Python Chunk Iterator

This PEP, therefore, proposes a simple and universal interface between web servers and web applications or frameworks: the Python Web Server Gateway Interface (WSGI). Syntax of the For Loop. x as well: Output with Print in Python 2. As mentioned. This kind of for loop is known in most Unix and Linux shells and it is the one which is implemented in Python. iterables Iterable Examples: lists, strings, dictionaries, file connections An object with an associated iter() method Applying iter() to an iterable creates an iterator Iterator Produces next value with next(). Various types of useful iterators and generators. This tries to simplify your overall code. By default, split() takes whitespace as the delimiter. Sometimes, the data we have to process reaches a size that is too much for a computer's memory to handle. Practice List, Set, Dictionary, and Tuple in Python. However, in the mongo shell, if the returned cursor is not assigned to a variable using the var keyword, then the cursor is automatically iterated up to 20 times to print up to the first 20 documents in the results. NB that if iter grows very large the reallocation every iteration above may begin to noticeably slow down the execution. Clearly we can’t put everything neatly into a Python list first and then start munching — we must process the information as it comes in. Block is a chunk of memory of a certain size. read() if c is None: print 'fp is at the eof' source : How to find out whether a file is at its `eof`?. While Python 2. They return a value. Suggest the Python environment to use, in your setup chunk. It means that with binary data we can’t reliably use readline and file object (as an iterator) to read the contents of a file because might be no newline character in a file. With this post, I intend help each one of you who is facing this trouble in python. Chapter 7 It’s time for the first of Python’s higher-level built-in data structures: lists and tuples. Each time we call the next method on the iterator gives us the next element. They are extracted from open source Python projects. In fact, this is a nice way to provide different ways to iterate over the data in a class in multiple ways. If used with NPY_ITER_EXTERNAL_LOOP, the inner loop for the caller may get larger chunks than would be possible without buffering, because of how the strides are laid out. iteratorをchunkに分ける. writelines 4) a trick I've found to implement the merge sort, which is a variant of the Schwartzian transform wherein the iterator is included in the transformed tuple. handlers # FileHandler, Formatter, getLogger, DEBUG. The mapping argument can be a mapping object (dict, jsontree, etc. Writing an iterator to load data in chunks (4) 100xp: In the previous exercises, you've only processed the data from the first DataFrame chunk. In this post, I describe a method that will help you when working with large CSV files in python. Question: Tag: iterator,rust I've written the following function to compare two iterators, element-by-element. python will provide you with a chunk of memory into which you must in-place construct your new C++ object. Then you type another command, which again produes an answer, and so on. In Python, and many other programming languages, you will need to loop commands several times, or until a condition is fulfilled. We have seen Perl, Python, PHP, C#, Java, Ruby, Rebol, and many, many more. Using The iterators Package Rich Calaway July 26, 2019 1 Introduction An iterator is a special type of object that generalizes the notion of a looping variable. I'll show examples to help you grok the GIL. 4+dfsg-0+deb8u1. In this article, I’ll show you – through a few practical examples – how to combine a for loop with another for loop and/or with an if statement!. In this post, I describe a method that will help you when working with large CSV files in python. Unleash the power of Python for your data analysis projects with For Dummies! Python is the preferred programming language for data scientists and combines the best features of Matlab, Mathematica, and R into libraries specific to data analysis and visualization. In that tutorial, what is Python list comprehension and how to use it? Along with this, we will learn syntax, list comprehension vs lambda expression in Python3. Return a iterator of chunks for the iterable. This time, you will aggregate the results over all the DataFrame chunks in the dataset. After that, in each iteration, we read a chunk of data and write it to the file opened and update the progress bar. Let's take a function to divide two numbers, and return the quotient. In each iteration step a loop variable is set to a value in a sequence or other data collection. How can I read only rows from 512*n to 512*(n+1). We can have a group and then join the group into something. A thorough understanding of Python will help you write more efficient and effective scripts, so let's get started with Python three essential training. This is a much more serious test and we start to see the size of the python interpreter process grow to accomodate the data structures used in the computation. But since all N items are in fact references to the same iterator iter(the_list) the result will be repeated calls to next() on the original iterator. Some object-oriented languages such as C#, C++ (later versions), Delphi (later versions), Go, Java (later versions), Lua, Perl, Python, Ruby provide an intrinsic way of iterating through the elements of a container object without the introduction of an explicit iterator object. GitHub Gist: instantly share code, notes, and snippets. cgitb: More comprehensive traceback formatting for Python scripts. When iterating over a Series, it is regarded as array-like, and basic iteration produce. Method 1: Using yield The yield keyword enables a function to comeback where it left off when it is called again. The Python Software Foundation ("PSF") does not claim ownership of any third-party code or content ("third party content") placed on the web site and has no obligation of any kind with respect to such third party content. For example, it allows to efficiently serialize Haskell values to lazy bytestrings with a large average chunk size. The Oracle - cx_Oracle - Python mappings are:. An iterator is a type of Python object which behaves in certain ways when operated on repeatedly. It is unique in that it combines the speed and XML feature completeness of these libraries with the simplicity of a native Python API, mostly compatible but superior to the well-known ElementTree API. This was the point where I decided to look at the itertools ((Python 2 documentation: itertools — Functions creating iterators for efficient looping)) module again. Few examples to show you how to split a String into a List in Python. A while loop statement in Python programming language repeatedly executes a target statement as long as a given condition is true. The size of the block can vary from 8 to 512 bytes and must be a multiple of eight (i. The amount of memory that Python holds depends on the usage patterns. com/gto76/python-cheatsheet/master/README. The code used in this article can be found in the following GitHub repo. Which in turn will group them into N sized tuples. if statements are a problem if your queryset is huge. Python [code ]read[/code] function reads up to the end of file. You would think iteration, you know looping over stuff, would be a solved problem in programming languages. Python Programming tutorials from beginner to advanced on a massive variety of topics. 3 #allows to split the original chunk into multiple chunks with smaller. Buffering is especially useful for Python code using the iterator, allowing for larger chunks of data at once to amortize the Python interpreter overhead. data = [] for chunks in df: data = data + [chunk] But this is quite useless as still the file has to be completelly opened and takes time. The other tests that fail under python 3 are the MSVS settings tests. Technically, in Python, an iterator is an object which implements the iterator protocol, which consist of the methods __iter__() and __next__(). Today we will talk about how to combine them. """Iterate through thing in chunks of size chunk_length. patch/etc/mpv. There are several helpers for the bulk API since its requirement for specific formatting and other considerations can make it cumbersome if used directly. According to the Python documentation it provides the developer with a high-level interface for asynchronously executing callables. 2 Installation. The “current thing”. I use it almost everyday to read urls or make POST requests. Possible Duplicate: How do you split a list into evenly sized chunks in Python? I am surprised I could not find a "batch" function that would take as input an iterable and return an iterable of. And so, chunks is a generator function that never ends. Usage eng_python(options) Arguments options Chunk options, as provided by knitrduring chunk execution. Problem Given a list and an integer chunk size, divide the list into sublists, where each sublist is of chunk size. You've learned a lot about processing a large dataset in chunks. You can also save this page to your account. To ensure no mixed types either set False, or specify the type with the dtype parameter. range() doesn't actually create the list; instead, it creates a range object with an iterator that produces the values until it reaches the limit If range() created the actual list, calling it with a value of 10^100 may not work, especially since a number as big as that may go over a regular computer's memory. Split in chunks. You can vote up the examples you like or vote down the ones you don't like. iterables Iterable Examples: lists, strings, dictionaries, file connections An object with an associated iter() method Applying iter() to an iterable creates an iterator Iterator Produces next value with next(). NB: last partial update for OpenViBE 1. zip(*(iter(the_list),) * N)-> will feed those list of iterators into zip. read_csv(), passing c_size to chunksize. By setting the chunksize kwarg for read_csv you will get a generator for these chunks, each one being a dataframe with the same header (column names). Some of the packages are small and brief. The examples are to be tried at the Python console. Writing an iterator to load data in chunks (3) 100xp: You're getting used to reading and processing data in chunks by now. In Python, this is easy to do on-the-fly and we don’t even need to uncompress the whole archive to disk. Python Program to Count the Number of Lines in a Text File Posted on April 15, 2017 by Manish This is a Python Program to count the number of lines in a text file. Updates to original script in comments below - 9/5/2019 With hurricane season upon us, I wanted to share a simple but powerful python script which locates assets that are in path of a major storm. Writing an iterator to load data in chunks (1) 100xp: Another way to read data too large to store in memory in chunks is to read the file in as: DataFrames of a certain length, say, 100. msg315229 -. In this article we will discuss the iterator invalidation with respect to std::vector in C++. (But I am new to python, so I am sure there are better ways to do everything!) Some are intended to illustrate literate programming and testing. An object which will return data, one element at a time. Iteration Simplifies our Turtle Program¶ To draw a square we'd like to do the same thing four times — move the turtle forward some distance and turn 90 degrees. Data may vary in size—some of them may fit into memory and some may … - Selection from Python Data Science Cookbook [Book]. {"categories":[{"categoryid":387,"name":"app-accessibility","summary":"The app-accessibility category contains packages which help with accessibility (for example. Welcome to Pebble's documentation!¶ Modern languages should natively support concurrency, threading and synchronization primitives. 5, are expressions (and not merely a different syntax for an if/else statement). Python excels in implementing this particular paradigm. And you want to separate it to equal size chunks. Furthermore, it avoids repetition and makes code reusable. By setting the chunksize kwarg for read_csv you will get a generator for these chunks, each one being a dataframe with the same header (column names). They are extracted from open source Python projects. A while loop statement in Python programming language repeatedly executes a target statement as long as a given condition is true. What is the most "pythonic" way to iterate over a list in chunks? How do you split a list into evenly sized chunks in Python?. we have some complicated code in bquery to iterate over chunks. Add Progress Bars To Your Python Loops. Iterator in python is any python type that can be used with a ‘for in loop’. A solution to this is to process an entire data source chunk by chunk, instead of a single go all at once. The simplification of code is a result of generator function and generator expression support provided by Python. Python has several built-in objects, which implement the iterator protocol and you must have seen some of these before: lists, tuples, strings, dictionaries and even files. Here are related articles and videos I recommend: Loop Like a Native, Ned Batchelder's PyCon 2013 talk. In Windows, assuming that Python has already been. Why are "broken iterators" broken?. It was created by Guido van Rossum during 1985- 1990. The iterator part iterates through each member e of the input sequence a_list. Iterators are new to R (REvolution Computing just released the iterators package to CRAN last month), but will be familiar to programmers of languages like Java or Python. py but not actually used in manager. Open a file and check for data. You've learned a lot about processing a large dataset in chunks. I created a function to read lines from an file into chunks. Output: 10 12 15 18 20. In other words, it zips two iterators together, into a single one. Almost any collection in Clojure can be converted to a "seq" (short for sequence) by calling a function called, you guessed it, seq. #compatibility PhotoScan Pro 1. Together, they form an “iterator algebra” making it possible to construct specialized tools succinctly and efficiently in pure Python. Although the Python interpreter closes the opened files automatically at the end of the execution of the Python program, explicitly closing the file via close() is good programming style, and should not be forgotten. When passed as an argument to a function that knows what to do with it, the iterator supplies a sequence of values. “[python] enumerate, iterators for large files” is published by peter_yun. When the slice len is not evenly divided by the chunk size, the last slice of the iteration will be the remainder. Here’s an efficient way to read an iterator of unicode chunks from a file using iterdecode(). nr rst2man-indent-level 0. Data may vary in size—some of them may fit into memory and some may … - Selection from Python Data Science Cookbook [Book]. The final goal is to export the functions & their instruct. Clearly we can't put everything neatly into a Python list first and then start munching — we must process the information as it comes in. The r everseRecursively() method reverse the linked list using recursion. Explaination : When passing iterators, zip internally invokes the next on the subsequent passed iterators before combining them. toDict()) and from (chunkify(), Chunk. What it does is split or breakup a string and add the data to a string array using a defined separator. Jump to: navigation, search. A table iterator is an iterator which satisfies the following: each item returned by the iterator is a sequence (e. Iteration Simplifies our Turtle Program¶ To draw a square we’d like to do the same thing four times — move the turtle forward some distance and turn 90 degrees. $ python array_string. Processing is not a single programming language, but an arts-centric system for learning, teaching, and making visual form with code. finished, default is False. So this function doesn't belong to itertools - it is a missing string or sequence method. csv', sep='\t', iterator=True, chunksize=1000) All good, i can for example print df. This is the opposite of concatenation which merges or combines strings into one. Now you could call him and say "thank you" for great generators in Python!. read_csv(filename, chunksize=100). zip(*(iter(the_list),) * N)-> will feed those list of iterators into zip. Reading chunks of a txt file create a chunk size, and then iterate over each of those chunks to give me what I need? If I was using Python I solely I would. But we must also remember that if we. You've learned a lot about processing a large dataset in chunks. This is a common problem faced by data scientists. The amount of memory that Python holds depends on the usage patterns. Files are horrible… let's use iterators! Dealing with files can get tedious. Begin Python chunks with ```{python}. patch/pip/commands/. More generator details and examples. Python provides a very straightforward and easy function to do that. After the #! shebang line and import statements, the program will check that there is more than one command line argument. This time, you will aggregate the results over all the DataFrame chunks in the dataset. An iterator is then an object for moving through the container, one item at a time. pc/01_desktop-path. Since the number of desired sublists may not evenly divide the length of. First up is a discussion of the basic data types that are built into Python. 3 For loop variations. In fact, this is a nice way to provide different ways to iterate over the data in a class in multiple ways. If we loop over a list, the loop variable is assigned to its elements one at a time:. Iterating Over Arrays¶ The iterator object nditer, introduced in NumPy 1. Generators are computed lazily. See project Pympler for unit tests. Files are horrible… let's use iterators! Dealing with files can get tedious. chunk to set up the reticulate Python engine (not required for knitr >= 1. patch/pip/. However, it would be great if I could just reuse something in the standard library. Using itertools. Basics of Loops in Python. To my delight many of my desired patterns were already implemented, present in the recipe section of the docs, or could be coded up quickly without much boilerplate. Iteration Simplifies our Turtle Program¶ To draw a square we'd like to do the same thing four times — move the turtle forward some distance and turn 90 degrees. Python List Comprehension. My conclusion was that the fundamental changes required to Python's iteration protocol would probably be more harmful. For example, it allows to efficiently serialize Haskell values to lazy bytestrings with a large average chunk size. Python Forums on Bytes. For very large result sets though, this could be expensive in terms of memory (and time to wait for the entire result set to come back). (iter(the_list),) * N-> will generate an N reference to the_list iterator. Iterators are new to R (REvolution Computing just released the iterators package to CRAN last month), but will be familiar to programmers of languages like Java or Python. Pythonのドキュメントに記述があるのそのまま. We then wrapped the iteration with a tqdm object, which will print a fancy progress bar. We previously used 8 lines of Python code to have alex draw the four sides of a square. In other words, it zips two iterators together, into a single one. c, /trunk/liblwgeom/lwgeodetic_tree. So we'll start off with some basics so everyone is on the same page. A while loop statement in Python programming language repeatedly executes a target statement as long as a given condition is true. Functions alen and itemsize have been updated. GitHub Gist: instantly share code, notes, and snippets. Some have many features. An object is called iterable if we can get an iterator from it. This data structure exercise is for beginners to understand and practice data structure in Python. Attachments (1) manager-patch. You will learn to write fast and thread-safe Python, and how to choose between threads and processes. 3? msg110533 - (view). Many of the items in the list are integer values returned from a function. The predicate checks if the member is an integer. In this post, focused on learning python programming, we learned how to use Python to go from raw JSON data to fully functional maps using command line tools, ijson, Pandas, matplotlib, and folium. Some of the packages are small and brief. The get_chunk() method directly returns the next chuck of the file. In Python, and many other programming languages, you will need to loop commands several times, or until a condition is fulfilled. headline) ``` #### Iterate over `QuerySet` by items ```python from django_chunked_iterator import iterator. Downloading files from web using Python Requests is a versatile HTTP library in python with various applications. Convert the articles to plain text (process Wiki markup) and store the result as sparse TF-IDF vectors. We describe two methods. You can vote up the examples you like or vote down the ones you don't like. Results: Five hundred thousand integers. Learn more about Python break and continue statement. With files this large, reading the data into pandas directly can be difficult (or impossible) due to memory constrictions, especially if you're working on a prosumer computer. Each has been recast in a form suitable for Python. 3 the convenient iterator protocol was introduced. c, /trunk/liblwgeom/lwgeodetic_tree. This update triggers a transition from libclamav7 to libclama9. ) If there is only one element in. But we must also remember that if we. Split in chunks. Basics of Loops in Python. pc/better-error-message. Basically concurrent. The syntax of hasattr() method is:. 5’s new with statement (dead link) seems to be a bit confusing even for experienced Python programmers. noun_chunks is a generator that yields spans Syntax iterators Learn more Python for data science interactively at www. Iterators are new to R (REvolution Computing just released the iterators package to CRAN last month), but will be familiar to programmers of languages like Java or Python. In this post, I describe a method that will help you when working with large CSV files in python. In this particular application what it does is that it looks at the file's individual name and size, compares that to what has already been uploaded in AWS. writelines 4) a trick I've found to implement the merge sort, which is a variant of the Schwartzian transform wherein the iterator is included in the transformed tuple. As our program grows larger and larger, functions make it more organized and manageable. Chunks (Python recipe) by Michael Lerner. Lists are inbuilt data structures in Python that store heterogeneous items and enable efficient access to these items. You can vote up the examples you like or vote down the ones you don't like. It is easy, and the loop itself only needs a few lines of code. Using this engine allows for shared state between Python chunks in a document - that is, variables defined by one Python chunk can be used by later Python chunks. If necessary, the iteration can be stopped by issuing a call to the stop() method on the returned iterator object. For Loop The for loop that is used to iterate over elements of a sequence, it is often. ```python [표현식 for 항목1 in 반복가능객체1 if 조건1. argv[0], which contains the Python script’s filename. Next, here are some common methods we can call on an iterator: a. all work as expected. Learn how to process images using the Pillow Python library. I have a list of arbitrary length, and I need to split it up into equal size chunks and operate on it. Python Team Training Write Pythonic code. Normally, you’d choose a particular coding style to meet a specific need, but using a common problem as an example makes it easier to compare the different styles. Note: A generator function can also be a method in a class. And so, chunks is a generator function that never ends. To overcome this problem we need to take one item out of the original iterator. Some module names are followed by an annotation indicating what platform they are available on. 2 — October 6, 2012 — Diff — Docs. An iterator is a type of Python object which behaves in certain ways when operated on repeatedly. It is by no means a complete collection but it has served me quite a bit in the past and I will keep extending it. Enumerate() method adds a counter to an iterable and returns it in a form of enumerate object. read() if c is None: print 'fp is at the eof' source : How to find out whether a file is at its `eof`?. for chunk in df: print chunk My problem is I don't know how to use stuff like these below for the whole df and not for just one chunk. Simplified Code. 3? msg110533 - (view). Basically I want to iterate through all functions in an IDB file and their instructions using ida python script. desktop' --- debian/. There are several helpers for the bulk API since its requirement for specific formatting and other considerations can make it cumbersome if used directly. chunk — Read IFF chunked data¶. These functions are throw-away functions, i. Learn more about Python break and continue statement. At a high level, you might simply view iteration as a way to process items in a sequence. The loop executes perfectly, but the last iteration of the loop invariably produces an empty raster with no spatial reference. I'm new to IDAPython. Notes for Professionals. This was all about the default arguments in Python Learn: Python Built-In Functions with Syntax and Examples. Add Progress Bars To Your Python Loops. We have set to 1024 bytes, iterate through each chunk, and write the chunks in the file until the chunks finished. In this exercise, you will read in a file using a bigger DataFrame chunk size and then process the data from the first chunk. This object implements the next method and can be used anywhere an iterator can be used in Python. We previously used 8 lines of Python code to have alex draw the four sides of a square. keys() and dict. Here are some examples. Chunk large QuerySets into small chunks, and iterate over them without killing your RAM. Here is an iterator that works like built-in xrange function. chain to create a chunk featuring this one item and n-1 more items. patch/etc/mpv. The first thing that comes in mind would be using for loop. We can't have a chunk with an iterator, because iterator over a string decomposes it into a group of pieces with no reverse function. Writing an iterator to load data in chunks (4) 100xp: In the previous exercises, you've only processed the data from the first DataFrame chunk. You can vote up the examples you like or vote down the ones you don't like. A generator function is a special function in Python, that can yield multiple values, instead of just a single return. Say you have an iterator (stream of objects of unknown length). For instance, maybe I have a flat list. The syntax of hasattr() method is:. Files are horrible… let’s use iterators! Dealing with files can get tedious. You've learned a lot about processing a large dataset in chunks. argv to accept command line arguments to Python scripts the first argument in sys. """ from influxdb import InfluxDBClient from influxdb import SeriesHelper # InfluxDB. As our program grows larger and larger, functions make it more organized and manageable. The Python for statement iterates over the members of a sequence in order, executing the block each time. Attachments (1) manager-patch. First up is a discussion of the basic data types that are built into Python. functions without a name. While C implemented chunks() is faster than manual iteration, speed up of real loops is not worth the use of special function. Iterate with Implicit Iterator. Introduction. In fact, this is a nice way to provide different ways to iterate over the data in a class in multiple ways. Python is an extremely readable and versatile programming language. Jump to: navigation, search. So this function doesn't belong to itertools - it is a missing string or sequence method. Iterators are a fundamental part of contemporary Python programming, where they form the basis for loops, list comprehensions and generator expressions. Let's say we are given a file 'number1. Next I tried a run of each method using 500,000 integers concatenated into a string 2,821 kB long. The iterator in Python is implemented via two distinct methods: __iter__ and __next__. get_chunks¶ get_chunks(iterable_obj, chunk_size=1): Receives the iterable object iterable_obj and divides the object in evenly sized chunks of size chunk_size. Python’s built-in iteration support to the rescue! Generators, iterators, iterables. The large average chunk size allows to make good. In this second Python Data Science Toolbox course, you'll continue to build your Python data science skills. py and query. We have seen Perl, Python, PHP, C#, Java, Ruby, Rebol, and many, many more. We can't have a chunk with an iterator, because iterator over a string decomposes it into a group of pieces with no reverse function. iterator() Showing 1-21 of 21 messages. Along with this, we will study conditionals and nested list comprehension in Python Programming Language. Splitting a list into even chunks. The common delimiter between words in a string is space. Python’s itertools library is a gem - you can compose elegant solutions for a variety of problems with the functions it provides. Internally process the file in chunks, resulting in lower memory use while parsing, but possibly mixed type inference. Python Programming tutorials from beginner to advanced on a massive variety of topics. Note that the last chunk can be smaller than chunk_length. 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: