I need to remove redundant data in xlsx file in which data is entered using pandas. Any help on how to remove the redundant data?
Thanks!
You can use pandas.read_excel to load your sheet as a pandas dataframe. Then use pandas.DataFrame.drop_duplicates to remove the redundant data. You can then write it as you want.
Related
Following up this question
I would like to ask if we could merge rows from multiple columns, i.e.
Following answers from #Dimitris Thomas, I would be able to obtain the following
And wondering how the code could be updated to get:
Thanks
IIUC:
Firstly:
df=pd.read_excel('filename.xlsx')
#read excel file in pandas
try via set_index() and to_excel()
df.set_index(df.columns[:-1].tolist()).to_excel('filename.xlsx',header=None)
#OR(Since you don't provide data as text so not sure which one should work)
df.set_index(df.columns.tolist()).to_excel('filename.xlsx',header=None)
This is perhaps one of those many times discussed questions with solutions more specific to actual system that outputs the data into a CSV file.
Is there a simple way to export data like 3332401187555, 9992401187000 into a CSV file in a way that later when opened in Excel, the columns won't show them in "scientific" format? Should this be important, the data is retrieved directly by an SQL SELECT statement from any DBMS.
This also means that I've tried solutions like surrounding the values with apostrophes '3332401187555' and the Excel cell recognizes those as text and doesn't do any conversions/masking. Was wondering if there was a more elegant way without actually it being a pre-set Excel template with text data fields.
1. Try exporting the numbers prefixed with single quote. Example: '3332401187555.
2. In excel, select the column containing number values
and then select Number in Format Cells.
Just have to save your file with Excel the option CSV file. And you have the in file in requested format.
I am trying to append several pandas dataframes to a csv file but I cannot know ahead of time which dataframe will be appended first as they are each generated on different worker machines. I can append each one to pre-made empty csv file by doing: df.to_csv('test.csv', mode='a', header=False) but then my csv has no header and I just has data.
If I make header=True then I have copies of the header every time I append which is redundant since they are all the same. Is there any direct way to overcome this? I suppose I can check the file each time I want to append to see if there is a header but that feels inefficient.
I need to use VBA to import a large CSV excel file into an Access table. The delimiter is "" (double quotes) except for some reason the first value is followed by " (only one quote) instead of two like every other value. The first row contains the column headers and are delimited the same way. At the bottom I have attached an example.
The CSV files are generated automatically by an accounting system daily so I cannot change the format. They are also quite large (150,000+ lines, many columns). I'm fairly new to VBA, so as much detail as is possible would be much appreciated.
Thanks in advance!
Example of format
That doesn't sound like a CSV file. Can you open it in Excel, convert it to a true CSV, and then import that into Access? You will find many VBA-driven import options at the URL below.
http://www.accessmvp.com/KDSnell/EXCEL_Import.htm
Also, take a look at these URLs.
http://www.erlandsendata.no/english/index.php?d=envbadacimportado
http://www.erlandsendata.no/english/index.php?d=envbadacimportdao
Platform : SSIS
I am new to SSIS and trying to check for duplicate rows while transferring the data from text file to excel file. Heard about cache transform can be used but I am not really sure about it. Any suggestions?
One simple way to handle this is use an Aggregate transform between the source and destination. In it, group by all the columns in the source to eliminate duplicates. I have used this technique, and it works well.
This could be slow if the source is large.