# Numpy Percentile Nan

quantile (a, q[, axis, out, overwrite_input, …]) Compute the q-th quantile of the data along the specified axis. nanquantile (a, q, axis=None, out=None, overwrite_input=False, interpolation='linear', keepdims=) [source] ¶ Compute the qth quantile of the data along the specified axis, while ignoring nan values. Returns: numpy. This function does not make sure that the percentiles are unique so it can happen that multiple measurements are scaled to one point or that there are NaN values in the output array. median, numpy. pip install numpy. percentile, and numpy. Returns the qth quantile(s) of the array elements. How do I do this using python data-science packages?. A histogram is a great tool for quickly assessing a probability distribution that is intuitively understood by almost any audience. The function numpy. Least-squares fitting in Python import numpy # Generate artificial data = straight line with a=0 and b=1 # plus some noise. You can also save this page to your account. Note, missing values in Python are noted "NaN. A third way to compute percentiles (presented below) is a weighted average of the percentiles computed according to the first two definitions. I'll try master too. corrcoef numpy. Additional features over raw numpy arrays:. The first step is to import the python libraries that we will use. By default the lower percentile is 25 and the upper percentile is 75. adding a new column the already existing dataframe in python pandas with an example. Python offers a handful of different options for building and plotting histograms. median, numpy. percentile is a lot faster than scipy. 0 International License. Here are the examples of the python api numpy. A Filter producing True for values where this Factor is anything but NaN, inf, or -inf. describe(). repeat might do it, but so far I haven’t had any luck because numpy. In alternativa, posso scaricare la libreria numpy usando uno dei tanti ambienti operativi di pyhon. xdata = numpy. But I have question. I have a poly line shapefile of some. voxelArrayShift = kwargs. Returns NaN for mean if data is empty or if any entry is NaN and NaN for standard deviation if data has less than two entries or if any entry is NaN. Access free and open data available on IBM's Analytics Exchange. Each row was assigned an index of 0 to N-1, where N is the number of rows in the DataFrame. Note, missing values in Python are noted "NaN. I am pleased to announce the availability of NumPy 1. In Matlab you would. arange # This function returns an ndarray object containing evenly spaced values within a given # Not a Number NaN import numpy as np a = np. If alias is not provided then to access the functions from numpy we shall write numpy. nan_to_num(x) ：将数组 x 中的下列数字替换掉，返回替换掉之后的新数组：. Returns: numpy. The output tells a few things about our DataFrame. percentile_between (min_percentile, max_percentile, mask=sentinel('NotSpecified')) ¶ Construct a Filter matching values of self that fall within the range defined by min_percentile and max_percentile. This lesson of the Python Tutorial for Data Analysis covers grouping data with pandas. My Map will look like this and it will always be sorted in ascending order on the keys- In which. nan)返回False，因为NaN首先就不是一个数 下列函数用于对这几个特殊的数进行转换： numpy. Begin Edit. series, what to keep in mind while using them and how to use them efficiently. Toggle navigation Research Computing in Earth Sciences. This function does not make sure that the percentiles are unique so it can happen that multiple measurements are scaled to one point or that there are NaN values in the output array. 58981 You can also access all of the values in a column meeting a certain criteria. Since they return the nan value, the warning is redundant and has been removed. median and percentile family of functions no longer warn about nan Functions like numpy. import numpy as np It is a general approach to import numpy with alias as 'np'. array ([40, 50, 60, 70, 75, 80, 83, 86, 89, 95]) >>> np. Some of the examples are somewhat trivial but I think it is important to show the simple as well as the more complex functions you can find elsewhere. py, which reads in the data (in dictionary form) and converts it into a sklearn-ready numpy array. histogram() and np. Au lieu de cela, vous pouvez utiliser le code suivant. feature_selection. Data Analysis is process of extracting information from raw data. percentile(arr, n, axis=None, out=None). TODO: this is a pure python implementation which probably has a much faster numpy impl. The 50th percentile has a value of 19. import numpy as np It is a general approach to import numpy with alias as 'np'. Returns the qth quantile(s) of the array elements. Moreover, the arrays can be modiﬁed in size dynamically. For example, if X is a matrix, then prctile(X,50,[1 2]) returns the 50th percentile of all the elements of X because every element of a matrix is contained in the array slice defined by dimensions 1 and 2. xdata = numpy. For example the highest income value is 400,000 but 95th percentile is 20,000 only. Is there any compelling reason to include NaN's in percentile calculations? It seesm Pandas handles this correctly, so I wonder why numpy would not make a similar implementation. Marks are 40 but percentile is 80%, what does this mean? 80% of CAT exam percentile means 20% are above & 80% are below; Percentiles help us in getting an idea on outliers. The module is not intended to be a competitor to third-party libraries such as NumPy, SciPy, or proprietary full-featured statistics packages aimed at professional statisticians such as Minitab, SAS and Matlab. The output tells a few things about our DataFrame. reshape, one of the new shape dimensions can be -1, in which case its value is inferred from the size of the array and the remaining dimensions. Remember that percentiles can be calculated by sorting the observations and selecting values at specific indices. 939851436401284. In the process of porting an existing (but abandoned) package to the latest version of Numpy, I stumbled upon a call to a 'numpy. def demean (self, mask = NotSpecified, groupby = NotSpecified): """ Construct a Factor that computes ``self`` and subtracts the mean from row of the result. nanpercentile()function used to compute the nth precentile of the given data (array elements) along the specified axis ang ignores nan values. q 要计算的百分位数，在 0 ~ 100 之间3. nanstd numpy. You have a numerical column, and would like to classify the values in that column into groups, say top 5% into group 1, 5-20% into group 2, 20%-50% into group 3, bottom 50% into group 4. Series([1,2,np. Hi Serberg and all following this thread, So I have a gridded data of shape (324,72,144) in order time,lat, and lon. percentile masked array aware (similiarly for other functions in the core library). float() Parameters. average() has a weights option, but numpy. Python numpy. As shown in the previous chapter, a simple fit can be performed with the minimize() function. xdata = numpy. For instance, in the above example for 20-th percentile the rank is 20. Explore Popular Topics Like Government, Sports, Medicine, Fintech, Food, More. nan]) Output 0 1. nanpercentile (a, q, axis=None, out=None, overwrite_input=False, interpolation='linear', keepdims=) [source] ¶ Compute the qth percentile of the data along the specified axis, while ignoring nan values. You can use the numpy method. correlate numpy. 37 The algorithm is from Numerical Recipes, Volume 2. The returned tensor and ndarray share the same memory. Numpy and Matplotlib¶These are two of the most fundamental parts of the scientific python "ecosystem". If multiple percentiles are given, first axis of the result corresponds to the percentiles. An object with fit method, returning a tuple that can be passed to a pdf method a positional arguments following an grid of values to evaluate the pdf on. median, numpy. These methods are very useful as you can operate the methods in a whole array or axis-wise according to your needs. So to finally answer the question, are theorists smarter than observers? Possibly, or there is a hidden bias in the way we educate or evaluate students. A Filter producing True for values where this Factor is anything but NaN, inf, or -inf. Estad stica b asica. How to get the documentation of the numpy add function from the command line? (★☆☆) 6. DataArray provides a wrapper around numpy ndarrays that uses labeled dimensions and coordinates to support metadata aware operations. Python Pandas - Descriptive Statistics - A large number of methods collectively compute descriptive statistics and other related operations on DataFrame. histogram() and np. (Python version: 2. defchararray. 285333333413 test mean loss = nan , accuracy = 0. Most people know a histogram by its graphical representation,. Comience la prueba gratis Cancele en cualquier momento. Masked data are excluded from calculations. data : numpy. exclude_percentile : float in the range of [0, 100], optional The percentage of masked pixels in a mesh, used as a threshold for determining if the mesh is excluded. nanpercentile (a, q, axis=None, out=None, overwrite_input=False, interpolation='linear', keepdims=) [source] ¶ Compute the qth percentile of the data along the specified axis, while ignoring nan values. I faced similar problem and saw that numpy handles NaN and Inf differently. zscore(boston_df)) print(z) show groupby object data statistics for each column by grouped element: grouped. percentile() takes the following arguments. For object data (e. If a label is not found in one Series or the other, the result will be marked as missing NaN. By voting up you can indicate which examples are most useful and appropriate. 100 numpy exercises. describe() now formats integer percentiles without decimal point were filled in resulting DataFrame with the string "nan" instead of numpy. The median (the 50th percentile) for the test scores is the 13th score: 77. This feature selection algorithm looks only at the features (X), not the desired outputs (y), and can thus be used for unsupervised learning. Not only were the names getting out of hand, some packages were unable to work with the postN suffix. sqrt(7) 100 loops, best of 1: 2. For numeric data, the result's index will include count, mean, std, min, max as well as lower, 50 and upper percentiles. median, numpy. I've also created some to reach the 100 limit. groupby(), using lambda functions and pivot tables, and sorting and sampling data. Au lieu de cela, vous pouvez utiliser le code suivant. At the extremes (> 97th percentile or < 3rd percentile), small differences in percentiles represent clinically important differences in BMI. Each row was assigned an index of 0 to N-1, where N is the number of rows in the DataFrame. Performing Fits and Analyzing Outputs¶. percentile and scipy. We wish to obtain the value of a dependent value at point for which we did not sample the independent value. First part may be found here. 2012-07-11 04:14 pramsey * Add note for future pain 2012-07-11 04:05 pramsey * Fix issue with projecting from the poles, retain the source longitude for more sensible result. NumPy specializes in numerical processing through multi-dimensional ndarrays, where the arrays allow element-by-element operations, a. To make it easier an alias 'np' is introduced so we can write np. If the result for either DOT_PRODUCT or EUCLIDEAN_DISTANCE is infinity, negative infinity, or NaN, null will be returned instead. This time we’ll be using Pandas and NumPy, along with the Titanic dataset. Returns the qth quantile(s) of the array elements. If False, try to avoid a copy and do inplace scaling instead. Analyzes both numeric and object series, as well as DataFrame column sets of mixed data types. scipy is the core package for scientific routines in Python; it is meant to operate efficiently on numpy arrays, so that numpy and scipy work hand in hand. nanpercentile to ignore null values. So now lets have a look at it in Python. It is useful in the middle of a script, to recover the resources held by accessing the dataset, remove file locks, etc. Gromacs produces graphs in the xmgrace ("xvg") format. n_samples : int, optional Maximum number of values to use. from_numpy (ndarray) → Tensor¶ Creates a Tensor from a numpy. I am trying to write a function that would create a regular grid of 5 pixels by 5 pixels inside a 2d array. mstats, > so I have to use np. sum()を使うとNumPy配列ndarrayの合計値、numpy. 64 μs per loop. median and percentile family of functions no longer warn about nan ¶ numpy. This docstring was copied from numpy. def demean (self, mask = NotSpecified, groupby = NotSpecified): """ Construct a Factor that computes ``self`` and subtracts the mean from row of the result. if the machine has a Millennium 120 standard MLC model, leaf data will have 120 dictionary items from 1 to 120. Matplotlib can be used to create histograms. This time we'll be using Pandas and NumPy, along with the Titanic dataset. Marks are 40 but percentile is 80%, what does this mean? 80% of CAT exam percentile means 20% are above & 80% are below; Percentiles help us in getting an idea on outliers. from the given elements in the array. percentile() Percentile (or a centile) is a measure used in statistics indicating the value below which a given percentage of observations in a group of observations fall. If q is a float, a Series will be returned where the. arange() Say you’re interested in analyzing length of delays and you want to put these lengths into bins that represent every 10 minute period. 0 International License. The steps shown here demonstrate one way of calculating percentiles, but there are several other acceptable methods. A single percentile still returns a scalar. A percentile rank is the proportion defined in percentile: for p-th percentile, rank is p. Alternatively, set this to an ascending sequence of percentile (e. , addition, subtraction, multiplication, etc. NumPy provides many other aggregation functions, but we won't discuss them in detail here. This module provides functions for calculating mathematical statistics of numeric (Real-valued) data. percentile, it seems not support only > nonmasked array? And there is no percentile function in scipy. amin() and numpy. And this is how you can get valuable percentiles data in Python with the numpy module. You can also save this page to your account. percentile() function used to compute the nth precentile of the given data (array elements) along the specified axis. Returns an array or scalar replacing Not a Number (NaN) with zero, (positive) infinity with a very large number and negative infinity with a very small (or negative) number. [SciPy-User] ANN: NumPy 1. Compute the index of qth percentile of the data along the specified axis. percentile, and numpy. I've also created some to reach the 100 limit. Descriptive or summary statistics in python - pandas, can be obtained by using describe function - describe(). median(axis = 0) will also give the same output. The bins of ten minute intervals will range from 50 minutes early (-50) to 200 minutes late (200). Since it reports order statistics (rather than, say, the mean) the five-number summary is appropriate for ordinal measurements, as well as interval and ratio measurements. This post was inspired by a question I answered on stack overflow. corrcoef(*args)¶ corrcoef(X) where X is a matrix returns a matrix of correlation coefficients for the columns of X. This argument is optional when feature_dependence="tree_path_dependent", since in that case we can use the number of training samples that went down each tree path as our background dataset (this is recorded in the model object). from the given elements in the array. The following are code examples for showing how to use numpy. NumPy Array : contains values of the same/homogeneous type Create empty array of size m x n numpy. APPLIES TO: SQL Server Azure SQL Database Azure SQL Data Warehouse Parallel Data Warehouse. Use the isnan or ismissing function to detect NaN values in an array. nanmean(a, axis=None, dtype=None, out=None, keepdims=False) [source] ¶ Compute the arithmetic mean along the specified axis, ignoring NaNs. 这篇文章收集了网友们使用pandas进行数据分析时经常遇到的问题, 这些问题也可以检验你使用pandas的熟练程度, 所以他们更像是一个学习教材, 掌握这些技能, 可以使你数据数据分析的工作事半功倍。. from scipy import stats import numpy as np z = np. Values with a NaN value are ignored from operations like sum, count, etc. _applyBinning (self. x (float, numpy. Note that for floating-point input, the mean is computed using the same precision the input has. There are two key components of a correlation value: magnitude - The larger the magnitude (closer to 1 or -1), the stronger the correlation; sign - If negative, there is an inverse correlation. 0 International License. Syntax : numpy. These are simple multi-column data files. median and percentile family of functions no longer warn about nan Functions like numpy. percentile would be returned with keepdims enabled. arange() numpy. median, numpy. If True, center the data before scaling. survived age sibsp parch fare pclass_1st pclass_2nd pclass_3rd sex_female sex_male embarked_Cherbourg embarked_Queenstown embarked_Southampton; count: 1306. nanmean numpy. Series([1,2,np. If positive, there is a regular correlation. I'm not sure how to properly eliminate these from my dataset. Compute the index of qth percentile of the data along the specified axis. It aims to build a model with predictive power. median, numpy. Modifications to the tensor will be reflected in the ndarray and vice versa. If True, shade in the area under the KDE curve (or draw with filled contours when data is bivariate). n_samples : int, optional Maximum number of values to use. Estimates the sample mean and the unbiased population standard deviation from the provided samples. NumPy is one of the most powerful Python libraries. nan)返回False，因为NaN首先就不是一个数 下列函数用于对这几个特殊的数进行转换： numpy. Se relleno el dato faltante en "Age", el de "Religion" tal vez sea mejor dejarlo asi ya que "NaN" representa un dato faltante a pesar de su significado Y para eliminar las filas que tienen datos faltantes en un cojunto de columnas. nanpercentile (a, q, axis=None, out=None, overwrite_input=False, interpolation='linear', keepdims=) [source] ¶ Compute the qth percentile of the data along the specified axis, while ignoring nan values. 13 Manual numpy. Values with a NaN value are ignored from operations like sum, count, etc. median and percentile family of functions no longer warn about nan Functions like numpy. In the question a user asked if it was possible to make a boxplot with box boundaries at arbitrary percentiles, using matplotlib. # Quantile vs Percentile. x (float, numpy. median numpy. A histogram shows the frequency on the vertical axis and the horizontal axis is another dimension. Y = prctile(X,p,vecdim) returns percentiles over the dimensions specified in the vector vecdim. nan_to_num(x, copy=True) [source] Replace nan with zero and inf with finite numbers. Raster objects. The standard approach is to use a simple import statement: >>> import numpy However, for large amounts of calls to NumPy functions, it can become tedious to write numpy. This is represented as a numpy. Some of the common functions of numpy are listed below -. Values are generated within the half - open interval[start, stop) (in other words, the interval including start but excluding stop). bmat() numpy. Visualization can be created in mlab by a set of functions operating on numpy arrays. L'elenco delle istruzioni e delle funzioni scientifiche del modulo numpy. # Load libraries import numpy as np from sklearn import q1, q3 = np. Syntax : numpy. This is just a brief public service announcement reporting something that I’ve just found: np. So now lets have a look at it in Python. A single percentile still returns a scalar. In the question a user asked if it was possible to make a boxplot with box boundaries at arbitrary percentiles, using matplotlib. statsmodels member josef-pkt commented Oct 20, 2015 AFAICS, it's numpy/numpy#6500 but I don't know any details since I don't understand the numpy internals for this. NumPy is the shorter version for Numerical Python. Modifications to the tensor will be reflected in the ndarray and vice versa. The 50th percentile has a value of 19. x (float, numpy. 10, Numpy version: 1. Values are generated within the half - open interval[start, stop) (in other words, the interval including start but excluding stop). These can be detected in a Series or DataFrame using notnull() which returns a boolean. > > Thanks for any comments. nanpercentile, and np. median, numpy. Most everything else is built on top of them. If True, shade in the area under the KDE curve (or draw with filled contours when data is bivariate). nan artificially pd. Is there any compelling reason to include NaN's in percentile calculations? It seesm Pandas handles this correctly, so I wonder why numpy would not make a similar implementation. quantile is relatively new, so most ArcGIS releases won't have that function in their numpy versions. Also try practice problems to test & improve your skill level. Notes： The arithmetic mean is the sum of the elements along the axis divided by the number of elements. array percentiles of reference data estimated through method of choice, must be same size as perc_src min_val: float, optional Minimum allowed value, output data is capped at this value max_val: float. A histogram shows the frequency on the vertical axis and the horizontal axis is another dimension. nanvar numpy. copy ()) def _initVoxelBasedCalculation (self): super (RadiomicsFirstOrder, self). This post was inspired by a question I answered on stack overflow. from the given elements in the array. A Series object has many attributes and methods that are useful for Data Analysis. The Python NumPy package has built in functions that are required to perform Data Analysis and Scientific Computing. 创建一个3x3矩阵，其值范围为0到8 (★☆☆) print(0 * np. The goal of the numpy exercises is to serve as a reference as well as to get you to apply numpy beyond the basics. L'elenco delle istruzioni e delle funzioni scientifiche del modulo numpy. nanmean numpy. from_numpy (ndarray) → Tensor¶ Creates a Tensor from a numpy. I'll try master too. percentile, and numpy. The command df. This function will work with integer and float rasters, as well as with on-disk rasters and in-memory arcpy. nanpercentile(a, q, axis=None, out=None, overwrite_input=False, interpolation='linear', keepdims=) 指定された軸に沿ってデータのq番目のパーセンタイルを計算し、一方ではnan値は無視します。 配列要素のq番目の百分位数を返します。. In simple terms, median represents the 50th percentile, or the middle value of the data, that separates the distribution into two halves. nanpercentile under nanfunctions is welcome, but in keeping with the model of mask array support seen for numpy. Per installare numpy su python posso usare l'installer pip. percentile returns an array instead of a list. They build full-blown visualizations: they create the data source, filters if necessary, and add the visualization modules. EfficientNetはAutoMLで作成された、パラメータ数の少なさに対して精度が非常に高いモデルです。 OfficialのTensorflowの実装だけでなく、PyTorchやKerasの実装も早速公開されており、使い方を知っておきたく試してみました。. The NumPy functions ndarray. percentile and np. mean(arr_2d) as opposed to numpy. I have a poly line shapefile of some. data : numpy. Syntax : numpy. median and percentile family of functions no longer warn about nan Functions like numpy. Here, float64 is a numeric type that NumPy uses to store double-precision (8-byte) real numbers, similar to the float type in Python. nan_to_num(X) you "replace nan with zero and inf with finite numbers". Since they return the nan value, the warning is redundant and has been removed. These can be detected in a Series or DataFrame using notnull() which returns a boolean. " You can use numpy to create missing value: np. Importing the NumPy module There are several ways to import NumPy. array([2,1,3,2]). Access free and open data available on IBM's Analytics Exchange. scipy is the core package for scientific routines in Python; it is meant to operate efficiently on numpy arrays, so that numpy and scipy work hand in hand. Write a NumPy program to print the NumPy version in your system. Alternatively, set this to an ascending sequence of percentile (e. Universal functions (ufunc for universal functions) are functions that can be applied term-by-term to the elements of an array. reshape((4,4)), 90)) # 13. I have a poly line shapefile of some. Download Open Datasets on 1000s of Projects + Share Projects on One Platform. The calculator gives you the 25th Percentile, which is the end of the first quartile, the 50th Percentile which is the end of the second quartile (or the median) and the 75th Percentile, which is the end of the third quartile. python计算分位数. Come installare numpy su python. percentile: scalar or ndarray If q is a single percentile and axis=None , then the result is a scalar. nanpercentile(a, q, axis=None, out=None, overwrite_input=False, interpolation='linear', keepdims=) 指定された軸に沿ってデータのq番目のパーセンタイルを計算し、一方ではnan値は無視します。 配列要素のq番目の百分位数を返します。. train mean loss = nan, accuracy = 0. The same as np. Tutorialspoint. Instead, it is common to import under the briefer name np:. Values with a NaN value are ignored from operations like sum, count, etc. 346 NaN 347 NaN 348 NaN 349 NaN Name: Virulence, Length: 350 # show the 12th row in the ShannonDiversity column print experimentDF["ShannonDiversity"][12] 1. You'd use it just like percentile(), but would input your q value in probability space (0. This function will work with integer and float rasters, as well as with on-disk rasters and in-memory arcpy. Per installare numpy su python posso usare l'installer pip. We can mark values as NaN easily with the Pandas DataFrame by using the replace() function on a subset of the columns we are interested in. Values that fall below the lowest bin are mapped to index 0. Gromacs produces graphs in the xmgrace ("xvg") format. We welcome contributions for these functions. nanmax numpy. NumPy has quite a few useful statistical functions for finding minimum, maximum, percentile standard deviation and variance, etc. nan_to_num(x, copy=True) [source] Replace nan with zero and inf with finite numbers. percentile(a, 30) # 30 パーセンタイル. A single percentile still returns a scalar. Returns: numpy. If ``mask`` is supplied, ignore values where ``mask`` returns False when computing row means, and output NaN anywhere the mask is False. 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: