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Merging multiple excel files into a master file using python with out any repeated values

I have multiple excel files with different columns and some of them have same columns with additional data added as additional columns. I created a masterfile which contain all the column headers from each excel file and now I want to export data from individual excel files into the masterfile. Ideally, each row representing all the information about one single item.
I tried merging and concatenating the files, it adds all the data as new rows so, now I have some columns with repeated data but they also contain additional data in different columns.
What I want now is to recognize the columns that are already present and fill in the new data instead of repeating the all columns using python. I cannot share the data or the code so, looking for some help or idea to get this done. Any help would be appreciated, Thanks in advance!

You are probably merging the wrong way.
Not sure about your masterfile, sounds not very intuitive.
Make sure your rows have a specific ID that identifies it.
Then always perform the merge with that id and the 'inner' merge type.

How to lively save pandas dataframe to file?

I have a Python program that is controlling some machines and stores some data. The data is produced at a rate of about 20 rows per second (and about 10 columns or so). The whole run of this program can be as long as one week, as a result there is a large dataframe.
What are safe and correct ways to store this data? With safe I mean that if something fails in the day 6, I will still have all the data from days 1→6. With correct I mean not re-writing the whole dataframe to a file in each loop.
My current solution is a CSV file, I just print each row manually. This solution is both safe and correct, but the problem is that CSV does not preserve data types and also occupies more memory. So I would like to know if there is a binary solution. I like the feather format as it is really fast, but it does not allow to append rows.

I can think of two easy options:
store chunks of data (e.g. every 30 seconds or whatever suits your use case) into separate files; you can then postprocess them back into a single dataframe.
store each row into an SQL database as it comes in. Sqlite will likely be a good start, but I'd maybe really go for PostgreSQL. That's what databases are meant for, after all.

Pandas position into an excel

Does anyone knows how can I insert a dataframe into an excel in a desired position ?
For example, I would like to start my dataframe into the cell "V78"

there is startrow and startcol argument in the .to_excel() method
df.to_excel('excel.xls', startrow=78, startcol=24)

I have a solution which may or may not fit your requirements.
I would not directly import it into an existing Excel file which may contain valuable data and furthermore keeping the files separate may be of use one day.
You could simply save the dataframe as an Excel file;
df.to_excel('df.xls')
And in the Excel file that you want to insert it into create an object of type file and link the two that way. See here.
Personally keeping them separate seems better as once two files become one there is no going back. You could also have multiple files this way for easy comparisons, without fiddling row/column numbers!
Hope was of some help!

How can I ensure unique rows in a large HDF5

I'm working on implementing a relatively large (5,000,000 and growing) set of time series data in an HDF5 table. I need a way to remove duplicates on it, on a daily basis, one 'run' per day. As my data retrieval process currently stands, it's far easier to write in the duplicates during the data retrieval process than ensure no dups go in.
What is the best way to remove dups from a pytable? All of my reading is pointing me towards importing the whole table into pandas, and getting a unique- valued data frame, and writing it back to disk by recreating the table with each data run. This seems counter to the point of pytables, though, and in time I don't know that the whole data set will efficiently fit into memory. I should add that it is two columns that define a unique record.
No reproducible code, but can anyone give me pytables data management advice?
Big thanks in advance...

See this releated question: finding a duplicate in a hdf5 pytable with 500e6 rows
Why do you say that this is 'counter to the point of pytables'? It is perfectly possible to store duplicates. The user is responsible for this.
You can also try this: merging two tables with millions of rows in python, where you use a merge function that is simply drop_duplicates().

Using Pandas to create, read, and update hdf5 file structure

We would like to be able to allow the HDF5 files themselves to define their columns, indexes, and column types instead of maintaining a separate file that defines structure of the HDF5 data.
How can I create an empty HDF5 file from Pandas with a specific table structure like:
Columns
id (Int)
name (Str)
update_date (datetime)
some_float (float)
Indexes
id
name
Once the HDF5 is created and saved to disk, how do I retrieve the column and index information without having to open the file completely each time since it will likely contain several GB of data.
Many thanks in advance...
-- UPDATE --
Thanks for the comments. To clarify a bit more:
We do have some experience with Pandas but by no means are really proficient. The part that is tripping us up is creating an empty data structure and reading that structure from a file that you will not want to fully open. In all of the Pandas examples there is data. The Pandas examples also only show two ways to retrieve data/structure which are to read the entire frame into memory or issue a where clause. In this case, we would like to be able to see the table structure without query operations if possible.
I know this is an odd case. Why the heck would you want an empty dataframe?? Well, we want to have a great deal of flexility in moving data around and want to be able to define a target dataframe structure prior to data writing, which could take place much later (e.g. hours or days). Since the HDF5 specification maintains all that information it seems directionally incorrect to store the table structure information separately. Thus our desire to crack the code on this subject.
-- UPDATE 2 --
To add more detail as #jeff requested.
We would like to abstract some of the common Pandas functions like summing data or merging two frames. Thus we would like to be able to ask each frame what their columns are so we can present a view for the user to select the result frame columns.
For example, if we imported a CSV with columns A, B, C, D, and V and saved the frame to HDF5 as my_csv.hdf then we would be able to determine the columns by opening the file.
However, in our use case it is likely that the import frame for the CSV could be cleared periodically and no longer contain the data. The reason knowing that the my_csv frame has certain columns and types is important because we want to enable a user to then select those columns for summing in a downstream operation. Lets say a user wants to sum column V by the values in columns A and B only and save the frame as my_sum. Since we can't ensure my_csv will always have data we would like to ensure it at least contains the structure.
Open to other suggestions obviously. It is also possible to store the table structure info in the user_block. This, again, is not ideal because the structure is now being kept in two different areas but I guess it would be possible to always update the user_block on save using the latest column and index information for the frame, although I believe the to_* operations in Pandas will blow away the user_block so...blah. I feel like I'm talking myself into maintaining a peer structure definition but I REALLY would love some suggestions to not have to do that.