# Pandas Groupby Percentile Rank

414 in the parent node, we can say that the player's weight caused a decrease of 0. It is a very powerful and versatile package which makes data cleaning and wrangling much easier and pleasant. If we compute a new variable, R , so that r i is the rank of x i , the distribution of R is uniform from 1 to n , regardless of the distribution of X. Seniors/Adults with Disabilities. Python pandas groupby object apply method duplicates first group My first SO question: I am confused about this behavior of apply method of groupby in pandas (0. Regards, · You can accomplish the second case. More generally, given position and field size, we can compute percentile rank: def PositionToPercentile(position, field_size): beat = field_size - position + 1 percentile = 100. Statistics - Module 3 Video 2 - Mean, Median, Mode, Percentiles and Quartiles - Problem 3-1B Peter Dalley. I am trying to take a numeric array in F#, and rank all the elements so that ties get the same rank. 1 What’s New 3 1. Specify one of these. egg\pandas\core\series. Samples: - 0: This customer ranks above the 90th percentile for annual spending amounts in Fresh, Frozen, and the Delicatessen categories. c v assumes its minimum value of zero for complete equality (all x i are equal). They are extracted from open source Python projects. Rank within a group & within a sub-group problem Using Excel 2010, RANK. mean() lowest = rank. 0),percentile(99. cume_dist: None: Assigns a cumulative distribution value between 0 and 1 to each data object in a window. Pandas describe() is used to view some basic statistical details like percentile, mean, std etc. min 计算组值的最小值: GroupBy. The axis labels are often referred to as index. COUNT operates like the COUNT_BIG function. This beginners' code covers the basics of matplotlib and its useful methods. Peer values are assigned the same rank. DataFrame: For 2D labeled tabular data. pandasticsearch Documentation, Release 0. I can get the window to pop up with my button and just cant seen. As described in the book, transform is an operation used in conjunction with groupby (which is one of the most useful operations in pandas). The percentile is essentially the same as a quantile, with quantiles indexed by fractions (so the. ffill() and pandas. J'ai toutes les équipes des années 1985-2012 dans une base de données; les 10 premières sont indiquées ci-dessous: il est actuellement trié par année. 输入搜索字词或模块，类或函数名称。 pandas. rank(ascending=False) / float(x. xx e um valor que é. Web開発はまさに芸術創造である!. Class implementing the. The College Panda's 10 Practice Tests for the SAT Math [Nielson Phu] on Amazon. rank(self, axis=0, method='average', numeric_only=None, na_option='keep', ascending=True, pct=False) Parameters:. 一、Pandas介绍 1、介绍. 5, meaning 50%, PERCENTILE_CONT will return the median value. 3 Name: 1985, dtype: float64 and directly on the WLPer column (although this is slightly different due to draws):. DataFrameGroupBy. In this article, I will offer an opinionated perspective on how to best use the Pandas library for data analysis. mix of pointers and values). Pandas Top 14 Job Locations. Pandas provides a similar function called (appropriately enough) pivot_table. The DataFrame. We will now learn a few statistical functions, which we can apply on Pandas ob. This was the second episode of my pandas tutorial series. 1 pip and virtualenv. Column A column expression in a DataFrame. Pandas is a vast library. Missing data in pandas dataframes. 由上述计算可知存在99个竞标者无竞标记录，其中29位来自训练集，70位来自测试集，而且这29位来自训练集的竞标者未被标记为机器人用户，所以可以针对测试集中的这70位用户后续标记为人类或者取平均值处理. This is a personal project I started to help me tie together using python for web scraping, data cleaning, data visualization, hypothesis testing, statistical modeling, machine learning, and more. groupby pandas. Data Wrangling with PySpark for Data Scientists Who Know Pandas with Andrew Ray 1. quantile DataFrameGroupBy. axis : {0, 1}, default 0 Ranks over columns (0) or rows (1) numeric_only : boolean, default None Include only float, int. We must compare period to period factor ranks rather than factor values to account for systematic shifts in the factor values of all names or names within a group. 0),percentile(99. Able Life Space Saver Lightweight Folding Travel Walker. The other axes are the axes that remain after the reduction of a. DataFrameGroupBy. co/products/elasticsearch). ranges() (pandasticsearch. Create a dataframe. The pandas package features two useful functions, cut and qcut, that can transform a metric variable into a qualitative one: cut expects a series of edge values used to cut the measurements or an integer number of groups used to cut the variables into equal-width bins. GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. Python is a great language for doing data analysis, primarily because of the fantastic ecosystem of data-centric python packages. A: A stanine score is simply another scale and is based on percentile ranks. X Required. In the example below, I manually do this by computing the decile rank using the kludgy argsort. pandas for data analysis, numpy for calculation and mathematics and lastly matplotlib for visualization of graphs. Information Loss 10. And, like the popular spreadsheet program, the basic units of operation are columns and rows of data that form tables. Pandas is a package that was started by Wes McKinney who worked as a quant in a hedge fund that wanted a better way to work with data than excel macros had to offer. It opens and works on the Mac but when I tried to open in my PC it doesn't work. groupby ([by, axis, level, as_index, sort, ]) Group series using mapper (dict or key function, apply given function: gt (other) head ([n]) Returns first n rows of Series: hist ([by, ax, grid, xlabelsize, xrot, ]) Draw histogram of the input series using matplotlib: idxmax ([axis, out, skipna]) Index of first occurrence of maximum of values. The last one (PERCENT_RANK) calculates percentile of records that fall within the current window. python pandas. describe() function to return a summary of a desired column (mean, stddev, count, min, and max) all as strings though. Calculating Percentiles [Ian Robertson, January 09, 2004] Percentiles are very handy for exploring the distribution of number sets using various EDA graphs, including the well-known (and still underused) boxplot. import pandas def getRankingHigherIsBetter(df, metric): return 1. com; PDF document) Boy Scouts and Woods Tools (May 15, 2017) Fitness Exercise Log Sample Fitness Plan for Tenderfoot Personal Fitness Merit Badge Personal Fitness Merit Badge Requirements…. The meaning of percentile can be captured by stating that the pth percentile of a. For object data cumcount can be used, though it would be up to the user to specify the desired order first. The Globalization of Crime A Transnational Organized Crime Threat Assessment “A ground-breaking assessment of transnational organized crime activities that INTERPOL will use in its work. This is a personal project I started to help me tie together using python for web scraping, data cleaning, data visualization, hypothesis testing, statistical modeling, machine learning, and more. isnull() 以布尔的方式返回空值 DataFrame. The 'Rank Change' column provides an indication of the change in demand within each location based on the same 6 month period last year. These metrics are more robust to pathologies in the data, but they are computationally more expensive (since you must sort a list). Percentile rank of a column in pandas python is carried out using rank() function with argument (pct=True). From the mean values, it can be seen that the for values for the various inflation categories vary greatly from each other and they are not the same or close to each, the mean values are 2329. 5) will compute the 50th percentile (that is, the median) of an expression. For example, a test score that is greater than 75% of the scores of people taking the test is said to be at the 75th percentile, where 75 is the percentile rank. Inside this string, you can write a Python expression between { and } characters that can refer to variables or literal values. quantile (self[, q, …]) Return group values at the given quantile, a la numpy. The DAX language offers a function that provides the ranking of an element sorted by using a certain. Home; web; books; video; audio; software; images; Toggle navigation. If we compute a new variable, R , so that r i is the rank of x i , the distribution of R is uniform from 1 to n , regardless of the distribution of X. The result is equal to a specific column value. plot attribute for groupby objects. Frederik Andersen is near the top of the charts with his Goals Saved Above Average. Update: Pandas version 0. pth percentile: p percent of observations below it, (100 – p)% above it. to_pandas ¶ Export the current query result to a Pandas DataFrame object. This situation arises, for example, when \(K\) is the linear kernel of a mixed model with fewer random effects than observations. pandas: powerful Python data analysis. A value that identifies the number of significant digits for the returned percentage value. Woodman, Neal. Introduction. The Energy Star score is a percentile rank, which means we would expect to see a uniform distribution, with each score assigned to the same number of buildings. MedCalc is capable of handling missing data, creating subgroups, calculating percentile ranks and power transformation. These functions differ only in the data types of their return values. Returns 0 if NR=1. The organization of the book follows the process I use when I start working with a dataset: Importing and cleaning: Whatever format the data is in, it usually takes some time and effort to read the data, clean and transform it, and check that everything made it through the translation process intact. The Pandas library is one of the most preferred tools for data scientists to do data manipulation and analysis, next to matplotlib for data visualization and NumPy , the fundamental library for scientific. Pandas lets you do this efficiently with the groupby function. So for example the first value of our output would be the final value in column (1) percentranked against all the values in column (1) and so on. argsort trick, and then use groupby. Business understanding. pandas pandas provides rich data structures and functions designed to make working with structured data fast, easy, and expressive. groupby("Subject")["Score"]. The PERCENTRANK function syntax has the following arguments: Array Required. The few differences between Pandas and PySpark DataFrame are: Operation on Pyspark DataFrame run parallel on different nodes in cluster but, in case of pandas it is not possible. Veterans, Service Members & Their Families. While it is exceedingly useful, I frequently find myself struggling to remember how to use the syntax to format the output for my needs. If q is a single percentile and axis=None, then the result is a scalar. import pandas as pd from pandas import DataFrame, Series Note: these are the recommended import aliases The conceptual model DataFrame object: The pandas DataFrame is a two-dimensional table of data with column and row indexes. Percentiles and Percentile Ranks 7. 3 Name: 1985, dtype: float64 and directly on the WLPer column (although this is slightly different due to draws):. They must include the desired percentile/percentile_rank. median, mode, percentiles and quartiles. python - Compute percentile for pandas dataframe row based on previous years data 分享于 推荐： 数据结构之--series,DataFrame. It is a very powerful and versatile package which makes data cleaning and wrangling much easier and pleasant. They are extracted from open source Python projects. groupby("date"). quantile(q=0. quantile¶ DataFrameGroupBy. Now let's read the data Here we can see the shape of our data and head (top 5 rows) with all the columns. Nothing fancy here, we just use some classical Python Data Science libraries: numpy, scikit-learn and pandas, plus pickle to save the result. Percentile ranks are useful for comparing measurements from different tests, or tests applied to different groups. Python Pandas - Statistical Functions - Statistical methods help in the understanding and analyzing the behavior of data. warning: pandas/src/sparse. percentile() spark sql中 sqlContext. size 计算组大小. Companies like- Aureus Analytics, C360, Metaome, Heckyl, Flutura, Sigmoid Analytics, Indix, Germin8, Bridgei2i, PromptCloud, Fractal Analytics, and many other leading companies offer great career in data science. Assuming that my percentile rank in my division is the same, how much slower should I expect to be? I can answer that question by converting my percentile rank in M4049 to a position in M5059. Returns: Series or DataFrame If q is an array, a DataFrame will be returned where the. Nº 348 enero-abril 2009. Column A column expression in a DataFrame. 如何使用星火找到中位数和分位数我怎样才能找到RDD使用分布式方法的整数，IPython和SPark？这个RDD大约有70万个元素，因此太大，无法收集和找到中位数。. For Educators, Parents & Students. edu is a platform for academics to share research papers. For example, a marketing analyst looking at inbound website visits might want to group data by channel, separating out direct email, search, promotional content, advertising, referrals, organic visits, and other ways people found the site. Rather than showing off all of pandas’ fanciest features, our goal will simply be to build intuition for the core abstractions that pandas gives us. train_test = pd. stable Getting Started. Supplying codes/labels and levels to the Categorical constructor is not supported anymore. There are a lot of ways that you can use groupby. This article will discuss the basics of why you might choose to use a weighted average to look at your data then walk through how to build and use this function in pandas. 0 (February 9, 2012) 45 pandas: powerful Python data analysis toolkit, Release 0. so ranking is done by subject wise # Rank by Group df["group_rank"] = df. Percentiles and percentile ranks are approximate and are calculated using the t-digest algorithm. max ([axis, numeric_only]). The other axes are the axes that remain after the reduction of a. The values and distances of the two nearest neighbors as well as the interpolation parameter will determine the percentile if the normalized ranking does not match the location of q exactly. python下的Pandas中DataFrame基本操作（一），基本函数整理。方法 描述 DataFrame([data, index, columns, dtype, copy]) 构造数据框 属性和数据 方法 描述 DataFrame. One goal of this document is to expose as much of Numpy and SciPy in a single document and a single Web page so that you have some ability to search for the function and capability you need. The ntile() function is used to divide the data into N bins. NIH Library Training - Essential Science Indicatorrs Percentile Ranking (Hands on) Date: Wednesday, 05 06, 2015; Speaker: CDR Leslie Wehrlen, United States Public. The iloc indexer syntax is data. The percentile rank of a score is the percentage of scores in its frequency distribution that are equal to or lower than it. The rank is returned on the basis of position after sorting. First step is to read the CSV files and load them as Pandas frames. For compatability with NumPy, the return value is the same (a tuple with an array of indices for each dimension), but it will always be a one-item tuple because series only have one dimension. quantile ( q=0. Finding z-score for a percentile. It features outlier detection, correlation and regression tools, Bland & Altman plotting, while also enabling you to run Anova, variance ratio, mean, propertion, Chi-Square, Fisher and T-tests. plot attribute for groupby objects. rank (pct = True) # Then, round it up, and multiply by 100 percentiles = 100 * percentiles. *FREE* shipping on qualifying offers. Seniors/Adults with Disabilities. astype(dtype[, copy, errors]) 转换数据类型 DataFrame. Class implementing the. As described in the book, transform is an operation used in conjunction with groupby (which is one of the most useful operations in pandas). bfill() where the fill within a grouping would not always be applied as intended due to the implementations' use of a non-stable sort (:issue:`21207`) Bug in pandas. If you use pip, I'd recommend using virtualenv, at the least, and even virtualenvwrapper, for extra convenience and flexibility. All of the Pandas, Spark, and Koalas DataFrames provide the same function describe() for obtaining such basic summary statistics, including the total number of rows, min, mean, max, and percentile of each of the columns of the DataFrame. By default, equal values are assigned a rank that is the average of the ranks of those values. In lesson 01, we read a CSV into a python Pandas DataFrame. You then nest your Index-command in: let Source = Table1, Partition = Table. They are extracted from open source Python projects. And, like the popular spreadsheet program, the basic units of operation are columns and rows of data that form tables. mean() # This doesn't work. I hope now you see that aggregation and grouping is really easy and straightforward in pandas… and believe me, you will use them a lot! Note: If you have used SQL before, I encourage you to take a break and compare the pandas and the SQL methods of aggregation. FreshPorts - new ports, applications. DataFrameNaFunctions Methods for handling missing data (null values). Solves an existing graph for a type of problem (e. rank(ascending=False) / float(x. Percentile Rank 百分比排名 The percentile ranks aggregation returns the percentile rankings for the values in the numeric field you specify. If no non-nan data, then return with rank 50 return nans_ranked if len(sub_df. Here are the examples of the python api pandas. dtypes (DataFrame) Data type in the database for each column. I upload table & join it with another table on some common key like Month_Id. Returns 0 if NR=1. Series is meant to store values, he definitely wants to groupby the values, if he make a clear request (I want to groupby the indexes), he would have a way to explicit that. function instead of pandas. to_pandas ¶ Export the current query result to a Pandas DataFrame object. It returns an array of indices of the same shape as a that index data along the given axis in sorted order. One goal of this document is to expose as much of Numpy and SciPy in a single document and a single Web page so that you have some ability to search for the function and capability you need. Data outliers…. Equal values are assigned a rank that is the average of the ranks of those values. Here are a couple of examples to help you quickly get productive using Pandas' main data structure: the DataFrame. randn(1000) In [204]: large_arr. Veterans, Service Members & Their Families. Bug in pandas. df1['Quantile_rank']=pd. Takeaways. Selecting pandas data using "iloc" The iloc indexer for Pandas Dataframe is used for integer-location based indexing / selection by position. This post has been updated to reflect the new changes. expanding_apply and pd. py", line 1247, in quantile. And, like the popular spreadsheet program, the basic units of operation are columns and rows of data that form tables. Notice how with method='min' , in the column min_rank_agency_seller_by_close_date , Julia's two home sales on August 1, 2012 are both given a tied rank of 1. The iloc indexer syntax is data. This article will discuss the basics of why you might choose to use a weighted average to look at your data then walk through how to build and use this function in pandas. Return the percentile rank of a row defined as (RK-1)/(NR-1), where RK is the RANK of the row and NR is the number of rows in the partition. I used Pandas here because of the « heavy » work needed to create the features, being very easy to do with Pandas and that would have been much more painful to do with numpy only for example. I'm guessing there is a built in "right way' to do a percentile analysis in pandas, but it is escaping me. APPLIES TO: SQL Server Azure SQL Database Azure SQL Data Warehouse Parallel Data Warehouse Calculates the relative rank of a row within a group of rows in SQL Server 2017. min() highest = rank. Very powerful and useful function. Typically, this aggregator will be used in conjunction with other. rank (pct = True) # Then, round it up, and multiply by 100 percentiles = 100 * percentiles. Data Wrangling with PySpark for Data Scientists Who Know Pandas with Andrew Ray 1. Update: Pandas version 0. This article explains why, and how to address this issue. bfill() where the fill within a grouping would not always be applied as intended due to the implementations’ use of a non-stable sort (:issue:`21207`) Bug in pandas. @therriault it 'worked' becuase the index that was the resultant of pd. I have seen a lot of versions, but I prefer a particular style since I feel the version I use is easy, intuitive, and scalable for different use cases. While you dont need a perfect 1600 on the exam to be admitted, successful applicants do tend to be in the top couple of percentiles. pandas 是基于NumPy 的一种工具，该工具是为了解决数据分析任务而创建的。Pandas 纳入了大量库和一些标准的数据模型，提供了高效地操作大型数据集所需的工具。pandas提供了大量能使我们快速便捷地处理数据的函数和方法。. 1 Introduction. Today I want to kick off a series of posts about slicing and dicing numerical data with Python. python下的Pandas中DataFrame基本操作（一），基本函数整理。方法 描述 DataFrame([data, index, columns, dtype, copy]) 构造数据框 属性和数据 方法 描述 DataFrame. quantile (self[, q, …]) Return group values at the given quantile, a la numpy. We'll be using pandas, a popular data analysis package for Python, to load and work with our data. I upload table & join it with another table on some common key like Month_Id. I think it would be great to implement a full SQL engine on top of pandas (similar to the SAS "proc sql"), and this new GroupBy functionality gets us closer to that goal. I realize I am computing percentile ranks constantly in my code. It is a very powerful and versatile package which makes data cleaning and wrangling much easier and pleasant. rank() where results did not scale to 100% when specifying method='dense' and pct=True. Return the percentile rank of a row defined as (RK-1)/(NR-1), where RK is the RANK of the row and NR is the number of rows in the partition. To use Pandas groupby with multiple columns we add a list containing the column names. The College Panda's ACT Essay: The Battle-tested Guide for ACT Writing [Nielson Phu] on Amazon. Entities located in space with a geometrical representation (such as points, lines or polygons) and a set of properties can be represented as features. preprocessing from sklearn_pandas import DataFrameMapper import pandas as pd Read the data. I want to calculate for each column, the percentile rank of todays price (last element in a column), against the full history of that particular column. rank(axis=0, numeric_only=None, method='average', na_option='keep', ascending=True)¶ Compute numerical data ranks (1 through n) along axis. Still, I generally have some issues with it. The oldest supported versions of all optional dependencies are now covered by automated tests (before, only the very latest. Analyzes both numeric and object series, as well as DataFrame column sets of mixed data types. heatmap (percentiles, vmin = 1, vmax = 99, annot = True). mean() Note: If single brackets are used to specify the column (e. 18 CategoricalIndex 0. Since the average percentile at this node is 0. By default, equal values are assigned a rank that is the average of the ranks of those values. nanpercentile(). frame """ DataFrame-----An efficient 2D container for potentially mixed-type time series or other labeled data series. stats as stats hv. statisticslectures. DataFrameNaFunctions Methods for handling missing data (null values). The last one (PERCENT_RANK) calculates percentile of records that fall within the current window. A value that identifies the number of significant digits for the returned percentage value. 집계(aggregation) 검색 결과에 다양한 연산 적용 RDBMS 의 groupby 와 유사 집계 종류에는 매트릭(metric), 버킷(bucket), 파이프라인(pipeline) 등이 있다. PERCENT_RANK. mean() lowest = rank. This doesn't seem to affect the alignment of key within people. Secured 706 All India rank with GATE score of 711 (99. 000 Elemente und daher zu groß, um den Median zu sammeln und zu finden. 317 versus 0. # First, calculate the percentile ranks of the whole dataset. Empty DataFrame Columns: [bidder_id, payment_account, address, outcome] Index: []. (GH5945) pd. There is a part 2 coming that will look at density plots with ggplot, but first I thought I would go on a tangent to give some examples of the apply family, as they come up a lot working with R. Significance Optional. [python] financial and economic data applications. The result is equal to a specific column value. groupby [source] ¶ Return group values at the given quantile, a la numpy. Apply function to multiple columns of the same data type; # Specify columns, so DataFrame isn't overwritten df[["first_name", "last_name", "email"]] = df. Documents Flashcards Grammar checker. ‘percentile(75. Puoi utilizzare Kibana per cercare, visualizzare e interagire con i dati archiviati negli indici ElasticSearch. max ([axis, numeric_only]). argsort(a, axis=-1, kind='quicksort', order=None)¶. Perform an indirect sort along the given axis using the algorithm specified by the kind keyword. I am trying to take a numeric array in F#, and rank all the elements so that ties get the same rank. The following are code examples for showing how to use numpy. Balance your portfolio with different projects. gt (other[, level, fill_value, axis]) Greater than of series and other, element-wise (binary operator gt ). algorithms""" Generic data algorithms. Generic data algorithms. Here are the examples of the python api pandas. 1 What’s New 3 1. Using pandas on the MovieLens dataset¶ To show pandas in a more "applied" sense, let's use it to answer some questions about the MovieLens dataset. But if X is skewed or has outliers, so does Z; in those cases, it is more robust to use percentile ranks. Data Wrangling: Clean, Transform, Merge, Reshape. Web開発はまさに芸術創造である!. One goal of this document is to expose as much of Numpy and SciPy in a single document and a single Web page so that you have some ability to search for the function and capability you need. Source code for pandas. There's been some data corruption! If you look at the Rank column, you'll notice that there are random dashes scattered throughout it. groupby("date"). Readability for Grouped Frequency Tables 11. Data Frames groupbymethod 27 Once groupby object is create we can calculate various statistics for each group: In [ ]: #Calculate mean salary for each professor rank: df. The kid can shoot the puck. Frequency Tables and Continuous Variables 12. The PERCENTILE function is a built-in function in Excel that is categorized as a Statistical Function. groupby pandas. First step is to read the CSV files and load them as Pandas frames. argsort trick, and then use groupby. I hope now you see that aggregation and grouping is really easy and straightforward in pandas… and believe me, you will use them a lot! Note: If you have used SQL before, I encourage you to take a break and compare the pandas and the SQL methods of aggregation. var ([ddof, split_every, split_out]) Compute variance of groups, excluding missing values. Bump Dots-Mixed-Sm Med Lg-Round Orange-Red-80-pk is a pack of assistive technology designed for those individuals with tow vision and the visually impaired to allow them to mark everyday items for identification by touch, and make their workplace more efficient and their daily life easier. One of the first steps of statistical analysis of your data is therefore to check the distribution of the different variables. Where Can You Start Your Career In data science? data science is an in-demand course to grab jobs in leading MNC’s. Returns: Series or DataFrame If q is an array, a DataFrame will be returned where the. Other readers will always be interested in your opinion of the books you've read. heatmap (percentiles, vmin = 1, vmax = 99, annot = True). For example, if the first two values tie for rank 1, the third value is assigned rank 3. I realize I am computing percentile ranks constantly in my code. 0-4), it appears to apply the function TWICE to the first row of a data frame. 0 (February 9, 2012) 45 pandas: powerful Python data analysis toolkit, Release 0. Return the relative rank of a row defined as NP/NR. At times, reality is not what we see or perceive. astype(dtype[, copy, errors]) 转换数据类型 DataFrame. Notice how with method='min' , in the column min_rank_agency_seller_by_close_date , Julia's two home sales on August 1, 2012 are both given a tied rank of 1. Grouped Frequency Distribution Tables 9. Group By FunctionThis is a quick look at Python groupby function. bfill() where the fill within a grouping would not always be applied as intended due to the implementations’ use of a non-stable sort (:issue:`21207`) Bug in pandas. The percentile rank of a score is the percentage of scores in its frequency distribution that are equal to or lower than it. 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: