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How do you combine multiple .csv files into a single file using the command line?
If you don have headers on any of the CSV files then a simple cat code or equivalent over your data files is sufficient as pretty much everyone has suggested. If there are italic headers you can take the header line from one file and strip it from the others before cat code ing them all together. However blindly assuming that each file shares the same column order is quite dangerous; even if youre generating all the CSV files yourself a simple code change that you forgot about might inadvertently rearrange your columns on newer files but not the older ones thus causing errors when processing thebined output. Hence I prefer to actually read italic the headers and use them to match the columns in each file regardless of the order in which theyre published. For this I personally use Miller in my day-to-day processing of securities trading data. As a single C executable it faster than every other Python solution Ive used and it processes all sorts of key-value file formats like CSV TSV JSON and the likeafter all tabular data is simply a list of records of KV pairs. As a bonus it also allows you to sanitize sort and even group and calculate various statistics on the data without having to write special Python or Perl scripts. For instance tobine all the CSV files in the current directory sort the entries by date and securities code then send the output to a different directory I simply do this mlr --rs lf --csv sort -f datecode *.csv output code Oh and almost nobody mentions which platform they like to have their problem solved on but it doesn matter in this case. Millerpiles just fine on *nix and MacOS platforms and recently added a Windows binary to its official releases s . Good luck!
How do you combine multiple .csv files into a single file in Windows Vista?
If you have Git Bash installed (on Windows!) or if youre on linux or MacOS you can use ofbination of awk and redirection to achieve what you want. Try thismand after you cd into the directory containing the .csv files awk 'FNR==1 && NR!=1next;print' *.csv code This will take all the data from the .csv files and use it to create a as the merged .csv file. It will take the header from the first file and skip adding the header from the other source .csv files. The reason I rmend the use of awk + redirection is because of how ridiculously fast it is. I was able to merge ~115 .csv files with ~1 rows each in less than 2 seconds.
What sort of information can I get from the new Open Payments data?
UPDATE I finally opened the research payments file - and merged it with the total payments to Doctors from the first file. I have added a question #17 to show the difference to answer #4. This time I have included Physician_Profile_ID's and increased the list to the Top 1. OK let's look at what we have here. I went to the page mentioned above and download the zip file - Inside this zip I found 4 files but the one that interests me is the data set for General Payments for the 213 program year = . See bottom of this post for info on the others. =============================n1) CSV file = GBn2) Total of 2626674 records) Contains payment records for both Hospitals and Doctors (Determined by record fields Teaching_Hospital_ID and Physician_Profile_ID) My questions aren1) What is the total spend across all the records and total by Hospitals and Doctors?n2) How many Doctors are in here?) How many Hospitals are in here?n4) How much did the top 2 Doctors receive?n5) How much did the top 2 hospitals receive?n6) How much did Doctors receive by US State - Top 2?n7) How much did Hospitals receive by top 2 US States?n8) What are the top 2 Zip Codes overall for amounts?n9) What are the top 1 Physician Specialties?n1) Top 1 Nature_of_Payment_or_Transfer_of_Valuen11) Nature of Payment - Hospitalsn12) Top 2 Manufacturers - Hospitalsn13) Amounts by Type of Doctorn14) Top 2 Drugs for Doctors - Amountsn15) Top 2 Cities for Doctors - Amountsn16) What is the distribution of payments if neither a drug or medical device are involved?n17) Including research amounts who are the top Doctors (vs. #4 above) Opening the file I can immediately see the records are fairly dirty. For example a number have a leading space for the First name of the Doctor. This makes me wonder if the Physician_Profile_ID is actually unique (Which I later found out was not - see below) Also for example in the nRecipient_City field a number of the records have street addresses instead. I found it interesting that when I looked online for other analyses of this data no one else hade up with these issues. italic It made me realize that many people just crank up their big data software and do not actually take the time to sift thru the data and determine poor data management. It reinforces the fact for me to never trust the data as clean and that when ites to any analysis Garbage in Garbage Out. ANSWERSn========n1) Total Payments $ Doctor Payments = $ (69%)nTotal Hospital Payments = $ (31%) 2) How many Doctors?nThis is tricky. Trouble is I found 436 Doctors with the same first name last name and same address with DIFFERENT Physician_Profile_ID's - (I removed all the Sr. II etc.). Of these 394 had two Physician_Profile_ID's 14 had 3 24 had 4 3 had 6 and 1 had 1 Physician_Profile_ID's! I would need to scrub this some more but my preliminary estimate is ~473188 Doctors. And this disagrees with the count in the CMS supplementary CSV file (see bottom) of 359924 Docs or 113264 less? 3) How many Hospitals?nThere are 837 Teaching_Hospital_ID's but I found two hospitals that had two Teaching_Hospital_ID's so it is really 835 4) How much did the top 2 Doctors receive?n n5) How much did the top 2 hospitals receive?n n6) How much did Doctors receive by US State - Top 2?n n7) How much did Hospitals receive by top 2 US States?n n8) What are the top 2 Zip Codes for Doctors overall for amounts?n n9) What are the Physician Specialties by Amounts Paid?n n1) Top Nature_of_Payment_or_Transfer_of_Valuen n11) Nature of Payment - HospitalsnRoyalty or License account for the most at $ or 69.3% n12) Top Manufacturers - Hospital paymentsn n13) Amounts by Type of Doctorn n14) Top 2 Name_of_Associated_Covered_Drug_or_Biological1 for DoctorsnNOTE Many of the drugs have multiple names so this list probably does not tell the whole story BUT if you tell me that a drug can be called A B H and G I can certainly figure out the total amount and which Doctors etc.n n15) Top 2 Cities for Doctorsn n16) What is the distribution of payments if neither a drug or medical device are involved? n17) Including research amounts who are the top Doctors (vs. #4 above)n nNOTE Now Page on issued a press releasen Page on www . Page on The data contains 4.4 million payments valued at nearly $3.5 billion attributable to 546 individual physicians and almost 136 teaching hospitals which is clearly different to my results above. I analyzed the data set for General Payments for the 213 program year. The other 4 smaller files are Research Payments Ownership and Investment Interest Information and a supplementary file that displays all of the physicians indicated as recipients of payments other transfers of value or ownership and investment interest in records reported in Open Payments. If you have any questionsments please let me know. Also if you'd like more specific analysis on this file I'd be happy to review it. If it's not too difficult Ill add it to this or otherwise I'll give you a quote -) To help you out with any suggestions for further analysis here's a list of the fields in the file.n=======================================nGeneral_Transaction_IDnProgram_YearnPayment_Publication_DatenSubmitting_Applicable_Manufacturer_or_Applicable_GPO_NamenCovered_Recipient_TypenTeaching_Hospital_IDnTeaching_Hospital_NamenPhysician_Profile_IDnPhysician_First_NamenPhysician_Middle_NamenPhysician_Last_NamenPhysician_Name_SuffixnRecipient_Primary_Business_Street_Address_Line1nRecipient_Primary_Business_Street_Address_Line2nRecipient_CitynRecipient_StatenRecipient_Zip_CodenRecipient_CountrynRecipient_ProvincenRecipient_Postal_CodenPhysician_Primary_TypenPhysician_SpecialtynPhysician_License_State_code1nPhysician_License_State_code2nPhysician_License_State_code3nPhysician_License_State_code4nPhysician_License_State_code5nProduct_IndicatornName_of_Associated_Covered_Drug_or_Biological1nName_of_Associated_Covered_Drug_or_Biological2nName_of_Associated_Covered_Drug_or_Biological3nName_of_Associated_Covered_Drug_or_Biological4nName_of_Associated_Covered_Drug_or_Biological5nNDC_of_Associated_Covered_Drug_or_Biological1nNDC_of_Associated_Covered_Drug_or_Biological2nNDC_of_Associated_Covered_Drug_or_Biological3nNDC_of_Associated_Covered_Drug_or_Biological4nNDC_of_Associated_Covered_Drug_or_Biological5nName_of_Associated_Covered_Device_or_Medical_Supply1nName_of_Associated_Covered_Device_or_Medical_Supply2nName_of_Associated_Covered_Device_or_Medical_Supply3nName_of_Associated_Covered_Device_or_Medical_Supply4nName_of_Associated_Covered_Device_or_Medical_Supply5nApplicable_Manufacturer_or_Applicable_GPO_Making_Payment_NamenApplicable_Manufacturer_or_Applicable_GPO_Making_Payment_IDnApplicable_Manufacturer_or_Applicable_GPO_Making_Payment_StatenApplicable_Manufacturer_or_Applicable_GPO_Making_Payment_CountrynDispute_Status_for_PublicationnTotal_Amount_of_Payment_USDollarsnDate_of_PaymentnNumber_of_Payments_Included_in_Total_AmountnForm_of_Payment_or_Transfer_of_ValuenNature_of_Payment_or_Transfer_of_ValuenCity_of_TravelnState_of_TravelnCountry_of_TravelnPhysician_Ownership_IndicatornThird_Party_Payment_Recipient_IndicatornName_of_Third_Party_Entity_Receiving_Payment_or_Transfer_of_ValuenCharity_IndicatornThird_Party_Equals_Covered_Recipient_IndicatornConual_InformationnDelay_in_Publication_of_General_Payment_Indicator
How do I merge 8 CSV files (49 million rows each) with a common column, and export the final output into a CSV in a Core i7 8GB RAM PC?
There already a very good answer s in StackOverflow exing how to do it with a Python script goes through all .csv files present in a folder prints them and thenbines them all together removing the headers. Ive tested running the script on 16 CSVs with a total of 153543233 rows x 3 columns in Spyder (Python 3.7) launched from Anaconda Navigator on a Asus Laptop Intel CoreTM i7-451U CPU @ 2. GHz 8 GBs RAM using Windows 1 and it worked fine. import os code import csv glob code code Dir = rCPathToSource code Avg_Dir = rCPathToDestinationOutput code code csv_file_list = ((Dir '*.csv')) code print (csv_file_list) code code with open((Avg_Dir '') 'w' newline='') as f code wf = (f lineterminator='n') code code for files in csv_file_list code with open(files 'r') as r code next(r) code rr = (r) code for row in rr code (row) code
How do I merge multiple csv files into 1 file? (Each file has more than a million rows)
Miller should be able tobine multi-million row CSVs with ease. I haven actually tried it with such large CSVs but I regularlybine thousands of CSVs each containing thousands of rows which is a scalability issue in a different dimension. Ive yet to see Miller fall over handling this case and as it written in C it also fast . Oh and if youre running Windows the author recently added a Windows binary to his official releases s . Good luck!
What is the easiest way to combine and organize contacts?
Tim you might like as we don't sync back specifically for that reason - who needs yet another polluted address book? conXt aims to be a clean relevant and organized online address book. You can import from multiple platforms or csv files (even custom ones - just match the fields) and conXt will merge duplicates (based on namebo). You can also easily tag contacts in order to filter sort andmunicate with one or more people directly from the conXt interface (but using your existing email in the 'from' field). conXt is free private and also makes it easy to start having your contacts fill out their *own* contact info in *your* address book by sending Contact Request Forms. Thanks!
How do I automate reports using the Tableau software?
1. Be more organized Do your colleagues have to nudge you time and again to give them updated reports? Get organized with Analytics Canvas Automation which is designed to generate reports whenever you need them. With this tool youll be able to get through the next reporting period with ease delivering updates to your clients faster without adding more resources or hiring new analysts. 2. Deliver more reports in the same amount of time Do you envy those people whose organization allows them time for flexibility and creative work? Once you have automated your reporting youll have time that you can then spend on innovative projects. What youre now doing manually could be performed by automation delivering the same results in a fraction of the time. Reducing such a time-consuming and repetitive task will make you more productive and more efficient. 3. Impress your boss 3 make more sophisticated reports Updating dashboards by campaign region or brand may take a while toplete. No matter how skilled you are it takes a great deal of time and effort to gather all the relevant data and run reports if you are doing it manually so often you will reduce the level of detail or scope of the report to reduce the effort required. Want to impress your boss by providing detailed sophisticated reports quickly and everytime? Automation software let you do just this. 4. Avoid having to be a programmer 3 or hire one. Use a visual interface to define queries and transforms Are you preparing your reports in Excel? If so chances are that youve heard of Macros and VBA programming. These programs are hard to learn and offer limited functionality. In contrast an automation tool can offer an visual interface that doesn require a writing code or programming from themand line. You can pull unsampled data from Google Analytics andbine it with other sources such as databases and Excel tables andbine it in one single report without writing a single line of code. In an automation tool queries can be built a step at a time rather than having to write a hugeplex query in SQL all at once. 5. No more working late or weekends in the office preparing that detailed report Remember the evening that you worked late to put together the report for the client meeting the next day? Automating your report generation means reduced workload and an increase in time and productivity letting you focus on more important things 3 like making money for yourpany. 6. No more typos and cut and paste errors Broken formulas and macros missing values and references 3 does this sound familiar? Updating an old report can be a hassle. Being able to deliver a report quickly is important but so is being able to ensure that things are accurate. Automating your reports eliminates the manual errors that inevitably happen from time to time. With reduced errors you reduce the headaches too and deliver a much greater level ofpliance. 7. Increase the frequency of analysis In our fast-paced world it seems there is never enough time to get everything done before the end of the day. Your clients rely on the analyses you provide for their business decisions. But if the effort required is too high some reports only get built once a month or once a quarter. By the time you see something going wrong its been going wrong for weeks- and maybe youve wasted money on bad advertising or missed out on potential sales. If youve automated your analysis you can run it every day or every week. Now you catch problems quickly respond and optimize quickly. Get More Free Videos - Subscribe u279c s s