pandas: powerful Python data analysis toolkit

Posted 2020-08-25

pandas.read_csv

pandas.read_csv(filepath_or_buffer, sep=‘, ‘, delimiter=None, header=‘infer‘, names=None, index_col=None, usecols=None, squeeze=False, prefix=None, mangle_dupe_cols=True, dtype=None, engine=None, converters=None, true_values=None, false_values=None, skipinitialspace=False, skiprows=None, nrows=None, na_values=None, keep_default_na=True, na_filter=True, verbose=False, skip_blank_lines=True, parse_dates=False, infer_datetime_format=False, keep_date_col=False, date_parser=None, dayfirst=False, iterator=False, chunksize=None, compression=‘infer‘, thousands=None, decimal=‘.‘, lineterminator=None, quotechar=‘"‘, quoting=0, escapechar=None, comment=None, encoding=None, dialect=None, tupleize_cols=False, error_bad_lines=True, warn_bad_lines=True, skipfooter=0, skip_footer=0, doublequote=True, delim_whitespace=False, as_recarray=False, compact_ints=False, use_unsigned=False, low_memory=True, buffer_lines=None, memory_map=False, float_precision=None)[source]

Read CSV (comma-separated) file into DataFrame

dataframe = pandas.read_csv(‘water_demand2009.csv‘,header =None, usecols=None, engine=‘python‘, skipfooter=0)

Parameters:

filepath_or_buffer : str, pathlib.Path, py._path.local.LocalPath or any object with a read() method (such as a file handle or StringIO)

header : int or list of ints, default ‘infer’

• Row number(s) to use as the column names, and the start of the data. Default behavior is as if set to 0 if no names passed, otherwise None. 

usecols : array-like, default None

• Return a subset of the columns. All elements in this array must either be positional (i.e. integer indices into the document columns) or strings that correspond to column names provided either by the user in names or inferred from the document header row(s). For example, a valid usecols parameter would be [0, 1, 2] or [‘foo’, ‘bar’, ‘baz’]. Using this parameter results in much faster parsing time and lower memory usage.

engine : {‘c’, ‘python’}, optional

• Parser engine to use. The C engine is faster while the python engine is currently more feature-complete.

skipfooter : int, default 0

• Number of lines at bottom of file to skip (Unsupported with engine=’c’)

Returns: result : DataFrame or TextParser