Pandas Udf Multiple Arguments

This distribution has fatter tails than a normal distribution and has two descriptive parameters (location and scale. Scalar UDFs are executed within a SELECT statement by specifying the UDF owner, name, and any parameters. To drop one or more rows from a Pandas dataframe, we need to specify the row indexes that need to be dropped and axis=0 argument. It is a shame, because the answers to them tend to clear up some ingrained misconceptions about functions that can lead to problems, particularly with locking and performance. User defined function in SQL Server can return : a) Scalar value b) Set of values c) Result set d) All of the mentioned View Answer. Applying function with multiple arguments to create a new pandas column. It shows how to register UDFs, how to invoke UDFs, and caveats regarding evaluation order of subexpressions in Spark SQL. apply ( square ) London 400 New York 441 Helsinki 144 dtype: int64. There are a lot of ways to pull the elements, rows, and columns from a DataFrame. SQLScript is the only language which is supported for table UDFs. Apache Arrow was introduced in Spark 2. When the parameter function appears in SQL syntax, replace it with one of the functions described in this section. Removing rows by the row index 2. Pass multi-area range to user-defined function if you want to pass a multiple range argument to your function, as written, you need to enter the formula. Executing UDFs. Wo what are Default Values for Arguments. Re: How to pass multiple non-contiguous ranges into a user defined function Your function would need to be completely rewritten to work with the different areas of the input ranges. DataFrame cannot be converted column literal. Pandas is a great module for data analysis and it uses some neat data structures such as Series and DataFrames. The function may take arguments(s) as input within the opening and closing parentheses, just after the function name followed by a colon. In my first real world machine learning problem, I introduced you to basic concepts of Apache Spark like how does it work, different cluster modes in Spark and What are the different data representation in Apache Spark. Here's an example: [code]CREATE PROCEDURE a. Here you'll use pandas, groupby objects and the principles of split-apply-combine to check out how Netflix movie ranges vary as a function of the year they were released. Apply a User Defined function with or without arguments to each row or column of a Dataframe. The methods have been discussed below. Download BigQuery table data to a pandas DataFrame by using the BigQuery client library for Python. Notice: Undefined index: HTTP_REFERER in /home/forge/blog. Python Arbitrary Arguments. User defined function in SQL Server can return : a) Scalar value b) Set of values c) Result set d) All of the mentioned View Answer. To make this easy, the pandas read_excel method takes an argument called sheetname that tells pandas which sheet to read in the data from. Understanding named arguments and optional arguments. Now let's dive into the code. It is a shame, because the answers to them tend to clear up some ingrained misconceptions about functions that can lead to problems, particularly with locking and performance. Here are a couple of examples to help you quickly get productive using Pandas' main data structure: the DataFrame. There are cases, however, where you need an interactive environment for data analysis and trying to pull that together in pure python, in a user-friendly manner would be difficult. User-Defined Functions - Scala. 5 seconds (approx) no matter how many URLs there are to check. """The config module holds package-wide configurables and provides a uniform API for working with them. Future versions of pandas_datareader will end support for Python 2. The parameters a and b accepts the passed arguments in the function definition. This is just a pandas programming note that explains how to plot in a fast way different categories contained in a groupby on multiple columns, generating a two level MultiIndex. Let’s add another method to the Column class that will make it easy to chain user defined functions (UDFs). Orca was originally designed to orchestrate UrbanSim simulations and so has capabilities for running a pipeline multiple times while iterating over a set of input data. The only reason I included in this table is that sometimes you may see the numpy types pop up on-line or in your own analysis. Pandas DataFrames. Is there a way to apply the same function with different arguments to multiple columns of pandas dataframe? For example: I have a dictionary with different values for each respective column and I am trying to apply the same function to the multiple columns within a single or chained lambda expression on a grouped pandas frame. SQL Server User-Defined Functions are good to use in most circumstances, but there just a few questions that rarely get asked on the forums. Each element of the tuple must be either an array with the length equal to the number of parameters, or a scalar (in which case the bound is taken to be the same for all parameters. arg ('x', pd. 3, Spark provides a pandas udf, which leverages the performance of Apache Arrow to distribute calculations. Applying Operations Over pandas Dataframes. Passing arguments By Reference is probably the most common way to return multiple values from a function. Each argument must evaluate to either TRUE or FALSE in order for this function to work. After defining the function name and arguments(s) a block of program statement(s) start at the next line and these statement(s) must be indented. Taking the idea from @Melba23s (btw, thanks ) ExtMsgBox and also making use of his StringSize UDF, I made a extended version of the InputBox function. Onboarding. A Pandas UDF is defined using the keyword pandas_udf as a decorator or to wrap the function, no additional configuration is required. In the row-at-a-time version, the user-defined function takes a double “v” and returns the result of “v + 1” as a double. merge() and some of the available arguments to pass. User-Defined Functions - Scala. User-defined functions help to decompose a large program into small segments which makes program easy to understand, maintain and debug. Updated for version: 0. Please see below. Before you create a UDF, determine where you want to define it, and whether you want to use CFML or CFScript to create it. takes an optional argument 'ax' which allows you to reuse an Axis to. On the other hand, Pandas UDF built atop Apache Arrow accords high-performance to Python developers, whether you use Pandas UDFs on a single-node machine or distributed cluster. Steps to write user defined functions in Python? In this section we will explain how to write a user-defined function in Python. // 1) Spark UDF factories do not support parameter types other than Columns // 2) While we can define the UDF behaviour, we are not able to tell the taboo list content before actual invocation. If repeated code occurs in a program. The parameters to the left of the comma always selects rows based on the row index, and parameters to the right of the comma always selects columns based on the column index. Since these functions are ready-only, only read-only statements can be used within the function. Clearly the fixed point of g is the root of f (x)=g (x)−x. When using the constructor function, the normal string escape rules (preceding special characters with \ when included in a string) are necessary. To define a scalar Pandas UDF, simply use @pandas_udf to annotate a Python function that takes in pandas. Python Pandas : Select Rows in DataFrame by conditions on multiple columns; Python Pandas : How to add rows in a DataFrame using dataframe. *args and **kwargs allow you to pass a variable number of arguments to a function. There is a VBA imposed limit that ' a string in a passed in array (e. User defined function. Add a new public enum class PandasUdfType in pyspark. Function VBAAND1(Logical1 As String, Optional Logical2 As String, Optional Logical3 As String). When running a user-defined function that reads two input arguments, it is possible to overwrite those. So, almost a year ago we launch Optimus. Understanding named arguments and optional arguments. Following are steps to be followed. User-defined functions can be called just like built-in ones (see section Function Calls), but it is up to you to define them--to tell awk what they should do. Hi, I am new in QTP. The pandas library is massive, and it's common for frequent users to be unaware of many of its more impressive features. You can also define functions with no input or output arguments, or multiple input or output arguments. com/public/pko2g00/09st. Selecting pandas data using "iloc" The iloc indexer for Pandas Dataframe is used for integer-location based indexing / selection by position. If you recall, a while back, we made new columns by doing something like df['Column2'] = df['Column1']*1. to_csv ('pandas. In this article, we take a look at pandas DataFrames and basic some of their basic functionality, including, indexing, masking, deletion, and reindexing. The plugin is disabled. Pandas is also an elegant solution for time series data. Table UDFs are read-only user-defined functions which accept multiple input parameters and return exactly one results table. (I will not be covering aggregate functions. If all of your prices are unique, you can use a simple, non-array, match. The arguments to this function is the set of all argument keys that were actually referred to in the format string (integers for positional arguments, and strings for named arguments), and a reference to the args and kwargs that was passed to vformat. The UDF definitions are the same except the function decorators: “udf” vs “pandas_udf”. apply (self, func, convert_dtype=True, args=(), **kwds) [source] ¶ Invoke function on values of Series. If a function, must either work when passed a DataFrame or when passed to DataFrame. Python Pandas is a Python data analysis library. Note that the return statement return x, y has the same result as return (x, y): the former actually packs x and yinto a tuple under the hood! # Define shout_all with parameters word1 and word2 def shout_all(word1, word2):. UDFs are currently only available on Windows. Re: Calling UDF with multiple inputs from command button I'm not sure where you are going with this. In this article we will discuss how to merge different Dataframes into a single Dataframe using Pandas Dataframe. This page is based on a Jupyter/IPython Notebook: download the original. Pandas is a very versatile tool for data analysis in Python and you must definitely know how to do, at the bare minimum, simple operations on it. Now we have multiple features h θ (x) = θ 0 + θ 1 x 1 + θ 2 x 2 + θ 3 x 3 + θ 4 x 4; For example. problem with a user defined function that has multiple parameters I am new to Excel programming and VBA in general. The routine fixed_point provides a simple iterative method using Aitkens sequence acceleration to estimate the fixed point of g given a starting point. In the function definition we use an asterisk (*) before the parameter name to denote this kind of argument. We will learn how to create. Most of the time, using pandas default int64 and float64 types will work. With the Batch Peak Analysis tool, you can: Run LabTalk Script to skip, filter or manipulate data prior to the analysis; Save custom settings to a reusable theme file. , data is aligned in a tabular fashion in rows and columns. Pandas DataFrame cannot be used as an argument for PySpark UDF. Crude looping in Pandas, or That Thing You Should Never Ever Do. When `f` is a user-defined function: Spark uses the return type of the given user-defined function as the return type of: the registered user-defined function. These arguments a and b are called formal arguments. Import Modules. To use a user function in a SQL expression, you must own or have EXECUTE privilege on the user function. result: A pandas DataFrame created by the Python script whose value becomes the tabular data that gets sent to the Kusto query operator that follows the plugin. Multi-statement Table-Valued User-Defined Function A Multi-Statement Table-Valued user-defined function returns a table. Where pandas visualisations can become very powerful for quickly analysing multiple data points with few lines of code is when you combine plots with the groupby function. Learn Data Science by completing interactive coding challenges and watching videos by expert instructors. Pipe enables user-defined methods in method chains. In this Python Tutorial for Beginners video I am going to show How to use Default Arguments, *args and **kwargs (Variable-length Arguments) in Python. Converts it to a dict with keys being the column header names 3. The UDF was designed to return only a single row for the parameters passed, despite having multiple joins inside the UDF itself. Removing rows that do not meet the desired criteria Here is the first 10 rows of the Iris dataset that will. The long version: Indexing a Pandas DataFrame for people who don't like to remember things. head function with no arguments gets the first five rows of data from the data frame so the output will be # head function in python with arguments iris. The User Defined Function demonstrated in the above picture extracts unique distinct values also considering lower and upper case letters. returnType can be optionally specified when f is a Python function but not when f is a user-defined function. Joining dataframe using on in an argument : In order to join dataframes we use on in an argument. An Inline Table-Value user-defined function returns a table data type and is an exceptional alternative to a view as the user-defined function can pass parameters into a T-SQL select command and in essence provide us with a parameterized, non-updateable view of the underlying tables. The function can also be applied over multiple columns of a DataFrame using apply. multiply¶ DataFrame. View this notebook for live examples of techniques seen here. Python Pandas Functions in Parallel July 6, 2016 Jay Data Science I’m always on the lookout for quick hacks and code snippets that might help improve efficiency. Crude looping in Pandas, or That Thing You Should Never Ever Do. Function can be used to include those codes and execute when needed by calling that function. The arguments should specify different features that all apply to the object. The decision as to which argument(s) should be passed by value or by reference is based on whether or not you want the called function to modify the argument and permanently change its value. It is not generally recommended to use non-contiguous ranges with UDFs though, so if at all possible I would rethink the layout of the data instead. The StringConcat Function Code. Equivalently, the root of f is the fixed_point of g (x)=f (x)+x. Most of the time, using pandas default int64 and float64 types will work. A function that needs to return multiple values can just return a tuple of the values. DataFrame cannot be converted column literal. Introduction; Argument and Return Types. Receives the entire row including column header names 2. The first example introduced us to the notion of functions and variable visibility or “scoping” across environments. Last released: Oct 31, 2019 Oct 31, 2019. // 1) Spark UDF factories do not support parameter types other than Columns // 2) While we can define the UDF behaviour, we are not able to tell the taboo list content before actual invocation. ; For a comprehensive overview of the available decorators and their options, check out the corresponding API docs: UDF decorators. Functions - arguments. apply() only has the raw keyword, see here. apply (self, func, *args, **kwargs) [source] ¶ Apply function func group-wise and combine the results together. One of the biggest advantages of having the data as a Pandas Dataframe is that Pandas allows us to slice and dice the data in multiple ways. any # Boolean True if any true. User-defined functions can be called just like built-in ones (see section Function Calls), but it is up to you to define them--to tell awk what they should do. In the syntax diagrams for SQL functions, arguments are indicated by their datatypes. I tried to iterate through rows, but Series objects aren't hashable so I couldn't create a dictionary that way. You have seen that functions constitute the most essential programming construct in R, which is in fact, a functional language. Following are steps to be followed. Applying Operations Over pandas Dataframes. import modules. In other words, a DataFrame is a matrix of rows and columns that have labels — column names for columns, and index labels for rows. We went from the basics of pandas DataFrames to indexing and computations. Following this answer I've been able to create a new column when I only need one column as an ar. apply(func). functions import udf. Consider a sample of floats drawn from the Laplace distribution. Working with Python Pandas and XlsxWriter. Pandas will try to call date_parser in three different ways, advancing to the next if an exception occurs: 1) Pass one or more arrays (as defined by parse_dates) as arguments; 2) concatenate (row-wise) the string values from the columns defined by parse_dates into a single array and pass that; and 3) call date_parser once for each row using one. Snowflake supports overloading of UDF names, i. In below function I have to read values from MsAccess table & stor. xls (personal. A function that needs to return multiple values can just return a tuple of the values. a user-defined function. This page is based on a Jupyter/IPython Notebook: download the original. Let us first load the pandas package. In this case, the use of threads will effectively reduce our URL lookup time to 1. The efficiency of data transmission between JVM and Python has been significantly improved through technology provided by Column Store and Zero Copy. We also try the amazing Pandas library, but our data was big enough to make it cry. A well-defined and thoughtfully written user-defined function can ease the application development process. Horizontal box plot in python with different colors:. # load pandas import pandas. apply to send a single column to a function. *args and **kwargs are mostly used in function definitions. On Jan 4, 2012, at 3:47 PM, Dan Abner wrote: Hello everyone, How does one pass multiple arguments of a user defined function to that function when called within sapply()?. We went from the basics of pandas DataFrames to indexing and computations. sort_values(by, axis=0, ascending=True, inplace=False, kind=’quicksort’, na_position=’last’) Parameters: by: List of columns by which dataset needs to be sorted. The return Statement. Lower and upper bounds on parameters. The Pandas DataFrame should contain at least two columns of node names and zero or more columns of node attributes. It is OK to put a function call in a lambda, and to pass arguments to that function. For example, you may often find yourself checking the size of a particular file or. Manually analyze a typical dataset in Peak Analyzer, and save your custom settings to a theme file. The parameters a and b accepts the passed arguments in the function definition. You’ll learn more about this when you tackle the Function Arguments section. By default, we return the first numeric column as a double. If repeated code occurs in a program. Pandas fluency is essential for any Python-based data professional, people interested in trying a Kaggle challenge, or anyone seeking to automate a data process. You will use logical operators such as || (or) and && (and) to execute a conditional or looping statement based on multiple criteria. In programming, argument refers to the variable passed to the function. Hi everyone! After briefly introducing the "Pandas" library as well as the NumPy library, I wanted to provide a quick introduction to building models in Python, and what better place to start than one of the very basic models, linear regression?. In Python, defining the function works as follows. We'll now learn to write our own user-defined functions. Sign in Sign up. 1k points). According to mysql, concat should accept multiple arguments. php(143) : runtime-created function(1) : eval()'d code(156) : runtime. I also tried set_index() with to_dict() but that seems to overwrite values. By default, we return the first numeric column as a double. The difference lies on how the computation is done. This Jira has been LDAP enabled, if you are an ASF Committer, please use your LDAP Credentials to login. Summary No matter how simple a UDF is, there’s a large performance penalty paid when they’re used. A passed user-defined-function will be passed a Series for evaluation. Example 1 The first argument is the delimiter, the example below shows you the result in cell C4 if you use no delimiting characters. inf with an appropriate sign to disable bounds on all or some parameters. User-Defined Functions - Scala. It shows how to register UDFs, how to invoke UDFs, and caveats regarding evaluation order of subexpressions in Spark SQL. multiple UDFs in the same schema can have the same name, as long as their argument signatures differ, either by the number of arguments or the argument types. If you're interested on learning Pandas, I recommend checking out 10 minutes to. Also, unless you turned the interactive mode on, the code will be paused until you close the gure window. Pandas UDFs are a lot faster than standard UDF. The parameters a and b accepts the passed arguments in the function definition. Parameters: x: array_like. group by and apply a function with multiple input arguments (PANDAS) - groupby_apply_multiple_inputs. Download BigQuery table data to a pandas DataFrame by using the BigQuery client library for Python. Pandas library is built on top of Numpy, meaning Pandas needs Numpy to operate. returnType – the return type of. It also contains link to a real-life example [2]. Parameters: name – name of the user-defined function in SQL statements. User-defined functions must be created as top-level functions or declared with a package specification before they can be named within a SQL statement. DataFrame cannot be converted column literal. In the following example, two series are made from same data. Let’s use the Pandas value_counts method to view the shape of our volume column. Free blog publishing tool from Google, for sharing text, photos and video. The Excel VBA user-defined function is a process that is written in VBA which accepts some inputs & in return gives a result when the function is called. SQLScript is the only language which is supported for table UDFs. To output all these gures at once, you should only have one plt. Specifying the Argument and Return Types. In below function I have to read values from MsAccess table & stor. These arguments are known as actual arguments. read_sql_query? (Python) - Codedump. For this, you can either use the sheet name or the sheet number. Specifying the Argument and Return Types. Pass multi-area range to user-defined function if you want to pass a multiple range argument to your function, as written, you need to enter the formula. If you want to select a set of rows and all the columns, you don't need to use a colon following a comma. What does variable mean here is that you do not know before hand that how many arguments can be passed to your function by the user so in this case you use these two keywords. import pandas as pd import numpy as np. In this case, this API works as if `register(name, f)`. Introduction; Argument and Return Types. It's similar in structure, too, making it possible to use similar operations such as aggregation, filtering, and pivoting. Python Pandas - DataFrame - A Data frame is a two-dimensional data structure, i. Alongside to catering to the tailored needs of students, professionals, corporates and educational institutions across multiple locations, ExcelR opened its offices in multiple strategic locations such as Australia, Malaysia for the ASEAN market, Canada, UK, Romania taking into account the Eastern Europe and South Africa. Series as arguments and returns another pandas. Parameters: name – name of the user-defined function in SQL statements. Make sure to download the attached workbook for this tutorial so that you can view working examples and easily copy/paste the UDF VBA Macro code. Inside the function body, the return statement determines the value to be returned. The pandas module provides objects similar to R’s data frames, and these are more convenient for most statistical analysis. Guidelines to write user defined functions in Python? In this section, we will check the steps to write user defined functions in Python. Pandas is arguably the most important Python package for data science. NumPy arrays can reach multiple dimensions, pandas data structures limit you to just 1 & 2D. Python Pandas - Statistical Functions - Statistical methods help in the understanding and analyzing the behavior of data. Registering a UDF. Removing rows that do not meet the desired criteria Here is the first 10 rows of the Iris dataset that will. An instance of Hashtable has two parameters that affect its performance: initial capacity and load factor. Quick introduction to linear regression in Python. *args and **kwargs allow you to pass a variable number of arguments to a function. For information on user-defined functions in legacy SQL, see User-Defined Functions in Legacy SQL. In addition to the built-in functions, this database supports user-defined Java functions. I am looking do some validation operation on the entire row of the DataFrame. We will learn how to create. Pandas has a method specifically for purging these rows called drop_duplicates(). The efficiency of data transmission between JVM and Python has been significantly improved through technology provided by Column Store and Zero Copy. Install from npm or github. Can be ufunc (a NumPy function that applies to the entire Series) or a Python function that only works on single values. Ask Question Dropping rows from a PANDAS dataframe where some of the columns have value 0. In this guide, you will learn: What is Pandas?. append() & loc[] , iloc[] Python Pandas : Count NaN or missing values in DataFrame ( also row & column wise) Python Pandas : How to add new columns in a dataFrame using [] or dataframe. Range objects and User Defined Functions (UDFs). User defined functions have a different method signature than the built-in SQL functions, so we need to monkey patch the Column class again. The resulting UDF is based Spark’s Pandas UDF and is currently limited to producing either a single value or an array of values of the same type per observation. To use these features, your data has to be in a Pandas DataFrame and it must take the form of what Hadley Whickam calls “tidy” data. Unlike other database objects, scalar UDFs disallow omitting the owner name, even if the owner of the UDF is the one calling the function. Pig UDFs can currently be implemented in six languages: Java, Jython, Python, JavaScript, Ruby and Groovy. Python and Pandas - How to plot Multiple Curves with 5 Lines of Code In this post I will show how to use pandas to do a minimalist but pretty line chart, with as many curves we want. Function names follow the same rules as other labels in PHP. PHP User Defined Functions. ) Types of UDFs. As shown in the charts, Pandas UDFs perform much better than row-at-a-time UDFs across the board, ranging from 3x to over 100x. I tried this in an User Defined Function, SomeFunction(@param1 int, @op_param bit = 0 ) When I call the function, SomeFunction(123, 1) --> it works. 20 Dec 2017. This method is elegant and more readable and you don't need to mention dataframe name everytime when you specify columns (variables). ) that stored on the database server and can be invoked using the SQL interface. Re: Calling UDF with multiple inputs from command button I'm not sure where you are going with this. Pandas make it easy to drop rows of a dataframe as well. >>> from pyspark. Let us first load the pandas package. show command at the very end. Use the drop function. read_json(filename, lines=True) test = df. Note, that here we are using a spark user-defined function (if you want to learn more about how to create UDFs, you can take a look here). Parameters are widely used in user defined functions (UDF) and stored procedures (SP) in SQL Server. Registering a UDF. You will learn the art of function writing in this first Python Data Science Toolbox course. Please see below. Install from npm or github. At QuantumBlack, we often deal with multiple terabytes of data to drive. io a pandas column with multiple columns as arguments? a new pandas. Lower and upper bounds on parameters. A Brief Overview of Pandas DataFrames. Currently, there are two types of Pandas UDF: Scalar and Grouped Map. In addition to the built-in functions, this database supports user-defined Java functions. Here is the syntax of a user defined function. Now we have multiple features h θ (x) = θ 0 + θ 1 x 1 + θ 2 x 2 + θ 3 x 3 + θ 4 x 4; For example. Parameters: func: function, string, dictionary, or list of string/functions. Download query results to a pandas DataFrame by using the BigQuery Storage API from the IPython magics for BigQuery in a Jupyter notebook. An overview of Pandas, a Python library, which is old but gold and a must-know if you're attempting to do any work with data in the Python world, and a glance of Seaborn, a Python library for making statistical visualizations. There is a VBA imposed limit that ' a string in a passed in array (e. You’ll learn more about this when you tackle the Function Arguments section. You can define a function in the following places: In a ColdFusion component. Apache Arrow was introduced in Spark 2. Introduced in Apache Spark 2. Understanding named arguments and optional arguments. In this case, this API works as if `register(name, f)`. Users who are familiar with SQL but new to pandas might be interested in a comparison with SQL. Note that the return statement return x, y has the same result as return (x, y): the former actually packs x and yinto a tuple under the hood! # Define shout_all with parameters word1 and word2 def shout_all(word1, word2):. js are, like in Python pandas, the Series and the DataFrame. Name of the MATLAB built-in function, operator, user-defined function, or MEX function to call specified as const char*. 20 Dec 2017. The lectures on this website are provided for informational purposes only and do not constitute an offer to sell, a solicitation to buy, or a recommendation or endorsement for any security or strategy, nor do they constitute an offer to provide investment advisory services by Quantopian. Merging is a big topic, so in this part we will focus on merging dataframes using common columns as Join Key and joining using Inner Join, Right Join, Left Join and Outer Join. Change data type of columns in Pandas. # load pandas import pandas. Python's Pandas library for data processing is great for all sorts of data-processing tasks. The resulting UDF is based Spark's Pandas UDF and is currently limited to producing either a single value or an array of values of the same type per observation. apply¶ GroupBy. Free blog publishing tool from Google, for sharing text, photos and video. 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: