pandas.read_csv
原型：
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)
参数很多，下面就常用的参数给出说明，详细请参考panda文档 ：
filepath_or_buffer : 要读取的csv文件的URL，本地或者远程文件均可。
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
namespassed, otherwiseNone. Explicitly passheader=0to 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 ifskip_blank_lines=True, so header=0 denotes the first line of data rather than the first line of the file.
names : array-like, default None
List of column names to use. If file contains no header row, then you should explicitly pass header=None. Duplicates in this list are not allowed unless mangle_dupe_cols=True, which is the default.
mangle_dupe_cols : boolean, default True
Duplicate columns will be specified as ‘X.0’...’X.N’, rather than ‘X’...’X’. Passing in False will cause data to be overwritten if there are duplicate names in the columns.
skip_blank_lines : boolean, default True
If True, skip over blank lines rather than interpreting as NaN values
返回值：Read CSV (comma-separated) file into DataFrame