laywerrobot/lib/python3.6/site-packages/pandas/io/json/json.py
2020-08-27 21:55:39 +02:00

929 lines
33 KiB
Python

# pylint: disable-msg=E1101,W0613,W0603
from itertools import islice
import os
import numpy as np
import pandas._libs.json as json
from pandas._libs.tslib import iNaT
from pandas.compat import StringIO, long, u, to_str
from pandas import compat, isna
from pandas import Series, DataFrame, to_datetime, MultiIndex
from pandas.io.common import (get_filepath_or_buffer, _get_handle,
_infer_compression, _stringify_path,
BaseIterator)
from pandas.io.parsers import _validate_integer
import pandas.core.common as com
from pandas.core.reshape.concat import concat
from pandas.io.formats.printing import pprint_thing
from .normalize import _convert_to_line_delimits
from .table_schema import build_table_schema, parse_table_schema
from pandas.core.dtypes.common import is_period_dtype
loads = json.loads
dumps = json.dumps
TABLE_SCHEMA_VERSION = '0.20.0'
# interface to/from
def to_json(path_or_buf, obj, orient=None, date_format='epoch',
double_precision=10, force_ascii=True, date_unit='ms',
default_handler=None, lines=False, compression=None,
index=True):
if not index and orient not in ['split', 'table']:
raise ValueError("'index=False' is only valid when 'orient' is "
"'split' or 'table'")
path_or_buf = _stringify_path(path_or_buf)
if lines and orient != 'records':
raise ValueError(
"'lines' keyword only valid when 'orient' is records")
if orient == 'table' and isinstance(obj, Series):
obj = obj.to_frame(name=obj.name or 'values')
if orient == 'table' and isinstance(obj, DataFrame):
writer = JSONTableWriter
elif isinstance(obj, Series):
writer = SeriesWriter
elif isinstance(obj, DataFrame):
writer = FrameWriter
else:
raise NotImplementedError("'obj' should be a Series or a DataFrame")
s = writer(
obj, orient=orient, date_format=date_format,
double_precision=double_precision, ensure_ascii=force_ascii,
date_unit=date_unit, default_handler=default_handler,
index=index).write()
if lines:
s = _convert_to_line_delimits(s)
if isinstance(path_or_buf, compat.string_types):
fh, handles = _get_handle(path_or_buf, 'w', compression=compression)
try:
fh.write(s)
finally:
fh.close()
elif path_or_buf is None:
return s
else:
path_or_buf.write(s)
class Writer(object):
def __init__(self, obj, orient, date_format, double_precision,
ensure_ascii, date_unit, index, default_handler=None):
self.obj = obj
if orient is None:
orient = self._default_orient
self.orient = orient
self.date_format = date_format
self.double_precision = double_precision
self.ensure_ascii = ensure_ascii
self.date_unit = date_unit
self.default_handler = default_handler
self.index = index
self.is_copy = None
self._format_axes()
def _format_axes(self):
raise com.AbstractMethodError(self)
def write(self):
return self._write(self.obj, self.orient, self.double_precision,
self.ensure_ascii, self.date_unit,
self.date_format == 'iso', self.default_handler)
def _write(self, obj, orient, double_precision, ensure_ascii,
date_unit, iso_dates, default_handler):
return dumps(
obj,
orient=orient,
double_precision=double_precision,
ensure_ascii=ensure_ascii,
date_unit=date_unit,
iso_dates=iso_dates,
default_handler=default_handler
)
class SeriesWriter(Writer):
_default_orient = 'index'
def _format_axes(self):
if not self.obj.index.is_unique and self.orient == 'index':
raise ValueError("Series index must be unique for orient="
"'{orient}'".format(orient=self.orient))
def _write(self, obj, orient, double_precision, ensure_ascii,
date_unit, iso_dates, default_handler):
if not self.index and orient == 'split':
obj = {"name": obj.name, "data": obj.values}
return super(SeriesWriter, self)._write(obj, orient,
double_precision,
ensure_ascii, date_unit,
iso_dates, default_handler)
class FrameWriter(Writer):
_default_orient = 'columns'
def _format_axes(self):
""" try to axes if they are datelike """
if not self.obj.index.is_unique and self.orient in (
'index', 'columns'):
raise ValueError("DataFrame index must be unique for orient="
"'{orient}'.".format(orient=self.orient))
if not self.obj.columns.is_unique and self.orient in (
'index', 'columns', 'records'):
raise ValueError("DataFrame columns must be unique for orient="
"'{orient}'.".format(orient=self.orient))
def _write(self, obj, orient, double_precision, ensure_ascii,
date_unit, iso_dates, default_handler):
if not self.index and orient == 'split':
obj = obj.to_dict(orient='split')
del obj["index"]
return super(FrameWriter, self)._write(obj, orient,
double_precision,
ensure_ascii, date_unit,
iso_dates, default_handler)
class JSONTableWriter(FrameWriter):
_default_orient = 'records'
def __init__(self, obj, orient, date_format, double_precision,
ensure_ascii, date_unit, index, default_handler=None):
"""
Adds a `schema` attribute with the Table Schema, resets
the index (can't do in caller, because the schema inference needs
to know what the index is, forces orient to records, and forces
date_format to 'iso'.
"""
super(JSONTableWriter, self).__init__(
obj, orient, date_format, double_precision, ensure_ascii,
date_unit, index, default_handler=default_handler)
if date_format != 'iso':
msg = ("Trying to write with `orient='table'` and "
"`date_format='{fmt}'`. Table Schema requires dates "
"to be formatted with `date_format='iso'`"
.format(fmt=date_format))
raise ValueError(msg)
self.schema = build_table_schema(obj, index=self.index)
# NotImplementd on a column MultiIndex
if obj.ndim == 2 and isinstance(obj.columns, MultiIndex):
raise NotImplementedError(
"orient='table' is not supported for MultiIndex")
# TODO: Do this timedelta properly in objToJSON.c See GH #15137
if ((obj.ndim == 1) and (obj.name in set(obj.index.names)) or
len(obj.columns & obj.index.names)):
msg = "Overlapping names between the index and columns"
raise ValueError(msg)
obj = obj.copy()
timedeltas = obj.select_dtypes(include=['timedelta']).columns
if len(timedeltas):
obj[timedeltas] = obj[timedeltas].applymap(
lambda x: x.isoformat())
# Convert PeriodIndex to datetimes before serialzing
if is_period_dtype(obj.index):
obj.index = obj.index.to_timestamp()
# exclude index from obj if index=False
if not self.index:
self.obj = obj.reset_index(drop=True)
else:
self.obj = obj.reset_index(drop=False)
self.date_format = 'iso'
self.orient = 'records'
self.index = index
def _write(self, obj, orient, double_precision, ensure_ascii,
date_unit, iso_dates, default_handler):
data = super(JSONTableWriter, self)._write(obj, orient,
double_precision,
ensure_ascii, date_unit,
iso_dates,
default_handler)
serialized = '{{"schema": {schema}, "data": {data}}}'.format(
schema=dumps(self.schema), data=data)
return serialized
def read_json(path_or_buf=None, orient=None, typ='frame', dtype=True,
convert_axes=True, convert_dates=True, keep_default_dates=True,
numpy=False, precise_float=False, date_unit=None, encoding=None,
lines=False, chunksize=None, compression='infer'):
"""
Convert a JSON string to pandas object
Parameters
----------
path_or_buf : a valid JSON string or file-like, default: None
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.json``
orient : string,
Indication of expected JSON string format.
Compatible JSON strings can be produced by ``to_json()`` with a
corresponding orient value.
The set of possible orients is:
- ``'split'`` : dict like
``{index -> [index], columns -> [columns], data -> [values]}``
- ``'records'`` : list like
``[{column -> value}, ... , {column -> value}]``
- ``'index'`` : dict like ``{index -> {column -> value}}``
- ``'columns'`` : dict like ``{column -> {index -> value}}``
- ``'values'`` : just the values array
The allowed and default values depend on the value
of the `typ` parameter.
* when ``typ == 'series'``,
- allowed orients are ``{'split','records','index'}``
- default is ``'index'``
- The Series index must be unique for orient ``'index'``.
* when ``typ == 'frame'``,
- allowed orients are ``{'split','records','index',
'columns','values', 'table'}``
- default is ``'columns'``
- The DataFrame index must be unique for orients ``'index'`` and
``'columns'``.
- The DataFrame columns must be unique for orients ``'index'``,
``'columns'``, and ``'records'``.
.. versionadded:: 0.23.0
'table' as an allowed value for the ``orient`` argument
typ : type of object to recover (series or frame), default 'frame'
dtype : boolean or dict, default True
If True, infer dtypes, if a dict of column to dtype, then use those,
if False, then don't infer dtypes at all, applies only to the data.
convert_axes : boolean, default True
Try to convert the axes to the proper dtypes.
convert_dates : boolean, default True
List of columns to parse for dates; If True, then try to parse
datelike columns default is True; a column label is datelike if
* it ends with ``'_at'``,
* it ends with ``'_time'``,
* it begins with ``'timestamp'``,
* it is ``'modified'``, or
* it is ``'date'``
keep_default_dates : boolean, default True
If parsing dates, then parse the default datelike columns
numpy : boolean, default False
Direct decoding to numpy arrays. Supports numeric data only, but
non-numeric column and index labels are supported. Note also that the
JSON ordering MUST be the same for each term if numpy=True.
precise_float : boolean, default False
Set to enable usage of higher precision (strtod) function when
decoding string to double values. Default (False) is to use fast but
less precise builtin functionality
date_unit : string, default None
The timestamp unit to detect if converting dates. The default behaviour
is to try and detect the correct precision, but if this is not desired
then pass one of 's', 'ms', 'us' or 'ns' to force parsing only seconds,
milliseconds, microseconds or nanoseconds respectively.
lines : boolean, default False
Read the file as a json object per line.
.. versionadded:: 0.19.0
encoding : str, default is 'utf-8'
The encoding to use to decode py3 bytes.
.. versionadded:: 0.19.0
chunksize: integer, default None
Return JsonReader object for iteration.
See the `line-delimted json docs
<http://pandas.pydata.org/pandas-docs/stable/io.html#io-jsonl>`_
for more information on ``chunksize``.
This can only be passed if `lines=True`.
If this is None, the file will be read into memory all at once.
.. versionadded:: 0.21.0
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 path_or_buf 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:: 0.21.0
Returns
-------
result : Series or DataFrame, depending on the value of `typ`.
Notes
-----
Specific to ``orient='table'``, if a :class:`DataFrame` with a literal
:class:`Index` name of `index` gets written with :func:`to_json`, the
subsequent read operation will incorrectly set the :class:`Index` name to
``None``. This is because `index` is also used by :func:`DataFrame.to_json`
to denote a missing :class:`Index` name, and the subsequent
:func:`read_json` operation cannot distinguish between the two. The same
limitation is encountered with a :class:`MultiIndex` and any names
beginning with ``'level_'``.
See Also
--------
DataFrame.to_json
Examples
--------
>>> df = pd.DataFrame([['a', 'b'], ['c', 'd']],
... index=['row 1', 'row 2'],
... columns=['col 1', 'col 2'])
Encoding/decoding a Dataframe using ``'split'`` formatted JSON:
>>> df.to_json(orient='split')
'{"columns":["col 1","col 2"],
"index":["row 1","row 2"],
"data":[["a","b"],["c","d"]]}'
>>> pd.read_json(_, orient='split')
col 1 col 2
row 1 a b
row 2 c d
Encoding/decoding a Dataframe using ``'index'`` formatted JSON:
>>> df.to_json(orient='index')
'{"row 1":{"col 1":"a","col 2":"b"},"row 2":{"col 1":"c","col 2":"d"}}'
>>> pd.read_json(_, orient='index')
col 1 col 2
row 1 a b
row 2 c d
Encoding/decoding a Dataframe using ``'records'`` formatted JSON.
Note that index labels are not preserved with this encoding.
>>> df.to_json(orient='records')
'[{"col 1":"a","col 2":"b"},{"col 1":"c","col 2":"d"}]'
>>> pd.read_json(_, orient='records')
col 1 col 2
0 a b
1 c d
Encoding with Table Schema
>>> df.to_json(orient='table')
'{"schema": {"fields": [{"name": "index", "type": "string"},
{"name": "col 1", "type": "string"},
{"name": "col 2", "type": "string"}],
"primaryKey": "index",
"pandas_version": "0.20.0"},
"data": [{"index": "row 1", "col 1": "a", "col 2": "b"},
{"index": "row 2", "col 1": "c", "col 2": "d"}]}'
"""
compression = _infer_compression(path_or_buf, compression)
filepath_or_buffer, _, compression, should_close = get_filepath_or_buffer(
path_or_buf, encoding=encoding, compression=compression,
)
json_reader = JsonReader(
filepath_or_buffer, orient=orient, typ=typ, dtype=dtype,
convert_axes=convert_axes, convert_dates=convert_dates,
keep_default_dates=keep_default_dates, numpy=numpy,
precise_float=precise_float, date_unit=date_unit, encoding=encoding,
lines=lines, chunksize=chunksize, compression=compression,
)
if chunksize:
return json_reader
result = json_reader.read()
if should_close:
try:
filepath_or_buffer.close()
except: # noqa: flake8
pass
return result
class JsonReader(BaseIterator):
"""
JsonReader provides an interface for reading in a JSON file.
If initialized with ``lines=True`` and ``chunksize``, can be iterated over
``chunksize`` lines at a time. Otherwise, calling ``read`` reads in the
whole document.
"""
def __init__(self, filepath_or_buffer, orient, typ, dtype, convert_axes,
convert_dates, keep_default_dates, numpy, precise_float,
date_unit, encoding, lines, chunksize, compression):
self.path_or_buf = filepath_or_buffer
self.orient = orient
self.typ = typ
self.dtype = dtype
self.convert_axes = convert_axes
self.convert_dates = convert_dates
self.keep_default_dates = keep_default_dates
self.numpy = numpy
self.precise_float = precise_float
self.date_unit = date_unit
self.encoding = encoding
self.compression = compression
self.lines = lines
self.chunksize = chunksize
self.nrows_seen = 0
self.should_close = False
if self.chunksize is not None:
self.chunksize = _validate_integer("chunksize", self.chunksize, 1)
if not self.lines:
raise ValueError("chunksize can only be passed if lines=True")
data = self._get_data_from_filepath(filepath_or_buffer)
self.data = self._preprocess_data(data)
def _preprocess_data(self, data):
"""
At this point, the data either has a `read` attribute (e.g. a file
object or a StringIO) or is a string that is a JSON document.
If self.chunksize, we prepare the data for the `__next__` method.
Otherwise, we read it into memory for the `read` method.
"""
if hasattr(data, 'read') and not self.chunksize:
data = data.read()
if not hasattr(data, 'read') and self.chunksize:
data = StringIO(data)
return data
def _get_data_from_filepath(self, filepath_or_buffer):
"""
read_json accepts three input types:
1. filepath (string-like)
2. file-like object (e.g. open file object, StringIO)
3. JSON string
This method turns (1) into (2) to simplify the rest of the processing.
It returns input types (2) and (3) unchanged.
"""
data = filepath_or_buffer
exists = False
if isinstance(data, compat.string_types):
try:
exists = os.path.exists(filepath_or_buffer)
# gh-5874: if the filepath is too long will raise here
except (TypeError, ValueError):
pass
if exists or self.compression is not None:
data, _ = _get_handle(filepath_or_buffer, 'r',
encoding=self.encoding,
compression=self.compression)
self.should_close = True
self.open_stream = data
return data
def _combine_lines(self, lines):
"""Combines a list of JSON objects into one JSON object"""
lines = filter(None, map(lambda x: x.strip(), lines))
return '[' + ','.join(lines) + ']'
def read(self):
"""Read the whole JSON input into a pandas object"""
if self.lines and self.chunksize:
obj = concat(self)
elif self.lines:
data = to_str(self.data)
obj = self._get_object_parser(
self._combine_lines(data.split('\n'))
)
else:
obj = self._get_object_parser(self.data)
self.close()
return obj
def _get_object_parser(self, json):
"""parses a json document into a pandas object"""
typ = self.typ
dtype = self.dtype
kwargs = {
"orient": self.orient, "dtype": self.dtype,
"convert_axes": self.convert_axes,
"convert_dates": self.convert_dates,
"keep_default_dates": self.keep_default_dates, "numpy": self.numpy,
"precise_float": self.precise_float, "date_unit": self.date_unit
}
obj = None
if typ == 'frame':
obj = FrameParser(json, **kwargs).parse()
if typ == 'series' or obj is None:
if not isinstance(dtype, bool):
dtype = dict(data=dtype)
obj = SeriesParser(json, **kwargs).parse()
return obj
def close(self):
"""
If we opened a stream earlier, in _get_data_from_filepath, we should
close it. If an open stream or file was passed, we leave it open.
"""
if self.should_close:
try:
self.open_stream.close()
except (IOError, AttributeError):
pass
def __next__(self):
lines = list(islice(self.data, self.chunksize))
if lines:
lines_json = self._combine_lines(lines)
obj = self._get_object_parser(lines_json)
# Make sure that the returned objects have the right index.
obj.index = range(self.nrows_seen, self.nrows_seen + len(obj))
self.nrows_seen += len(obj)
return obj
self.close()
raise StopIteration
class Parser(object):
_STAMP_UNITS = ('s', 'ms', 'us', 'ns')
_MIN_STAMPS = {
's': long(31536000),
'ms': long(31536000000),
'us': long(31536000000000),
'ns': long(31536000000000000)}
def __init__(self, json, orient, dtype=True, convert_axes=True,
convert_dates=True, keep_default_dates=False, numpy=False,
precise_float=False, date_unit=None):
self.json = json
if orient is None:
orient = self._default_orient
self.orient = orient
self.dtype = dtype
if orient == "split":
numpy = False
if date_unit is not None:
date_unit = date_unit.lower()
if date_unit not in self._STAMP_UNITS:
raise ValueError('date_unit must be one of {units}'
.format(units=self._STAMP_UNITS))
self.min_stamp = self._MIN_STAMPS[date_unit]
else:
self.min_stamp = self._MIN_STAMPS['s']
self.numpy = numpy
self.precise_float = precise_float
self.convert_axes = convert_axes
self.convert_dates = convert_dates
self.date_unit = date_unit
self.keep_default_dates = keep_default_dates
self.obj = None
def check_keys_split(self, decoded):
"checks that dict has only the appropriate keys for orient='split'"
bad_keys = set(decoded.keys()).difference(set(self._split_keys))
if bad_keys:
bad_keys = ", ".join(bad_keys)
raise ValueError(u("JSON data had unexpected key(s): {bad_keys}")
.format(bad_keys=pprint_thing(bad_keys)))
def parse(self):
# try numpy
numpy = self.numpy
if numpy:
self._parse_numpy()
else:
self._parse_no_numpy()
if self.obj is None:
return None
if self.convert_axes:
self._convert_axes()
self._try_convert_types()
return self.obj
def _convert_axes(self):
""" try to convert axes """
for axis in self.obj._AXIS_NUMBERS.keys():
new_axis, result = self._try_convert_data(
axis, self.obj._get_axis(axis), use_dtypes=False,
convert_dates=True)
if result:
setattr(self.obj, axis, new_axis)
def _try_convert_types(self):
raise com.AbstractMethodError(self)
def _try_convert_data(self, name, data, use_dtypes=True,
convert_dates=True):
""" try to parse a ndarray like into a column by inferring dtype """
# don't try to coerce, unless a force conversion
if use_dtypes:
if self.dtype is False:
return data, False
elif self.dtype is True:
pass
else:
# dtype to force
dtype = (self.dtype.get(name)
if isinstance(self.dtype, dict) else self.dtype)
if dtype is not None:
try:
dtype = np.dtype(dtype)
return data.astype(dtype), True
except (TypeError, ValueError):
return data, False
if convert_dates:
new_data, result = self._try_convert_to_date(data)
if result:
return new_data, True
result = False
if data.dtype == 'object':
# try float
try:
data = data.astype('float64')
result = True
except (TypeError, ValueError):
pass
if data.dtype.kind == 'f':
if data.dtype != 'float64':
# coerce floats to 64
try:
data = data.astype('float64')
result = True
except (TypeError, ValueError):
pass
# do't coerce 0-len data
if len(data) and (data.dtype == 'float' or data.dtype == 'object'):
# coerce ints if we can
try:
new_data = data.astype('int64')
if (new_data == data).all():
data = new_data
result = True
except (TypeError, ValueError):
pass
# coerce ints to 64
if data.dtype == 'int':
# coerce floats to 64
try:
data = data.astype('int64')
result = True
except (TypeError, ValueError):
pass
return data, result
def _try_convert_to_date(self, data):
""" try to parse a ndarray like into a date column
try to coerce object in epoch/iso formats and
integer/float in epcoh formats, return a boolean if parsing
was successful """
# no conversion on empty
if not len(data):
return data, False
new_data = data
if new_data.dtype == 'object':
try:
new_data = data.astype('int64')
except (TypeError, ValueError, OverflowError):
pass
# ignore numbers that are out of range
if issubclass(new_data.dtype.type, np.number):
in_range = (isna(new_data.values) | (new_data > self.min_stamp) |
(new_data.values == iNaT))
if not in_range.all():
return data, False
date_units = (self.date_unit,) if self.date_unit else self._STAMP_UNITS
for date_unit in date_units:
try:
new_data = to_datetime(new_data, errors='raise',
unit=date_unit)
except ValueError:
continue
except Exception:
break
return new_data, True
return data, False
def _try_convert_dates(self):
raise com.AbstractMethodError(self)
class SeriesParser(Parser):
_default_orient = 'index'
_split_keys = ('name', 'index', 'data')
def _parse_no_numpy(self):
json = self.json
orient = self.orient
if orient == "split":
decoded = {str(k): v for k, v in compat.iteritems(
loads(json, precise_float=self.precise_float))}
self.check_keys_split(decoded)
self.obj = Series(dtype=None, **decoded)
else:
self.obj = Series(
loads(json, precise_float=self.precise_float), dtype=None)
def _parse_numpy(self):
json = self.json
orient = self.orient
if orient == "split":
decoded = loads(json, dtype=None, numpy=True,
precise_float=self.precise_float)
decoded = {str(k): v for k, v in compat.iteritems(decoded)}
self.check_keys_split(decoded)
self.obj = Series(**decoded)
elif orient == "columns" or orient == "index":
self.obj = Series(*loads(json, dtype=None, numpy=True,
labelled=True,
precise_float=self.precise_float))
else:
self.obj = Series(loads(json, dtype=None, numpy=True,
precise_float=self.precise_float))
def _try_convert_types(self):
if self.obj is None:
return
obj, result = self._try_convert_data(
'data', self.obj, convert_dates=self.convert_dates)
if result:
self.obj = obj
class FrameParser(Parser):
_default_orient = 'columns'
_split_keys = ('columns', 'index', 'data')
def _parse_numpy(self):
json = self.json
orient = self.orient
if orient == "columns":
args = loads(json, dtype=None, numpy=True, labelled=True,
precise_float=self.precise_float)
if len(args):
args = (args[0].T, args[2], args[1])
self.obj = DataFrame(*args)
elif orient == "split":
decoded = loads(json, dtype=None, numpy=True,
precise_float=self.precise_float)
decoded = {str(k): v for k, v in compat.iteritems(decoded)}
self.check_keys_split(decoded)
self.obj = DataFrame(**decoded)
elif orient == "values":
self.obj = DataFrame(loads(json, dtype=None, numpy=True,
precise_float=self.precise_float))
else:
self.obj = DataFrame(*loads(json, dtype=None, numpy=True,
labelled=True,
precise_float=self.precise_float))
def _parse_no_numpy(self):
json = self.json
orient = self.orient
if orient == "columns":
self.obj = DataFrame(
loads(json, precise_float=self.precise_float), dtype=None)
elif orient == "split":
decoded = {str(k): v for k, v in compat.iteritems(
loads(json, precise_float=self.precise_float))}
self.check_keys_split(decoded)
self.obj = DataFrame(dtype=None, **decoded)
elif orient == "index":
self.obj = DataFrame(
loads(json, precise_float=self.precise_float), dtype=None).T
elif orient == 'table':
self.obj = parse_table_schema(json,
precise_float=self.precise_float)
else:
self.obj = DataFrame(
loads(json, precise_float=self.precise_float), dtype=None)
def _process_converter(self, f, filt=None):
""" take a conversion function and possibly recreate the frame """
if filt is None:
filt = lambda col, c: True
needs_new_obj = False
new_obj = dict()
for i, (col, c) in enumerate(self.obj.iteritems()):
if filt(col, c):
new_data, result = f(col, c)
if result:
c = new_data
needs_new_obj = True
new_obj[i] = c
if needs_new_obj:
# possibly handle dup columns
new_obj = DataFrame(new_obj, index=self.obj.index)
new_obj.columns = self.obj.columns
self.obj = new_obj
def _try_convert_types(self):
if self.obj is None:
return
if self.convert_dates:
self._try_convert_dates()
self._process_converter(
lambda col, c: self._try_convert_data(col, c, convert_dates=False))
def _try_convert_dates(self):
if self.obj is None:
return
# our columns to parse
convert_dates = self.convert_dates
if convert_dates is True:
convert_dates = []
convert_dates = set(convert_dates)
def is_ok(col):
""" return if this col is ok to try for a date parse """
if not isinstance(col, compat.string_types):
return False
col_lower = col.lower()
if (col_lower.endswith('_at') or
col_lower.endswith('_time') or
col_lower == 'modified' or
col_lower == 'date' or
col_lower == 'datetime' or
col_lower.startswith('timestamp')):
return True
return False
self._process_converter(
lambda col, c: self._try_convert_to_date(c),
lambda col, c: ((self.keep_default_dates and is_ok(col)) or
col in convert_dates))