Python pandas读取数据文件时,如何指定字段类型(如字符串)? – Python量化投资

Python pandas读取数据文件时,如何指定字段类型(如字符串)?




import pandas as pd




下面好长一段就是这个的对应帮助文档,为了阅读方便, 我把他放在最下面了。


 data = pd.read_csv(fpath,dtype=object)


pd.read_csv(file_path, converters={'date':str, 'stockcode':str, 'open':np.float16}, 'amt':int)


Signature: pd.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, skipfooter=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=b'.', lineterminator=None, quotechar='"', quoting=0, escapechar=None, comment=None, encoding=None, dialect=None, tupleize_cols=False, error_bad_lines=True, warn_bad_lines=True, 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)
Read CSV (comma-separated) 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

filepath_or_buffer : str, pathlib.Path, py._path.local.LocalPath or any object with a read() method (such as a file handle or StringIO)
    The string could be a URL. Valid URL schemes include http, ftp, s3, and
    file. For file URLs, a host is expected. For instance, a local file could
    be file ://localhost/path/to/table.csv
sep : str, default ','
    Delimiter to use. If sep is None, will try to automatically determine
    this. Separators longer than 1 character and different from '\s+' will be
    interpreted as regular expressions, will force use of the python parsing
    engine and will ignore quotes in the data. Regex example: '\r\t'
delimiter : str, default “None“
    Alternative argument name for sep.
delim_whitespace : boolean, default False
    Specifies whether or not whitespace (e.g. “' '“ or “'    '“) will be
    used as the sep. Equivalent to setting “sep='\+s'“. If this option
    is set to True, nothing should be passed in for the “delimiter“

    .. versionadded:: 0.18.1 support for the Python parser.

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“. 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.
names : array-like, default None
    List of column names to use. If file contains no header row, then you
    should explicitly pass header=None
index_col : int or sequence or False, default None
    Column to use as the row labels of the DataFrame. If a sequence is given, a
    MultiIndex is used. If you have a malformed file with delimiters at the end
    of each line, you might consider index_col=False to force pandas to _not_
    use the first column as the index (row names)
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.
squeeze : boolean, default False
    If the parsed data only contains one column then return a Series
prefix : str, default None
    Prefix to add to column numbers when no header, e.g. 'X' for X0, X1, …
mangle_dupe_cols : boolean, default True
    Duplicate columns will be specified as 'X.0'…'X.N', rather than 'X'…'X'
dtype : Type name or dict of column -> type, default None
    Data type for data or columns. E.g. {'a': np.float64, 'b': np.int32}
    (Unsupported with engine='python'). Use `str` or `object` to preserve and
    not interpret dtype.
engine : {'c', 'python'}, optional
    Parser engine to use. The C engine is faster while the python engine is
    currently more feature-complete.
converters : dict, default None
    Dict of functions for converting values in certain columns. Keys can either
    be integers or column labels
true_values : list, default None
    Values to consider as True
false_values : list, default None
    Values to consider as False
skipinitialspace : boolean, default False
    Skip spaces after delimiter.
skiprows : list-like or integer, default None
    Line numbers to skip (0-indexed) or number of lines to skip (int)
    at the start of the file
skipfooter : int, default 0
    Number of lines at bottom of file to skip (Unsupported with engine='c')
nrows : int, default None
    Number of rows of file to read. Useful for reading pieces of large files
na_values : str or list-like or dict, default None
    Additional strings to recognize as NA/NaN. If dict passed, specific
    per-column NA values.  By default the following values are interpreted as
    NaN: `''`, `'#N/A'`, `'#N/A N/A'`, `'#NA'`, `'-1.#IND'`, `'-1.#QNAN'`, `'-NaN'`, `'-nan'`, `'1.#IND'`, `'1.#QNAN'`, `'N/A'`, `'NA'`, `'NULL'`, `'NaN'`, `'nan'`.
keep_default_na : bool, default True
    If na_values are specified and keep_default_na is False the default NaN
    values are overridden, otherwise they're appended to.
na_filter : boolean, default True
    Detect missing value markers (empty strings and the value of na_values). In
    data without any NAs, passing na_filter=False can improve the performance
    of reading a large file
verbose : boolean, default False
    Indicate number of NA values placed in non-numeric columns
skip_blank_lines : boolean, default True
    If True, skip over blank lines rather than interpreting as NaN values
parse_dates : boolean or list of ints or names or list of lists or dict, default False

    * boolean. If True -> try parsing the index.
    * list of ints or names. e.g. If [1, 2, 3] -> try parsing columns 1, 2, 3
      each as a separate date column.
    * list of lists. e.g.  If [[1, 3]] -> combine columns 1 and 3 and parse as
        a single date column.
    * dict, e.g. {'foo' : [1, 3]} -> parse columns 1, 3 as date and call result

    Note: A fast-path exists for iso8601-formatted dates.
infer_datetime_format : boolean, default False
    If True and parse_dates is enabled, pandas will attempt to infer the format
    of the datetime strings in the columns, and if it can be inferred, switch
    to a faster method of parsing them. In some cases this can increase the
    parsing speed by ~5-10x.
keep_date_col : boolean, default False
    If True and parse_dates specifies combining multiple columns then
    keep the original columns.
date_parser : function, default None
    Function to use for converting a sequence of string columns to an array of
    datetime instances. The default uses “dateutil.parser.parser“ to do the
    conversion. Pandas will try to call date_parser in three different ways,
    advancing to the next if an exception occurs: 1) Pass one or more arrays
    (as defined by parse_dates) as arguments; 2) concatenate (row-wise) the
    string values from the columns defined by parse_dates into a single array
    and pass that; and 3) call date_parser once for each row using one or more
    strings (corresponding to the columns defined by parse_dates) as arguments.
dayfirst : boolean, default False
    DD/MM format dates, international and European format
iterator : boolean, default False
    Return TextFileReader object for iteration or getting chunks with
chunksize : int, default None
    Return TextFileReader object for iteration. `See IO Tools docs for more
    <>`_ on
    “iterator“ and “chunksize“.
compression : {'infer', 'gzip', 'bz2', 'zip', 'xz', None}, default 'infer'
    For on-the-fly decompression of on-disk data. If 'infer', then use gzip,
    bz2, zip or xz if filepath_or_buffer is a string ending in '.gz', '.bz2',
    '.zip', or 'xz', respectively, and no decompression otherwise. If using
    'zip', the ZIP file must contain only one data file to be read in.
    Set to None for no decompression.

    .. versionadded:: 0.18.1 support for 'zip' and 'xz' compression.

thousands : str, default None
    Thousands separator
decimal : str, default '.'
    Character to recognize as decimal point (e.g. use ',' for European data).
lineterminator : str (length 1), default None
    Character to break file into lines. Only valid with C parser.
quotechar : str (length 1), optional
    The character used to denote the start and end of a quoted item. Quoted
    items can include the delimiter and it will be ignored.
quoting : int or csv.QUOTE_* instance, default None
    Control field quoting behavior per “csv.QUOTE_*“ constants. Use one of
    Default (None) results in QUOTE_MINIMAL behavior.
escapechar : str (length 1), default None
    One-character string used to escape delimiter when quoting is QUOTE_NONE.
comment : str, default None
    Indicates remainder of line should not be parsed. If found at the beginning
    of a line, the line will be ignored altogether. This parameter must be a
    single character. Like empty lines (as long as “skip_blank_lines=True“),
    fully commented lines are ignored by the parameter `header` but not by
    `skiprows`. For example, if comment='#', parsing '#empty\na,b,c\n1,2,3'
    with `header=0` will result in 'a,b,c' being
    treated as the header.
encoding : str, default None
    Encoding to use for UTF when reading/writing (ex. 'utf-8'). `List of Python
    standard encodings
dialect : str or csv.Dialect instance, default None
    If None defaults to Excel dialect. Ignored if sep longer than 1 char
    See csv.Dialect documentation for more details
tupleize_cols : boolean, default False
    Leave a list of tuples on columns as is (default is to convert to
    a Multi Index on the columns)
error_bad_lines : boolean, default True
    Lines with too many fields (e.g. a csv line with too many commas) will by
    default cause an exception to be raised, and no DataFrame will be returned.
    If False, then these "bad lines" will dropped from the DataFrame that is
    returned. (Only valid with C parser)
warn_bad_lines : boolean, default True
    If error_bad_lines is False, and warn_bad_lines is True, a warning for each
    "bad line" will be output. (Only valid with C parser).

result : DataFrame or TextParser
File:      d:\anaconda3\lib\site-packages\pandas\io\
Type:      function


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