Numpy 2d Array Indexing

Processing large NumPy arrays with memory mapping. I got it to work but I used really basic methods for solving it. He also walks through two sample big-data projects: one using NumPy to analyze weather patterns and the other using Pandas to analyze the popularity of baby names over the last century. …Let's go to the Python notebook,…and select the Exercise File for. We can think of a 2D array as an advanced version of lists of a list. NumPy cannot use double-indirection to access array elements, so indexing modes that would require this must produce copies. 20368021]] print a [1 ,2] 0. Scalars are zero dimensional. NumPy is a numerical mathematics extension to the Python programming language. newaxis (or "None" for short) is a very useful tool; you just stick it in an index expression and it adds an axis of length one there. I like to use numpy. Creating arrays in NumPy 3. In this post we are going to look at indexing, slicing and reshaping NumPy arrays. In many cases, it is helpful to use a uniquely valued identifying field of the data as its index. If a is any numpy array and b is a boolean array of the same dimensions then a[b] selects all elements of a for which the corresponding value of b is True. And, if you need to do mathematical computation on arrays and matrices, you are much better off using something like NumPy library. But what is very easy described, turned out to be a little bit harder to put into code. This is because NumPy cannot represent all the types of data that can be held in extension arrays. Boolean Indexing. Python doesn't have any syntax that directly corresponds to this. 16 this returns a view containing only those fields. So, we cannot index a numpy array with a float number. Additionally, numpy arrays support boolean indexing. This section is under construction. In real-world Often tasks have to store rectangular data table. Also, lists are faster than arrays. Coordinate conventions¶. If we change one float value in the above array definition, all the array elements will be coerced to strings, to end up with a homogeneous array. Creating arrays in NumPy 3. For one-dimensional numpy arrays, you only need to specific one index value to access the elements in the numpy array (e. NumPy’s reshape function takes a tuple as input. The values corresponding to True positions are retained in the output. So numpy provides a convenience function, ix_() for doing this:. Axis 1 is the axis that runs horizontally across the columns of the NumPy arrays. NumPy has put python lists out of the job as NumPy arrays are more efficient, convenient and makes it faster to read or write an item. Numpy array basics¶. NumPy-Arrays als universelle Datenstruktur für Bilder, extrahierte Feature-Punkte, Faltungsmatrizen und vieles mehr erleichtern die Entwicklung und das Debugging von Algorithmen zur Bildverarbeitung. Let's create a simple array of 15 numbers: nums = np. This lets us compute on arrays larger than memory using all of our cores. NumPy arrays also use much less memory than built-in Python sequences. Coordinate conventions¶. It must be of the correct shape (the same shape as arr, excluding axis). An important constraint on NumPy arrays is that for a given axis, all the elements must be spaced by the same number of bytes in memory. Basic operations on NumPy arrays. The main scenario considered is NumPy end-use rather than NumPy/SciPy development. Counting number of trues in a 1000 line answer. csv',delimiter=',') - From a CSV file np. loadtxt('file. numpy - 2d array indexing. Learn the basics of the NumPy library for Python in this tutorial from Keith Galli. For example, maybe you want to plot column 1 vs column 2, or you want the integral of data between x = 4 and x = 6, but your vector covers 0 < x < 10. Ndarray is the n-dimensional array object defined in the numpy which stores the collection of the similar type of elements. In this Python Programming video tutorial you will learn about advanced indexing operation in NumPy arrays in detail. dtype is not used for inferring the array type. Indexing 2D NumPy arrays¶ NumPy arrays need not be one-dimensional. We can also see that the type is a "numpy. Numpy is the standard module for doing numerical computations in Python. This is because NumPy cannot represent all the types of data that can be held in extension arrays. A numpy array object has a pointer to a dense block of memory that stores the data of the array. Because scikit-image represents images using NumPy arrays, the coordinate conventions must match. Take the following array. e element-wise addition and multiplication as shown in figure 15 and figure 16. Any arrays can be single or multidimensional. Other objects are built on top of these. We will index an array C in the following example by using a Boolean mask. If you have some knowledge of Cython you may want to skip to the ‘’Efficient indexing’’ section. Before we move on to more advanced things time. Note that when data is a NumPy array, data. pandas and NumPy arrays explained. We will also go over how to index one array with another boolean array. Large parts of this manual originate from Travis E. This means. Some of the key advantages of Numpy arrays are that they are fast, easy to work with, and give users the opportunity to perform calculations across entire arrays. PEP 465 -- A dedicated infix operator for matrix multiplication numpy, for example, it is technically possible to switch between the conventions, because numpy provides two different types with different __mul__ methods. * NumPy arrays are directly supported in numba. array), an n-dimensional array that is also present in some form in array-oriented languages such as Fortran 90, R, and MATLAB, as well as predecessors APL and J. Numpy arrays are great alternatives to Python Lists. where() kind of oriented for two dimensional arrays. Structured arrays. General Broadcasting Rules; Byte-swapping. Just like Python lists, the index starts from zero and goes up to 1 less than the size of the array here too. 666667 Name: ounces, dtype: float64 #calc. NumPy: creating and manipulating numerical data¶. csv',delimiter=',') - From a CSV file np. This allows NumPy to seamlessly and speedily integrate with a wide variety of databases. An important constraint on NumPy arrays is that for a given axis, all the elements must be spaced by the same number of bytes in memory. In contrast, integer array indexing allows you to construct arbitrary arrays using the data from another array. Numpy Arrays Getting started. * numba is able to generate ufuncs/gufuncs. One needs to use specific functions for linear algebra (though for matrix multiplication, one can use the @ operator in python 3. dtype is not used for inferring the array type. NumPy will interpret the structure of the data it receives to determine the dimensionality and shape of the array. Yes and no. Three types of indexing methods are available − field access, basic slicing and advanced indexing. frecuency and numbers - Numpy - mean, histogram and more. Nested Arrays or elements can be accessed by adding additional comma-separated parameters. Figure 14: Example of List indexing Street analogy for list indexing. Indexing with Boolean Arrays We create a 2D array and store our condition in b. NumPy stands for Numerical Python and it is a core scientific computing library in Python. This is because NumPy cannot represent all the types of data that can be held in extension arrays. All NumPy arrays (column-major, row-major, otherwise) are presented to R as column-major arrays, because that is the only kind of dense array that R understands. savez() function, which archives several arrays into a NumPy. NumPy provides a conversion function from zero-dimensional arrays to Python scalars, which is described in the section "Returning arrays from C functions". compress (condition, a[, axis, out]) Return selected slices of an array along given axis. We will slice the matrice "e". Pandas’ some functions return result in form of NumPy array. See the user guide section on Structured arrays for more information on multifield indexing. Finally, we will dive into some of NumPy’s more advanced features. set_printoptions(threshold=sys. Creating Arrays from Python Sequences¶. Dask Array implements a subset of the NumPy ndarray interface using blocked algorithms, cutting up the large array into many small arrays. This has led many libraries (including dask and h5py) to attempt to define a: safe subset of NumPy-style indexing that is equivalent to outer indexing, e. " We call that address an "index. loadtxt('file. In practice this means that numba code running on NumPy arrays will execute with a level of efficiency close to that of C. Values other than 0, None, False or empty strings are considered True. Join Charles Kelly for an in-depth discussion in this video, NumPy, data science, IMQAV, part of NumPy Data Science Essential Training. Structured arrays. PEP 465 -- A dedicated infix operator for matrix multiplication numpy, for example, it is technically possible to switch between the conventions, because numpy provides two different types with different __mul__ methods. That is, an ndarray can be a “view” to another ndarray, and the data it is referring to is taken care of by the “base” ndarray. NumPy’s “advanced” indexing support for indexing array with other arrays is one of its most powerful and popular features. Indexing 2D arrays 2D arrays work the same way, so if we create a 2D array of random numbers from numpy import a = random. # Find the number of civilian deaths in battles with less than 500 deaths civ_deaths = civilian_deaths [few_civ_deaths] civ_deaths. Saving NumPy Arrays •NumPy provides its own functions to read and write arrays to binary files. numpy describes 2D arrays by first listing the number of rows then the number columns. Indexing NumPy Arrays. The main actor of Numpy is the numpy array, which…. [say more on this!] Such tables are called matrices or two-dimensional arrays. Slicing Arrays Explanation Of Broadcasting. Take the following array. It provides a high-performance multidimensional array object, and tools for working with these arrays. In the 2-D case with inputs of length M and N, the outputs are of shape (N, M) for ‘xy’ indexing and (M, N) for ‘ij’ indexing. Operations on these arrays in all dimensionalities including 2D are element-wise operations. You'll need to be more specific. However, there is a better way of working Python matrices using NumPy package. Sort index. Like Python lists, numpy arrays are also composed of ordered values (called elements) and also use indexing to organize and manipulate the elements in the numpy arrays. Sort columns. ndarray (also known as numpy. >>> import numpy as np Use the following import convention: Creating Arrays. Fancy Indexing. Numpy is also incredibly fast, as it has bindings to C libraries. ” If you’re familiar with computing in general, and Python specifically, you’re probably familiar with indexes. * NumPy arrays are directly supported in numba. Basic slices are just views of this data - they are not a new copy. Structured arrays. An array as an indexed sequence of objects, all of which are of the same type. This is of course a useful tool for storing data, but it is also possible to manipulate large numbers of values without writing inefficient python loops. values) in numpy arrays using indexing. Oliphant’s book Guide to Numpy (which generously entered Public Domain in August 2008). All examples expect an import numpy as np. NumPy is the fundamental package for array computing with Python. One-dimensional Numpy Arrays. Is there any way to create a zero 2D array without numpy and without loop? The first way is: n=10 Grid=[[0]*n]*n Grid[1][1]=1 Quote:[0, 1, 0, 0, 0, 0, 0, 0, 0, 0]. NumPy is a package for scientific computing which has support for a powerful N-dimensional array object. This lets us compute on arrays larger than memory using all of our cores. Posted by: admin November 1, 2017 Leave a comment. Secondly, this is probably just a display issue. save() function, which writes a single array to a NumPy. Authors: Emmanuelle Gouillart, Didrik Pinte, Gaël Varoquaux, and Pauli Virtanen. Performance of Pandas Series vs NumPy Arrays September 5, 2014 September 5, 2014 jiffyclub python pandas numpy performance snakeviz I recently spent a day working on the performance of a Python function and learned a bit about Pandas and NumPy array indexing. One unfortunate consequence of numpy's list-of-locations indexing syntax is that users used to other array languages expect it to pick out rows and columns. random ((2 ,4)) print a [[ 0. In many cases, it is helpful to use a uniquely valued identifying field of the data as its index. I have the following six 2D arrays like this- array([[2, 0],. You've seen it with your own eyes: Python lists and numpy arrays sometimes behave differently. All NumPy arrays (column-major, row-major, otherwise) are presented to R as column-major arrays, because that is the only kind of dense array that R understands. The reference documentation for many of the functions are written by numerous contributors and developers of Numpy, both prior to and during the Numpy Documentation Marathon. Index arrays; Indexing Multi-dimensional arrays; Boolean or "mask" index arrays; Combining index arrays with slices; Structural indexing tools; Assigning values to indexed arrays; Dealing with variable numbers of indices within programs; Broadcasting. import numpy as np # Optionally you may set a random seed to make sequence of random numbers # repeatable between runs (or use a loop to run models with a repeatable # sequence of random…. The core of numpy is written in the low-level C programming language, so all computations are executed very fast. arrayname[index,]). You simply pass in the index you want. Oliphant's book Guide to Numpy (which generously entered Public Domain in August 2008). This is accomplished with either: - np. In this tutorial, you will learn how to perform many operations on NumPy arrays such as adding, removing, sorting, and manipulating elements in many ways. Replace rows an columns by zeros in a numpy array. Changing the Index of a DataFrame. Numpy is a vast toolbox with a host of powerful mathematical features. The multidimensional arrays described in this sheet are homogeneous, meaning that the values are all of the same type. # Map predictions to outcomes (only possible outcomes are 1 and 0) predictions[predictions >. mean) group a 6. empty with the following syntax: numpy. To make a sequence of numbers, similar to range in the Python standard library, we use arange. Numpy package of python has a great power of indexing in different ways. We'll create a two-dimensional NumPy array by reshaping our xa_high array from having shape (44,) to having shape (11, 4). The official home of the Python Programming Language. Arrays The central feature of NumPy is the array object class. If you are new to Python, you may be confused by some of the pythonic ways of accessing data, such as negative indexing and array slicing. So, we cannot index a numpy array with a float number. (The same array objects are accessible within the NumPy package, which is a subset of SciPy. Oliphant’s book Guide to NumPy (which generously entered Public Domain in August 2008). array class, which. save() function, which writes a single array to a NumPy. I am quite new to python and numpy. NumPy support in Numba comes in many forms: * numba understands NumPy ufuncs and is able to generate equivalent native code for many of them. For example, # printing the first three indexes in our numpy array. Applying the ndim method to our scalar, we get the dimension of the array. MATLAB® uses 1 (one) based indexing. This is of course a useful tool for storing data, but it is also possible to manipulate large numbers of values without writing inefficient python loops. Also for 2D arrays, the NumPy rule applies: an array can only contain a single type. Take values from the input array by matching 1d index and data slices. The following program creates two arrays pand qin lines 3 and 6, then it stacks them into array newa in line 7. In contrast, indexing by 1D arrays along: at least one dimension in the style of outer indexing is much more acheivable. Understanding the internals of NumPy to avoid unnecessary array copying. 31309302 Dr Ben Dudson Introduction to Programming - Lab 3 (11 of 16). It provides efficient multi-dimensional array objects and various operations to work with these array objects. This section introduces NumPy arrays then explains the difference between Python lists and NumPy arrays. You can create numpy array casting python list. A boolean array can be created manually by using dtype=bool when creating the array. To make a sequence of numbers, similar to range in the Python standard library, we use arange. The official home of the Python Programming Language. The default dtype of numpy array is float64. Installing Numpy. In this article, we will see how we can identify and select parts of our arrays, whether 1d or 2d. genfromtxt('file. NumPy defines a new data type called the ndarray or n-dimensional array. choose (a, choices[, out, mode]) Construct an array from an index array and a set of arrays to choose from. NumPy replaces a lot of the functionality of Matlab and Mathematica, but in contrast to those products, it is free and open source. Indexing with 1-D Arrays. The mathematical operations for 3D numpy arrays follow similar conventions i. Indexing with Boolean Arrays We create a 2D array and store our condition in b. savez() function, which archives several arrays into a NumPy. Values other than 0, None, False or empty strings are considered True. Understanding the internals of NumPy to avoid unnecessary array copying. i: a SWIG Interface File for NumPy; Testing the numpy. Here is an example of concatenating 3 NumPy arrays row-wise. NumPy creates an appropriate scale index at the time of array creation. This means. Arrays are the main data structure used in machine learning. Zero-dimensional Arrays in Numpy. Large parts of this manual originate from Travis E. Welcome to my new course Python Essentials with Pandas and Numpy for Data Science In this course, we will learn the basics of Python Data Structures and the most important Data Science libraries like NumPy and Pandas with step by step examples!. This lets us compute on arrays larger than memory using all of our cores. > Even if we have created a 2d list , then to it will remain a 1d list containing other list. Searching, Sorting and splitting Array Mathematical functions and Plotting numpy arrays. As part of working with Numpy, one of the first things you will do is create Numpy arrays. Related Posts: Sorting 2D Numpy Array by column or row in Python; Python Numpy : Select an element or sub array by index from a Numpy Array; Delete elements, rows or columns from a Numpy Array by index positions using numpy. In case of slice, a view or shallow copy of the array is returned but in index array a copy of the original array is returned. In response to Ticket numpy#4724, explain that the 'index_array' returned by 'argparse' can only be used. For those of you who are new to the topic, let's clarify what it exactly is and what it's good for. As the name gives away, a NumPy array is a central data structure of the numpy. NumPy DataCamp Learn Python for Data Science Interactively The NumPy library is the core library for scientific computing in Python. Coordinate conventions¶. Replace rows an columns by zeros in a numpy array. Arrays make operations with large amounts of numeric data very fast and are. In NumPy, Array is an Object class little similar to lists in Python. 20368021]] print a [1 ,2] 0. loadtxt(“valle_aburra. ” We call that address an “index. You can also index with other arrays: In [14]: a = np. It will give you a jumpstart with data structure. Numpy arrays are great alternatives to Python Lists. Doing this for one dimensional arrays is fairly straightforward, but dealing with multidimensional arrays is a bit trickier. BSON-NumPy 0. The following code is used to produce a Numpy Multiplication Matrix; * is used for array multiplication. For example,. I've always included a python course as well, but that's just bonus content (in case you haven't used python before. The three types of indexing methods that are followed in numpy − field access, basic slicing, and advanced indexing. Website: h. savez() function, which archives several arrays into a NumPy. Integer array indexing: When you index into numpy arrays using slicing, the resulting array view will always be a subarray of the original array. Replace rows an columns by zeros in a numpy array. my_array = np. You can also index with other arrays: In [14]: a = np. This allows NumPy to seamlessly and speedily integrate with a wide variety of databases. The above function is used to make a numpy array with elements in the range between the start and stop value and num_of_elements as the size of the numpy array. So, to access the fifth element of the above NumPy array, we execute the following command:. A simple example The following function calculates the sum of the diagonal elements of a two-dimensional array, verifying that the array is in fact two-dimensional and of type PyArray_DOUBLE. Subsetting 2D NumPy Arrays If your 2D numpy array has a regular structure, i. Indexing NumPy arrays with Booleans Boolean indexing is indexing based on a Boolean array and falls in the family of fancy indexing. A first_index_et function is given as. Oliphant, PhD Dec 7, 2006 This book is under restricted distribution using a Market-Determined, Tempo-rary, Distribution-Restriction (MDTDR. Arrays can be stacked into a single array by calling Numpy function hstack. Structured arrays. We coordinate these blocked algorithms using Dask graphs. savetxt('file. In NumPy, Array is an Object class little similar to lists in Python. diag (v[, k]) Extract a diagonal or construct a diagonal array. In NumPy the basic type is a multidimensional array. You can create an array from a Python list or tuple by using NumPy’s array function. In Python, data is almost universally represented as NumPy arrays. Indexing with Boolean Arrays We create a 2D array and store our condition in b. In the following example, you will first create two Python lists. array), an n-dimensional array that is also present in some form in array-oriented languages such as Fortran 90, R, and MATLAB, as well as predecessors APL and J. Ways of creating numpy arrays? There are mainly two ways to create numpy arrays. NumPy arrays have an index. Here are some ways Numpy arrays can be manipulated: Create ndarray. Oliphant's book Guide to Numpy (which generously entered Public Domain in August 2008). topas2numpy 0. The mathematical operations for 3D numpy arrays follow similar conventions i. General Broadcasting Rules; Byte-swapping. Giving the string ‘ij’ returns a meshgrid with matrix indexing, while ‘xy’ returns a meshgrid with Cartesian indexing. Array newa is split into three arrays with equal shape in line 10. Machine learning data is represented as arrays. MATLAB® uses 1 (one) based indexing. For those of you who are new to the topic, let's clarify what it exactly is and what it's good for. NumPy has an extensive list of methods to generate random arrays and single numbers, or to randomly shuffle arrays. They build full-blown visualizations: they create the data source, filters if necessary, and add the. I had a code challenge for a class I'm taking that built a NN algorithm. And, if you need to do mathematical computation on arrays and matrices, you are much better off using something like NumPy library. Advanced indexing using `np. find nearest value in numpy array: stackoverflow: Finding the nearest value and return the index of array in Python: stackoverflow: Numpy minimum in (row, column) format: stackoverflow: Numpy: get the column and row index of the minimum value of a 2D array: stackoverflow: numpy : argmin in multidimensional arrays: bytes. Just like Python lists, the index starts from zero and goes up to 1 less than the size of the array here too. This allows NumPy to seamlessly and speedily integrate with a wide variety of databases. To get a range of values in an array, we will use the slice notation ':' just like in Python. " We call that address an "index. Passing multidimensional numpy arrays to C with cffi I've been playing with using numpy with the cffi library to allow c functions to manipulate numpy arrays directly. This chapter gives an overview of NumPy, the core tool for performant numerical computing with Python. Indexing into a structured array can also be done with a list of field names, e. 20368021]] print a [1 ,2] 0. An important constraint on NumPy arrays is that for a given axis, all the elements must be spaced by the same number of bytes in memory. That is, it will become an array with 11 rows and 4 columns. dtype is not used for inferring the array type. But arrays are also useful because they interact with other NumPy functions as well as being central to other package functionality. NumPy is an incredible library to perform mathematical and statistical operations. The following program creates two arrays pand qin lines 3 and 6, then it stacks them into array newa in line 7. One way to make numpy array is using python list or nested list; We can also use some numpy built-In methods; Creating numpy array from python list or nested lists. I've always included a python course as well, but that's just bonus content (in case you haven't used python before. I am quite new to python and numpy. If you are new to Python, you may be confused by some of the pythonic ways of accessing data, such as negative indexing and array slicing. ndarray (also known as numpy. NumPy Array: Numpy array is a powerful N-dimensional array object which is in the form of rows and columns. Try adding this line before you print the array: np. In order to access a single or multiple items of an array, we need to pass array of indexes in square brackets. Data manipulation in Python is nearly synonymous with NumPy array manipulation: even newer tools like Pandas are built around the NumPy array. In today's world of science and technology, it is all about speed and flexibility. NumPy provides two fundamental objects: an N-dimensional array object and a universal function object. Large parts of this manual originate from Travis E. In this follow-on to our first look at Python arrays we examine some of the problems of working with lists as arrays and discover the power of the NumPy array. Only integer scalar arrays can be converted to a scalar index. Arbitrary data-types can be defined. Although index 1 and 7 are repeated but they are incremented only once. array) is different from the standard Python array. A boolean index array is of the same shape as the array-to-be-filtered and it contains only True and False values. The generic format in NumPy multi-dimensional arrays is: Array[row_start_index:row_end_index, column_start_index: column_end_index] NumPy arrays can also be accessed using boolean indexing. NumPy provides an N-dimensional array type, the ndarray, which describes a collection of "items" of the same type. In this article we will discuss how to remove elements , rows and columns from 1D & 2D numpy array using np. (Another Python module called array defines one-dimensional arrays, so don’t confuse the arrays of NumPy with this other type. Axis 1 is the axis that runs horizontally across the columns of the NumPy arrays. BSON-NumPy 0. NumPy is a Python library used in data science and big data that works with arrays when performing scientific computing with Python. Indexing using index arrays. Numpy offers several ways to index into arrays. This section will try to explain in detail how numpy indexing works and why we adopt the convention we do for images, and when it may be appropriate to adopt other conventions. NumPy arrays are used to store lists of numerical data and to represent vectors, matrices, and even tensors. In this lecture, we introduce NumPy arrays and the fundamental array processing operations provided by NumPy. 05098369] [ 0. Strings, Lists, Arrays, and Dictionaries¶. txt') - From a text file np. Installing Numpy. Access to Numpy arrays is very efficient, as indexing is lowered to memory accessing when possible. 3D Numpy Arrays. set_printoptions(threshold=sys. 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: