Dynamic Dataframe Name Python

For Series, the row labels are prefixed. It's tightly integrated with NumPy and provides Pandas with dataframe-equivalent structures — the dask. We can use Pandas melt function to reshape the data frame to a longer form that satisfies the tidy data principles. It also features dynamic name resolution (late binding), which binds method and variable names during program execution. The following are code examples for showing how to use pandas. There are multiple methods you can use to take a standard python datastructure and create a panda’s DataFrame. We only need the state name and the town name and can remove everything else. DataFrame, IMO, should have a. Line plots of observations over time are popular, but there is a suite of other plots that you can use to learn more about your problem. Pandas DataFrame Functions (Row and Column Manipulations) - DZone. In R, when manipulating our data, we often need to rename column of data frame. We built a method to allow Python Excel macro functions to be added and executed, all from Python. In a notebook, to enable the Python interpreter, click on the Gear icon and select Python. It has API support for different languages like Python, R, Scala, Java. Leveraging Python in Excel spreadsheets can be a fantastic way to enhance your productivity and remove the need for importing and exporting data into and out of Excel. The Pandas Python library is an extremely powerful tool for graphing, plotting, and data analysis. The present proposal brings this ease-of-use to dynamic attribute access too. It is actually quite easy but I had a hard time finding out how to do that, no doubt due to my limited knowledge of python, so here's a simple example. In the last post, we have demonstrated how to load JSON data in Hive non-partitioned table. rotation to get correct values, but I have to re-enter the code everytime I rotate the data frame. lower() for x in hgcallvar] 2: string contains method. Here is an example with dropping three columns from gapminder dataframe. But before we begin, here is a template that you can use to create a database in Python using sqlite3:. # create empty dataframe in r with column names mere_husk_of_my_data_frame <- originaldataframe[FALSE,] In the blink of an eye, the rows of your data frame will disappear, leaving the neatly structured column heading ready for this next adventure. as_matrix (columns=None) values, counts = np. sort_values ([' time', ' name']) Now my data is ready to be exported, analyzed and visualized! I decided to visualize this particular data as a highly interactive map using HighChart's Highmaps. columns = new_column_name_list However, the same doesn't work in pyspark dataframes created using sqlContext. save() Write a DataFrame to Hive, specifying partitions. There isn't one piece of code that will work on all databases. This sample will update the first data frame's name and refresh the table of contents so the change can be see in the application. This is set via the Size and Position Tab on the Properties dialog box in ArcMap. simple tables in a web app using flask and pandas with Python. Re: referring to a data-frame column using a variable On 13/11/2007, at 8:42 AM, Bernd Jagla wrote: > Hi, > > > > I would like to refer to a column in a data frame using a variable. val df = spark. date_range(). Recommended from our users: Dynamic Network Monitoring from WhatsUp Gold from IPSwitch. It is with this property that strings can be read and modified. # filter rows for year 2002 using the boolean variable >gapminder_2002 = gapminder[is_2002] >print(gapminder_2002. Questions: I come from pandas background and am used to reading data from CSV files into a dataframe and then simply changing the column names to something useful using the simple command: df. within a single dataframe using. We'll use this labeled array as an example:. Can be thought of as a dict-like container for Series. Python's design offers some support for functional programming in the Lisp tradition. In a paragraph, use %python to select the Python interpreter and then input all commands. Because the data we desire is in nested dicts, I used custom code, the list comprehension. Please note the following: The keyword in the argument list and the function (i. Python Programming tutorials from beginner to advanced on a massive variety of topics. For example, mean, max, min, standard deviations and more for columns are easily calculable:. , data is aligned in a tabular fashion in rows and columns. CameronLaird calls the yearly decision to keep TkInter "one of the minor traditions of the Python world. In case of a MultiIndex, only rename labels in the. png) or on a portion of text in the name. Time series lends itself naturally to visualization. It also features dynamic name resolution (late binding), which binds method and variable names during program execution. The name does not matter as long as it is discrete from the other table names and reserved SQL-statements such as select. On September 17th, 2014, I published my first article which means that today is the 5th birthday of Practical Business Python. Example – Import into Python a CSV File that has a Variable Name. DataFrame API dataframe. Pandas DataFrame Functions (Row and Column Manipulations) - DZone. option("header","true"). Renaming columns in a data frame Problem. saveAsTable("tableName", format="parquet", mode="overwrite") The issue I'm having isn't that it won't create the table or write the data using saveAsTable, its that spark doesn't see any data in the the table if I go back and try to read it later. For example, R has a nice CSV reader out of the box. It has API support for different languages like Python, R, Scala, Java. (1) pandas DataFrame의 칼럼 이름 바꾸기 : df. While it is exceedingly useful, I frequently find myself struggling to remember how to use the syntax to format the output for my needs. These libraries enable you to. NAME" trailer can be used, as in: x. you cannot use =TODAY() as part of a dynamic array. CameronLaird calls the yearly decision to keep TkInter "one of the minor traditions of the Python world. how to assign column names of my data frame by taking values from. Using the Python Interpreter. For anyone new to data exploration, cleaning, or analysis using Python, Pandas will quickly become one of your most frequently used and reliable tools. I have started with it not a long time ago and it always amazes me how easy it is to pick up and do things. wide_df = pd. The following script demonstrates how the CURRENT keyword can be used within the Python window. Creating Dynamic Data Frames in Python. save() Write a DataFrame to Hive, specifying partitions. Spark’s primary data abstraction is an immutable distributed collection of items called a resilient distributed dataset (RDD). The benefit of the eval() method is that columns can be referred to by name. To construct a DataFrame with missing data, we use np. This introduction to pandas is derived from Data School's pandas Q&A with my own notes and code. Duplicate column names are allowed, but you need to use check. Existing text elements can be cloned and deleted. Most of the times when you are working with data frames, you are changing the data and one of the several changes you can do to a data frame is adding column or row and as the result increase the dimension of your data frame. sort_values ([' time', ' name']) Now my data is ready to be exported, analyzed and visualized! I decided to visualize this particular data as a highly interactive map using HighChart's Highmaps. DataFrame (data=None, index=None, columns=None, dtype=None, copy=False) [source] ¶ Two-dimensional size-mutable, potentially heterogeneous tabular data structure with labeled axes (rows and columns). If you don't set it, you get empty dataframe. Line plots of observations over time are popular, but there is a suite of other plots that you can use to learn more about your problem. Python Pandas : Replace or change Column & Row index names in DataFrame; Pandas : How to create an empty DataFrame and append rows & columns to it in python; Pandas : Sort a DataFrame based on column names or row index labels using Dataframe. show all the rows or columns from a DataFrame in Jupyter QTConcole. In this post "Python use case - Dynamic UNPIVOT using pandas - SQL Server 2017" we are going to learn how we can leverage the power of Python's pandas module in SQL Server 2017. e remove_punct) parameters have the same name. In case of R, the input variable is a data frame. Anonymous lambda functions in Python are useful for these tasks. Line plots of observations over time are popular, but there is a suite of other plots that you can use to learn more about your problem. Python has become one of the most popular dynamic programming languages, along with Ruby, Perl, etc. No data, just these column names. val colNames = Seq("c1", "c2") df. We'll use this labeled array as an example:. You can vote up the examples you like or vote down the ones you don't like. The entry point to programming Spark with the Dataset and DataFrame API. you can access the field of a row by name naturally row. A name can refer to an integer, and then to a string, and then to a function, and then to a module. Learn the latest GIS technology through free live training seminars, self-paced courses, or classes taught by Esri experts. It's also possible to use R base functions, but they require more typing. IT'S DATABASE SPECIFIC In Python, it works with libraries, connection libraries. Here, the top_models is an arbitrary name defined and used only within the query. dataframes — that are based on lazy loading and can be used to perform dataframe operations in chunks and in parallel. Related course: Data Analysis with Python Pandas. class pyspark. Use a parameterized query to insert dynamic data into a MySQL table in Python. While we could have cleaned these strings in the for loop above, Pandas makes it easy. It also defines names for some object types that are used by the standard Python interpreter, but not exposed as builtins like int or str are. Showing only the rows where the year is greater than 2012 OR name is "Frank":. Imagine we want to list all the details of local surfers, split by gender. DataFrame(data) wide_df Name Weight BP 0 John 150 120 1 Smith 170 130 2 Liz 110 100 Reshaping with Pandas Melt. png) or on a portion of text in the name. After subsetting we can see that new dataframe is much smaller in size. (1) pandas DataFrame의 칼럼 이름 바꾸기 : df. See also the bar charts examples. level: int or level name, default None. Python makes this easy, but it's not always clear what the correct approach is. In short, basic iteration (for i in object. xlsx') dictionary = {} for sheet_name in workbook. Dynamic typing. The file names are based on date and time of when the log file was created. append() & loc[] , iloc[] Python Pandas : Count NaN or missing values in DataFrame ( also row & column wise) Python Pandas : Drop columns in DataFrame by label Names or by Index Positions. js) First, let’s design the front end which will be a basic html page (“index. They are extracted from open source Python projects. Converting Spark RDD to DataFrame and Dataset. registerFunction(name, f, returnType=StringType)¶ Registers a python function (including lambda function) as a UDF so it can be used in SQL statements. Azure Data Explorer is a fast and highly scalable data exploration service for log and telemetry data. import pdpipe as pdp drop_name = pdp. e remove_punct) parameters have the same name. In a paragraph, use %python to select the Python interpreter and then input all commands. up vote 3 down vote favorite. sheet_names: df = workbook. columns = new_column_name_list However, the same doesn't work in pyspark dataframes created using sqlContext. It has API support for different languages like Python, R, Scala, Java. We will prepare a data frame so that we can practice renaming its columns in the below sections. 06/03/2019; 4 minutes to read +4; In this article. registerTempTable("table_name"). Dynamic text is text placed on a map layout that changes dynamically based on the current properties of the map document, data frame, and Data Driven Pages. I then sorted my data frame by length of membership, found in the "time" column, and alphabetical order of the country names. contains method and regular expressions. append() & loc[] , iloc[] Python Pandas : Count NaN or missing values in DataFrame ( also row & column wise) Python Pandas : Drop columns in DataFrame by label Names or by Index Positions. groupby() method. In case of a MultiIndex, only rename labels in the. DataFrame([course_dict(item) for item in data]) Keeping related data together makes the code easier to follow. It also defines names for some object types that are used by the standard Python interpreter, but not exposed as builtins like int or str are. HWC follows Hive semantics for overwriting data with and without partitions and is not affected by the setting of spark. parse(sheet_name) dictionary[sheet_name] = df Note: the parse() method takes many arguments like read_csv() above. Python is a high-level programming language which is an interpreted language (execute line by line) instead of compiled language. You can also reference a raster field as well. Python Pandas : How to create DataFrame from dictionary ? Pandas: Sort rows or columns in Dataframe based on values using Dataframe. Filter using query A data frames columns can be queried with a boolean expression. min(col)¶ Aggregate function: returns the minimum value of the expression in a group. The reason for this is that the dynamic look-thru for CAS actions and table parameters only happens if there isn’t a real Python attribute or method defined. Showing only the rows where the year is greater than 2012 OR name is "Frank":. In this post "Python use case - Dynamic UNPIVOT using pandas - SQL Server 2017" we are going to learn how we can leverage the power of Python's pandas module in SQL Server 2017. Web apps are a great way to show your data to a larger audience. However, for some use cases, the repartition function doesn't work in the way as required. The name does not matter as long as it is discrete from the other table names and reserved SQL-statements such as select. The Python has gained popularity because of its user friendliness. Paste the following code into the Python window within a new ArcMap document. Pandas rename() method is used to rename any index, column or row. This is an interactive grads script to get the climatology distribution meteorological parameters during some special events which occur at different periods on different years (First active spell of Indian Summer Monsoon). Python has become one of the most popular dynamic programming languages, along with Ruby, Perl, etc. Python is a case-sensitive language which means that HOME and home are two different variables. No, not the endangered species that has bamboo-munched its way into our hearts and the Japanese lens blur that makes portraits so beautiful, the Python Data Analysis Library and the Bokeh visualization tool. Given a Data Frame, we may not be interested in the entire dataset but only in specific rows. We can create pandas dataframe from lists using dictionary using pandas. From this Dataset class, you can: Read a dataset as a Pandas dataframe. Creating variables with dynamic names is typically a bad practice. Importing Data into Hive Tables Using Spark. When you do so Spark stores the table definition in. You can also reference a raster field as well. Python's design offers some support for functional programming in the Lisp tradition. We can then use this boolean variable to filter the dataframe. If True then value of copy is ignored. In case of R, the input variable is a data frame. Python's built-in id() function, which returns a unique object identifier for a given variable name, can be used to trace what is happening under the hood. I want to subset each of these data frames and put them in a new data frame with a dynamic name. # Each Excel sheet’ in a Python dictionary workbook = pd. Most of the analysts prepare data in MS Excel. What I decided to are use are the widely available shapefiles and python matplotlib packages, using mainly pandas and numpy to analyse data. Filter using query A data frames columns can be queried with a boolean expression. Adding and Modifying Columns. In order to change the schema, I try to create a new DataFrame based on the content of the original DataFrame using the following script. Let's discuss how to get column names in Pandas dataframe. In this post "Python use case - Dynamic UNPIVOT using pandas - SQL Server 2017" we are going to learn how we can leverage the power of Python's pandas module in SQL Server 2017. The following are code examples for showing how to use pandas. This is not an efficient approach. sqrt(col)¶ Computes the square root of the specified float value. sort_values ('price', axis = 0, ascending = False) 7. columnName). These names can also be modified after the objects are created so they may not always be particularly trustworthy. I have a chunk of code that I received that only works with pandas dataframes as input. Spark dataframe provides the repartition function to partition the dataframe by a specified column and/or a specified number of partitions. dataframes — that are based on lazy loading and can be used to perform dataframe operations in chunks and in parallel. png) or on a portion of text in the name. Use the New Text tool on the Draw toolbar to add new text to the layout. In a notebook, to enable the Python interpreter, click on the Gear icon and select Python. So there will be a column 25041 with value as 1 or 0 if 25041 occurs in that particular row in any dxs columns. Preparation. Thank you to all my readers and all those that have supported me through this process!. Dynamic Arrays have been refactored with v0. Here, the top_models is an arbitrary name defined and used only within the query. In Spark, dataframe is actually a wrapper around RDDs, the basic data structure in Spark. In short, basic iteration (for i in object. How to join (merge) data frames (inner, outer, right, left join) in pandas python We can merge two data frames in pandas python by using the merge() function. Python is a great programming language that supports OOP. Source: Author’s conception. They are extracted from open source Python projects. 0 to be proper legacy arrays: To edit a dynamic array with xlwings >= v0. Thus, a data frame's rows can include values like numeric, character, logical, and so on. However, not all operations on data frames will preserve duplicated column names: for example matrix-like subsetting will force column names in the result to be unique. dynamically rename data frame in Python. level: int or level name, default None. Let's discuss how to get column names in Pandas dataframe. Parsing HTML Tables in Python with BeautifulSoup and pandas Something that seems daunting at first when switching from R to Python is replacing all the ready-made functions R has. 3 Welcome to part three of the web-based data visualization with Dash tutorial series. It is conceptually equivalent to a table in a relational database or a data frame in R/Python, but with richer optimizations under the hood. append() & loc[] , iloc[] Python Pandas : Count NaN or missing values in DataFrame ( also row & column wise) Python Pandas : Drop columns in DataFrame by label Names or by Index Positions. In short, basic iteration (for i in object. Make a data frame from vectors in R. DataFrame([course_dict(item) for item in data]) Keeping related data together makes the code easier to follow. I think the best solution for your problem is to store your dataframes into a dictionary and dynamically generate the name of the key to access each dataframe. See also the bar charts examples. you can access the field of a row by name naturally row. Creating new columns by iterating over rows in pandas dataframe. txt file is not given but have to assign as a "income" column name in my data frame. Each row is a measurement of some instance while column is a vector which contains data for some specific attribute/variable. In R, when manipulating our data, we often need to rename column of data frame. We built a method to allow Python Excel macro functions to be added and executed, all from Python. 💡 Merge Dataframes with Different Column Names So we've talked about how to merge data using different ways — left, right, inner, and outer. It is an excellent language for building data-centric applications. Programmatically Specifying the Schema - The second method for creating DataFrame is through programmatic interface that allows you to construct a schema and then apply it to an existing RDD. The terms "arguments" and "parameters" are often used interchangeably and that's sloppy. It has API support for different languages like Python, R, Scala, Java. My data frame is called df1 and the column name is NameDevice. We can use printSchema() function to see the schema. ListLayers example 2: The following script will find a layer called Lakes in a data frame named County Maps, turn the layer on (to be visible) and set the transparency to 50%. Photo by Start Digital on Unsplash. After reading this tutorial, you will be familiar with the concept of loop and will be able to apply loops in real world data wrangling tasks. 이번 포스팅에서는 Python pandas DataFrame의 칼럼 이름 바꾸는 방법(how to change column name in python pandas DataFrame), index 이름을 바꾸는 방법(how to change index name in python pandas DataFrame)을 소개하겠습니다. We can create pandas dataframe from lists using dictionary using pandas. xlsx') dictionary = {} for sheet_name in workbook. Converting a character string into a data frame name and performing assignments to that data frame. Python keywords. Example - Import into Python a CSV File that has a Variable Name. simple tables in a web app using flask and pandas with Python. This is about as simple as it gets (even simpler, the nodes could be represented by numbers instead of names, but names are more convenient and can easily be made to carry more information, such as city names). Converting Spark RDD to DataFrame and Dataset. This is an extremely inefficient process since R needs to reallocated memory every time you use something like a <- rbind(a, b). I need to convert this into a pandas dataframe. Example of one. The more you learn about your data, the more likely you are to develop a better forecasting model. That’s just how indexing works in Python and pandas. if the df has a lot of rows or columns, then when you try to show the df, pandas will auto detect the size of the displaying area and automatically hide some part of the data by replacing with. Setup Apache Spark. val colNames = Seq("c1", "c2") df. test(df,201612) The output of new dataframe is: df_new_201612. From this Dataset class, you can: Read a dataset as a Pandas dataframe. 💡 Merge Dataframes with Different Column Names So we've talked about how to merge data using different ways — left, right, inner, and outer. Data visualization is an important part of being able to explore data and communicate results, but has lagged a bit behind other tools such as R in the past. It is actually quite easy but I had a hard time finding out how to do that, no doubt due to my limited knowledge of python, so here's a simple example. Adding text to a geodatabase annotation feature class. sheet_names: df = workbook. concat() Python 분석과 프로그래밍/Python 데이터 전처리 2016. level: int or level name, default None. str() methods again here, we could also use applymap() to map a Python callable to each element of the DataFrame. class pyspark. Unlike two dimensional array, pandas dataframe axes are labeled. As the name suggests, FILTER is used in Spark SQL to filter out records as per the requirement. How to get column names in Pandas dataframe While analyzing the real datasets which are often very huge in size, we might need to get the column names in order to perform some certain operations. Photo by Start Digital on Unsplash. Together, they represent an powerful set of tools that make it easy to retrieve, analyze, and visualize open data. The result will be a DataFrame with the same index as the input Series, and with one column whose name is the original name of the Series (only if no other column name provided). partitionOverwriteMode to static or dynamic. You can vote up the examples you like or vote down the ones you don't like. For the purposes of these examples, I’m going to create a DataFrame with 3 months of sales information for 3 fictitious companies. Instance Variable: What's the Difference? A Python class attribute is an attribute of the class (circular, I know), rather than an attribute of an instance of a class. Python is a great language for doing data analysis, primarily because of the fantastic ecosystem of data-centric python packages. For example, you can filer on the file type (. In this tutorial, you will learn how to rename the columns of a data frame in R. Python Numpy : Create a Numpy Array from list, tuple or list of lists using numpy. We will prepare a data frame so that we can practice renaming its columns in the below sections. sort_index() Select Rows & Columns by Name or Index in DataFrame using loc & iloc | Python Pandas. Python is a case-sensitive language which means that HOME and home are two different variables. Display pandas dataframes clearly and interactively in a web app using Flask. The DataFrame in Python is similar in many ways. Pandas rename() method is used to rename any index, column or row. This is one of the important concept or function, while working with real-time data. eval() for Column-Wise Operations¶ Just as Pandas has a top-level pd. Here, the top_models is an arbitrary name defined and used only within the query. level: int or level name, default None. Let’s say that you want to import into Python a CSV file, where the file name is changing on a daily basis. When executing SQL queries using Spark SQL, you can reference a DataFrame by its name previously registering DataFrame as a table. The following recipe shows you how to rename the column headers in a Pandas DataFrame. - Proficient in R, More. DataFrame API dataframe. Takeaways— Python on Spark standalone clusters: Although standalone clusters aren’t popular in production (maybe because commercially supported distributions include a cluster manager), they have a smaller footprint and do a good job as long as multi-tenancy and dynamic resource allocation aren’t a requirement. isin ( [x]). This also allows the use of subscripted elements in an augmented assignment, as in "x[12] += 1". I have a Data Frame DF1 with three columns A, B and C with values 3, 2 and 100. Given a Data Frame, we may not be interested in the entire dataset but only in specific rows. CameronLaird calls the yearly decision to keep TkInter "one of the minor traditions of the Python world. Using the Python Interpreter. The following are code examples for showing how to use pyspark. I just started using python a couple of days ago, so I am a complete beginner. array() Python : Get number of elements in a list, lists of lists or nested list; Python Pandas : How to convert lists to a dataframe; How to Merge two or more Dictionaries in Python ? Pandas : How to Merge Dataframes using Dataframe. Create Pivot table in Pandas python In this tutorial we will be dealing on how to create pivot table from a Pandas dataframe in python with aggregate function – mean ,count and sum. In this article, we will show how to retrieve a column or multiple columns from a pandas DataFrame object in Python. You can vote up the examples you like or vote down the ones you don't like. Tkinter is not the only GuiProgramming toolkit for Python. Python keywords. Lets see how to create pivot table in pandas python with an example. columnName). Part 1: Defining the front end (html, d3. Importing Data into Hive Tables Using Spark. I know that if we are using LOAD_CSV directly from the cypher-shell, we would include PERIODIC COMMIT 20000 to make it commit every 20000 lines of the CSV. Also this is a reverse search for the variable name. I can't quite figure out how to make it work though. Nothing gets initialized properly, and anything that assumes the constructor is called breaks. Python is a great language for doing data analysis, primarily because of the fantastic ecosystem of data-centric python packages. The row with index 3 is not included in the extract because that’s how the slicing syntax works. Spark dataframe provides the repartition function to partition the dataframe by a specified column and/or a specified number of partitions. DataFrames can be created from various sources such as:. A data frame is a table-like data structure available in languages like R and Python. Requirement. Creating Dynamic Data Frames in Python. Missing data. The following are code examples for showing how to use pandas. HWC follows Hive semantics for overwriting data with and without partitions and is not affected by the setting of spark. columns = ['a', 'b']. show all the rows or columns from a DataFrame in Jupyter QTConcole. apply to create dynamic columns. I have a chunk of code that I received that only works with pandas dataframes as input. Filtering rows of a DataFrame is an almost mandatory task for Data Analysis with Python. name reports year Helsinki Rebecca 31 2014 Query String. I want to subset each of these data frames and put them in a new data frame with a dynamic name. In a notebook, to enable the Python interpreter, click on the Gear icon and select Python. Aggregation functions will not return the groups that you are aggregating over if they are named columns, when as_index=True, the default. 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: