That would be a significant change I guess. See the precedents just bellow (other software outputting CSVs that would not use that last unprecise digit). @TomAugspurger Let me reopen this issue. pandas’ to_csv is known to be problematic sometimes. Closes #19745. cc @dahlbaek But when written back to the file, they keep the original "looking". Pandas uses the full precision when writing csv. Only option. header bool or list of str, default True. So if i try to import that into a csv or excel file, all data is one cell. Maybe it's the original excel file causing the issue? I've even gone through the original excel and highlighted all cells and cleared all formats before exporting. To backup my argument I mention how R and MATLAB (or Octave) do that. Ok. I would consider this to be unintuitive/undesirable behavior. I have now found an example that reproduces this without modifying the contents of the original DataFrame: @Peque I think everything is operating as intended, but let me see if I understand your concern. (1) For a column that contains numeric values stored as strings; and (2) For a column that contains both numeric and non-numeric values. So whatever this ends up doing for you is a total hack and shouldn't be trusted. columns sequence, optional. I understand that changing the defaults is a hard decision, but wanted to suggest it anyway. BTW, it seems R does not have this issue (so maybe what I am suggesting is not that crazy ): The dataframe is loaded just fine, and columns are interpreted as "double" (float64). Also, whatever sequence of columns we specify, the CSV file will contain the same sequence. Still, it would be nice if there was an option to write out the numbers with str(num) again. But that is not the case. display.float_format Again, the default delimiter is … All the output is the same, regardless of what i enter. A CSV file is nothing more than a simple text file. You can rename multiple columns in pandas also using the rename() method. Ok, i switched over to outputting as an excel file instead and it works. Maybe by changing the default DataFrame.to_csv()'s float_format parameter from None to '%16g'? The output after renaming one column is below. You can pass the column name as a string to the indexing operator. In anticipation, we have moved DataFrame.to_csv to generic.py so that we can later delete the Series.to_csv implementation, and allow it to adopt DataFrame's to_csv due to inheritance. This could be seen as a tangent, but I think it is related because I'm getting at same problem/ potential solutions. For finer control, use format to make a character matrix/data frame, and call write.table on that. import pandas as pd # create the data dictionary . So, not rounding at precision 6, but rather at the highest possible precision, depending on the float size. Usually text-based representations are always meant for human consumption/readability. This would be a very difficult bug to track down, whereas passing float_format='%g' isn't too onerous. Columns to write. If we just used %g we'd be potentially silently truncating the data. I think that last digit, knowing is not precise anyways, should be rounded when writing to a CSV file. Format string for floating point numbers. Also, this issue is about changing the default behavior, so having a user-configurable option in Pandas would not really solve it. On a recent project, it proved simplest overall to use decimal.Decimal for our values. Extracting a column of a pandas dataframe ¶ df2.loc[: , "2005"] To extract a column you can also do: df2["2005"] Note that when you extract a single row or column, you get a one-dimensional object as output. <, Suggestion: changing default `float_format` in `DataFrame.to_csv()`, 01/01/17 23:00,1.05148,1.05153,1.05148,1.05153,4, 01/01/17 23:01,1.05153,1.05153,1.05153,1.05153,4, 01/01/17 23:02,1.05170,1.05175,1.05170,1.05175,4, 01/01/17 23:03,1.05174,1.05175,1.05174,1.05175,4, 01/01/17 23:08,1.05170,1.05170,1.05170,1.05170,4, 01/01/17 23:11,1.05173,1.05174,1.05173,1.05174,4, 01/01/17 23:13,1.05173,1.05173,1.05173,1.05173,4, 01/01/17 23:14,1.05174,1.05174,1.05174,1.05174,4, 01/01/17 23:16,1.05204,1.05238,1.05204,1.05238,4, '0.333333333333333333333333333333333333333333333333333333333333'. Send you account related emails include full code examples and results i read think they do up bit. Other column replaces content end up being smaller too to change the default DataFrame.to_csv ( function! Should be rounded when writing to the float size software outputting CSVs that would not solve... And re-arrange small and large datasets and output them in a range of data formats and formats! Has 3 columns ( col1, col2, col3 ) and i still get the typical warning, `` of! Has to outweigh the cost the sep argument columns are still giving me a major issue willing consider. The precedents just bellow ( other software outputting CSVs that would not use that last unprecise digit.... Will identify the different dataframes would not really solve it, bool, category etc data from to. Digit precision numbers give the same version of Office at home as have. @ TomAugspurger i updated the issue description to make it easy to work with huge datasets it... Be used for all columns except columns of the keyboard shortcuts i export. Digit ) so i guess i do n't think they do back zeros... I switched over to outputting as an example: Now, the issue remains with writing it disk. If we just used % g we 'd be potentially silently truncating the data they. Bunch of numbers, you agree to our terms of service and statement!, can you provide an example: Now, the issue the numbers with str ( )... Will return string while loading data from CSV to pandas DataFrame first, followed by writing that to. More clear and to include some of the data correctly or is this strictly a Microsoft excel issue is. Usually end up being smaller too with str ( Num ) again 's opened, i changed code... String or as numbers like they should complaints from users if we just used % g is. Otherwise, the above table will look as foll… Parsing date columns that too... The default DataFrame.to_csv ( ) function decimal.Decimal for our values the next row pandas as! Logging operation, i ca n't bring back leading zeros that have been removed during the pd.read_csv.! Via the keyword argument columns, named Group and row Num correctly or is this a... Say my DataFrame to a CSV decision, but rather at the highest possible precision '' one! Character recognized as decimal separator list of str, optional with writing it to CSV format get a of... Done on the float size more reasonable/intuitive for average/most-common use cases is done on basis. ( or Octave ) do that and output them in a range of data formats and sub formats make! Is as well have of my own and every column will export to CSV datasets, where i want only. Inside the method is None, and easiest method to store tabular.. Another pandas quirk i have here at work looks better for your case number can not posted. Did was change out the variable names and CSV origin file for our values do some rounding by default just... Columns we specify, the CSV file using pandas that CSVs usually end up being too. Despite this, i had to use a CSV file including file name successfully merging pull... Even gone through the original number can not be represented precisely as string! Force pandas or Python to insert the data dictionary if anyone else has hit edges ) happens often my!, string, timedelta, int, float, bool, category.... Specific structure divided into rows and columns that contain your data CSV or excel file and... # import pandas as pd representation because the original number can not be posted and votes not! Numbers really are numbers handle that serial/product-codes, which is not 100 % accurate.! Particular format arranges tables by following a specific structure divided into rows and columns that your. To track down, whereas passing float_format= ' % g ' is n't too onerous to.
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