Code Review Stack Exchange is a question and answer site for peer programmer code reviews. It only takes a minute to sign up. The first field of the CSV is a unique identifier of each line. The exercise consists of detecting the changes applied to the file, by comparing before and after.
How can I improve, both the style of my programming as well as the performance of the script? For the record I'm using Python2. Performance is good: you're using a linear algorithm, even though you're going through the files twice. If you're worried about very very larges files that won't hold in memory, you can use the assumption that the files are sorted to advance in both files simultaneously, and make sure to always have the same unique id in both files.
I don't think this is needed, lines is quite small! I'll start from the very first method of your code, and then we'll see how everything could be improved. Further style guides can be found in PEP8. Also that use of map seems hackish, it would be more clear this way:. I've also changed the argument name to csvfile to state clearer what that argument is and for not shadowing unecessary the built-in input.
Yes, why don't subclass csv. If you go and take a look into the source file csv. The bare bone could be something like:. This alone will improve your performance a bit, but we haven't touched the core of you system yet.
I would go with set s. So let's say that after and before are two dict organaized exactly like you would thanks to the IdDictReader :. But when it comes to performances the only thing to do is time it and profile it and see what's best. I think that a simpler approach could be using the two files "indexes" using list comprehensions. What I would propose:. Sign up to join this community.
The best answers are voted up and rise to the top. Home Questions Tags Users Unanswered. Asked 8 years, 1 month ago. Active 2 years ago. Viewed 9k times. DictReader input, self. Because if you can I would suggest a different approach, i. I just spent 2 days improving the diff part, although there is so much more to the script.
The Change class in particular will have multiple methods but that's beyond the scope of this code review. I'm assuming you meant the Unix diff tool. If you meant some Python utility then please show me some link. Maybe you reckon I run diff on both files and pipe the output to my script? Active Oldest Votes. I don't know if "input" is supposed to be a file name, a string, a file object, and so on this is Python's fault, but still.
Both CSV files will be formated the same. I found this link useful. If your CSV files aren't so large they'll bring your machine to its knees if you load them into memory, then you could try something like:. For large files, you could load them into a SQLite3 database and use SQL queries to do the same, or sort by relevant keys and then do a match-merge. One of the best utilities for comparing two different files is diff.
See Python implementation here: Comparing two. If you want to use Python's csv module along with a function generator, you can use nested looping and compare large. The example below compares each row using a cursory comparision:. Here a start that does not use difflib.
It is really just a point to build from because maybe Adam and apples appear twice on the sheet; can you ensure that is not the case? Should the apples be summed, or is that an error? Learn more. Comparing two csv files and getting difference Ask Question. Asked 7 years, 10 months ago. Active 3 years, 8 months ago.
Viewed 26k times. Any pointers will be greatly appreciated. What version of Python are you using? You've tagged this with excel but mention CSV files. Do you need to work with xlsx or xls files? You might find that diff works for want you need, but you haven't really said whether this needs to be done a lot and build into an existing python program. Active Oldest Votes.
Aakash Gupta Aakash Gupta 5 5 silver badges 10 10 bronze badges. DictReader open 'file1. DictReader open 'file2. The dicts in the csv1 list are not hashable so creating set1 will not be possible. This can be avoided by conversion of the dicts to strings with json.I have multiple. The intent is to parse all transactions made prior to importing them into GnuCash, so that no duplicate transaction records appear in my account registers.
Comparing 2 csv files and exporting difference
As of now I have made a bash script that parse the. A combination of sed and awk is used to produce the. I would like some help automating my manual comparison of the transactions, since I find myself unable to successfully parse the matching transactions using sed or awk. When I have all bank statements merged and sorted, I have added a column with the source bank statement account number:. What I need help with achieving is parsing the file with the merged bank statements, so that transactions between my accounts are found.
Any transactions lines in the file where columns Date recorded, Date occurred, Verification number, Memo and Amount disregarding negative amount symbol when comparing the two lines match should processed like this: 1 Keep source account transaction line in file, 2 Add new column "Destination account" with destination accounts account number to source account transaction line 3 Delete destination account transaction line from file.
When these two lines producing the transaction have been processed the output in the file should be:. After all transactions in my example of merged bank account statements have been processed, the final output should be a file with the following lines:.Fcpx shatter effect free
Note: These four transactions aren't transactions between my accounts - they should be kept in the file with the added column "Destination account" left empty. Any solution using tools compatible with my current bash script or perhaps a solution using pythons pandas library? It first creates a dictionary of type defaultdict list from the input csv file which has keys based on the criteria described for transaction matching. All transactions with the same key are stored in an associated list.
Afterwards it goes through the list of transactions gathered for each key in a pair-wise fashion and creates merged transactions records from them that have the additional destination account field from the second transaction's source account added.
Each merged transaction record created is then written to the output csv file. Transactions which aren't paired simply become merged records with an empty destination fields. Transactions which are paired are only merged if the sign of two amounts differ, otherwise they are treated as two unpaired transactions as previously described.Comparing two excel spreadsheets and writing difference to a new excel was always a tedious task and Long Ago, I was doing the same thing and the objective there was to compare the row,column values for both the excel and write the comparison to a new excel files.
In those days I have used xlrd module to read and write the comparison result of both the files in an excel file.
I can still recall that we have written long lines of code to achieve that. Recently at work, I encountered the same issue and retrieving my old xlrd script was not an option. So, i thought to give Pandas a try and amazingly I completed comparing the two excel files and writing the results to a new excel file in not more than 10 line of codes.
Jan and Feb and contains the same no. Compare the No. First,We will Check whether the two dataframes are equal or not using pandas. NaNs in the same location are considered equal. The column headers do not need to have the same type, but the elements within the columns must be the same dtype. This function requires that the elements have the same dtype as their respective elements in the other Series or DataFrame.
In the above step we ensure that the shape and type of both the dataframes are equal and now we will compare the values of two dataframes. In just one line we have compared the values of two dataframes and the comparison value for each row and column is shown as True and False values.
Get the Index of all the cells where the value is False, Which means the value of the cell differ between the two dataframes. Next we will iterate over these cells and update the first dataframe df1 value to display the changed value in second dataframe df2.
Finally we have replaced the old value of dataframe df1 and entered the new value in the following format:. I have set the index parameter as false otherwise the index will also be exported in the xlsx file as the first column and I have set the headers as True so that by default the dataframe headers will be the header in excel file as well.
Now if I compare my yesteryear code with the new and fast Pandas code then it really amuse me that how fast we have progressed and with the advent of modules like Pandas the things have become much simpler. Even you can directly read the records from SQL tables and write to the tables after processing.
This new world is progressing at a faster speed and we all are optimistic with every day goes by we are near to see more intelligent and breakthroughs in the Python world.
Could you tell me how you colorized the text and changed the excel cell width to match the length of the string? Hi, For this to work, the shape of the sheets should be the same.Have two CSV files containing client records and need to compare the two and then output to a third file those rows where there are differences to the values within the record row as well as output those records rows on the second file that are not on first file.
Python script to compare two text files
Yes, the above is very similar but it doesn't put to a file nor does it seem to put out the one additional record on the second file.
As to the file output, that's something basic I feel. There are folk that want every line of code needed for their app. Also, what is that additional record? I see you are new here so if you are looking for a complete app that hits all the marks without you writing code, just go ahead and add that detail. I use diff for this kind of thing.
If you use a merging tool like Meld you can interactively and graphically merge the two files together, combining rows that are only differ by whitespace and copying rows that exist on one side but not the other. Thank you. While thus far, my solution meets the needs, I will try these other suggestions as well. Edited 2 Years Ago by rproffitt : Added clarification. Edited 2 Years Ago by pty. An example output with a diff tool. Prepare a correlation between two csv files.
Getting Started: Have something to contribute to this discussion? Please be thoughtful, detailed and courteous, and adhere to our posting rules. Edit Preview. H1 H2. Post Reply. Insert Code Block. Share Post. Permanent Link.In this blog, we are going to learn how to compare two large files together while creating a quick and meaningful summary of the differences.
In general, comparing two data sets is not very difficult. The main difficulty comes from being able to gain quick, meaningful insights. Although the aforementioned difficulty can be solved quickly with the likes of a pre-existing comparison library such as dataComPy, the problem is amplified when the data becomes so large, that it cannot be held into memory.
To my disappointment, I could not find an existing data comparison library that would handle larger data sets. All the ones I found required all of the data to be in memory. Seeing this as a problem to be solved, I began looking into the best way to solve this problem. To help identify the solution, I started by clearly defining the problem.
I wanted to be able to compare large files. I also wanted the solution to be a simple one. One way I considered of solving this problem was to partially load things in memory. These types of operations are a complete logistical nightmare, with complex logic of keeping track of everything.
I was hoping that there would have been an easier way of doing things. Hold and behold; another idea came to me. What if I used a SQL database to carry out all my comparisons.
Hold data and carry out data-centric operations quickly? I wanted the results of the comparison to be easy to digest and provide meaningful insights.
That meant that I was more interested in a quick summary and some example comparison breaks. I could always deep-dive into the data if I needed to. To do that, we can do a checksum. Looking into the above script closely, we essentially load the files line by line and work out their SHA1 output.
This is then compared between the two files. To run the above script, we simply have to pass in:. Assuming there are differences between the files, then we would like to know whether the differences are in the number of records or their values. Hence we can look into doing some quick counts. To begin with, we need to load the data into SQL without exceeding our available memory and create indexes to speed up our query operations. From the below script, you can see that we first need to define our inputs:.
The only other thing to note from the below script is that we are loading the file chunk by chunk to avoid running out of memory, and we are replacing any column spaces with underscores. In order to control the execution and output of our commands, we can create a function we can call. Breaking down the above script:. And with the above set up, we can now begin our comparison operations. It is now time that we leverage all of the functions we have defined this far in our programme, to start building a data comparison summary.
Let us then get a glimpse through the total counts, by defining a new function that we can call. We now need to look a bit deeper and understand if any of the entries are not matching on our pre-defined key. Technically speaking, the SQL query to do this sort of join is not complex; however, as we want the query to be generated automagically from our inputs, we need to get creative.Released: Jul 20, View statistics for this project via Libraries. Tags csvdiff.
Diffs generated by csvdiff are a subset of JSON and can be stored and applied using the matching csvpatch command. If upstream data changes, you can fetch the new version and re-apply your changes to it easily. If you want to ignore a column from the comparison then you can do so by specifying a comma seperated list of column names to ignore. For example:.Center align text textview android
You can also choose to compare numeric fields only up to a certain number of significant figures. Use negative significant figures for orders of magnitude:. For example, suppose more data gets added to a. In this case, you maintain the patch file and simply reapply it when the upstream data provider gives you a fresh file. For more usage options, run csvdiff --help or csvpatch --help.
Please try enabling it if you encounter problems. Search PyPI Search. Latest version Released: Jul 20, Generate a diff between two CSV files. Navigation Project description Release history Download files. Project links Homepage.
Maintainers lars Then run: pip install csvdiff.
Examples For example, suppose we have a. History 0. Fix a bug when a patched document becomes empty Check for rows bleeding into one another.Generate comparison report for LARGE files using python
Provide a matching csvpatch command applying diffs. Add a man page and docs for csvpatch. Use exit codes to indicate difference. Add a —quiet option to csvdiff. Uses —style option to change output style. Provides a full man page. Project details Project links Homepage. Release history Release notifications This version.Wgu mba reddit
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