pandas: powerful Python data analysis toolkit，,pandas.rea
pandas: powerful Python data analysis toolkit，,pandas.rea
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 nonamespassed, otherwiseNone.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 innamesor inferred from the document header row(s). For example, a validusecolsparameter 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
pandas: powerful Python data analysis toolkit
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