of array n = 3 # For legacy Python you'll need a. spark dataframe distinct by column (4) Please suggest pyspark dataframe alternative for Pandas df['col']. pandas will do this by default if an index is not specified. In Spark, dataframe is actually a wrapper around RDDs, the basic data structure in Spark. 5, you are provided with numbers of date processing functions and you can use these functions in your case. Spark SQL can convert an RDD of Row objects to a DataFrame. dataframe from pyspark. The easiest way to create a DataFrame visualization in Databricks is to call display(). >>> from pyspark. DataFrameNaFunctions Methods for handling missing data (null values). Indexing, Slicing and Subsetting DataFrames in Python. repartition('id') creates 200 partitions with ID partitioned based on Hash Partitioner. Make sure that sample2 will be a RDD, not a dataframe. As you can see, there are some blank rows. In this case first and the last row. To see the first n rows of a Dataframe, we have head() method in PySpark, just like pandas in python. Adding and removing columns from a data frame Problem. Not seem to be correct. Filter Pyspark dataframe column with None value I'm trying to filter a PySpark dataframe that has None as a row Select rows from a DataFrame based on values. They are basically a collection of rows, organized into named columns. Returns the new DataFrame. Let us quickly understand it in it with the help of script. fill() 互为同名函数。 value: 替换的值,可以是 int, long, float, string, or dict,如果是 dict 的话 key 应当是列值, value 应该是空值的替换值,如果是 dict 则 subset 不生效。 subset: 指定需要忽略替换的列。. 1 - I have 2 simple (test) partitioned tables. This page shows how to operate with Hive in Spark including: Create DataFrame from existing Hive table Save DataFrame to a new Hive table Append data. The following are code examples for showing how to use pyspark. apply(), you must define the following: A Python function that defines the computation for each group; A StructType object or a string that defines the schema of the output DataFrame. The datasets are stored in pyspark RDD which I want to be converted into the DataFrame. To find all rows matching a specific column value, you can use the filter() method of a dataframe. This page shows how to operate with Hive in Spark including: Create DataFrame from existing Hive table Save DataFrame to a new Hive table Append data. Spark DataFrame expand on a lot of these concepts, allowing you to transfer that knowledge easily by understanding the simple syntax of Spark DataFrames. The volume of unstructured text in existence is growing dramatically, and Spark is an excellent tool for analyzing this type of data. It will return a boolean series, where True for not null and False for null values or missing values. PySpark is the python API to Spark. As you already know, we can create new columns by calling withColumn() operation on a DataFrame, while passing the name of the new column (the first argument), as well as an operation for which values should live in each row of that column (second argument). Imagine we would like to have a table with an id column describing a user and then two columns for the number of cats and dogs she has. To see the first n rows of a Dataframe, we have head() method in PySpark, just like pandas in python. DataFrameNaFunctions Methods for handling missing data (null values). Spark DataFrames include some built-in functions for statistical processing. What is difference between class and interface in C#; Mongoose. Method 1 is somewhat equivalent to 2 and 3. I have a pyspark dataframe like: A B C 1 NA 9 4 2 & I want to delete rows which contain value "NA". pdf), Text File (. Running the following command right now: %pyspark. Iteratively appending rows to a DataFrame can be more computationally intensive than a single concatenate. Hence the expected output is like this value in pyspark dataframe select columns so. Does CBO work if the select is completely Pyspark based ? 0 Answers. If you want to do distributed computation using PySpark, then you'll need to perform operations on Spark dataframes, and not other python data types. 4 was before the gates, where. Then, reshape your array into a 2D array which each line contains the one-hot encoded value for the color input. So you have to pull the right element from the original data. How to show rows as selected in JQuery Datatables. filter method; but, on the one hand, I needed some more time to experiment and confirm it and, on the other hand, I knew that Spark 1. pdf), Text File (. Former HCC members be sure to read and learn how to activate your account here. In this post, we will see how to replace nulls in a DataFrame with Python and Scala. sql("select. It does not affect the data frame column values. (Disclaimer: not the most elegant solution, but it works. A Databricks table is a collection of structured data. head(10) To see the number of rows in a data frame we need to call a method count(). In general, the numeric elements have different values. I don't know why in most of books, they start with RDD rather than Dataframe. 4 release, DataFrames in Apache Spark provides improved support for statistical and mathematical functions, including random data generation, summary and descriptive statistics, sample covariance and correlation, cross tabulation, frequent items, and mathematical functions. DataFrameNaFunctions Methods for handling missing data (null values). Kindly guide me. 1 – see the comments below]. If values is a DataFrame, then both the index and column labels must match. One external, one managed - If I query them via Impala or Hive I can see the data. •In an application, you can easily create one yourself, from a SparkContext. There are 1,682 rows (every row must have an index). I need to implement a auto increment column in my spark sql table, how could i do that. Consider a pyspark dataframe consisting of 'null' elements and numeric elements. I plan to do this by maintaining a list of all rows elements and mapping it to individual row values. e, if we want to remove duplicates purely based on a subset of columns and retain all columns in the original dataframe. Filter PySpark Dataframe based on the Condition. Python For Data Science Cheat Sheet PySpark - RDD Basics Learn Python for data science Interactively at www. Each row was assigned an index of 0 to N-1, where N is the number of rows in the DataFrame. I have a dataframe with 10609 rows and I want to convert 100 rows at a time to JSON and send them back to a webservice. To select rows based on a criteria use the filter method. Reset index of DataFrame to row numbers, moving Select rows meeting logical condition, and only the specific. distinct() #Returns distinct rows in this DataFrame df. First, let'se see how many rows the crimes dataframe has: print(" The crimes dataframe has {} records". Scale column values into a certain range (i. Spark DataFrame expand on a lot of these concepts, allowing you to transfer that knowledge easily by understanding the simple syntax of Spark DataFrames. You want to add or remove columns from a data frame. I want to access values of a particular column from a data sets that I've read from a csv file. sql import Row from pyspark. filter("tag == 'php'"). Methods 2 and 3 are almost the same in terms of physical and logical plans. count())) The crimes dataframe has 6481208 records We can also see the columns, the data type of each column and the schema using the commands below. Kindly guide me. For each adjacent pair of rows in the clock dataframe, rows from the dataframe that have time stamps between the pair are grouped. Pyspark Joins by Example This entry was posted in Python Spark on January 27, 2018 by Will Summary: Pyspark DataFrames have a join method which takes three parameters: DataFrame on the right side of the join, Which fields are being joined on, and what type of join (inner, outer, left_outer, right_outer, leftsemi). functions import explode explodedDF = df. I'm trying to transform spark dataframe row value as a relation of every other value of the same row. It's hard to mention columns without talking about PySpark's lit. e the entire result)? Or is the sorting at a partition level? If the later, then can anyone suggest how to do an orderBy across the data? I have an orderBy right at the end. Don't worry, this can be changed later. No errors - If I try to create a Dataframe out of them, no errors. spark dataframe distinct by column (4) Please suggest pyspark dataframe alternative for Pandas df['col']. 4) def dropDuplicates (self, subset = None): """Return a new :class:`DataFrame` with duplicate rows removed, optionally only considering certain columns. std_id); Pyspark Left Join Example. How would I go about changing a value in row x column y of a dataframe? In pandas this would be df. PySpark can be a bit difficult to get up and running on your machine. As you can see, there are some blank rows. This type of join returns all rows from the left dataset even if there is no matching values in the right dataset. I have a data frame in pyspark with more than 300 columns. A SparkSession can be used create DataFrame, register DataFrame as tables, execute SQL over tables, cache tables, and read parquet files. python for GroupBy column and filter rows with maximum value in Pyspark Select rows from a DataFrame based on values in a column in pandas value in a group. For example: Column_1 column_2 null null null null 234 null 125 124 365 187 and so on When I want to do a sum of column_1 I am getting a Null as a result, instead of 724. case (dict): case statements. I have a very large dataset that is loaded in Hive. Pyspark DataFrames Example 1: FIFA World Cup Dataset. It is similar to a row in a Spark DataFrame, except that it is self-describing and can be used for data that does not conform to a fixed schema. To select a column from the data frame, DataFrame` omitting rows with null values. The iloc indexer syntax is data. 4 was before the gates, where. iloc[, ], which is sure to be a source of confusion for R users. How would I go about changing a value in row x column y of a dataframe? In pandas this would be df. StructType` as its only field, and the field name will be "value", each record will also be wrapped into a tuple, which can be converted to row later. rows are constructed by passing a ("FROM src SELECT key, value. Get the maximum value of column in python pandas : In this tutorial we will learn How to get the maximum value of all the columns in dataframe of python pandas. HiveContext: 访问Hive数据的主入口 1. alias ("d")) display (explodedDF) explode() accepts a column name to "explode" (we only had one column in our DataFrame, so this should be easy to follow). query = "SELECT Id, {} as. I want to convert all empty strings in all columns to null (None, in Python). For the reason that I want to insert rows selected from a table (df_rows) to another table, I need to make sure that The schema of the rows selected are the same as the schema of the table Since the function pyspark. When I do an orderBy on a pyspark dataframe does it sort the data across all partitions (i. shape X = x_3d. Filter Pyspark dataframe column with None value I'm trying to filter a PySpark dataframe that has None as a row Select rows from a DataFrame based on values. sort_index() Select Rows & Columns by Name or Index in DataFrame using loc & iloc | Python Pandas; Pandas: Sort rows or columns in Dataframe based on values using Dataframe. drop()#Omitting rows with null values df. But for the purpose of this tutorial, I had filled the missing rows by the above logic but practically tampering with the data with no data-driven logic to back it up is usually not a good idea. Row A row of data in a DataFrame. Introduction to DataFrames - Python. What is difference between class and interface in C#; Mongoose. sql import functions as F Select >>> df. The easiest way to create a DataFrame visualization in Databricks is to call display(). To find the data within the specified range we use between method in the pyspark. If schema inference is needed, ``samplingRatio`` is used to determined the ratio of. insertInto , which inserts the content of the DataFrame to the specified table, requires that the schema of. For example: Column_1 column_2 null null null null 234 null 125 124 365 187 and so on When I want to do a sum of column_1 I am getting a Null as a result, instead of 724. We are going to load this data, which is in a CSV format, into a DataFrame and then we. The below will return a DataFrame which only contains rows where the author column has a value of todd:. My solution is to take the first row and convert it in dict your_dataframe. In part 2 we will learn about Spark Dataframes. I need to implement a auto increment column in my spark sql table, how could i do that. For every row custom function is applied of the dataframe. As an extension to the existing RDD API, DataFrames features seamless integration with all big data tooling and infrastructure via Spark. format(crimes. “iloc” in pandas is used to select rows and columns by number, in the order that they appear in the data frame. The input data contains all the rows and columns for each group. Spark Window Functions have the following traits: perform a calculation over a group of rows, called the Frame. In part 2 we will learn about Spark Dataframes. In the example below, the rows 1,3,5, and 7 are removed. dtypes like in pandas or just df. std_id = dpt_data. 5, you are provided with numbers of date processing functions and you can use these functions in your case. When I do an orderBy on a pyspark dataframe does it sort the data across all partitions (i. Operations in PySpark DataFrame are lazy in nature but, in case of pandas we get the result as soon as we apply any operation. Personally I would go with Python UDF and wouldn't bother with anything else: Vectors are not native SQL types so there will be performance overhead one way or another. How to select particular column in Spark(pyspark)? Either you convert it to a dataframe and then apply select or do a map operation over with x being an RDD row. The median longitude and median latitude values are located for each time step. For example: Column_1 column_2 null null null null 234 null 125 124 365 187 and so on When I want to do a sum of column_1 I am getting a Null as a result, instead of 724. where() #Filters rows using the given condition df. and add calculated values as new columns of the. A dataframe in Spark is similar to a SQL table, an R dataframe, or a pandas dataframe. Select columns in. In the example below, the rows 1,3,5, and 7 are removed. filter() #Filters rows using the given condition df. Lets create DataFrame with sample data Employee. Spark’s primary data abstraction is an immutable distributed collection of items called a resilient distributed dataset (RDD). One external, one managed - If I query them via Impala or Hive I can see the data. GroupedData Aggregation methods, returned by DataFrame. My solution is to take the first row and convert it in dict your_dataframe. If you've used R or even the pandas library with Python you are probably already familiar with the concept of DataFrames. Import modules. How is it possible to replace all the numeric values of the dataframe by a constant numeric value (for example by the value 1)? Thanks in advance!. How to create a column in pyspark dataframe with random values within a range? Filtering a row in Spark DataFrame based on matching values from a list. dataframe from pyspark. y[0] is the rating. pdf), Text File (. Creates a new row for each key-value pair in a map by ignoring null & empty. By itself, calling dropduplicates() on a DataFrame drops rows where all values in a row are duplicated by another row. 4) def dropDuplicates (self, subset = None): """Return a new :class:`DataFrame` with duplicate rows removed, optionally only considering certain columns. By default the data frames are merged on the columns with names they both have, but separate specifications of the columns can be given by by. Dropping rows and columns in pandas dataframe. A dataframe in Spark is similar to a SQL table, an R dataframe, or a pandas dataframe. In part 2 we will learn about Spark Dataframes. One important feature of Dataframes is their schema. SEMI JOIN Select only rows from the side of the SEMI JOIN where there is a match. LEFT ANTI JOIN Select only rows from the left side that match no rows on the right side. It does not affect the data frame column values. for example 100th row in above R equivalent codeThe getrows() function below should get the specific rows you want. Reset index of DataFrame to row numbers, moving Select rows meeting logical condition, and only the specific. 许多数据分析师都是用HIVE SQL跑数,这里我建议转向PySpark:PySpark的语法是从左到右串行的,便于阅读、理解和修正;SQL的语法是从内到外嵌套的,不方便维护;PyS. - Pyspark with iPython - version 1. ix[x,y] = new_value. Kindly guide me. Edit: Consolidating what was said below, you can't modify the existing dataframe as it is immutable, but you can return a new dataframe with the desired modifications. I need to implement a auto increment column in my spark sql table, how could i do that. 5, you are provided with numbers of date processing functions and you can use these functions in your case. We can either drop all the rows which have missing values in these columns or we can fill in those by the above logic. Column A column expression in a DataFrame. PySpark is a Spark Python API that exposes the Spark programming model to Python - With it, you can speed up analytic applications. In Spark, dataframe is actually a wrapper around RDDs, the basic data structure in Spark. apache spark sql and dataframe guide spark sql can convert an rdd of row object to a dataframe. 0, you can easily read data from Hive data warehouse and also write/append new data to Hive tables. DataFrameNaFunctions Methods for handling missing data (null values). It is estimated to account for 70 to 80% of total time taken for model development. Select a column out of a DataFrame: df. Make sure that sample2 will be a RDD, not a dataframe. PySpark has no concept of inplace, so any methods we run against our DataFrames will only be applied if we set a DataFrame equal to the value of the affected DataFrame ( df = df. colName return before non-null values. They are not null because when I ran isNull() on the data frame, it showed false for all records. Spark SQL can convert an RDD of Row objects to a DataFrame. Filter Pyspark dataframe column with None value (Python) - Codedump. posexplode_outer(e: Column) Creates a new row for each key-value pair in a map including null & empty. If you've used R or even the pandas library with Python you are probably already familiar with the concept of DataFrames. complete: All rows will be written to the sink every time there are updates. In these columns there are some columns with values null. If I have a function that can use values from a row in the dataframe as input, then I can map it to the entire dataframe. dtypes like in pandas or just df. It is a wrapper over PySpark Core to do data analysis using machine-learning algorithms. 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. A simple word count application. When a key matches the value of the column in a specific row, the respective value will be assigned to the new column for that row. spark dataframe派生于RDD类,但是提供了非常强大的数据操作功能。 当然主要对类SQL的支持。 在实际工作中会遇到这样的情况,主要是会进行两个数据集的筛选、合并,重新入库。. Returns: DataFrame. See my attempt below. When we implement spark, there are two ways to manipulate data: RDD and Dataframe. *, dpt_data. PySpark is a Spark Python API that exposes the Spark programming model to Python - With it, you can speed up analytic applications. The slides give an overview of how Spark can be used to tackle Machine learning tasks, such as classification, regression, clustering, etc. filter() #Filters rows using the given condition df. So you can convert them back to dataframe and use subtract from the original dataframe to take the rest of the rows. Method 1 is somewhat equivalent to 2 and 3. 0, you can easily read data from Hive data warehouse and also write/append new data to Hive tables. Pyspark flatten RDD error:: Too many values to unpack json dataframe apache-spark pyspark nested. value:要代替空值的值有int,long,float,string或dict. Dataframes are data tables with rows and columns, the closest analogy to understand them are spreadsheets with labeled columns. 1 Selecting Columns. It does not affect the data frame column values. It's hard to mention columns without talking about PySpark's lit. To get a list of column names use df. 0: Added with the default being 0. sum If fewer than min_count non-NA values are present the result will be NA. Recently, I have been playing with PySpark a bit and decided I would write a blog post about using PySpark and Spark SQL. 4 release, DataFrames in Apache Spark provides improved support for statistical and mathematical functions, including random data generation, summary and descriptive statistics, sample covariance and correlation, cross tabulation, frequent items, and mathematical functions. DISTINCT is very commonly used to seek possible values which exists in the dataframe for any given column. sql import DataFrame, Row: from functools import reduce. •The DataFrame data source APIis consistent,. select A SparkSession can be used create DataFrame, register DataFrame as tables, dfomitting rows. Selecting pandas data using “iloc” The iloc indexer for Pandas Dataframe is used for integer-location based indexing / selection by position. A DataFrame is equivalent to a relational table in Spark SQL, and can be created using various functions in SparkSession: 分布在命名列中的分布式数据集合。. 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: