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35 to read a csv file as a pandas dataframe, you'll need to use pd.read_csv, which has sep=',' as the default I managed to get pandas to read . But this isn't where the story ends
Data exists in many different formats and is stored in different ways so you will often need to pass additional parameters to read_csv to ensure your data is read in properly. The string nan is a possible value, as is an empty string Read_csv takes an encoding option to deal with files in different formats
I am trying to read a large csv file (aprox
6 gb) in pandas and i am getting a memory error The pandas.read_csv() function has a keyword argument called parse_dates using this you can on the fly convert strings, floats or integers into datetimes using the default date_parser (dateutil.parser.parser) 9 i'm using pandas.read_csv() and i found quotechar and quoting parameters in it, pandas.read_csv(filepath_or_buffer, sep=', ' , quotechar='', quoting=0) what is the exact use of these parameters I checked the documentation, but i can't comprehend it.
The pandas read_csv () method interprets 'na' as nan (not a number) instead of a valid string In the simple case below note that the output in row 1, column 2 (zero based count) is 'nan' instead of 'na'. You could use pandas.to_datetime() as recommended in the documentation for pandas.read_csv() If a column or index contains an unparseable date, the entire column or index will be returned unaltered as an object data type.
I can't see how not to import it beca.
With python or pandas when you use read_csv or pd.read_csv, both of them look into current working directory, by default where the python process have started So you need to use os module to chdir() and take it from there. I'm using the pandas library to read in some csv data In my data, certain columns contain strings
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