![]() Option 1 is to share the codebooks (which are also copyrighted and can’t be publicly distributed) with the researcher and haggle back and forth via email to iron out the details of the request. This poses a challenge for the data librarian, who somehow has to communicate to the researcher what’s available in the files and mediate the request. Another stipulation is that the files cannot be shared in their entirety, even for members of the licensed institution researchers must request individual extracts of variables and observations to answer a specific research question. Naturally, the microdata is copyrighted and licensed for non-commercial research purposes to members of the university or institution who are covered by the license agreement, and cannot be shared outside the institution. The microdata is valuable to social science researchers who use the responses to conduct statistical analyses. These files contain the anonymized, individual responses to the surveys. As part of the package, subscribing institutions also receive microdata files for some of the surveys, in STATA and SPSS formats. Many academic libraries subscribe to an online database called Gallup Analytics, which lets users explore and download summary statistics from a number of on-going polls and surveys conducted by the Gallup Organization, such as the US Daily Tracker poll, World Poll, and SPSS polling series. Gallup Analytics microdata serves as the example. This is especially useful when your code is complicated.In this post I demonstrate how export a list of variables from a STATA dta file to an Excel spreadsheet, and how to create a STATA do file by using Python to read in a list of variables from a spreadsheet the do file will generate an extract of attributes and observations from a larger dta file. Whereas for method 2, the dataframes can be in different scopes and it will still work. This means if you have a dataframe outside of the current scope, you will have to bring it in first. The differenceįirst of all, because of the with block in method 1, all your dataframes have to be in the same scope. however, the mechanism is quite different. It’s true that both methods do exactly the same thing – saving the two dataframes into a single Excel file. writer2 = pd.ExcelWriter('mult_sheets_2.xlsx')ĭf_1.to_excel(writer2, sheet_name = 'df_1', index = False)ĭf_2.to_excel(writer2, sheet_name = 'df_2', index = False)īy now you must think that these two methods are the same! Well, yes and no. ![]() Let me show you how it looks like then tell you why I prefer this over method 1. ![]() ![]() with pd.ExcelWriter('mult_sheets_1.xlsx') as writer1:ĭf_1.to_excel(writer1, sheet_name = 'df_1', index = False)ĭf_2.to_excel(writer1, sheet_name = 'df_2', index = False) This is the method demonstrated on the official pandas documentation. The two methods are slightly different in syntax but they work the same way. The idea is pretty much the same between the 2 methods: we create an ExcelWriter, then pass it into the df.to_excel() for saving dataframe into an Excel file. We’ll go through 2 methods of saving multi-sheets Excel files. import pandas as pdĭf_1 = pd.DataFrame(np.random.rand(20,10))ĭf_2 = pd.DataFrame(np.random.rand(10,1)) We create 2 dataframes, the first one is a 20 row by 10 columns random numbers and the second dataframe is 10 rows by 1 column. Let’s create some mock-up dataframes, so we have something to work with. ![]()
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