# Pandas Compare Two Series

def answer_six(): statewiththemost=census_df. Download it once and read it on your Kindle device, PC, phones or tablets. Not only does it give you lots of methods and functions that make working with data easier, but it has been optimized for speed which gives you a significant advantage compared with working with numeric data using Python’s built-in functions. Getting Started. 原因是由于在python中 or 和 and 的声明需要 truth-values, 即真实的True或者False. Because the result is just another Series, we have all of the regular pandas functions at our disposal. You can do this by taking advantage of Pandas' pivot table functionality. Often while working with a big data frame in pandas, you might have a column with string/characters and you want to find the number of unique elements present in the column. Pandas offers several options but it may not always be immediately clear on when to use which ones. 1: Uses for the plot() method of the pandas Series. [Pandas] Difference between two datetime columns I've got a data frame in which there are two columns with dates in form of string. The following are code examples for showing how to use pandas. PANDAS is considered as a diagnosis when there is a very close relationship between the abrupt onset or worsening of OCD, tics, or both, and a strep infection. Now, we need to tokenize the sentences into words aka terms. Series is a one-dimensional labeled array capable of holding data of any type (integer, string, float, python objects, etc. Part 3 - Accessing data within a DataFrame. " A possible confusing point about pandas data types is that there is some overlap between pandas, python and numpy. To counter this, pass a single-valued list if you require DataFrame output. You can vote up the examples you like or vote down the ones you don't like. Also it gives an intuitive way to compare the dataframes and find the rows which are common or uncommon between two dataframes. The Cast the First Stone silver coins are one of five Biblical Series coins featured in Doc’s Holiday Collection. First,We will Check whether the two dataframes are equal or not using pandas. isin (self, values) [source] ¶ Check whether values are contained in Series. We have two dimensions - i. Pandas DataFrame Exercises, Practice and Solution: Write a Pandas program to combining two series into a DataFrame. I want to keep the series going by highlighting some other tasks that you commonly execute in Excel and show how you can perform similar functions in pandas. rolling_mean(). Learned the basics of plotting with matplotlib. Welcome to Part 5 of our Data Analysis with Python and Pandas tutorial series. Pandas is one of those packages and makes importing and analyzing data much easier. They are extracted from open source Python projects. In comparison with SAS PROC COMPARE which can operate on datasets that are on disk, this could be a constraint if you're using very large dataframes. df <- data. I have two Series of different lengths, and I want to get the indices for which both the indices and the amount are the same in both series. Compare two Pandas DataFrames. This is the beginning of a four-part series on how to select subsets of data from a pandas DataFrame or Series. 7, Python 3. 6, the second edition of this hands-on guide is packed with practical case studies that show you how to solve a broad set of data analysis problems effectively. So we have seen using Pandas – Merge, Concat and Equals how we can easily find the difference between two excel, csv’s stored in dataframes. This is just a pandas programming note that explains how to plot in a fast way different categories contained in a groupby on multiple columns, generating a two level MultiIndex. Pandas typically stores a Series’ values as a numpy array. They are extracted from open source Python projects. Pandas' origins are in the financial industry so it should not be a surprise that it has robust capabilities to manipulate and summarize time series data. Discover how to prepare data with pandas, fit and evaluate models with scikit-learn, and more in my new book, with 16 step-by-step tutorials, 3 projects, and full python code. First we will use Pandas iterrows function to iterate over rows of a Pandas dataframe. This tutorial will go over, 1) What is. get column name. > Summing two Series: 0 3 1 5 2 7 3 9 Name: , dtype: dtype(int) Return a new Series created by a map along a Series. Another way to merge two data frames is to keep all the data in the two data frames. Suppose you have a dataset containing credit card transactions, including: the date of the transaction; the credit card number; the type of the expense. GitHub Gist: instantly share code, notes, and snippets. If you want to use the standard library, you can use the datetime module, but it's a bit awful. Your humoristic style is awesome, keep up the good work! And you can look our website about proxy list. I don't know if i properly understood your problem. In this series of videos we create a function that plots any two time series on the same graph. The following code loads the olympics dataset (olympics. Features like gender, country, and codes are always repetitive. You can vote up the examples you like or vote down the ones you don't like. Also, operator [] can be used to select columns. Pandas Series. equals, This function allows two Series or DataFrames to be compared against each other to see if they have the same shape and elements. There's two gotchas to remember when using iloc in this manner: Note that. 23 2 3 Stack Exchange Network Stack Exchange network consists of 175 Q&A communities including Stack Overflow , the largest, most trusted online community for developers to learn, share their knowledge, and build their careers. The results demonstrated on each dataset provide a baseline of performance that can be used to compare more sophisticated methods, such as SARIMA, ETS, and even machine learning methods. The Hands-On, Example-Rich Introduction to Pandas Data Analysis in Python Today, analysts must manage data characterized by extraordinary variety, velocity, and volume. 7, Python 3. Python Pandas - Categorical Data - Often in real-time, data includes the text columns, which are repetitive. Method chaining, where you call methods on an object one after another, is in vogue at the moment. The second one we calculate the cumulative sum for this series - as you can see np. All questions are weighted the same in this assignment. subtract() function is used for finding the subtraction of dataframe. Pandas library in Python easily let you find the unique values. Another way to merge two data frames is to keep all the data in the two data frames. To compare two time series simply estimate the COMMON appropriate arima model for each time series separately AND then estimate it globally ( putting the second series behind the first ). The following are code examples for showing how to use pandas. Perform data analysis with python using the pandas library. In this series of videos we create a function that plots any two time series on the same graph. iloc returns a Pandas Series when one row is selected, and a Pandas DataFrame when multiple rows are selected, or if any column in full is selected. As a rule of thumb, if you calculate more than one column of results, your result will be a Dataframe. Series data. Since we have no idea were bayFails comes from, the only advice would be to read the Pandas docs since extracting data would be rountinely done by many programmers (I would guess by using itertuples or iteritems). The Python example below compares two pandas series objects of string literals and prints the resulting series on to the console. When you do mathematical operations on two pandas Series (e. I want to calculate the scipy. Decomposition provides a useful abstract model for thinking about time series generally and for better understanding problems during time series analysis and forecasting. The rest roam in unprotected areas nearby. To counter this, pass a single-valued list if you require DataFrame output. Pandas makes it simple to structure and manipulate data. from datetime import datetime import pandas as pd % matplotlib inline import matplotlib. Another way to merge two data frames is to keep all the data in the two data frames. Theres two gotchas to remember when using iloc in this manner: 1. Examples on how to plot data directly from a Pandas dataframe, using matplotlib and pyplot. Perform data analysis with python using the pandas library. DataFrame(data_tuples, columns=['Month','Day']) Month Day 0 Jan 31 1 Apr 30 2 Mar 31 3 June 30 3. Essentially, we would like to select rows based on one value or multiple values present in a column. csv), which was derrived from the Wikipedia entry on All Time Olympic Games Medals, and does some basic data cleaning. Have another way to solve this solution? Contribute your code (and comments) through Disqus. Use cpc in combination with a comparison operator and 500. Boolean Operators. In this exercise, noisy measured data that has some dropped or otherwise missing values has been loaded. a 2D data frame with height and width. StringsMethods object. corr (self, callable: callable with input two 1d ndarrays and returning a float. In 2007, Laura Wattenburg of babynamewizard. There’s two gotchas to remember when using iloc in this manner: Note that. It's always been a style of programming that's been possible with pandas, and over the past several releases, we've added methods that enable even more chaining. The results are returned as a separate pandas Series, consisting of test results as Boolean values - True and False. gt() is used to compare two series and return Boolean value for every. Thus in this example, the axis is referring to. Pandas is a software library written for the Python programming language. Pandas Series. Pandas is a NUMFocus sponsored project. But first, let’s generate two distinct data samples for comparison: >>> >>>. As a rule of thumb, if you calculate more than one column of results, your result will be a Dataframe. Compare the No. I want to calculate the scipy. To counter this, pass a single-valued list if you require DataFrame output. Pandas is one of those packages and makes importing and analyzing data much easier. Pandas is arguably the most important Python package for data science. In Pandas, can we compare the values of two columns in the same dataframe? Answer. Some of Pandas reshaping capabilities do not readily exist in other environments (e. We have two dimensions - i. What is the idea behind the fact that when inserting a Series that does not have the same index as the DataFrame, it will be conformed to the DataFrame's index? When creating a DataFrame from series, the resulting index covers all individual series indexes. Learn how to resample time series data in Python with pandas. Groupby output format – Series or DataFrame? The output from a groupby and aggregation operation varies between Pandas Series and Pandas Dataframes, which can be confusing for new users. Python Data Science Handbook , Essential Tools for Working With Data, by Jake VanderPlas. I wanted to know if this. 5 are all available. Pandas dataframe. Returns ----- pandas. The axis labels are collectively called index. If these are not the same between the 2 objects. The Python example below compares two pandas series objects of string literals and prints the resulting series on to the console. There are many other things we can compare, and 3D Matplotlib is. rolling_mean(). Accessing Data from Series with Position in python pandas; Retrieve Data Using Label (index) in python pandas; Accessing data from series with position: Accessing or retrieving the first element: Retrieve the first element. diff (self, periods=1) [source] ¶ First discrete difference of element. Part 2 - Basics. Pandas offers a wide variety of options for subset selection which necessitates multiple…. cumsum(skipna=False) which will result in: 0 2. Similar thing happened with AO series. Breaking up a string into columns using regex in pandas. In this post you will discover some quick and dirty recipes for Pandas to improve the understanding of your data in terms of it's structure, distribution and relationships. pyplot as pyplot. Country Company). You can also reuse this dataframe when you take the mean of each row. Introduction. You will have to access the data within the class. I have two data frames df1 and df2 and I would like to merge them into a single data frame. Example - Comparing elements from two series objects using ge(): # Python example program to compare pandas series objects. The Cast the First Stone silver coins are one of five Biblical Series coins featured in Doc’s Holiday Collection. Import modules. In this series of videos we create a function that plots any two time series on the same graph. 2013-04-23 12:08. corr (self, callable: callable with input two 1d ndarrays and returning a float. 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. In this article we will discuss different ways to select rows and columns in DataFrame. So if you take two columns as pandas series, you may compare them just like you would do with numpy arrays. corr ¶ Series. In the same way elements of a pandas Series can be compared with any Python sequence such as list and tuple. Artisan Series - Cornflower Blue (Renewed), do not wait for the end-of the 30 days to review your finances. Merge, join, and concatenate¶. The following are code examples for showing how to use pandas. If you're brand new to Pandas, here's a few translations and key terms. Specific objectives are to show you how to:. While a Pandas Series is a flexible data structure, it can be costly to construct each row into a Series and then access it. In this short guide, I’ll show you how to compare values in two Pandas DataFrames. In our case with real. Artisan Series - Cornflower Blue (Renewed) periodically to make certain that there aren't any extra costs. Part 2: Working with DataFrames, dives a bit deeper into the functionality of DataFrames. Getting Started. Often while working with a big data frame in pandas, you might have a column with string/characters and you want to find the number of unique elements present in the column. 2013-04-23 12:08. So we have seen using Pandas – Merge, Concat and Equals how we can easily find the difference between two excel, csv’s stored in dataframes. Use cpc in combination with a comparison operator and 500. In This tutorial we will learn how to access the elements of a series in python pandas. R language was once more powerful in doing mathematical statistics than Python. All other comparison operators also give expected results with operands (s,d), and seem to compare them as unscaled integers when the order is (d,s). Series([1,2]) s1 0 1 1 2 dtype: int64. pyplot as pyplot. frame(a=rnorm(5), b=rnorm(5), c=rnorm(5), d=rnorm(5), e=rnorm(5)) df[, c("a", "c","e")] or. This was the second episode of my pandas tutorial series. Elements of one pandas Series object can be compared with the corresponding elements of another pandas Series object, and checked whether the first element is greater than the second. There are times when working with different pandas dataframes that you might need to get the data that is ‘different’ between the two dataframes (i. ,g Comparing two pandas dataframes and getting the. labels_right (label, pandas. This will help ensure the success of development of pandas as a world-class open-source project, and makes it possible to donate to the project. What is the idea behind the fact that when inserting a Series that does not have the same index as the DataFrame, it will be conformed to the DataFrame's index? When creating a DataFrame from series, the resulting index covers all individual series indexes. Compare plans; Contact Sales Sponsor pandas-dev/pandas mjnicky changed the title compare two series objects compare two series objects ignores index Sep 12, 2014. such as ARIMA models that can help determine how similar they are. This is confirmed by the df. How to compare two rows of two pandas series? I have two python pandas series df10 and df5. [Pandas] Difference between two datetime columns I've got a data frame in which there are two columns with dates in form of string. Python is a great language for doing data analysis, primarily because of the fantastic ecosystem of data-centric python packages. The repo for the code is here. Pandas introduced two new types of objects for storing data that make analytical tasks easier and eliminate the need to switch tools: Series, which have a list-like structure, and DataFrames, which have a tabular structure. The goal is to compare two time series, and then look at summary statistics of the differences. Pandas is a NUMFocus sponsored project. All questions are weighted the same in this assignment. Download it once and read it on your Kindle device, PC, phones or tablets. DataFrame is a main object of pandas. append() to add rows in a dataframe. But first, let's generate two distinct data samples for comparison: >>> >>>. Dragoons regiment company name preTestScore postTestScore 4 Dragoons 1st Cooze 3 70 5 Dragoons 1st Jacon 4 25 6 Dragoons 2nd Ryaner 24 94 7 Dragoons 2nd Sone 31 57 Nighthawks regiment company name preTestScore postTestScore 0 Nighthawks 1st Miller 4 25 1 Nighthawks 1st Jacobson 24 94 2 Nighthawks 2nd Ali 31 57 3 Nighthawks 2nd Milner 2 62 Scouts regiment. Now I want to visualize the vote_count for the timestamps and do some analysis on that further. Groupby output format – Series or DataFrame? The output from a groupby and aggregation operation varies between Pandas Series and Pandas Dataframes, which can be confusing for new users. The "==" operator works for multiple values in a Pandas Data frame too. Not only does it give you lots of methods and functions that make working with data easier, but it has been optimized for speed which gives you a significant advantage compared with working with numeric data using Python’s built-in functions. ) function has provisions for creating data frames from lists. The following code loads the olympics dataset (olympics. Another way to merge two data frames is to keep all the data in the two data frames. Here are the Series: ipdb> s1 s1 000007720. Following two examples will show how to compare and select data from a Pandas Data frame. Python Pandas is a Python data analysis library. Series data. Pandas makes it simple to structure and manipulate data. Pandas offers a wide variety of options for subset selection which necessitates multiple…. In other words, a DataFrame is a matrix of rows and columns that have. Flatten hierarchical indices created by groupby. Part 3: Using pandas with the MovieLens dataset. If these are not the same between the 2 objects. I don't know if i properly understood your problem. Fears are mounting that Donald Trump's trade war could have a pair of unintended victims: the two giant pandas that are on lease to Washington D. Store the log base 2 dataframe so you can use its subtract method. Series as specialized dictionary¶. There's also arrow, a third party library for working with dates. Let us see examples of how to loop through Pandas data frame. Here, the column means the column heading, title, label, etc, and the series is a pandas. Often while working with a big data frame in pandas, you might have a column with string/characters and you want to find the number of unique elements present in the column. StringsMethods object. Related Posts: Pandas : Find duplicate rows in a Dataframe based on all or selected columns using DataFrame. Have you ever tried to do math with a pandas Series that you thought was numeric, but it turned out that your numbers were stored as strings? In this video, I'll demonstrate two different ways to. Pandas is a NUMFocus sponsored project. Or, if you have two strings such as "cat" and "hat" you could concatenate (add) them together to get "cathat. If you only need to check whether or not two dataframes are exactly the same, you should look at the testing capabilities within Pandas and Numpy:. Hello Readers, This post continues directly from exploring baby names in Part 3 of the Python and Pandas Series. In the same way elements of a pandas Series can be compared with any Python sequence such as list and tuple. In this tutorial we will do some basic exploratory visualisation and analysis of time series data. Pandas is one of those packages and makes importing and analyzing data much easier. 1: Uses for the plot() method of the pandas Series. This is part 2 of a four-part series on how to select subsets of data from a pandas DataFrame or Series. Series object -- basically the whole column for my purpose today. The goal is to compare two time series, and then look at summary statistics of the differences. Python Pandas - Working with Text Data - In this chapter, we will discuss the string operations with our basic Series/Index. Because the result is just another Series, we have all of the regular pandas functions at our disposal. In this post, we'll be going through an example of resampling time series data using pandas. 23 2 3 Stack Exchange Network Stack Exchange network consists of 175 Q&A communities including Stack Overflow , the largest, most trusted online community for developers to learn, share their knowledge, and build their careers. Steps to Compare Values in two Pandas DataFrames Step 1: Prepare the datasets to be compared. For example, there are fast element-wise addition and substring operations. I've benchmarked a lot of different operations, against quite a few different data sets (a variety of security types, granularity of data from tick up to inter-day, etc), and generally KDB is at least a magnitude faster than the best Python tests. *args - Additional arguments to pass to callable comp_func. To start, let's quickly review the fundamentals of Pandas data structures. Boolean Operators. @mlevkov Thank you, thank you! Have long been vexed by Pandas SettingWithCopyWarning and, truthfully, do not think the docs for. Quick Tip: Comparing two pandas dataframes and getting the differences Posted on January 3, 2019 January 3, 2019 by Eric D. You can compare bullion dealers and find the lowest prices for gold coins at FindBullionPrices. 0 1 NaN 2 NaN 3 NaN 4 NaN dtype: float64. To counter this, pass a single-valued list if you require DataFrame output. We will learn how to create a pandas. When you do mathematical operations on two pandas Series (e. Free Bonus: Click here to download an example Python project with source code that shows you how to read large. DataFrame object from an input data file, plot its contents in various ways, work with resampling and rolling calculations, and identify correlations and periodicity. Accessing Data from Series with Position in python pandas; Retrieve Data Using Label (index) in python pandas; Accessing data from series with position: Accessing or retrieving the first element: Retrieve the first element. The code below names your cohorts in a format like 2019-05 (that’s May 2019). Now that we have a robust framework for grid searching simple model hyperparameters, let’s test it out on a suite of standard univariate time series datasets. In this article, I analyze the race that took place in stage 14 of the 2019 Tour de France in a Jupyter Notebook using Python, Pandas and Plotly and based on the Strava performance data published by Steven Kruijswijk, Thomas de Gendt, Thibaut Pinot and Marco Haller. Now we would like to combine AO and NAO Series. The Cast the First Stone silver coins are one of five Biblical Series coins featured in Doc’s Holiday Collection. In This tutorial we will learn how to access the elements of a series in python pandas. In this tutorial you’re going to learn how to work with large Excel files in Pandas, focusing on reading and analyzing an xls file and then working with a subset of the original data. Series¶ In Arrow, the most similar structure to a pandas Series is an Array. As someone who works with time series data on almost a daily basis, I have found the pandas Python package to be extremely useful for time series manipulation and analysis. Is s[1:] == s[:-1] the main use case though, where you need to have this not completely intuitive == operator?. ghost assigned jreback Apr 23, 2013 This comment has been minimized. Perform data analysis with python using the pandas library. We wanted to optimize how we assigned personnel to specific jobs. Some of Pandas reshaping capabilities do not readily exist in other environments (e. In this tutorial we will learn how to get the list of column headers or column name in python pandas using list() function with an example. The goal is to compare two time series, and then look at summary statistics of the differences. Two-thirds of the world’s wild pandas live in 67 nature reserves in the bamboo-rich, old-growth forests above China’s Sichuan Basin. Pandas is free software released under the three-clause BSD license. csv), which was derrived from the Wikipedia entry on All Time Olympic Games Medals, and does some basic data cleaning. The first two are ways to apply column-wise functions on a dataframe column: use_column: use pandas column operation; use_panda_apply: use pandas apply function; Next are the three different approaches for accessing the variable by using pandas indexing methods inside a for-loop: 3. You can vote up the examples you like or vote down the ones you don't like. We have two dimensions – i. Numpy and Pandas have implementations of many efficient operations for numpy arrays. Introduction. For example, let's create a simple Series in pandas:. In our case with real. Specific objectives are to show you how to:. , data is aligned in a tabular fashion in rows and columns. In this post, we'll be going through an example of resampling time series data using pandas. else I am trying to apply a filter on a series of values stored in a pandas series object. The repo for the code is here. Artisan Series - Cornflower Blue (Renewed), do not wait for the end-of the 30 days to review your finances. Pandas is one of those packages and makes importing and analyzing data much easier. First, take the log base 2 of your dataframe, apply is fine but you can pass a DataFrame to numpy functions. Pandas library in Python easily let you find the unique values. Thank you to all for the positive feedback. Learning pandas - Second Edition: High performance data manipulation and analysis using Python - Kindle edition by Michael Heydt. The first element in the series is assigned the index 0, while the last element is at index N-1, where N is the total number of elements in the series. human-readable reporting on the difference between two dataframes. Resampling time series data with pandas. If your data had only one column, ndim would return 1. Look at your account KitchenAid RRK150CO 5 Qt. Pandas Series example DataFrame: a pandas DataFrame is a two (or more) dimensional data structure - basically a table with rows and columns. I wanted to know if this. equals, This function allows two Series or DataFrames to be compared against each other to see if they have the same shape and elements. append() to add rows in a dataframe. Out of interest, is there a way to figure out whether s1 and s2 are a view on the same underlying series and in this case have s1 == s2 do the current comparison. As was the case with Series, we can use the associated object's arithmetic method and pass any desired fill_value to be used in place of missing entries. Method Chaining. Pandas is a NUMFocus sponsored project. Rather than giving a theoretical introduction to the millions of features Pandas has, we will be going in using 2 examples: 1) Data from the Hubble Space Telescope. This is just a pandas programming note that explains how to plot in a fast way different categories contained in a groupby on multiple columns, generating a two level MultiIndex. Is s[1:] == s[:-1] the main use case though, where you need to have this not completely intuitive == operator?. Data Structures. In this tutorial we will be covering difference between two dates in days, week , and year in pandas python with example for each. The following are code examples for showing how to use pandas. Fears are mounting that Donald Trump's trade war could have a pair of unintended victims: the two giant pandas that are on lease to Washington D. This is basically an amalgamation of my two previous blog posts on pandas and SciPy. I have been very excited by the response to the first post in this series. Pandas library in Python easily let you find the unique values. The pandas. I had 2 dataframes whose values are equal. You can also save this page to your account. a 2D data frame with height and width. However, there are times when you will have data in a basic list or dictionary and want to populate a DataFrame. The basic Pandas structures come in two flavors: a DataFrame and a Series. Rather than giving a theoretical introduction to the millions of features Pandas has, we will be going in using 2 examples: 1) Data from the Hubble Space Telescope. I want to calculate the scipy. Have another way to solve this solution? Contribute your code (and comments) through Disqus. Learn how to resample time series data in Python with pandas. The recommended approach for multi-dimensional (>2) data is to use the Xarray Python library. Assignment 2 - Pandas Introduction. Now we would like to combine AO and NAO Series. Now that we're clear that DataFrames are made up of Series, let's return to our problem. A pandas Series Object is more flexible as you can use define your own labeled index to index and access elements of an array. The following code loads the olympics dataset (olympics. Now Python becomes neck and neck with its special package pandas, which needs more maturity to thoroughly outpace its rival. This is used to combine two series into one. array_equal¶ numpy. In Pandas, can we compare the values of two columns in the same dataframe? Answer. Is s[1:] == s[:-1] the main use case though, where you need to have this not completely intuitive == operator?. About The NOAA Precipitation Data Used In This Lesson. Python Data Science Handbook , Essential Tools for Working With Data, by Jake VanderPlas. csv), which was derrived from the Wikipedia entry on All Time Olympic Games Medals, and does some basic data cleaning. Stacked bar plot with two-level group by, normalized to 100%. You can vote up the examples you like or vote down the ones you don't like. What happens when you compare 2 pandas Series. 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. In addition to the above functions, pandas also provides two methods to check for missing data on Series and DataFrame objects. I encourage you to review it so that you're aware of the concepts. 0 1 NaN 2 NaN 3 NaN 4 NaN dtype: float64. Pandas Exercises, Practice, Solution: pandas is a Python package providing fast, flexible, and expressive data structures designed to make working with relational or labeled data both easy and intuitive. a 2D data frame with height and width. This is because pandas understood the data in the date column as strings, not as dates. 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: