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I a importing a .csv file in python with pandas.
Here is the file format from the .csv :
a1;b1;c1;d1;e1;... a2;b2;c2;d2;e2;... ..... here is how get it :
from pandas import * csv_path = "C:...." data = read_csv(csv_path) Now when I print the file I get that :
0 a1;b1;c1;d1;e1;... 1 a2;b2;c2;d2;e2;... And so on... So I need help to read the file and split the values in columns, with the semi color character ;.
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pandas. read_csv(filepath_or_buffer, sep=', ', delimiter=None, header='infer', names=None, index_col=None, ....) It reads the content of a csv file at given path, then loads the content to a Dataframe and returns that. It uses comma (,) as default delimiter or separator while parsing a file.
Pandas provide a method to split string around a passed separator/delimiter. After that, the string can be stored as a list in a series or it can also be used to create multiple column data frames from a single separated string.
The argument delim_whitespace controls whether or not whitespace (e.g. ' ' or ' ' ) will be used as separator. See pandas. read_csv for details.
Pandas - Space, tab and custom data separators Data files need not always be comma separated. Space, tabs, semi-colons or other custom separators may be needed. Consider storing addresses where commas may be used within the data, which makes it impossible to use it as data separator.
We will read the text file with pandas using the read_csv () function. Along with the text file, we also pass separator as a single space (‘ ’) for the space character because, for text files, the space character will separate each field.
For Excel to be able to read a CSV file with a field separator used in a given CSV file, you can specify the separator directly in that file. For this, open your file in any text editor, say Notepad, and type the below string before any other data:
Consider storing addresses where commas may be used within the data, which makes it impossible to use it as data separator. Let us examine the default behavior of read_csv (), and make changes to accommodate custom separators. The default separator for read_csv () is comma.
read_csv takes a sep param, in your case just pass sep=';' like so:
data = read_csv(csv_path, sep=';') The reason it failed in your case is that the default value is ',' so it scrunched up all the columns as a single column entry.
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