pandas reading CSV data formatted with comma for thousands separator

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I am trying to create a dataframe in pandas using a CSV that is semicolon-delimited, and uses commas for the thousands separator on numeric data. Is there a way to read this in so that the type of the column is float and not string?


Pass param thousands=',' to read_csv to read those values as thousands:

In [27]:
import pandas as pd
import io

t="""id;value
0;123,123
1;221,323,330
2;32,001"""
pd.read_csv(io.StringIO(t), thousands=r',', sep=';')

Out[27]:
   id      value
0   0     123123
1   1  221323330
2   2      32001

Support parsing thousands separators in floating point data � Issue , I am trying to create a dataframe in pandas using a CSV that is semicolon- delimited, and uses commas for the thousands separator on numeric data. Is there a� Read a comma-separated values (csv) file into DataFrame. Also supports optionally iterating or breaking of the file into chunks. Additional help can be found in the online docs for IO Tools. Parameters filepath_or_buffer str, path object or file-like object. Any valid string path is acceptable. The string could be a URL.


Take a look at the read_csv documentation there is a keyword argument 'thousands' that you can pass the ',' into. Likewise if you had European data containing a '.' for the separator you could do the same.

Pandas show thousand separator, xref #584 It seems that the decimal format works ok for the decimal sign or Example import pandas as pd from StringIO import StringIO data = """A;B csv_import: Thousands separator works in floating point numbers #4598. I am trying to read a csv file with commas as thousands separators into a pandas dataframe. The one time I am running the code it runs fine, while the other time it does not parse one of the columns correctly. Please find an example of my code below:


The answer to this question should be short:

df=pd.read_csv('filename.csv', thousands=',')

pandas.read_csv — pandas 1.1.1 documentation, Between the read & write, pandas can probably make short work of whatever Jan 25, 2018 � CSV files, comma separator and decimal number format 1,000. It uses comma (,) as default delimiter or separator while parsing a file. But we can also specify our custom separator or a regular expression to be used as custom separator. To use pandas.read_csv() import pandas module i.e. import pandas as pd. Using read_csv() with custom delimiter


pandas.read_csv — pandas 0.21.1 documentation, Read a comma-separated values (csv) file into DataFrame. Row number(s) to use as the column names, and the start of the data. If True and parse_dates is enabled, pandas will attempt to infer the format of the Thousands separator. pandas read_csv parameters. Lets now try to understand what are the different parameters of pandas read_csv and how to use them. sep. If the separator between each field of your data is not a comma, use the sep argument.For example, we want to change these pipe separated values to a dataframe using pandas read_csv separator.


In addition, as row indices are not available in such a format, the index_col parameter will be ignored. squeeze : boolean, default False. If the parsed data only� Also, the format spec '{:,}' doesn't contain any type '%'/'f'/etc., so it will default to 's' and try to apply it to all types. That's supposed to work, but if it doesn't, you'd have to fall back to column-specific, type-specific format specifiers/ custom formatters. It helps when reporting issues to say which version of pandas. – smci Apr 30


pd.read_csv(file_name, header=0) sep. Sep is the separator variable used to separate you columns. Most files use commas between columns in csv format, however you can sometimes have / or | separators (or others) in files.