Pandas Crosstab Vs Pivot

Try building a pivot table that shows the max temperature for each city and month based on the raw data in the table below. Pivot and Crosstab Queries. Therefor, it can’t deal with duplicate values for one index/column pair. The information from each crosstab query is used to create metrics that populate a dashboard report. So, if you build a crosstab that shows "Year" accross the top, and "Country" and "City" down the side, with "Quantity" as the displayed measure, you would set our data item to use the following expression:. We took a look at how to create cross-tab queries in SQL Server 2000 in this previous tip and in this tip we will look at the SQL Server PIVOT. A pivot table provides a summary of large amounts of data. • Python for DataAnalysis• Wes McKinney• Lead developer ofpandas• Quantitative FinancialAnalyst 4. In this part, we will continue to deep dive further into the Pandas library and look at how it can be used along with other Python functions for. Create a dataframe. pivot (self, index=None, columns=None, values=None) [source] ¶ Return reshaped DataFrame organized by given index / column values. crosstab and the Pandas pivot table seem to provide the exact same functionality. I have a dataframe such as the one below that I pivoted to apply some. I wouldnt use Panda to browse data (but you could), and I wouldn't use Excel as a tool to clean up data or automate tasks (but you could). Along with 16+ years of hands on experience he holds a Masters of Science degree and a number of database certifications. Read Excel column names We import the pandas module, including ExcelFile. The previous pivot table article described how to use the pandas pivot_table function to combine and present data in an easy to view manner. pivot_table - Create a pivot table - HERE pandas. By default, a pivot table uses general formatting. It usually involves aggregation of data e. An example. It is not a as stark a comparison as UNPIVOT vs CROSS APPLY, but using an old-school CrossTab query form is again more readbale (in my opinion) and will generally be equivalent or better on performance than using PIVOT. Ask Question Asked 3 years, 5 months ago. Pivot Tables. However, there are limited options for customizing the output and using Excel's features to make your output as useful as it could be. The drag and drop functions make it easy to aggregate and filter the data in any way. By default computes a frequency table of the factors unless an. The information from each crosstab query is used to create metrics that populate a dashboard report. The solution is easy; however, it takes time to find the correct property or “discover” the correct button that does the trick. 利用python的pandas库进行数据分组分析十分便捷,其中应用最多的方法包括:groupby、pivot_table及crosstab,以下分别进行介绍。 Pandas中最为常 Pandas分组统计函数:groupby、pivot_table及crosstab - CSDN博客. There is a similar command, pivot, which we will use in the next section which is for reshaping data. We took a look at how to create cross-tab queries in SQL Server 2000 in this previous tip and in this tip we will look at the SQL Server PIVOT. Read More: Pandas Reference (fillna) #4 – Pivot Table. While it is exceedingly useful, I frequently find myself struggling to remember how to use the syntax to format the output for my needs. Alternatively, given the crosstab output above, you can present it in a different format that may be easier for further analysis. The "Crosstab, Rotate, Pivot" article from earlier this year should have been named "Crosstab, Rotate, Pivot, Denormalize" because it showed how to take a perfectly nice table like this and muck it up to look like this without hard-coding any of the data values in the SQL. How-to install MinGW on Windows. # re: Understanding SQL 2005's new PIVOT clause You can think of this like a two dimensional GROUP BY. Sometimes, the easiest of report requests give report developers the hardest time. They are extracted from open source Python projects. We will learn. Pandas Datetime: Exercise-25 with Solution. 03/16/2017; 5 minutes to read +2; In this article. Working with Missing Data Working with missing Data - HERE Working with Tables pandas. In this post, I'll exemplify some of the most common Pandas reshaping functions and will depict their work with diagrams. I want to calculate the scipy. pandas に関して学ん pandasのcrosstabでクロス集計(カテゴリ毎の出現回数・頻度を算出) pandasでstack, unstack, pivotを使って. Any Series passed will have their name attributes used unless row or. size) will construct a pivot table for each value of X. Power Pivot is a feature of Microsoft Excel. Asking for help, clarification, or responding to other answers. crosstab(index, columns, values=None, rownam 博文 来自: 学习笔记 pandas列联表 crosstab 透视图pivot_table总结. Python Pandas Tutorial 13. Excel easily summarizes flat, tabular data. , where the months are represented by columns. Pivot tables can be formatted in a variety of ways, including the border styles and a full range of options for formatting text, numbers, cell color and pattern. If you don't want create a new data frame after sorting and just want to do the sort in place, you can use the argument "inplace = True". Using pivot table user can do quick data analysis and it is quite easy to use as well. proximacentauri October 2018. Pandas makes it very easy to output a DataFrame to Excel. crosstab and the Pandas pivot table seem to provide the exact same functionality. The library provides. Thanks for contributing an answer to Stack Overflow! Please be sure to answer the question. By selecting items in the detail, you're only selecting from the master implicitly. I know how to convert that. We will learn. I want to show MULTIPLE (not jus. Thing is, I didn’t care about the Payment Type, and never would. While pivot tables may display the same data as crosstabs can, pivot tables let you drag, drop and otherwise rearrange data to create additional reports right on the spot. The Pandas crosstab and pivot has not much difference it works almost the same way. The function pivot_table() can be used to create spreadsheet-style pivot tables. A Pivot Table Slicer enables you to filter the data when you select one or more than one options in the Slicer box (as shown below). This article will focus on explaining the pandas pivot_table function and how to use it for your data analysis. pandas pivot table to data frame. 470 in all other columns). Pivot table rows or columns can be hidden to focus on other parts of the table. The solution is easy; however, it takes time to find the correct property or "discover" the correct button that does the trick. melt()는 ID가 칼럼으로 존재하는 반면에, pivot_table()은 ID가 index로 들어갔습니다. In the examples, I will use pandas to manipulate the data and use it to drive the visualization. Uses unique values from specified index / columns to form axes of the resulting DataFrame. A pivot table provides a summary of large amounts of data. Let's take a look. Pivot tables give us a different way to see our data. The good news is that you certainly can create two pivot tables in a single worksheet and even multiple pivot tables in the same Excel Worksheet, in case you wish to do so. I know how to convert that. unstack (self, level=-1, fill_value=None) [source] ¶ Pivot a level of the (necessarily hierarchical) index labels, returning a DataFrame having a new level of column labels whose inner-most level consists of the pivoted index labels. These approaches are all powerful data analysis tools but it can be confusing to know whether to use a groupby, pivot_table or crosstab to build a summary table. pivot_table(xgroup, rows='Y', cols='Z', margins=False, aggfunc=numpy. If you don't want create a new data frame after sorting and just want to do the sort in place, you can use the argument "inplace = True". Learn more about Teams. Fields can be added to crosstabs as row groups or column groups. A column or expression that will display as the first column in the pivot table. We use pivot queries when we need to transform data from row-level to columnar data. Along with 16+ years of hands on experience he holds a Masters of Science degree and a number of database certifications. But the answer only answers the specific question with little explanation. You can vote up the examples you like or vote down the ones you don't like. Sun 23 October 2011 Fast and easy pivot tables in pandas 0. Pandas will allow you to use any function that is part of Numpy or even create your own function. Create a crosstab table by company and regiment. Unlike other query types, the Query Designer for Crosstabs has an extra Crosstab row to specify each of the columns. Creating Cross Tab Queries and Pivot Tables in SQL For those times when you absolutely, positively got to perform a cross tab query in SQL, Keith Fletcher's T-SQL stored procedure will allow you to do it "on the fly". Sometimes, the easiest of report requests give report developers the hardest time. They will save you a lot of time by allowing you to quickly summarize large amounts of data into a meaningful report. If you're new to pandas, you might want to first read through 10 Minutes to pandas to familiarize yourself with the library. Pandas はまさにうってつけのツールですが、分析に集中できるようにはなっていません。 代わりに、ユーザは複雑で過剰な構文を覚えることを強要されます。 私は Minimally Sufficient Pandas の定義として、以下を提案します。. Create a cross tab / pivot table in Python. Counting the number of observations by regiment and category. Therefore we need to use full path of column like df. Our data table contains sales amount of a company by year (rows) and by continent (columns). Hey, it looks like just a crosstab report? So why the title? What is the difference between a crosstab and pivot reports? When we do a crosstab report inside VFP using either _GenXTab, FastXTab or any other cross tabulation utility, then we are placing the cross-tabbed result into a new cursor/table. Try building a pivot table that shows the max temperature for each city and month based on the raw data in the table below. Import Modules. Crosstab - Duration: Microsoft Excel Pivot Table Tutorial for Beginners. pivot(index='date', columns='item', values='status') 2. proximacentauri October 2018. Read the basics first if you are not familiar with this: PostgreSQL Crosstab Query; The original id is carried over as "extra column". Dependent Variables Chapter 7: Independent vs Dependent Variable Prof. Highlight the cell where you'd like to create the pivot table. pivot_table (data, values=None, index=None, columns=None, aggfunc='mean', fill_value=None, margins=False, dropna=True, margins_name='All', observed=False) [source] ¶ Create a spreadsheet-style pivot table as a DataFrame. Builtin Python functions vs Pandas methods with the same name. 0 Performance and Securing Data With Microsoft SQL Server 2008 R2. What I wanted was a much smaller source table that collapsed the PaymentType column and aggregated. As you can see in the cross tab screen shot, the amounts are just repeated for each province - the pivot table screen shot shows how it. Namely how the columns look. Crosstabs are advanced tables where two or more fields are tabulated one against the other, and are h eavily used in survey research. For example, we might have data like. Pivot table rows or columns can be hidden to focus on other parts of the table. This Python course will get you up and running with using Python for data analysis and visualization. Pandas Doc 1 Table of Contents. insert( , { // options writeConcern: , ordered: } ) You may want to add the _id to the document in advance, but. Generating Frequency Tables. Re: How to create a crosstab in Excel I have have changed your Pivot Table slightly in the attached. SQL PIVOT is an operator which can be used to rotate rows into separate columns. Kaci Page 1 of 7 Chapter Seven: Cross Tabulations and Correlations between variables In this chapter, we'll look at how PASW for Windows can be used to create contingency tables, oftentimes called cross tabulations (or crosstabs), bivariate, or two-variable. With only a couple of mouse clicks, you can summarize and analyze your data from different perspectives. Learn more about Teams. *pivot_table summarises data. Uses unique values from specified index / columns to form axes of the resulting DataFrame. We will learn how to create. In this course, you'll learn how to leverage pandas' extremely powerful data manipulation engine to get the most out of your data. I'd like to be able to programatically change the data in the data tab so that when the file is opened, the pivot table can be updated. This chart can also show various calculations on the values of the measure field such as running total, percentage total, etc. how to edit kvp data as crosstab/pivot. , June 99th). API Reference. We will learn. Typically, I use the groupby method but find pivot_table to be more readable. Inserting a variable in MongoDB specifying _id field. pivot (data, index=None, columns=None, values=None) [source] ¶ Return reshaped DataFrame organized by given index / column values. pandas includes automatic tick resolution adjustment for regular frequency time-series data. If you're new to pandas, you might want to first read through 10 Minutes to pandas to familiarize yourself with the library. 0 documentation. In this post, we’ll walk through how to use sqlite3 to create, query, and update databases. Thanks in advance. Which shows the sum of scores of students across subjects. import pandas as pd import numpy as np. Anyone familiar with crosstab software is already familiar with two-way tables. Sun 16 October 2011 Speeding up pandas's file parsers with Cython. They put flexibility and analytical power in your hands. 0 documentation; 出現回数ではなく、カテゴリごとの平均値などを算出したい場合はピボットテーブルpandas. If you had created the original sales chart using a pivot table, you would be privy to drill-down and other features that let you look deeper into the. Now, let's talk about Pivot Tables. , in an externally created twinx), you can choose to suppress this behavior for alignment purposes. txt) or read book online for free. 2) The default aggregate function is average (mean) in case of pivot while "count" is the default for crosstab. pivot_table on a data set with 100000 entries and 25 groups. Crosstabs can be used with any level of data (nominal, ordinal, interval, or ratio), and usually display the summarized data, contained in the report variables, in the form of a dynamic table. Go back to Pivot Tables to learn how to create this pivot table. If you selected Crosstab when you created a view, as described in Ad Hoc View Types, the following sections explain tasks specific to your crosstab development. Mar 28, 2016 · Both the pandas. It usually involves aggregation of data e. You may want to index ptable using the xvalue. csv") \pima" is now what Pandas call a DataFrame object. Time Series Analysis: The major disadvantage of using tableau desktop or tableau prep is that it doesn't provide any feature to perform time series analysis. You need to aggregate the data before creating the pivot table. '' 没有指定values,默认为count数量, 列 行. But, on the other side. python使用pandas的交叉表crosstab出现问题 对数据分析时使用到pandas,下面的代码是从数据库中获取数据再转换成DataFrame结构 sql = 'select * from content;' cur. unstack¶ DataFrame. In the crosstab query, which is a special type of Totals query, the Total row that appears in the query design grid will always be active. I am keeping data in key/value pairs. The moment you add numbers to your pivot table, you'll want to adjust the formatting. The equivalency of pivot_table and pd. Crosstabs can be used with any level of data (nominal, ordinal, interval, or ratio), and usually display the summarized data, contained in the report variables, in the form of a dynamic table. Mar 23, 2017 · For anyone who is still interested in the difference between pivot and pivot_table, there are mainly two differences: pivot_table is a generalization of pivot that can handle duplicate values for one pivoted index/column pair. As an alternative, construct a dataframe and use df. Create a pivot table of group score counts, by company and regiments. Merge large datasets taken from various data file formats. What about going the other way?. Cross tab in python pandas (cross table) In this tutorial we will learn how to create cross tab in python pandas ( 2 way cross table or 3 way cross table or contingency table) with example. You will pivot, unstack. Let's take a look. crosstab(data. Now we can feed it to crosstab() using the safe 2-parameter form for missing attributes. The second is the data in the cross tab format. You can vote up the examples you like or vote down the ones you don't like. pivot_table (data, values=None, index=None, columns=None, aggfunc='mean', fill_value=None, margins=False, dropna=True, margins_name='All', observed=False) [source] ¶ Create a spreadsheet-style pivot table as a DataFrame. For data analysis, pandas is phenomenally more agile than SQL, letting you easily create many types of plots, compute statistics, etc. This article will focus on explaining the pandas pivot_table function and how to use it for your data analysis. insert( , { // options writeConcern: , ordered: } ) You may want to add the _id to the document in advance, but. In this post, I'll exemplify some of the most common Pandas reshaping functions and will depict their work with diagrams. Excel: Pivot tables are my go-to #1 in Excel. pivot (self, index=None, columns=None, values=None) [source] ¶ Return reshaped DataFrame organized by given index / column values. Pivot Tables Explained. If you can not find a good example below, you can try the search function to search modules. How-to install MinGW on Windows. I am using Filemaker Pro 14 Advanced (Mac) and I'm having problems with the setup of Layouts/Reports. pandas is very fast as I've invested a great deal in optimizing the indexing infrastructure and other core algorithms related to things such as this. A couple of weeks ago in my inaugural blog post I wrote about the state of GroupBy in pandas and gave an example application. Create Two Pivot Tables in Single Worksheet. It is not exactly what you wanted but hopefully gives you what you need. pivot_table and crosstabs rows and cols keyword. This tutorial covers pivot and pivot table functionality in pandas. Create a cross tab / pivot table in Python. pivot_table()を使う。以下の記事を参照。 関連記事: pandasのピボットテーブルでカテゴリ毎の統計量などを算出; ここでは、. Pivot is used to transform or reshape dataframe into a different format. Use the table to display fields from a dataset either as detail data or as grouped data in a grid or free-form layout. To see what it can do and how, browse the examples below or check out the documentation wiki for full details. At its core, it is. By default ``crosstab`` computes a frequency table of the factors: unless an array of values and an aggregation function are. pandas: a Foundational Python Library for Data Analysis and Statistics Wes McKinney F Abstract—In this paper we will discuss pandas, a Python library of rich data structures and tools for working with structured data sets common to statistics, finance, social sciences, and many other fields. This concept is probably familiar to anyone that has used pivot tables in Excel. There is a similar command, pivot, which we will use in the next section which is for reshaping data. If you'd prefer to work with your data as a list, you can. Besides Power Query there are several other ways to add data to the Data Model, you can use Excel’s get external data features or even directly import in Power Pivot. That's pretty easy to do, but I can't open your attachment (my Tableau is an older version at the moment). A Pivot Table Slicer enables you to filter the data when you select one or more than one options in the Slicer box (as shown below). unstack¶ DataFrame. Write a Pandas program to create a heatmap (rectangular data as a color-encoded matrix) for comparison of the top 10 years in which the UFO was sighted vs each Month. Python Pandas Pivot Table Index location Percentage calculation on Two columns – XlsxWriter pt2 Python Bokeh plotting Data Exploration Visualization And Pivot Tables Analysis Save Python Pivot Table in Excel Sheets ExcelWriter Save Multiple Pandas DataFrames to One Single Excel Sheet Side by Side or Dowwards – XlsxWriter. Other than crosstab to table you will also learn some. Understanding exactly how a pivot table works will really help you unleash the full potential of this amazing tool. See: Pivot on Multiple Columns using Tablefunc; Your question leaves room for interpretation. Create a pivot table of group score counts, by company and regiments. Because we were awed by its grandeur, we didn't initially want to add our own contribution. Re: How to create a crosstab in Excel I have have changed your Pivot Table slightly in the attached. This tool. Convert this Weigth/Score DataFrame into List of Coulmn name with sorted according to their Weigth/Score Matrix Format DataFrame in Python Pandas; Pandas: How to analyse data with start and end timestamp? How to convert a column with values like 6200000 to 6. Prerequisites. Other then displaying or reporting on data there isn't a real need for this type of query. Pivot tables give us a different way to see our data. This article provides introduction to the PIVOT and UNPIVOT relational operators introduced in Sql Server 2005 with extensive list of examples. crosstab and the Pandas pivot table seem to provide the exact same functionality. Some of Pandas reshaping capabilities do not readily exist in other environments (e. Koop, DSC 201, Fall 2016 12 See Table 9-2 for a summary of pivot_table methods. Pandas is an open-source, BSD-licensed Python library providing high-performance, easy-to-use data structures and data analysis tools for the Python programming language. In pivot table display can I repeat the aggregate row values (on the left, not the data)? In Access 2007 the product does a neat display of not repeating those values, but I have unique need to repeat them on every row. There is a similar command, pivot, which we will use in the next section which is for reshaping data. Pandas Datetime: Exercise-24 with Solution. Product], columns=df. In this tip we look at how you can dynamically create the pivot command to handle these unknown values. To ensure that it calculates the right values, you need to include the pivot-columns in the "For" clause. how to edit kvp data as crosstab/pivot. You can imagine that the client side pivot grid displays the first 3 columns as hierarchies which can be collapsed and expanded. It helps you in trying out. The function pivot_table() can be used to create spreadsheet-style pivot tables. Published on August 28, 2019: In this video, we will learn to append two dataframes using python. Cheat sheet for the python pandas library. It is a T-SQL operator introduced in SQL Server 2008. Pandas makes it very easy to output a DataFrame to Excel. I wouldnt use Panda to browse data (but you could), and I wouldn't use Excel as a tool to clean up data or automate tasks (but you could). The pandas library is very powerful and offers several ways to group and summarize data. unstack¶ DataFrame. crosstab(index, columns, values=None, rownames=None, colnames=None, aggfunc=None, margins=False, dropna=True, normalize=False) Compute a simple cross-tabulation of two (or more) factors. Reshape data (produce a "pivot" table) based on column values. Solved: Hi All, Could you please explain and give an example for CROSSTABLE Vs CrossTab in script. QlikView : get data filtered in pivot table. It's the same sort of idea as Excel's pivot tables, or the Matrix control from Reporting Services. It cannot be used on disaggregated views of data because a crosstab is by definition an aggregated view of data. A crosstab query calculates a sum, average, or other aggregate function, and then groups the results by two sets of values— one set on the side of the datasheet and the other set across the top. Supplying codes/labels and levels to the Categorical constructor is not supported anymore. Viewed 103 times 1. crosstab ¶ pandas. you will need to format Closure Rate as a percentage and add Rep Location as. I put in a little work on a new crosstab function in the main pandas namespace. Our data table contains sales amount of a company by year (rows) and by continent (columns). Viewed 103 times 1. Drop or delete the row in python pandas with conditions In this tutorial we will learn how to drop or delete the row in python pandas by index, delete row by condition in python pandas and delete the row in python pandas by position. Are there any differences? How is a Pandas crosstab different. execute(sql) rows = cur. What is Crosstab April 9, 2008 Editorial Team + Data Warehouse Basics No comments Crosstab, or Cross Tabulation, is a process or function that combines and/or summarizes data from one or more sources into a concise format for analysis or reporting. You can vote up the examples you like or vote down the ones you don't like. Mon 31 October 2011 PyHPC 2011 Pre-print paper on pandas. Time Series Analysis: The major disadvantage of using tableau desktop or tableau prep is that it doesn't provide any feature to perform time series analysis. Also known as contingency tables or cross-tabulations, two-way tables are ideal for analyzing relationships between categorical variables. They are extracted from open source Python projects. compat and pandas. Read the basics first if you are not familiar with this: PostgreSQL Crosstab Query; The original id is carried over as "extra column". In this post, I am sharing an example of CROSSTAB query of PostgreSQL. However, there are limited options for customizing the output and using Excel's features to make your output as useful as it could be. We use cookies for various purposes including analytics. The data is categorical, like this: var1 var2 0 1 1 0 0 2 0 1 0 2 He. The final result we are trying to achieve should look similar to Figure 1. 在Python和pandas中,可以通过本章所介绍的groupby功能以及(能够利用层次化索引的)重塑运算制作透视表。DataFrame有一个pivot_table方法,此外还有一个顶级的pandas. Prerequisites. pivot_table options Function name Description values Column name or names to aggregate. Comparison with SAS¶. What about going the other way?. It has not actually computed anything yet except for some intermediate data about the group key df['key1']. PostgreSQL: CREATE PIVOT TABLE to arrange Rows into Columns form. 炼数成金»论坛 › 商业智能 › Python与数据分析 › Pandas分组统计函数:groupby、pivot_table及crosstab. This function operates like pivot tables in Excel by creating an index and applying an aggregation function over a specified value. Pandas offers several options for grouping and summarizing data but this variety of options can be a blessing and a curse. Input/Output. In many situations, we split the data into sets and we apply some functionality on each subset. And the third is the data in an excel pivot table. One of the many new features added in Spark 1. The pandas read_json() function can create a pandas Series or pandas DataFrame. I won't dive into details of this operation, but in addition to the code above, you can chain the unstack() method and then the reset_index() method to pivot the DataFrame so each row is a unique combination of a value from sex and day with the appropriate count of. 1 documentation. chi2_contingency() for two columns of a pandas DataFrame. The data produced can be the same but the format of the output may differ. pandas提供了几种分析和汇总数据的函数,比如gourpby,pivot_table和crosstab,可以说功能强大,十分优秀,是您居家旅行,行走江湖杀人灭口的必备工具。 但有时候工具多也不一定是好事,用的时候想不起来,想的起来又不知道怎么用,脑子容易乱。. Now, let's talk about Pivot Tables. Finally, we describe how to sort a Pivot Table, so that you can easily analyse your data. Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data. pivot (self, index=None, columns=None, values=None) [source] ¶ Return reshaped DataFrame organized by given index / column values. Python for Data AnalysisAndrew HenshawGeorgia Tech Research Institute 2. First off, before going any further make sure you have read the hall of fame SQLTeam article by Rob Volk on generating crosstab results using a flexible, dynamic stored procedure that has been viewed over 100,000 times!. I just started with pandas and was planning to use pandas widely. This chart can also show various calculations on the values of the measure field such as running total, percentage total, etc. Example data (before query): KEYID ID Class a1dog a1 dog. In these cases it may make sense to construct a dynamic pivot. For this example, we would like to determine the student’s name that. How to use Pivot and Unpivot? (how to change rows to column and column to rows) - Duration: 10:47. What is Crosstab? Definition. Given that the two columns-you want to perform division with, contains int or float type of values, you can do this using square brackets form, for example: [code. CREATE TABLE weather (city text, when timestamp, temperature. This is followed by a guide on how to create a more advanced, two-dimensional Excel Pivot Table. Pandas offers several options for grouping and summarizing data but this variety of options can be a blessing and a curse. Pivot tables are a useful feature all accountants should be familiar with. Pandas, along with Scikit-learn provides almost the entire stack needed by a data scientist. 90K in pandas DataFrame?. Are you using React? Check out the React port: react-pivottable!. js is an open-source Javascript Pivot Table (aka Pivot Grid, Pivot Chart, Cross-Tab) implementation with drag'n'drop functionality written by Nicolas Kruchten. It is possible to manually calculate the relative frequencies after running pivot_table so crosstab isn’t all that necessary. Using pandas, is it possible to compute a single cross-tabulation (or pivot table) containing values calculated from two different functions? import pandas as pd import numpy as np c1 = np. frame objects, statistical functions, and much more - pandas-dev/pandas. APPLIES TO: SQL Server Azure SQL Database Azure SQL Data Warehouse Parallel Data Warehouse You can use the PIVOT and UNPIVOT relational operators to change a table-valued expression into another table. This lesson of the Python Tutorial for Data Analysis covers grouping data with pandas. The easiest way to do this is to link your table to MS Access and then use MS Access's crosstab wizard. The "Crosstab, Rotate, Pivot" article from earlier this year should have been named "Crosstab, Rotate, Pivot, Denormalize" because it showed how to take a perfectly nice table like this and muck it up to look like this without hard-coding any of the data values in the SQL. 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: