Pandas read in table without headers

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How can I read in a .csv file (with no headers) and when I only want a subset of the columns (say 4th and 7th out of a total of 20 columns), using pandas? I cannot seem to be able to do usecols

In order to read a csv in that doesn’t have a header and for only certain columns you need to pass params header=None and usecols=[3,6] for the 4th and 7th columns:

df = pd.read_csv(file_path, header=None, usecols=[3,6])

See the docs

Previous answers were good and correct, but in my opinion, an extra names parameter will make it perfect, and it should be the recommended way, especially when the csv has no headers.


Use usecols and names parameters

df = pd.read_csv(file_path, usecols=[3,6], names=['colA', 'colB'])

Additional reading

or use header=None to explicitly tells people that the csv has no headers (anyway both lines are identical)

df = pd.read_csv(file_path, usecols=[3,6], names=['colA', 'colB'], header=None)

So that you can retrieve your data by

# with `names` parameter

instead of

# without `names` parameter


Based on read_csv, when names are passed explicitly, then header will be behaving like None instead of 0, so one can skip header=None when names exist.

Make sure you specify pass header=None and add usecols=[3,6] for the 4th and 7th columns.

As per documentation :
headerint, list of int, default ‘infer’
Row number(s) to use as the column names, and the start of the data. Default behavior is to infer the column names: if no names are passed the behavior is identical to header=0 and column names are inferred from the first line of the file, if column names are passed explicitly then the behavior is identical to header=None. Explicitly pass header=0 to be able to replace existing names. The header can be a list of integers that specify row locations for a multi-index on the columns e.g. [0,1,3]. Intervening rows that are not specified will be skipped (e.g. 2 in this example is skipped). Note that this parameter ignores commented lines and empty lines if skip_blank_lines=True, so header=0 denotes the first line of data rather than the first line of the file.

namesarray-like, optional
List of column names to use. If the file contains a header row, then you should explicitly pass header=0 to override the column names. Duplicates in this list are not allowed.

columts = ['Day', 'PLMN', 'RNCname']
tempo = pd.read_csv("info.csv", sep=';', header=0, names=columts, index_col=False)

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