# Igraph Create Graph From Edge List Python

If you're involved in analytics in any capacity, this course will be a huge help, teaching you how the R sna and igraph modules works and how to format data for analysis, create graphs, analyze network graphs, and visualize networks. Let's see what we can do strictly sticking to Python. The club's president and the instructor were involved in a dispute, resulting in a split of this group. for a rectangle) vertex. In this post, we'll use this package to animate the simulated spread of a disease through a network. , the nodes are numbered from 0 to N-1, where N is the number of nodes in the graph, and the kth value in the array or in the list is a pointer to node k). > Is there any interest in a (hypothetical) standard graph API (with > 'graph' meaning a network, consisting of nodes and edges)? 1) Yes! 2) Only if it's in C. Figuerola: A walk on Python-igraph Import/export graphs from/to disk · igraph deals with major graph file formats · it has generic load and save methods. Vertex ids in these objects integers starting from 1 (in igraph version prior to 0. A finite set of ordered pair of the form (u, v) called as edge. For example, consider the graph shown in Fig. Graph plotting functionality is provided by the Cairo library, so make sure you install the Python bindings of Cairo if you want to generate publication-quality graph plots. atlas(n)” 과 동일하다. Full(3) >>> es = EdgeSeq(g) >>> restricted_es = EdgeSeq(g, [0, 1]) The individual edges can be accessed by indexing the edge sequence object. The index of the array represents a vertex and each element in its linked list represents the other vertices that form an edge with the vertex. With your preparation out of the way, you can now get started actually using Python to draw a graph from a CSV file. Examples of how to make line plots, scatter plots. To begin, we're going to modify the notebook first by converting it to a. Figuerola: A walk on Python-igraph Populating the graph: vertices in python-igraph every node (vertices) has an unique ID, which is an automatic number vertices can be added with add_vertices(n) vertices are renumbered starting from 0 in python-igraph, vertices are encapsulated as an iterable list: Graph. add_edges: Add edges to a graph in igraph: Network Analysis and Visualization rdrr. ndarray" in Python. edges Browse other questions tagged python igraph edge-list. The first edge points from edges[1] to edges[2], the second from edges[3] to edges[4], etc. Выявление блоков/компонент двусвязности графа (найти количество блоков в связном. Recommend：creating a bipartite graph in R with igraph with specific edge list has 2115 nodes and another one has 4) with specified edge list. We can always emulate an undirected edge between vertices x and y with directed edges from x to y and from y to x. Its argument is a two-column matrix, each row defines one edge. the vertex corresponding to the first row will be vertex 0 in the graph, etc. and various random graphs. Sometimes it is useful to work with a standard representation of a graph, like an edge list. collections. Each edge in the graph contains information about the road, including a list of points along this road. You have to collapse multiple edges into a single edge and assign the original edge count to the new edge as an edge attribute. pyplot as plt then I try to plot as plt. Open the CSV file and create a reader object from it. Reading in the file and converting to an igraph object is straightforward. 本文仅用于学习和交流目的，不代表图灵社区观点。非商业转载请注明作译者、出处，并保留本文的原始链接。. GitHub Gist: instantly share code, notes, and snippets. Now if the graph is undirected, we also need to create an edge from dest to src in the adjacency list as shown below:. network <- as. Stackoverflow. You have adjacency matrix in which row. Now, I insist on a pure python minimal system with the least. Package igraph. igraph is a collection of software packages for graph theory and network analysis. On the right graph, A and B belongs to the same group, D and E are grouped together and C is alone in his group. Each graph, node, and edge can hold key/value attribute pairs in an associated attribute dictionary (the keys must be hashable). Graph objects: igraph_graph = igraph. Returns the adjacency edge list representation of the graph. I also was confused why there was integer overflow when python's maxint is much higher than any integer in the list. SageMath is a free open-source mathematics software system licensed under the GPL. A graph database represents and stores data in three aspects: nodes, edges and properties. To see the proper mathematical definition of a graph, you can have a look at our previous chapter Graphs in Python. Start_Airport: the airport we start with. gexf” function. 3+ installed on your development machine. Other implementations of this class are also possible. In a weighted graph, the edges have weights associated with them. 2 for an example. The following are code examples for showing how to use networkx. layout_myclass() This method takes the return value of create_layout. Comparison with Popular Python Implementations: NetworkX and iGraph Both implementations (exact solution and power method ) are much faster than their correspondent methods in NetworkX. With the edgelist format simple edge data can be stored but node or graph data is not. collections. NetworkX是一个用Python语言开发的图论与复杂网络建模工具，内置了常用的图与复杂网络分析算法，可以方便的进行复杂网络数据分析、仿真建模等工作。. > but does that mean igraph cannot handle multigraph isomorphism? It can, with a trick. It easily scales to massive networks with hundreds of millions of nodes, and billions of edges. , 1991), a typical benchmark dataset that is also provided in our package. The multi-line adjacency list format is useful for graphs with nodes that can be meaningfully represented as strings. r,graph,nodes,igraph,edges. Alternatively, you may take a look at the Graph. However, if there is no root node, then we must use an array or a linked list of (pointers to) graph nodes to ensure access to every node (i. In the code below a function tags is defined. You have to collapse multiple edges into a single edge and assign the original edge count to the new edge as an edge attribute. vs · · · · A walk on. Learn about the components that make up a graph - vertices and edges - along with the graph vocabulary and the various types of graphs. • The model sets an edge between each pair. First, we load the edge list into R, and create an undirected network graph using the igraph package. Network Plot with plotly and graphviz. Directed edges are instances of the Edge class. fast) as possible to enable the analysis of large graphs. In this quickstart, you've learned how to create an Azure Cosmos DB account, create a graph using the Data Explorer, create vertices and edges, and traverse your graph using the Gremlin console. The basic principles are shown in the attached workbook. It has tools for data mining (Google, Twitter and Wikipedia API, a web crawler, a HTML DOM parser), natural language processing (part-of-speech taggers, n-gram search, sentiment analysis, WordNet), machine learning (vector space model, clustering, SVM),. The following example shows how to create a basic adjacency matrix from one of the NetworkX-supplied graphs: import networkx as nx G = nx. • any Python object is allowed as edge data and it is assigned and stored in a Python dictionary (default empty) NetworkX is all based on Python • Instead, other projects use custom compiled code and Python: Boost Graph, igraph, Graphviz • Focus on computational network modeling not software tool development. The plot below is generated in igraph, and part of the documentation. IPython supports Python 2. Weighted_Adjacency() if you want to preserve the original values in the matrix as weights. It has two modes of operatation, depending whether the vertices argument is NULL or not. This software provides a suitable data structure for representing graphs and a whole set of important algorithms. append((first, second)). You can use the following CLI command to create a new Service Principal and this file at the same time: az ad sp create-for-rbac --sdk-auth > mycredentials. I have a Graph G1 with 50 nodes and 100 edges. A node is an entity, such as a person or business. It’s a commonly used input format for graphs. from igraph import * g = Graph. EdgeSeq(builtins. The goal would be, in other words, to define how a graph (or various kinds of graphs) would be expected to behave (possibly from different perspectives. IPython is open source (BSD license), and is used by a range of other projects; add your project to that list if it uses IPython as a library, and please don’t forget to cite the project. Second, the mathematical concept of partition lattices is transferred to the network theory in order to demonstrate how partitioning the node and edge set of a graph into supernodes and superedges. size Size of the node (default is 15) vertex. How to make 3D Network Graphs in Python. While they propose numerous algorithms for graph analysis, they offer little or no support for hierarchical. python中igraph包说明v0. R's igraph package provides a simple and flexible set of utilities for working with graphs. You can use the Gremlin language to create graph entities (vertices and edges), modify properties within those entities, perform queries and traversals, and delete entities. The adjacency list for the graph we made in the first example is as follows: An adjacency list is efficient in terms of storage because we only need to store the values for the edges. Assuming. Skip to content. The byte stream representing the object can then be transmitted or stored, and later reconstructed to create a new object with the same characteristics. Edge Lists¶ Read and write NetworkX graphs as edge lists. and various random graphs. If this is FALSE then the diagonal is zerod out first. python igraph: nodes and edges color according to a number associated to the node I'm really new to python and igraph, so the question may be really easy. After all, ‘ Good Things Happen When You Expect Them Least To Happen’. An edge-table contains source and target nodes in the first two columns and optionally additional columns with edge attributes. The igraph library provides versatile options for descriptive network analysis and visualization in R, Python, and C/C++. A graph and its equivalent adjacency list representation is shown below. SNAP is a general purpose, high performance system for analysis and manipulation of large networks. All on topics in data science, statistics and machine learning. A graph can be directed (arrows) or undirected. Sign in Sign up. The old plotter or the new one can then be used to create different graphs. Unfortunately, it is of limited use to us unless we customise its activities somehow. matrix(x) g1 <- graph. layout("tree") plot(g, layout = layout) You need the Cairo library for plotting though. adjacency_matrix(G) print(A. Skip to content. The specification of the combination of (vertex or edge) attributes can be given as a character scalar, a function object or a list of character scalars and/or function objects. py, which is not the most recent version. Treat the code on this page as a starting point, not as a final version of the algorithm that works for all situations. For example in a graph of roads that connect one city to another, the weight on the edge might represent the distance between the two cities or traffic status. , the nodes are numbered from 0 to N-1, where N is the number of nodes in the graph, and the kth value in the array or in the list is a pointer to node k). I have a vertices list as (vertexID) and a edge list as (srcID, dstID). I didn’t find a way to use pygraphviz to create 3d version of graphs. Their constructors allow to set the graph’s name, the filename for the DOT source and the rendered graph, a comment for the first source code line, etc. In any case, the igraph package is the best tool to read that kind of data and transform it into a graph object that is required to make a chart. A graph can be directed (arrows) or undirected. Option 1 Use a dual-axis graph to create a network graph. Overrides: igraph. Stackoverflow. TupleList() as the best solution. It has two modes of operatation, depending whether the vertices argument is NULL or not. rinohtype is a batch document processor that renders structured documents to PDF based on a document template and style sheet. If you're a curious Python programmer, a data scientist, or a CNA specialist interested in mechanizing mundane tasks, you'll increase your productivity exponentially. Learn More on Gephi Platform ». It easily scales to massive networks with hundreds of millions of nodes, and billions of edges. However, you cannot directly change the number of nodes or edges in the graph by modifying these tables. DataCamp offers interactive R, Python, Sheets, SQL and shell courses. collections. As evident from above code, in a directed graph we only creates an edge from src to dest in the adjacency list. The picture shown above is not a digraph. Выявление блоков/компонент двусвязности графа (найти количество блоков в связном. For some graphs, it may not make sense to represent them explicitly. Most of the concepts of Graph Theory have been covered. igraph2pandas(ig) Under current bindings, transform an IGraph into a pandas edges dataframe and a nodes dataframe. In the following graph, there are 3 back edges, marked with a cross sign. I don't mind (re)writing pure Python graph containers, it's the speed of a pure Python graph that's the bigger issue to me (mostly object inspection and/or. In a directed graph, the edge will flow from u to v. Creating graphs, graph. GraphFrames provide several simple graph queries, such as node degree. First, we load the edge list into R, and create an undirected network graph using the igraph package. A self-loop is an edge that connects a vertex to itself. But there is no straightway to combine these two at present in. edge for a graph G. Create Graph. Skip navigation Sign in. Fortunately, Python's object-oriented features, combined with the design of the SGMLParser class, provide a means of customising it fairly easily. Graph nodes can be any hashable Python objects. This is one of several commonly used representations of graphs for use in computer programs. visualizing clr network in cytoscape. Create an edge list in Python. keys() positions = node_positions. 2 for various directed and undirected, simple and non-simple graphs. Tries to identify the format of the graph stored in the file with the given filename. CRAN: Contributed Documentation - Another long list of tutorials, in different languages. rinohtype is written in Python 3 and supports CommonMark (Markdown) and reStructuredText input. The position on the X (horizontal) and Y (vertical) axis represents the values of the 2. I'm using a dictionary to represent a graph in my Python program. Practical statistical network analysis (with R and igraph) G? abor Cs? ardi [email protected] In this case adjacency lists are more efficient than igraph graphs. the python igraph? thank you, simone. Convert a graph to an edge list Description. In NetworkX, you can put these two lists together into a single network object that understands how nodes and edges are related. hu Department of Biophysics, KFKI Research Institute for Nuclear and Particle Physics of the Hungarian Academy of Sciences, Budapest, Hungary Currently at Department of Medical Genetics, University of Lausanne, Lausanne, Switzerland. You can vote up the examples you like or vote down the ones you don't like. add_edge (u, v, key=None, attr_dict=None, **attr) [source] ¶ Add an edge between u and v. 6 - Python 2. Then you should convert this matrix file to an edge list file: graph_adj=as. # Create the graph and assign random edge weights > g 是真的很有帮助，这次我的大部分python代码的完成都是靠着igraph官方的API文档。. I also find it a little annoying that I have to create a tuple/list/whatever to actually add the edge to the graph - I'd rather just pass in the two end-points. Note: this page is part of the documentation for version 3 of Plotly. Basically a graph is represented as a Python dictionary, whose keys are the string names of the vertices, and each vertex name is mapped to its adjacency list. It then creates a graph using the cycle_graph() template. each vertex has a set of outgoing edges. GitHub Gist: instantly share code, notes, and snippets. W ith the tool, we can start the plan to travel around the world. Use this vertex-edge tool to create graphs and explore them. Returns the adjacency edge list representation of the graph. Create network with igraph in 4 simple steps. Let us call the edges within a community "internal" and the edges connecting the vertices of a community with the rest of the graph "external". This video will show you simple functions of igraph library to add vertices and edges to graph. The Open Graph Viz Platform. The power method is also faster than the iGraph native implementation, which is also an eigenvector based solution. make_graph can create some notable graphs. This page serves to be a quick view of the algorithms. Disables Lightweight Edges in new databases by default. The R and Python graph galleries are 2 websites providing hundreds of chart example, always providing the reproducible code. I also find it a little annoying that I have to create a tuple/list/whatever to actually add the edge to the graph - I'd rather just pass in the two end-points. R igraph迭代边缘列表并获得属性 R igraph iterate through edge list and edges to create a graph knn graph from image, using python and igraph. set_edge_label() Set the edge label of a given edge. numpy (adjacency matrix as complete matrix). Watts and Steven Strogatz in their joint 1998 Nature paper. Returns: the calculated layout, which may (and usually will) have more rows than the number of vertices; the remaining rows correspond to the dummy nodes introduced in the layering step. up vote 2 down vote favorite I have some relational data in the format of a list of lists that I would like to import into an iGraph. An index-based edge list can be used to reconstruct a graph using the undocumented syntax Graph[vertexList, indexEdgeList], e. com The reason for this is that is we want R igraph to be consistent with Python igraph and C igraph, as far as file formats go. Convert a graph to an edge list Description. neighbors(2) # Prints [0,1,3] g. igraph supports: - graph generators, creating both regular structures like trees, lattices, etc. Returns the adjacency edge list representation of the graph. Tries to identify the format of the graph stored in the file with the given filename. 但就效率而言，后者要比前者高出很多。原因在于：NetworkX is a pure-python implementation, whereas igraph is implemented in C. The tbl_graph object. closeness method for connected graph is good and right calculate, and for Disconnected graph in not Good!. weighted graph. Implicit representations. iGraph Python, convert edge list to tuple and add. The third variable is the weight of the link. py is a Python interface for SNAP. iGraph to GePhi GExF format Conversion. To see the proper mathematical definition of a graph, you can have a look at our previous chapter Graphs in Python. Networkx () is a graph library in python which has good visualization. graph_from_data_frame creates igraph graphs from one or two data frames. The following problems appeared in the programming assignments in the coursera course Applied Social Network Analysis in Python. Once we have the dictionary built we can create the graph. predecessors(1) To calculate. table(graph_adj, "graph_adj. names and col names are your genes. iGraph Python，将边缘列表转换为元组和add. net NetSciX 2016 School of Code Workshop, Wroclaw, Poland Contents. See the details in §1. It has two modes of operatation, depending whether the vertices argument is NULL or not. Finding communities in networks is a common task under the paradigm of complex systems. Gephi is a complementary solution to R. The nodes connect to each. ) which use the attribute and the type of the attribute (strings representing legal values of that type). Network Plot with plotly and graphviz. It seems like the C core is what is actually handling creating the edgelist, which I'm. The set methods are unfortunately not very good at using attributes, a workaround solution would be to create edge lists from the graphs, merge the edge lists and then create a new igraph. Because it is based on Python, it also has much to offer for experienced programmers and researchers. Starting from igraph 0. It easily scales to massive networks with hundreds of millions of nodes, and billions of edges. The edges could represent distance or weight. Pattern is a web mining module for the Python programming language. How can I make a weighted graph from this list. Drew Conway, PhD student in NYU\'s Department of Politics, provides an introduction to mining social graph data from the Internet that focuses on the technical, substantive and ethical concerns related to this type of analysis. After I run sudo pip install python-igraph I got the following log:. Skip to content. At this point you could simply load your vertex and edge list into your favorite graph plotting software (mine is cytoscape) and go from there…. Description. We used a categorical color scale. In add_edge function. rinohtype is written in Python 3 and supports CommonMark (Markdown) and reStructuredText input. The flags is to specify the color type of a loaded image: Image properties include number of rows, columns and channels, type of image data, number of pixels etc. View source: R/data_frame. I have opted to. Practical statistical network analysis (with R and igraph) G? abor Cs? ardi [email protected] tuple for edge in graph. Create an edge list in Python. layout("tree") plot(g, layout = layout) You need the Cairo library for plotting though. The igraph package includes some classic random graphs like the. The adjacency list for the graph we made in the first example is as follows: An adjacency list is efficient in terms of storage because we only need to store the values for the edges. How can I make a weighted graph from this list. Sign in Sign up. The results include the measurement of a narrow, magnetic field aligned electron flux at the edge of an arc. Tuesday, May 13, 2014. We use a very compact, dictionary-of-dictionaries (DD) implementation of the adjacency list representation of the graph. graph, which takes numeric vertex ids directly. publication-quality graphs. igraph supports: - graph generators, creating both regular structures like trees, lattices, etc. After I run sudo pip install python-igraph I got the following log:. R's igraph package provides a simple and flexible set of utilities for working with graphs. Every function expects an igraph object and it doesn’t matter how you initially formatted the data. nemo, 2013/11/14 Re: [igraph] How to identify negative vertices in graph , Gábor Csárdi , 2013/11/15 Prev by Date: Re: [igraph] Igraph won't import on Python 3. However, is there a way to directly creating a graph instead of writing it to a file. is_loop() This method calls the is_loop method of the Graph class with this edge as the first argument, and returns the result. Create network with igraph in 4 simple steps. and does not use it as a vertex id in the edge list. 20 Dec 2014. How to read Edge List from file and Create a graph : Networkx Tutorial # 2 This video will show how to use files for reading edge list from them. A node is an entity, such as a person or business. python - Networkx : Convert multigraph into simple graph with weighted edges I have a multigraph object and would like to convert it to a simple graph object with weighted edges. net NetSciX 2016 School of Code Workshop, Wroclaw, Poland Contents 1. R code for igraph animation Posted on 2013/06/27 by Raffael Vogler For my personal code archive and everybody who finds interest in it I publish the R code which I used to create the frames for the animations showing the carpoolings formed through the booking system until a certain date. vertices contracts many vertices into a single one, the attributes of the vertices can be combined and stores as the vertex attributes of the new graph. Graph, and plot it with Fruchterman Reingold layout (networkx does not provide the Kamada-Kawai layout). I have a list of edges say [(a,b,3. EdgeSeq(builtins. Its core data structures and algorithms are implemented in C++, with heavy use of Template metaprogramming , based on the Boost Graph Library. We can use Kruskal’s Minimum Spanning Tree algorithm which is a greedy algorithm to find a minimum spanning tree for a connected weighted graph. Alternatively, this can be a character scalar, the name of a notable graph. After all, ‘ Good Things Happen When You Expect Them Least To Happen’. Imagine data points that are connected to other data points, such as how one web page is connected to another web page through hyperlinks. The return value should be in the form of an edge list with a to and from column giving the indexes of the terminal nodes of the edge. This function should not be used directly by igraph users, it is useful only in the case when the underlying igraph object must be passed to another C code through Python. I have tried downloading quite a few python programs. The first one provides the links between nodes. For BFS in directed graphs, each edge of the graph either connects two vertices at the same level, goes down exactly one level, or goes up any number of levels. The list below suggests the diverse range of systems where graphs are appropriate starting points for understanding structure. This section describes how to create an IoT hub using the Azure portal. fast) as possible to enable the analysis of large graphs. And then there's a number on the edges that represents how many times they had lunch together. If image is grayscale, tuple. This command now creates a regular edge. That is, the data for the boxplot is in the form of a list of lists, or list of arrays, or a tuple of arrays etc. If vertices is NULL , then the first two columns of d are used as a symbolic edge list and additional columns as edge attributes. • any Python object is allowed as edge data and it is assigned and stored in a Python dictionary (default empty) NetworkX is all based on Python • Instead, other projects use custom compiled code and Python: Boost Graph, igraph, Graphviz • Focus on computational network modeling not software tool development. The descriptions of the problems are taken from the assignments. The first edge points from edges[1] to edges[2], the second from edges[3] to edges[4], etc. Its output is similar to the output of print but it does not print the edge list to avoid cluttering up the display for large graphs. It can be used as an iterable as well, or even in a list comprehension:. Updated on 1 November 2019 at 00:33 UTC. graph_from_data_frame creates igraph graphs from one or two data frames. Skip to content. The order of the vertices are preserved, i. PACKAGE DESCRIPTION: igraph is originally a C library for graphs, but has interfaces to high level languages like R, Python and Ruby. edge for a graph G. It's a commonly used input format for graphs. It has two modes of operatation, depending whether the vertices argument is NULL or not. Graph nodes can be any hashable Python objects. Comparison with Popular Python Implementations: NetworkX and iGraph Both implementations (exact solution and power method ) are much faster than their correspondent methods in NetworkX. and various random graphs. add_vertices([(2,2)]) · every edge has an ID (a sequential number) · we can delete an edge with: X. It identifies most file formats based on the extension of the file (and not on syntactic evaluation). Lost a graph? Click here to email you a list of your saved graphs. However, you cannot directly change the number of nodes or edges in the graph by modifying these tables. Usage as_edgelist(graph, names = TRUE). Planning our trip. The only exception is the adjacency matrix format and the edge list format: the first few lines of the file are evaluated to decide between the two. This allows you to create fully featured games and multimedia programs in the python language. With your preparation out of the way, you can now get started actually using Python to draw a graph from a CSV file. Skip to content. Now this python code 1) imports our edge list from the SPSS dataset and turn it into a networkx graph, 2) reduces the set of edges into connected components, 3) makes a new SPSS dataset where each row is a list of those subgraphs, and 4) makes a macro variable to identify the end variable name (for subsequent transformations). This is a small python library built on matplotlib to make publication quality plots of weighted, directed graphs/networks of medium size (10-100 nodes). 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: