Json Dict To Pandas Dataframe

to_html - 13 examples found. Pandas is one of those packages and makes importing and analyzing data much easier. Filtering In Pandas Dataframe July 13, 2019. Python's pandas library provide a constructor of DataFrame to create a Dataframe by passing objects i. 24- Pandas DataFrames: JSON File Read and Write Noureddin Sadawi. The following are code examples for showing how to use pandas. align not returning the sub-class (GH12983) Bug in aligning a Series with a DataFrame (GH13037) 18. DataFrame(). FirstName LastName MiddleName password username John Mark Lewis 2910 johnlewis2. So, How do I write a GeoPandas dataframe into a single file (preferably JSON or GeoPackage)?. Introduction. GitHub Gist: instantly share code, notes, and snippets. but as you can see the weather column needs to saperate 3 different columns. chunksize: int, optional. sort_index(). DataFrameで扱いたい. However, I did not find a starightforward way to read the JSON objects into DataFrames, so here is one way I had found to complete the task. This method works great when our JSON response is flat, because dict. Dear Python Users, I am using python 3. Values along which we partition our blocks on the index. DataFrameとして読み込むことができる。pandas. I followed the documentation scrupulously on Accessing and creating content | ArcGIS for Developers paragraph "i mporting data from a pandas data frame". I tried with read_json() but got the error: UnicodeDecodeError:'charmap' codec can't decode byte 0x81 in position 21596351:charac. keys() only gets the keys on the first "level" of a dictionary. Name Age 0 Mike 23 1 Eric 25 2 Donna 23 3 Will 23 Now I want to find Will and then print the details. I will also review the different JSON formats that you may apply. screen_name'], (i. Keys can either be integers or column labels. from_dict (). A JSON parser transforms a JSON text into another representation must accept all texts that conform to the JSON grammar. Doing this by default is problematic on many levels in the DataFrame constructor (though I wanted you to try it and see, maybe it IS possible to infer these types of multi-level dicts). Most pandas users quickly get familiar with ingesting spreadsheets, CSVs and SQL data. Filtering In Pandas Dataframe July 13, 2019. See matplotlib documentation online for more on this subject; If kind = 'bar' or 'barh', you can specify relative alignments for bar plot layout by position keyword. Let's see the example dataset to understand it better. Contents List ManipulationConcatenate two python listsConvert a python string to a list of charactersJSON ManipulationConvert a dictionary to a json stringConvert a json string back to a python dictionaryLoad a json file into a pandas data frameDataFrame ManipulationGroup by a column and keep the …. Hello, it will be nice if to_dict method could provide same orient parameter as to_json. Output: Method #4: By using a dictionary We can use a Python dictionary to add a new column in pandas DataFrame. The corresponding writer functions are object methods that are accessed like DataFrame. By the way, Pandas provides a convenient method for reading JSON into a DataFrame, pd. Sure, like most Python objects, you can attach new attributes to a pandas. When schema is a list of column names, the type of each column will be inferred from data. Indication of expected JSON string format. py of this book's code bundle:. Also, if ignore_index is True then it will not use indexes. Let's pretend that we're analyzing the file with the content listed below:. Programs always start from natural language. Pandas is one of those packages and makes importing and analyzing data much easier. screen_name'], (i. to_dict¶ DataFrame. A Data frame is a two-dimensional data structure, i. These are some python code snippets that I use very often. com/pulse/rdd-datarame-datasets. Apologies in advance if I missed it. The following are code examples for showing how to use pandas. Pandas is a great library for Python that makes it really easy to explore various kinds of data (JSON, CSV etc). Note that the dates in our JSON file are stored in the ISO format, so we're going to tell the read_json() method to convert dates:. You can rate examples to help us improve the quality of examples. JSON only support string keys, and therefore won't accept our tuple from Pandas multiindex. limit(limit) df = pd. def registerFunction (self, name, f, returnType = StringType ()): """Registers a lambda function as a UDF so it can be used in SQL statements. import pandas as pd stops = pd. json_normalize does a pretty good job of flatting the object into a pandas dataframe: from pandas. read_feather() to store data in the R-compatible feather binary format that is super fast (in my hands, slightly faster than pandas. Pandas DataFrame conversions work by parsing through a list of dictionaries and converting them to df rows per dict. One of the fastest way to convert Python json dict list to csv file with only 2 lines of code by pandas. align not returning the sub-class (GH12983) Bug in aligning a Series with a DataFrame (GH13037) 18. JSON is easy to read and write. They are extracted from open source Python projects. Still the same thing where it has 'results' and 'status' as headers while the rest of the json data appear as dicts in each cell. JSON only support string keys, and therefore won't accept our tuple from Pandas multiindex. Convert pandas multiindex dataframe to nested dictionary; Pandas: Convert DataFrame with MultiIndex to dict; How to rotate Pandas Dataframe MultiIndex Rows into MultiIndex Columns? pandas, how to add columns to a multiindex column DataFrame; Pandas: append row to DataFrame with multiindex in columns; How to subtract columns in a multiindex. I think the solution to this problem would be to change the format of the data so that it is not subdivided into 'results' and 'status' then the data frame will use the 'lat', 'lng', 'elevation', 'resolution' as the separate headers. Indication of expected JSON string format. ) It's not apparent to me how to do it, either from a short google search or skimming the docs. DataFrame. py Find file Copy path simonjayhawkins CLN: replace Dict with Mapping to annotate arguments ( #29155 ) 2ca2161 Oct 22, 2019. I welcome any and all feedback please. Values along which we partition our blocks on the index. Please help! { "Meta Data": { "1. A dataframe is basically a 2d numpy array with rows and columns, that also has labels for columns and rows. One of the fastest way to convert Python json dict list to csv file with only 2 lines of code by pandas. DataFrameをjsonにする方法。 to_json()を使う。 ただ、これの戻り値は、文字列strなので、json. Pandas can also be used to convert JSON data (via a Python dictionary) into a Pandas DataFrame. データフレームpandas. I generate a dataframe by joining the lists in a dictionary and then converting with pandas. dumps(dump string) is used when we need the JSON data as a string for parsing or printing. 20 Dec 2017. GitHub Gist: instantly share code, notes, and snippets. but as you can see the weather column needs to saperate 3 different columns. import pandas as pd pd. The type of the key-value pairs can be customized with the parameters (see below). Pandas is a powerful resource for you as a Data Scientist. In this tutorial, I'll show you how to export pandas DataFrame to JSON file using a simple example. to_json() pandas. We use cookies for various purposes including analytics. Is there a simple way of grabbing nested keys when constructing a Pandas Dataframe from JSON. They are extracted from open source Python projects. Convert a pandas dataframe to a json blob. Keys are used as column names. copy([deep]) 复制数据框 DataFrame. One of the fastest way to convert Python json dict list to csv file with only 2 lines of code by pandas. json") 人によりけりだとは思うが、簡単で直感的だ。. DataFrame(dict) - From a dict, keys for col-umns names, values for data as lists. FirstName LastName MiddleName password username John Mark Lewis 2910 johnlewis2. In this tutorial, I’ll show you how to export pandas DataFrame to JSON file using a simple example. read_json, but it relies on the JSON data being "flat". DataFrame, pandas. The following are code examples for showing how to use pandas. Load A JSON File Into Pandas. It’s available via pip install pandas. Questions: I am interested in knowing how to convert a pandas dataframe into a numpy array, including the index, and set the dtypes. DataFrame(), DataFrame. The term Panel data is derived from econometrics and is partially responsible for the name pandas − pan(el)-da(ta)-s. I learned how to load and read json file in pandas dataframe. DataFrameに列や行を追加(assign, appendなど) Pythonでメソッドチェーンを改行して書く; pandas. json') as json_file: dict_lst = json. DataFrameとして読み込むことができる。pandas. Does not try to reinvent the wheel and uses pandas json_normalize from typing import Dict Read and normalize a given JSON array into a pandas DataFrame. com/pulse/rdd-datarame-datasets. Pandas provides. Name Age 0 Mike 23 1 Eric 25 2 Donna 23 3 Will 23 Now I want to find Will and then print the details. You have two main ways of selecting data: select pandas rows by exact match from a list filter pandas rows by partial match from a list Related resources: Video Notebok Also pandas offers big. A JSON parser transforms a JSON text into another representation must accept all texts that conform to the JSON grammar. Pandas allow you to convert a list of lists into a Dataframe and specify the column names separately. JSON; Making Pandas Play Nice With Native Python Datatypes; Map Values; Map from Dictionary; Merge, join, and concatenate; Meta: Documentation Guidelines; Missing Data; MultiIndex; Pandas Datareader; Pandas IO tools (reading and saving data sets) pd. I created a Pandas dataframe from a MongoDB query. from_records(), and. , data is aligned in a tabular fashion in rows and columns. json') In this tutorial, I’ll review the steps to load different JSON strings into Python using pandas. Also, before using the to_dict() method, use set_index() to control the minor keys inside of each nested dictionary in the output. pandas documentation: Create a DataFrame from a dictionary of lists. In this post you can find information about several topics related to files - text and CSV and pandas dataframes. In this post I wanted to focus on how I used Pandas and Python to help me gather some insight into data that I’ve collected. Returns a GeoDataFrame when the geometry column is kept as geometries, otherwise returns a pandas DataFrame. Load A JSON File Into Pandas. Dictionary to DataFrame | Creating a Pandas DataFrame Using Scalar Values Trying to make a a Pandas DataFrame from a dictionary but getting the, "If using all scalar values, you must pass an index" error?. flatten_json on Python Package Index (PyPI) The final result as a Pandas dataframe: Get unlimited access to the best stories on Medium — and support writers while you're at it. If there are too many child structures in your dicts, such as a "list of dicts containing another list of dicts" times 2, then you need to restructure you data model. By typing the values in Python itself to create the DataFrame; By importing the values from a file (such as an Excel file), and then creating the DataFrame in Python based on the values imported; Method 1: typing values in Python to create pandas DataFrame. Note that the dates in our JSON file are stored in the ISO format, so we're going to tell the read_json() method to convert dates:. to_dict(outtype='split1234') is understood as df. How to read XML file into pandas dataframe using lxml This is probably not the most effective way, but it's convenient and simple. DataFrame(list(c)) Right now one column of the dataframe corresponds to a document nested within the original MongoDB document, now typed as a dictionary. to_dict (self, orient='dict', into=) [source] ¶ Convert the DataFrame to a dictionary. In addition to a name and the function itself, the return type can be optionally specified. So, How do I write a GeoPandas dataframe into a single file (preferably JSON or GeoPackage)?. This is because index is also used by DataFrame. A JSON parser transforms a JSON text into another representation must accept all texts that conform to the JSON grammar. , data is aligned in a tabular fashion in rows and columns. You can create a DataFrame from a list of simple tuples, and can even choose the specific elements of the tuples you want to use. Create dataframe :. The following are code examples for showing how to use pandas. Python Pandas Tutorial 2: Dataframe Basics Python Tutorial: Working with JSON Data using the json Module - Duration: 20:34. XMLファイルをPandas. json import json_normalize json_normalize(sample_object) However flattening objects with embedded arrays is not as trivial. DataFrameの構造と基本操作について説明する。. Note − Observe, df2 DataFrame is created with a column index other than the dictionary key; thus, appended the NaN's in place. DataFrame, pandas. Load JSON File # Create URL to JSON file (alternatively this can be a. This is because index is also used by DataFrame. One Dask DataFrame operation triggers many operations on the constituent Pandas DataFrames. Wow that must seem super obvious to people who have been working with pandas for a while, but I didn't realize I could just use the parsed json directly like that (thought I needed to use the from_json method). 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. apply; Read MySQL to DataFrame; Read SQL Server to Dataframe; Reading files into. how do I get the 'screen_name' from the 'user' key without flattening the JSON). from_dict (data, orient='columns', dtype=None, columns=None) [source] ¶ Construct DataFrame from dict of array-like or dicts. We can easily create a pandas Series from the JSON string in the previous example. to_jsonの基本的な使い方 JSON形式の文字列に変換. DataFrameは二次元の表形式のデータ(テーブルデータ)を表す、pandasの基本的な型。DataFrame — pandas 0. meta: pandas. to_pickle() on numeric data and much faster on string data). I followed the documentation scrupulously on Accessing and creating content | ArcGIS for Developers paragraph "i mporting data from a pandas data frame". read_json(). json import json_normalize print json_normalize(your_json) This will Normalize semi-structured JSON data into a flat table. One of the methods provided by Pandas is json_normalize. Whats New pandas: powerful Python data analysis toolkit, Release 0. I've a problem to import data from a pandas data frame on ArcGIS OnLine. Pandas DataFrame conversions work by parsing through a list of dictionaries and converting them to df rows per dict. 24- Pandas DataFrames: JSON File Read and Write Noureddin Sadawi. Please help! { "Meta Data": { "1. I created a Pandas dataframe from a MongoDB query. DataFrame 创建,索引,增添,删除 You can treat a DataFrame semantically like a dict of like-indexed Series objects. the keys in di refer to index values; the keys in di refer to df['col1'] values; the keys in di refer to index locations (not the OP’s question, but thrown in for fun. Still pandas API is more powerful than Spark. We use cookies for various purposes including analytics. Load JSON File # Create URL to JSON file (alternatively this can be a. read_json that enables us to do. If such data contained location information, it would be much more insightful if presented as a cartographic map. loads(response. Pandas is a great library for Python that makes it really easy to explore various kinds of data (JSON, CSV etc). , data is aligned in a tabular fashion in rows and columns. 2 documentation pandas. pandas / pandas / io / json / _json. If such data contained location information, it would be much more insightful if presented as a cartographic map. In this post we have learned how to write a JSON file from a Python dictionary, how to load that JSON file using Python and Pandas. DataFrameとして読み込むことができる。pandas. You can vote up the examples you like or vote down the ones you don't like. A DataFrame can be created from a list of dictionaries. They are extracted from open source Python projects. Whats New pandas: powerful Python data analysis toolkit, Release 0. Arithmetic operations align on both row and column labels. Load JSON File # Create URL to JSON file (alternatively this can be a. 《本文首发于公众号:深度学习与python》Python的卓越灵活性和易用性使其成为最受欢迎的编程语言之一,尤其是对于数据处理和机器学习方面来说,其强大的数据处理库和算法库使得python成为入门数据科学的首选语言。. Nested JSON structure means that each key can have more keys associated with it. frame with me: print(abc) cyl mpg 0 4 21. to_json DataFrame. Pandas is an open source library, providing high-performance, easy-to-use data structures and data analysis tools for Python. read_excel Read an Excel file into a pandas DataFrame. to_json() to denote a missing Index name, and the subsequent read_json() operation. to_dict (self, orient='dict', into=) [source] ¶ Convert the DataFrame to a dictionary. from_dict (). DataFrameとして読み込むことができる。pandas. It's basically a way to store tabular data where you can label the rows and the columns. json() I couldn't think of a way to remove these repeated loops and still have legible code. File path or object. with open('d:\\data\\json\\data. DataFrameのindex, columns属性を更新行名・列名をすべて変更 行名・列名をすべて変更 それぞれの方法についてサンプル. Parsing of JSON Dataset using pandas is much more convenient. DataFrame object. json") 人によりけりだとは思うが、簡単で直感的だ。. json') とすればよい。 そして、このDataFrameをJSONとして保存する場合、以下のように書けば良い。 df. json_normalize does a pretty good job of flatting the object into a pandas dataframe: from pandas. They are extracted from open source Python projects. Both NA and null values are automatically excluded from the calculation. DataFrameの構造と基本操作について説明する。. when I choose each column to turn data frame I can. to_json() pandas. Create dataframe :. A DataFrame is a table much like in SQL or Excel. astype(dtype[, copy, errors]) 转换数据类型 DataFrame. When schema is a list of column names, the type of each column will be inferred from data. Python DataFrame. In his post about extracting data from APIs, Todd demonstrated a nice way to massage JSON into a pandas DataFrame. How can I do this for dataframe with same datatype and different dataypes. I generate a dataframe by joining the lists in a dictionary and then converting with pandas. Here we will create a DataFrame using all of the data in each tuple except for the last element. An empty pandas. For this project, our goal is to retrieve data from an API and transform it into a Tableau Hyper file, a consumable format for analytics. 2 documentation ここではまずはじめにpandas. Here is a article that i wrote about RDD, DataFrames and DataSets and it contain samples with JSON text file https://www. to_dict()メソッドを使うとpandas. to_read()において引数orient='records'で読み書きできる形式。. DataFrame A distributed collection of data grouped into named columns. In this post we have learned how to write a JSON file from a Python dictionary, how to load that JSON file using Python and Pandas. 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. The second most common format I found online is, all the images are present inside a single directory and their respective classes are mapped in a CSV or JSON file, but Keras doesn’t support. They are extracted from open source Python projects. read_json — pandas 0. A little script to convert a pandas data frame to a JSON object. DataFrame() — pandas 0. tl;dr We benchmark several options to store Pandas DataFrames to disk. Does not try to reinvent the wheel and uses pandas json_normalize from typing import Dict Read and normalize a given JSON array into a pandas DataFrame. How to read XML file into pandas dataframe using lxml This is probably not the most effective way, but it's convenient and simple. I found that there were some nested json. DataFrame. This outputs JSON-style dicts, which is highly preferred for many tasks. This is similar to how a SAX parser handles XML parsing, it fires events for each node in the document instead of processing it al. read_feather() to store data in the R-compatible feather binary format that is super fast (in my hands, slightly faster than pandas. Categorical dtypes are a good option. Python DataFrame. By typing the values in Python itself to create the DataFrame; By importing the values from a file (such as an Excel file), and then creating the DataFrame in Python based on the values imported; Method 1: typing values in Python to create pandas DataFrame. DataFrame(dict) - From a dict, keys for col-umns names, values for data as lists. The axis labels are collectively c. DataFrame(dict):从字典对象导入数据,Key是列名,Value是数据. To interpret the json-data as a DataFrame object Pandas requires the same length of all entries. DataFrame with names, dtypes, and index matching the expected output. Does not try to reinvent the wheel and uses pandas json_normalize from typing import Dict Read and normalize a given JSON array into a pandas DataFrame. How to convert an xml file to pandas dataframe? Converting a pandas data-frame to a dictionary. dataframe: label A B C ID 1 NaN 0. Convert pandas multiindex dataframe to nested dictionary; Pandas: Convert DataFrame with MultiIndex to dict; How to rotate Pandas Dataframe MultiIndex Rows into MultiIndex Columns? pandas, how to add columns to a multiindex column DataFrame; Pandas: append row to DataFrame with multiindex in columns; How to subtract columns in a multiindex. I want this pandas df to convert to JSON. Load A JSON File Into Pandas. read_json()やpandas. optional Dict of functions for converting values in certain columns. In this tutorial we will learn how to assign or add new column to dataframe in python pandas. Work with JSON Data in Python Python Dictionary to JSON. DataFrameは二次元の表形式のデータ(テーブルデータ)を表す、pandasの基本的な型。DataFrame — pandas 0. Python | Pandas Dataframe. Let's design tests from natural language, its interactive behavior driven development. My first dataframe was created off a JSON file seen here. to_dict(outtype='series') which is quite strange but df. Let's pretend that we're analyzing the file with the content listed below:. By continuing to use Pastebin, you agree to our use of cookies as described in the Cookies Policy. Create a DataFrame from Dict of Series. to_dict() Python is a great language for doing data analysis, primarily because of the fantastic ecosystem of data-centric Python packages. How to read XML file into pandas dataframe using lxml This is probably not the most effective way, but it's convenient and simple. Creates a DataFrame from an RDD, a list or a pandas. Working with pandas¶ One of the most important features of xarray is the ability to convert to and from pandas objects to interact with the rest of the PyData ecosystem. 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. to_html extracted from open source projects. to_dict()メソッドを使うとpandas. Output: Method #4: By using a dictionary We can use a Python dictionary to add a new column in pandas DataFrame. to_read()において引数orient='records'で読み書きできる形式。. The term Panel data is derived from econometrics and is partially responsible for the name pandas − pan(el)-da(ta)-s. loads(response. Working with pandas¶ One of the most important features of xarray is the ability to convert to and from pandas objects to interact with the rest of the PyData ecosystem. Hi, I'm trying to create a pandas DataFrame from some json, which has a series of arrays. to_jsonの基本的な使い方 JSON形式の文字列に変換. keys() only gets the keys on the first "level" of a dictionary. I've written functions to output to nice nested dictionaries using both nested dicts and lists. Parsing a JSON string which was loaded from a CSV using Pandas. I followed the documentation scrupulously on Accessing and creating content | ArcGIS for Developers paragraph "i mporting data from a pandas data frame". to_dict (self, orient='dict', into=) [source] ¶ Convert the DataFrame to a dictionary. Output: Method #4: By using a dictionary We can use a Python dictionary to add a new column in pandas DataFrame. Seriesを辞書(dict型オブジェクト)に変換できる。pandas. to_json()没有给我足够的灵活性来实现我的目标. com/channel/UC2_-PivrHmBdspaR0klV. Objective: convert pandas dataframe to an aggregated json-like object. They are extracted from open source Python projects. read_json (r'Path where you saved the JSON file\File Name. keys() only gets the keys on the first "level" of a dictionary. The question is about constructing a data frame from a list of dicts, JSON to pandas DataFrame. GitHub Gist: instantly share code, notes, and snippets. DataFrame A distributed collection of data grouped into named columns. You can vote up the examples you like or vote down the ones you don't like. 2 documentation ここではまずはじめにpandas. See matplotlib documentation online for more on this subject; If kind = 'bar' or 'barh', you can specify relative alignments for bar plot layout by position keyword. Python Pandas Tutorial 2: Dataframe Basics Python Tutorial: Working with JSON Data using the json Module - Duration: 20:34. Pandas is one of those packages and makes importing and analyzing data much easier. Given a dataframe: id value 0 1 a 1 2 b 2 3 c I want to get a new dataframe that is basically the cartesian product of each row with each other row excluding itself: id value id_2 value_2 0 1 a 2 b 1 1 a 3 c 2 2 b 1 a 3 2 b. This method works great when our JSON response is flat, because dict. Converting a string to JSON is done with the function to_json(), and selecting a column of a pandas data frame is done with the following syntax: dataframe_name['column_name'] More helpful pandas syntax can be found in their Intro to Data Structures documentation. from_dict (data, orient='columns', dtype=None, columns=None) [source] ¶ Construct DataFrame from dict of array-like or dicts. In this post we have learned how to write a JSON file from a Python dictionary, how to load that JSON file using Python and Pandas. DataFrameは二次元の表形式のデータ(テーブルデータ)を表す、pandasの基本的な型。DataFrame — pandas 0. Arithmetic operations align on both row and column labels. The second most common format I found online is, all the images are present inside a single directory and their respective classes are mapped in a CSV or JSON file, but Keras doesn’t support. I want to convert a json file into a dataframe in pandas (Python). Extract the JSON data from the response with its json() method, and assign it to data. Hi, I'm trying to create a pandas DataFrame from some json, which has a series of arrays. DataFrameは二次元の表形式のデータ(テーブルデータ)を表す、pandasの基本的な型。DataFrame — pandas 0. The listings are under the "businesses" key in data. The object supports both integer- and label-based indexing and provides a host of methods for performing operations involving the index. DataFrameとして読み込んでしまえば、もろもろのデータ分析はもちろん、to_csv()メソッドでcsvファイルとして保存したりもできるので、pandas. You can by the way force the dtype giving the related dtype argument to read_table. Python | Pandas DataFrame. DataFrame (data=None, index=None, columns=None, dtype=None, copy=False) [source] ¶ Two-dimensional size-mutable, potentially heterogeneous tabular data structure with labeled axes (rows and columns). Let's design tests from natural language, its interactive behavior driven development. I use repeated list comprehensions in loops over the JSON object data; where data = response. Pandas DataFrame is two-dimensional size-mutable, potentially heterogeneous tabular data structure with labeled axes (rows and columns). Create a DataFrame from Dict of Series. Keys can either be integers or column labels. read_json(). Output: Method #4: By using a dictionary We can use a Python dictionary to add a new column in pandas DataFrame. DataFrameとして読み込むことができる。pandas. DataFarmeの行ラベルindex、列ラベルcolumns、値valuesをどのように辞書のkey, valueに割り当てるかの形式を指定できる。. I have a pandas dataframe containing windows 10 logs. 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: