How To Fill Missing Values In Dataset

If you use some ML as a service platform, data cleaning can be automated. One of the frustrating data formats that can make data unusable in Tableau is report-formatted spreadsheet that have headers with following blanks. Make sure that any missing values in your data set are indicated with NA. If your Fill Method is Temporal Trend, this number is the number of values that exist in your time series for that Location ID (for instance, if you were only missing one value in your time series, it would report the number of time steps in your dataset minus 1). The missing value fill pattern is set to solid fill (0) using cnMissingValFillPattern, and the missing value fill color is set to blue using cnMissingValFillColor. A simple way to handle missing data is to remove those instances that have one or more missing values. In our dataset, there are two categorical columns. Missing values in data is a common phenomenon in real world problems. In a multiple imputation method, each variable with missing data is modeled conditionally using the other variables in the data before filling in the missing values. In R, missing values are often represented by NA or some other value that represents missing values (i. For my blog post about Modifying Parameter values in powerbi. I am clueless on how to fill the missing values using interpolation method. This paper presents several practical usages of the RETAIN statement. Detecting missing values is. , the first time X has a missing value, observation 3) the queue only has a missing value in it since no other value had been passed to it yet. omit(mydata) Advanced Handling of Missing Data. Missing Data can also refer to as NA(Not Available) values in pandas. For example, remember how our original data set had one metric column for each year (2010, 2011, 2012, etc)? If we tried merging the raw life_expectancy and sanitation data frames without renaming the columns, and without setting by parameters, R would have tried merging the two data frames by all common columns — namely country. , the first time X has a missing value, observation 3) the queue only has a missing value in it since no other value had been passed to it yet. Handle Missing Values in Objects Description. Implicitly, i. Missing data: this occurs when there are no records in the underlying data set. Fill in Missing Values in an Astronomy Dataset SynthesizeMissingValues allows for missing data to be imputed based on an estimation of the underlying distribution of the dataset. mean/median is fine on a small data set but on large data sets with many outliers and missing values, I think it's not a good approach to fill 40-50% values in a column with median/mean. Let's quickly understand this. Explore Popular Topics Like Government, Sports, Medicine, Fintech, Food, More. You learn something new every day. Use a global constant to fill the missing values 4. Fill NaN values by propogating values forward. Every dataset we come across will almost have some missing values which need to be dealt with. F = fillmissing(A,'constant',v) fills missing entries of an array or table with the constant value v. This How-To will walk you through writing a simple Python script to see if your data set has null or empty values, and if so, it will propose two options for how to modify your data. Best practices Make sure you know what values are missing. In the example below, I’m requesting that if the value in B2 is greater than the value in C2, apply a green background color. Its purpose is to add observations to the dataset if there are gaps, but the only non-missing values ever created are in identifier and time variables. The Lookup/ZN function worked like a charm. The cell values represent the phenomenon portrayed by the raster dataset such as a category, magnitude, height, or spectral value. In this way, nonmissing values are copied in a cascade down the current sort order. Sometimes you would like to carry the last value forward, you can then use tidyr::fill. If the data has missing values, they will become NaNs in the resulting Numpy arrays. Excel Conditional Formatting with a Formula Microsoft has greatly enhanced this feature over the years and now predefines popular examples so you don’t need to rely on formulas as much. For more advanced data manipulation in R Commander, explore the Data menu, particularly the Data / Active data set and Data / Manage variables in active data set menus. Fill the DataFrame forward (that is, going down) along each column using linear interpolation. , the first time X has a missing value, observation 3) the queue only has a missing value in it since no other value had been passed to it yet. If the average of the 30 responses on the question is a 4. Hi, I am looking for an easy way to set all missing values to zero for all variables in a dataset. ggplot your missing data 01 Dec 2015 R Missing Data rbloggers. Select “Get Values from a query” Set Dataset to the same dataset your values will be populated from. RapidMiner Studio has many different operators to help you increase your data quality but for this post, we'll focus on replacing missing values and replacing values. For example, the following replaces null values in column "A" with string "unknown", and null values in column "B" with numeric value 1. Missing data is a problem that most people have to deal with at some point, and there are different approaches to doing so. The Microsoft Excel SMALL function returns the nth smallest value from a set of values. Let us get started. The fill option implies the type of join: inner or full outer. We have illustrated that MI is a feasible method to try to deal with bias due to missing data in longitudinal studies, including attrition and nonresponse, and should be considered in combination with analysis of sensitivity in longitudinal studies. Furthermore, missing values may themselves be informative. from_dict (d) Convert a dictionary into an xarray. 12 Sep 5, 2019 11:58 AM ( in response to Brian Davis ) This is a problem for example when the data comes from a source you can't control. a zero for body mass index or blood pressure is invalid. This variable holds the number of missing values over a set of variables that we'd like to analyze together. I'm using Pandas to store stock prices data using Data Frames. Fills in (imputes) missing values with estimated values using regression or EM methods; however, multiple imputation is generally considered to provide more accurate results. …And in pandas you can remove the missing values…by using the dropna method. Small gaps between non-overlapping and/or non-adjacent datasets become NODATA (The MERGE or MOSAIC function will not interpolate to fill missing data between adjacent rasters). In the above example, missing COL0 and COL2 attributes of TEST_TABLE second row represents null values. imputeTS: Time Series Missing Value Imputation in R by Steffen Moritz and Thomas Bartz-Beielstein Abstract The imputeTS package specializes on univariate time series imputation. Explore Popular Topics Like Government, Sports, Medicine, Fintech, Food, More. Excel Conditional Formatting with a Formula Microsoft has greatly enhanced this feature over the years and now predefines popular examples so you don’t need to rely on formulas as much. A Study on Handling Missing Values and Noisy Data using WEKA Tool (IJSRD/Vol. If Fill finds that a primary key exists for a table, it will overwrite data in the DataSet with data from the data source for rows where the primary key column value in the data source matches that in the DataSet. A simple way to handle missing data is to remove those instances that have one or more missing values. So let’s say we would like to know the maximum and minimum values within a specific letter grade range. To continue with your YouTube experience, please fill out the form below. Generally, the median is the best choice in comparison to mean an mean can be affected by the outliers present in our dataset while the median value is unaffected. Fill the missing value manually. How to fill in missing data without the need of a Scaffold data set - Alteryx by Juliana Camparotti | Oct 29, 2019 Last week at the Data School, we learned how to do Data Scaffolding using Tableau Prep Builder and Alteryx. If this assumption holds, resulting estimates (i. when data is not available) and then handle missing values (provide defaults or fill with previous values). I tried using a calculated field with some LOD, but once the data is null, the calculated field automatically get's a null ( or 0 after using ZN() ), I think the only thing that will solve my issue is to be able to manually fill those missing dates in the table, but Tableau doesn't allow it and the data itself is too big, so modifying it will take eternity. F = fillmissing(A,'constant',v) fills missing entries of an array or table with the constant value v. The best way to donate data sets is to fill out our web form which will allow you to upload your data file into our repository. In some cases, an event is represented by a function of the random variable which we refer to as g(X). Data prep and pattern. Populate an Excel Worksheet from. Now, let's impute the missing values. Deleting Rows. The queue stores the new (missing. Fill the missing date values in a Pandas Dataframe column. , the fields TARGET_B and TARGET_D are not included in the validation data set. While this method maintains the sample size and is easy to use, the variability in the data is reduced, so the standard deviations and the variance estimates tend to be underestimated. Once the formats have been created, you can continue to use them throughout your SAS session, making the format a very efficient and powerful tool. when data is not available) and then handle missing values (provide defaults or fill with previous values). Hi, I am struggling with the following problem, hope somebody can help. The answer would depend on the percentage of those missing values in the dataset, the variables affected by missing values, whether those missing values are a part of dependent or the independent variables, etc. Ask a question. In our example, the missing value will remain missing (which is better than. The presence of Missing Values in a data set can affect the performance of a classifier constructed using that data set as a training sample. na()) only sums those where your value is assigned NA in the dataset. If you compare the missing values in the picture above (cells with dots “. Optionally, users may directly upload a distance matrix on which to match. Dear all, I have some csv-files (originating from Excel-files) containing empty cells. As a worksheet function, the LARGE function can be entered as part of a formula in a cell of a worksheet. Then, the report should work as desired. Decide on a new global constant value, like "unknown", "N/A" or minus infinity, that will be used to fill all the missing. Here, we are filling with value zero; instead we can also fill with any other value. Note that in this case, the Fisher calculation results in a warning (but still plots). Missing data is a problem that most people have to deal with at some point, and there are different approaches to doing so. use Fill down or up, for the value column, use Replaces values as 0. I have a column called StudentIBFlag which is a Boolean and a column called YearSem which gives me a year and a semester. The presence of Missing Values in a data set can affect the performance of a classifier constructed using that data set as a training sample. How to Visualize and Compare Distributions in R By Nathan Yau Single data points from a large dataset can make it more relatable, but those individual numbers don’t mean much without something to compare to. replace missing values with mean (or modes) of these nearest neighbors. However, some attributes are not. How to Extract the Missing Values through VBA In this article, we are going to create user defined function to find out the missing number in a range through VBA in Microsoft Excel. Missing values are missing for reasons related to another variable, Age, that is completely observed. In your case, you have an easy task to fill in missing values for a string variable which you know should be constant. st: How to balance an unbalanced panel data set. R Replace NA with 0 (10 Examples for Data Frame, Vector & Column) A common way to treat missing values in R is to replace NA with 0. # ' # ' \code{FillIn} uses values of a variable from one data set to fill in missing values in another. When missing values are not at least MAR, missingness is said to benot missing at random (NMAR or MNAR) or nonignorable (there is a distinction between MNAR and. Various methods are available for fill missing values in data. Assuming that there are no missing values in either of Education or Self Employed, try this : Compute the mean based on Education and Self Employed table = train_data. Missing values are missing for reasons related to another variable, Age, that is completely observed. Repeats the process for multiple times, say m times and stores all the m complete(d)/imputed datasets. mysql - query to fill in missing datetime values. Let’s assume that females generally don’t want to reveal their ages! Here the missing value in age variable is impacted by gender variable) In the first two cases, it is safe to remove the data with missing values depending upon their occurrences, while in the third case removing observations with missing values can produce a bias in the. a) Missing completely at random (MCAR): Suppose variable Y has some missing values. when data is not available) and then handle missing values (provide defaults or fill with previous values). And there you have it; a simple approach to filling in missing data that will perform well, without the need for blending. In our missing data, we have to decide which dataset to use to fill missing values. I'm interested in "balance" this panel, keeping. Data prep and pattern. Populate an Excel Worksheet from. Then once I have the median of that row I need to replace all potential 'NA's with the median of the corresponding column! Link to image of dataset. NET though that I had previously running is DataTable serialization I have in use in a few older apps. Using the concepts of filling discussed in the ReIndexing Chapter we will fill the missing values. imputeDF = imputeDF. TestId == iTestID orderby wlr. In your case, you have an easy task to fill in missing values for a string variable which you know should be constant. ## Warning: Removed 130 rows containing missing values (geom_errorbar). rows) this line showing error"System. In this section, we will try to evaluate a the Linear Discriminant Analysis (LDA) algorithm on the dataset with missing values. This option sets all remaining variables without values to missing. You can easily convert it to a numpy array and fill the missing value using the given number by default: Note that -999. If it is ok with your data there will be average calculated. By creating your own custom format to categorize missing vs. a zero for body mass index or blood pressure is invalid. I'm using Pandas to store stock prices data using Data Frames. to build an all-zero. While this method maintains the sample size and is easy to use, the variability in the data is reduced, so the standard deviations and the variance estimates tend to be underestimated. How to identify missing numbers sequence in Excel? Let's say you have a long list of sequence numbers to mark items, such as check numbers in bank statements, normally we scrolling through and locate the missing sequence numbers manually. If this assumption holds, resulting estimates (i. Raw: The raw rgb, depth and accelerometer data as provided by the Kinect. If it is ok with your data there will be average calculated. Next, select only the empty cells. Justify in general. 0 Unported License. When we expand the data, we will inevitably create missing values for other variables. Excel Tactics Learn how to use Excel with tutorials, tips and tricks on functions, formulas, and features. na() returns a logical vector equal to TRUE if any of the variable in a dataset is missing and to FALSE otherwise. , you have no missing values or outliers), or you are performing some of the more straightforward statistical tests, you may only need to know the basics of data setup (see Data Setup and Entry). In numeric columns, missing values are denoted by an asterisk (*). Pandas provides various methods for cleaning the missing values. I want to import a file which has missing value in it, and the missing values are. Does anyone have a clever way of effectively filling down data from x number of rows above? Similar to the way where you can double-click in excel and have the data fill in from above. Mark Missing Values Extract, Transform, and Save CSV data. When the missing value for X is encountered, the LAG function looks to the queue to get the lagged value (of missing) and passes it back to X. DataSet' does not contain a definition for 'Rows' and no extension method 'Rows' accepting a first argument of type 'System. We use Amelia R package on The Analytics Edge competition data. Currently unused. The basic missing value for numeric variables is represented by a dot ". Hi, I using Weka to classify my data set, which has a lot of missing values, and two classes. This is then passed to complete() function. The below example marks the missing values in the dataset, as we did in the previous section, then attempts to evaluate LDA using 3-fold cross validation and print the mean accuracy. Reshaping data frames. If your data contains gaps – data points without values – you can easily make the chart display a gap as well. Missing value analysis helps address several concerns caused by incomplete data. Can any one help me with this please. SELECT LEFT(stat_start,16) AS `time`, AVG(`stat_values`) AS `data` FROM stats s WHERE s. Thank you for taking the time to write it. 0 here signifies missing value. Once SAS processes all observations in a data set, all subsequent observations in the new data set have missing values for the variables that are unique to that data set. The dataset is modified to include missing values in SECTION variable. Mark Missing Values Extract, Transform, and Save CSV data. This causes the modeling function to call one of the missing value filter functions. For related questions, for example, like those often presented in a matrix, if the participant responds with all "4s", assume that the missing value is a 4. When an operation affects rows in multiple tables, or when a duplicate row causes an update to existing values, the rows-affected statistics change in odd ways. Fill the DataFrame forward (that is, going down) along each column using linear interpolation. You can set constraint information for a DataTable by using the PrimaryKey and Unique properties. In which case, it is important to minimize balances or distortions. Explore Popular Topics Like Government, Sports, Medicine, Fintech, Food, More. While importing a dataset while making a machine learning model, often we find missing data. Usually, these tables were sorted manually in Excel, using the Data, Sort command. This is an algorithm that does not work when there are missing values in the dataset. from_dataframe (dataframe, sparse) Convert a pandas. Step 3) Replace the NA Values. • Does not produce imputed datasets. Example: Respondents with high income less likely to report income. F = fillmissing(A,'constant',v) fills missing entries of an array or table with the constant value v. I have a Table similar to:. Fill in Missing Values in an Astronomy Dataset SynthesizeMissingValues allows for missing data to be imputed based on an estimation of the underlying distribution of the dataset. We often leave them in and fill the values with some smart estimates. Mean imputation is a method replacing the missing values with the mean value of the entire feature column. Drop missing values; Dropping a complete row. DataSet' does not contain a definition for 'Rows' and no extension method 'Rows' accepting a first argument of type 'System. It's an array and the NaN values are missing. 5 Missing values. You don't want to fill too many values. How to fill in missing data without the need of a Scaffold data set - Alteryx by Juliana Camparotti | Oct 29, 2019 Last week at the Data School, we learned how to do Data Scaffolding using Tableau Prep Builder and Alteryx. Reshaping data frames. Click the Example 5 worksheet. SELECT LEFT(stat_start,16) AS `time`, AVG(`stat_values`) AS `data` FROM stats s WHERE s. How to Programmatically add new columns to a. A radio station asks for call-in responses to a question concerning city recycling. The missing value fill pattern is set to solid fill (0) using cnMissingValFillPattern, and the missing value fill color is set to blue using cnMissingValFillColor. We have 10 missing values in row numbers indicated by the first column. A time series data set may have gaps and sometimes we may want to fill in the gaps so the time variable will be in consecutive order. Eliminating and Imputing are two different strategies for handling samples or features with missing values. What technique is used to fill up missing values in a data set? Get the answers you need, now!. What technique is used to fill up missing values in a data set? Get the answers you need, now!. This short blog post shows you through example how to replace missing values with zero in SAS. This is an array formula so accept it by CTRL + SHIFT + ENTER. Data Analysis Code Book. When v is a vector, each element specifies the fill value in the corresponding column of A. In this example, you see missing data represented as np. Compute derived variables, indices and make data-set ana-lysis ready. Values in a data set are missing completely at random (MCAR) if the events that lead to any particular data-item being missing are independent both of observable variables and of unobservable parameters of interest, and occur entirely at random. There are applications where a scaffold data set may still require blending, for example; when trying to represent values from multiple data sources a scaffold can be useful to fill in gaps in both data sources. Implicitly, i. Missing value handling in Weka. I followed your guides to fill the missing values of the sparse depth maps of kitti dataset (I gonna use it as my ground truth in training). People with high salaries generally do not want to reveal their incomes in surveys) or missing value is dependent on some other variable’s value (e. Deleting Rows. efficient way to make NAs of empty cells in a factor (or character). table method consists of an additional argument cols, which when specified looks for missing values in just those columns specified. Filling In Missing Values Using the T-SQL Window Frame Since SQL Server delivered the entire range of window functions, there has been far less justification for using the non-standard ex-Sybase 'Quirky Update' tricks to perform the many permutations of running totals in SQL Server. Use the attribute mean (or majority nominal value) for all samples belonging to the same class. Description. Excel Tactics Learn how to use Excel with tutorials, tips and tricks on functions, formulas, and features. It's very easy to calculate it with SAS. hi, I have a question about importing data in R. Traditional time series analysis is commonly directed toward scalar-valued data, and can be represented by traditional Box-Jenkins’ autoregressive,. fillna( 'NA' ) This way, the vectorizer will create additional column =NA for each feature with NAs. Deleting Rows. # ' @param D1 the data frame with the variable you would like to fill in. Whatever the value of StudentIBFlag is in the most current YearSem, I want that to populate all rows for. XSL Transformation is applied to dataset and XML for excel is generated. v Fills in (imputes) missing values with estimated values using regression or EM methods; however, multiple imputation is generally considered to provide more accurate results. This How-To will walk you through writing a simple Python script to see if your data set has null or empty values, and if so, it will propose two options for how to modify your data. Google’s AI Can Fill In the Missing Frames in a Video Sequence. Missing Data Mechanisms Missing Completely at Random (MCAR) Missing value (y) neither depends on x nor y Example: some survey questions asked of a simple random sample of original sample Missing at Random (MAR) Missing value (y) depends on x, but not y Example: Respondents in service occupations less likely to report income. NET though that I had previously running is DataTable serialization I have in use in a few older apps. If it is ok with your data there will be average calculated. To do that, use control-G to bring up the GoTo special window, then select Blank, and click OK. Next, let's look at how we can remove instances with missing values from our dataset. Generating Zeroes in PROC TRANSPOSE Output. The COUNTIF function counts cells that meet supplied criteria, returning the number of occurrences found. dta” to open the dataset P14. There are three types of missing values (Allison, 2001): data missing completely at random (MCAR), data missing at random (MAR) and data not missing at random (NMAR). to build an all-zero. Missing Data Data Set 1 Data. TestId ascending select wlr; DataSet ds = ;. Filling missing values: We have to fill those missing data cells with 6 possible ways. However, the choice of what should be done is largely dependent on the nature of our data and the missing values. Hi Statalisters, I have a very unbalanced panel data set, composed for a lot of panel and 12 years. Replace them with t he variable mean (if numerical) or the most frequent value (if categorical). You must enter at least 4 values to build the box plot. Sometimes I want to use R to fill in values that are missing in one data frame with values from another. Before starting any research on a dataset the missing. In this way, nonmissing values are copied in a cascade down the current sort order. The MySQL manual explains how this works, so I don’t want to go into it; I just want to point out that you should expect odd values. Note how the last entry in column 'a' is interpolated differently, because there is no entry after it to use for interpolation. Far to many people just delete those records, which often contain useful training data. In other words, if you used Clean Missing Data on Dataset A and generated a mean value of 0. dta" to open the dataset P14. Missing Data Data Set 1 Data. rm=TRUE to each of the functions. A named list that for each variable supplies a single value to use instead of NA for missing combinations. Every data value has an affect on the mean, unlike the median and the mode. The length of the bar is based on the value of the cell compared to the rest of the selected cells. After I clicked “OK” on impute missing data, I noted random missing data is still available in the dataset of imputation_1, imputation_2, imputation_3, imputation_4 and imputation_5. The problem of missing data is relatively common in almost all research and can have a significant effect on the conclusions that can be drawn from the data []. I want to import a file which has missing value in it, and the missing values are. preprocessing. But always remember: if you're ever confused by the behaviour of NA in R, think about what. Furthermore, missing values may themselves be informative. Each strategy can perform better for certain datasets and missing data types but may perform much worse on other types of datasets. For example for the variable ’prcp’: The values of each variable were stored in a masked array. The mice package in R, helps you imputing missing values with plausible data values. Delete the case with missing observations. This is an excellent deep dive on ggplot. If the cell is blank, enter a value into the cell below it. NULL data: this occurs when the underlying data set contains records but the measure value(s) is NULL or blank. There are a number of different ways to impute data when you have missing values. The Dataset snapshot is displayed below: The time series data does not contain the values for Saturday and Sunday. We often leave them in and fill the values with some smart estimates. This class is designed to help you managing the data used to create the charts. Fill the missing date values in a Pandas Dataframe column. Any numerical value - fill fields with given value, full outer join. This post will show you the proper way to setup or organize your source data for a pivot table. Determine how many values are missing. You can vote up the examples you like or vote down the ones you don't like. Multiple Imputation Analysis (MIA) (Little and Rubin, 2002) is a method used to fill in missing observations. If the COMPUTE command puzzles you, see Compute A = B = C for an explanation. Fill NA Forward and Backward. By adding an index into the dataset, you obtain just the entries that are missing. This approach begins with the expectation step, during which the parameters (e. mysql - query to fill in missing datetime values. These generic functions are useful for dealing with NAs in e. After partitioning the data, the most popular test, called the t-test of mean difference, is carried out in order to check whether there exists any difference in the sample between the two data-sets. to build an all-zero. I need to bind data to a datatable, everything works except binding my DS to a DT. You will find a summary of the most popular approaches in the following. How to identify missing numbers sequence in Excel? Let's say you have a long list of sequence numbers to mark items, such as check numbers in bank statements, normally we scrolling through and locate the missing sequence numbers manually. It will now select all by default. We can add 'Group By' step to group the data by Product values (A or B) before running 'fill' command operation. Works by randomly choosing the missing value from a set of related and similar variables. So just the other day, as the lesson on Fill/Series… took us deep into Weekday territory, I got served with a rather unorthodox use of the command Fill/Justify described here in Fill/Series secondary. The first method is Multiple Imputation (MI). In DataFrame sometimes many datasets simply arrive with missing data. Currently unused. The missing values for Variable_1 in rows 5 and 12 (renamed to Variable_1_fix) and Variable_2 (renamed to Variable_2_fix) have been replaced by 100, while the values for Variable_3 remain untouched. Examples of data manipulation in SAS: Filling in "known" missing data Chong Ho Yu, Ph. Employee data with missing values][1] To get the most out of this, open the experiment in Studio. This is an entire branch of statistical research, so be careful with this one. Ignore the tuple is the simplest and not effective method. Sometimes I want to use R to fill in values that are missing in one data frame with values from another. In this section, we will try to evaluate a the Linear Discriminant Analysis (LDA) algorithm on the dataset with missing values. Prediction models: Here, we create a predictive model to estimate values that will substitute the missing data. Summary Here we have seen how Pandas handles null/NA values, and seen a few DataFrame and Series methods specifically designed to handle these missing values in a uniform way. to build an all-zero. Fill the missing date values in a Pandas Dataframe column. SQL JOINs make it easy to find and fix missing data by Jeff Davis in Data Management on April 30, 2002, 12:00 AM PST Many people are confused by various JOIN operations supported by SQL. We can easily work with missing values and in this section you will learn how to: Test for missing values; Recode missing values; Exclude. Variable-by- belonging to the same class to fill in the missing variable data cleaning is straightforward filter value approach (those values that are suspicious due to • Regression or classification methods: Develop a their relationship to a specific probability regression or classification model based on distribution, say a normal. Handle Missing Values in Objects Description. Define your missing value. Assign serial numbers to observations in a data set in SAS. Python - NetCDF reading and writing example with plotting by Chris Slocum is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3. 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: