def __init__(self, data=None, index=None, columns=None, dataframe=None):
if dataframe is None:
if not data is None:
if isinstance(data, dict):
columns = data.keys()
dlist = []
n = 1
for v in data.values():
if isinstance(v, (list, tuple)):
n = len(v)
v = np.array(v)
elif isinstance(v, np.NDArray):
n = len(v)
dlist.append(v)
for i in range(len(dlist)):
d = dlist[i]
if not isinstance(d, np.NDArray):
d = [d] * n
d = np.array(d)
dlist[i] = d
data = dlist
if isinstance(data, np.NDArray):
n = len(data)
data = data._array
else:
dlist = []
n = len(data[0])
for dd in data:
dlist.append(dd._array)
data = dlist
if index is None:
index = range(0, n)
else:
if n != len(index):
raise ValueError('Wrong length of index!')
if isinstance(index, np.NDArray):
index = index.tolist()
if isinstance(index, Index):
self._index = index
else:
self._index = Index.factory(index)
if data is None:
self._dataframe = MIDataFrame(self._index._index)
else:
self._dataframe = MIDataFrame(data, self._index._index,
columns)
else:
self._dataframe = dataframe
self._index = Index.factory(index=self._dataframe.getIndex())
def __init__(self, data=None, index=None, columns=None, dataframe=None):
if dataframe is None:
if not data is None:
if isinstance(data, dict):
columns = data.keys()
dlist = []
n = 1
for v in data.values():
if isinstance(v, (list, tuple)):
n = len(v)
v = minum.array(v)
elif isinstance(v, MIArray):
n = len(v)
dlist.append(v)
for i in range(len(dlist)):
d = dlist[i]
if not isinstance(d, MIArray):
d = [d] * n
d = minum.array(d)
dlist[i] = d
data = dlist
if isinstance(data, MIArray):
n = len(data)
data = data.array
else:
dlist = []
n = len(data[0])
for dd in data:
dlist.append(dd.array)
data = dlist
if index is None:
index = range(0, n)
else:
if n != len(index):
raise ValueError('Wrong length of index!')
if isinstance(index, (MIArray, DimArray)):
index = index.tolist()
if isinstance(index, Index):
self._index = index
else:
self._index = Index.factory(index)
if data is None:
self._dataframe = MIDataFrame(self._index._index)
else:
self._dataframe = MIDataFrame(data, self._index._index, columns)
else:
self._dataframe = dataframe
self._index = Index.factory(index=self._dataframe.getIndex())
def read_table(cls, filepath, **kwargs):
'''
Create DataFrame by reading column oriented data from a file.
:param filepath: (*string*) File path for reading.
:param delimiter: (*string*) Variable delimiter character. Default is ``None``, means space or tab
delimiter.
:param format: (*string*) Colomn format of the file. Default is ``None``, means all columns were
read as string variable. ``%s``: string; ``%i``: integer; ``%f``: float; ``%{yyyyMMdd...}D``:
date time.
:param skiprows: (*int*) Lines to skip at beginning of the file. Default is ``0``.
:param skipfooter: (*int*) Number of lines at bottom of file to skip.
:param encoding: (*string*) Character encoding scheme associated with the file. Default is ``UTF8``.
:param names: (*array_like*) List of column names to use. If file contains no header row, then you should
explicitly pass header=None. Default is None.
:param header: (*int*) Row number to use as the column names. If column names are passed explicitly
then the behavior is identical to ``header=None``.
:param index_col: (*int*) Column to use as the row labels (index) of the DataFrame.
:param index_format: (*string*) Index column format.
:returns: (*DataFrame*) The DataFrame.
'''
delimiter = kwargs.pop('delimiter', None)
format = kwargs.pop('format', None)
skiprows = kwargs.pop('skiprows', 0)
skipfooter = kwargs.pop('skipfooter', 0)
encoding = kwargs.pop('encoding', 'UTF8')
names = kwargs.pop('names', None)
header = kwargs.pop('header', 0)
index_col = kwargs.pop('index_col', -1)
index_format = kwargs.pop('index_format', None)
midf = MIDataFrame.readTable(filepath, delimiter, skiprows, format,
encoding, index_col, index_format, names,
header, skipfooter)
return DataFrame(dataframe=midf)
def read_table(cls, filepath, **kwargs):
'''
Create DataFrame by reading column oriented data from a file.
:param filepath: (*string*) File path for reading.
:param delimiter: (*string*) Variable delimiter character. Default is ``None``, means space or tab
delimiter.
:param format: (*string*) Colomn format of the file. Default is ``None``, means all columns were
read as string variable. ``%s``: string; ``%i``: integer; ``%f``: float; ``%{yyyyMMdd...}D``:
date time.
:param skiprows: (*int*) Lines to skip at beginning of the file. Default is ``0``.
:param skipfooter: (*int*) Number of lines at bottom of file to skip.
:param encoding: (*string*) Character encoding scheme associated with the file. Default is ``UTF8``.
:param names: (*array_like*) List of column names to use. If file contains no header row, then you should
explicitly pass header=None. Default is None.
:param header: (*int*) Row number to use as the column names. If column names are passed explicitly
then the behavior is identical to ``header=None``.
:param index_col: (*int*) Column to use as the row labels (index) of the DataFrame.
:param index_format: (*string*) Index column format.
:param usecols: (*list*) Return a subset of the columns. If list-like, all elements
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).
:returns: (*DataFrame*) The DataFrame.
'''
delimiter = kwargs.pop('delimiter', None)
format = kwargs.pop('format', None)
skiprows = kwargs.pop('skiprows', 0)
skipfooter = kwargs.pop('skipfooter', 0)
encoding = kwargs.pop('encoding', 'UTF8')
names = kwargs.pop('names', None)
header = kwargs.pop('header', 0)
index_col = kwargs.pop('index_col', -1)
index_format = kwargs.pop('index_format', None)
usecols = kwargs.pop('usecols', None)
if usecols is None:
midf = MIDataFrame.readTable(filepath, delimiter, skiprows, format, encoding,
index_col, index_format, names, header, skipfooter)
else:
midf = MIDataFrame.readTable(filepath, delimiter, skiprows, format, encoding,
index_col, index_format, names, header, skipfooter, usecols)
return DataFrame(dataframe=midf)
class DataFrame(object):
'''
Two-dimensional size-mutable, potentially heterogeneous tabular data structure with
labeled axes (rows and columns). Arithmetic operations align on both row and column
labels. Can be thought of as a dict-like container for Series objects.
:param data: (*array_like*) Two-dimensional array data or list of one-dimensional arrays.
:param index: (*list*) Data index list. Values must be unique and hashable, same length as data.
:param columns: (*list*) Column labels to use for resulting frame. Will default to
arange(n) if no column labels are provided
'''
def __init__(self, data=None, index=None, columns=None, dataframe=None):
if dataframe is None:
if not data is None:
if isinstance(data, dict):
columns = data.keys()
dlist = []
n = 1
for v in data.values():
if isinstance(v, (list, tuple)):
n = len(v)
v = np.array(v)
elif isinstance(v, np.NDArray):
n = len(v)
dlist.append(v)
for i in range(len(dlist)):
d = dlist[i]
if not isinstance(d, np.NDArray):
d = [d] * n
d = np.array(d)
dlist[i] = d
data = dlist
if isinstance(data, np.NDArray):
n = len(data)
data = data._array
else:
dlist = []
n = len(data[0])
for dd in data:
dlist.append(dd._array)
data = dlist
if index is None:
index = range(0, n)
else:
if n != len(index):
raise ValueError('Wrong length of index!')
if isinstance(index, np.NDArray):
index = index.tolist()
if isinstance(index, Index):
self._index = index
else:
self._index = Index.factory(index)
if data is None:
self._dataframe = MIDataFrame(self._index._index)
else:
self._dataframe = MIDataFrame(data, self._index._index,
columns)
else:
self._dataframe = dataframe
self._index = Index.factory(index=self._dataframe.getIndex())
#---- index property
def get_index(self):
return self._index
def set_index(self, value):
if isinstance(value, series.Series):
value = value.values
self._index = Index.factory(value, self._index.name)
self._dataframe.setIndex(self._index._index)
index = property(get_index, set_index)
#---- data property
def get_data(self):
r = self._dataframe.getData()
if isinstance(r, Array):
r = np.array(r)
else:
rr = []
for d in r:
rr.append(np.array(d))
r = rr
return r
def set_data(self, value):
value = np.array(value)
self._dataframe.setData(value._array)
values = property(get_data, set_data)
#---- columns property
def get_columns(self):
return self._dataframe.getColumns()
def set_columns(self, value):
self._dataframe.setColumns(value)
columns = property(get_columns, set_columns)
#---- shape property
def get_shape(self):
s = self._dataframe.getShape()
s1 = []
for i in range(len(s)):
s1.append(s[i])
return tuple(s1)
shape = property(get_shape)
#---- dtypes property
def get_dtypes(self):
colnames = list(self.columns.getNames())
datatypes = list(self.columns.getDataTypes())
r = series.Series(datatypes, colnames, 'DataTypes')
return r
dtypes = property(get_dtypes)
@property
def loc(self):
'''
Access a group of rows and columns by label(s) or a boolean array.
'''
return LocIndexer(self)
@property
def iloc(self):
'''
Purely integer-location based indexing for selection by position.
'''
return ILocIndexer(self)
@property
def at(self):
'''
Access a single value for a row/column label pair.
'''
return AtIndexer(self)
@property
def iat(self):
'''
Access a single value for a row/column pair by integer position.
'''
return IAtIndexer(self)
def __getitem__(self, key):
if isinstance(key, basestring):
data = self._dataframe.getColumnData(key)
if data is None:
return data
idx = self._index[:]
r = series.Series(np.array(data), idx, key)
return r
hascolkey = True
if isinstance(key, tuple):
ridx = key[0]
cidx = key[1]
if isinstance(ridx, int) and isinstance(cidx, int):
if ridx < 0:
ridx = self.shape[0] + ridx
if cidx < 0:
cidx = self.shape[1] + cidx
return self._dataframe.getValue(ridx, cidx)
elif isinstance(ridx, int) and isinstance(cidx, basestring):
if ridx < 0:
ridx = self.shape[0] + ridx
return self._dataframe.getValue(ridx, cidx)
else:
key = (key, slice(None))
hascolkey = False
k = key[0]
if isinstance(k, Index):
k = k.data
if isinstance(k, int):
if k < 0:
k = self.shape[0] + k
rowkey = k
elif isinstance(k, basestring):
sidx = self._index.index(k)
if sidx < 0:
return None
eidx = sidx
step = 1
rowkey = Range(sidx, eidx, step)
elif isinstance(k, slice):
if isinstance(k.start, basestring):
sidx = self._index.index(k.start)
if sidx < 0:
sidx = 0
else:
sidx = 0 if k.start is None else k.start
if sidx < 0:
sidx = self.shape[0] + sidx
if isinstance(k.stop, basestring):
eidx = self._index.index(k.stop)
if eidx < 0:
eidx = self.shape[0] + eidx
else:
eidx = self.shape[0] - 1 if k.stop is None else k.stop - 1
if eidx < 0:
eidx = self.shape[0] + eidx
step = 1 if k.step is None else k.step
rowkey = Range(sidx, eidx, step)
elif isinstance(k, (list, tuple, np.NDArray, series.Series)):
if isinstance(k[0], (int, bool)):
if isinstance(k, (list, tuple)):
rowkey = k
else:
rowkey = k.asarray()
else:
tlist = []
for tstr in k:
idx = self._index.index(tstr)
if idx >= 0:
tlist.append(idx)
rowkey = tlist
else:
rowkey = self._index.get_loc(k)
if not hascolkey:
colkey = Range(0, self.shape[1] - 1, 1)
else:
k = key[1]
if isinstance(k, int):
sidx = k
if sidx < 0:
sidx = self.shape[1] + sidx
eidx = sidx
step = 1
colkey = Range(sidx, eidx, step)
elif isinstance(k, slice):
sidx = 0 if k.start is None else k.start
if sidx < 0:
sidx = self.shape[1] + sidx
eidx = self.shape[1] - 1 if k.stop is None else k.stop - 1
if eidx < 0:
eidx = self.shape[1] + eidx
step = 1 if k.step is None else k.step
colkey = Range(sidx, eidx, step)
elif isinstance(k, list):
if isinstance(k[0], int):
colkey = k
else:
colkey = self.columns.indexOfName(k)
elif isinstance(k, basestring):
col = self.columns.indexOf(k)
colkey = Range(col, col + 1, 1)
else:
return None
r = self._dataframe.select(rowkey, colkey)
if r is None:
return None
if isinstance(r, MISeries):
r = series.Series(series=r)
else:
r = DataFrame(dataframe=r)
return r
def __setitem__(self, key, value):
if isinstance(value, datetime.datetime):
value = miutil.jdatetime(value)
if isinstance(value, (list, tuple)):
if isinstance(value[0], datetime.datetime):
value = miutil.jdatetime(value)
value = np.array(value)
if isinstance(value, np.NDArray):
value = value._array
if isinstance(key, basestring):
if isinstance(value, series.Series):
value = value.values._array
self._dataframe.setColumn(key, value)
return
hascolkey = True
if isinstance(key, tuple):
ridx = key[0]
cidx = key[1]
if isinstance(ridx, int) and isinstance(cidx, int):
if ridx < 0:
ridx = self.shape[0] + ridx
if cidx < 0:
cidx = self.shape[1] + cidx
self._dataframe.setValue(ridx, cidx, value)
return
elif isinstance(ridx, int) and isinstance(cidx, basestring):
if ridx < 0:
ridx = self.shape[0] + ridx
self._dataframe.setValue(ridx, cidx, value)
return
else:
key = (key, slice(None))
hascolkey = False
k = key[0]
if isinstance(k, int):
if k < 0:
k = self.shape[0] + k
rowkey = k
elif isinstance(k, basestring):
sidx = self._index.index(k)
if sidx < 0:
return None
eidx = sidx
step = 1
rowkey = Range(sidx, eidx, step)
elif isinstance(k, slice):
if isinstance(k.start, basestring):
sidx = self._index.index(k.start)
if sidx < 0:
sidx = 0
else:
sidx = 0 if k.start is None else k.start
if sidx < 0:
sidx = self.shape[0] + sidx
if isinstance(k.stop, basestring):
eidx = self._index.index(k.stop)
if eidx < 0:
eidx = self.shape[0] + eidx
else:
eidx = self.shape[0] - 1 if k.stop is None else k.stop - 1
if eidx < 0:
eidx = self.shape[0] + eidx
step = 1 if k.step is None else k.step
rowkey = Range(sidx, eidx, step)
elif isinstance(k, list):
if isinstance(k[0], int):
rowkey = k
else:
tlist = []
for tstr in k:
idx = self._index.index(tstr)
if idx >= 0:
tlist.append(idx)
rowkey = tlist
else:
return
if not hascolkey:
colkey = Range(0, self.shape[1] - 1, 1)
else:
k = key[1]
if isinstance(k, int):
sidx = k
if sidx < 0:
sidx = self.shape[1] + sidx
eidx = sidx
step = 1
colkey = Range(sidx, eidx, step)
elif isinstance(k, slice):
sidx = 0 if k.start is None else k.start
if sidx < 0:
sidx = self.shape[1] + sidx
eidx = self.shape[1] - 1 if k.stop is None else k.stop - 1
if eidx < 0:
eidx = self.shape[1] + eidx
step = 1 if k.step is None else k.step
colkey = Range(sidx, eidx, step)
elif isinstance(k, list):
if isinstance(k[0], int):
colkey = k
else:
colkey = self.columns.indexOfName(k)
elif isinstance(k, basestring):
col = self.columns.indexOf(k)
colkey = Range(col, col + 1, 1)
else:
return
self._dataframe.setValues(rowkey, colkey, value)
def _getitem_loc(self, key):
if not isinstance(key, tuple):
key = (key, None)
k = key[0]
rkeys = key[0]
if isinstance(k, slice):
sidx = 0 if k.start is None else self._index.index(k.start)
if sidx < 0:
raise KeyError(key)
eidx = self.shape[0] - 1 if k.stop is None else self._index.index(
k.stop)
if eidx < 0:
raise KeyError(key)
step = 1 if k.step is None else k.step
rowkey = Range(sidx, eidx, step)
else:
rloc = self._index.get_loc(k, outkeys=True)
if isinstance(rloc, tuple):
rowkey = rloc[0]
rkeys = rloc[1]
else:
rowkey = rloc
rkeys = None
if len(rowkey) == 0:
raise KeyError(key)
k = key[1]
if k is None:
colkey = Range(0, self.shape[1] - 1, 1)
else:
if isinstance(k, slice):
sidx = 0 if k.start is None else self.columns.indexOfName(
k.start)
if sidx < 0:
raise KeyError(key)
eidx = self.shape[
1] - 1 if k.stop is None else self.columns.indexOfName(
k.stop)
if eidx < 0:
raise KeyError(key)
step = 1 if k.step is None else k.step
colkey = Range(sidx, eidx, step)
elif isinstance(k, list):
colkey = self.columns.indexOfName(k)
elif isinstance(k, basestring):
col = self.columns.indexOfName(k)
if col < 0:
raise KeyError(key)
colkey = [col]
else:
return None
if isinstance(rowkey, (int, Range)):
r = self._dataframe.select(rowkey, colkey)
else:
if isinstance(colkey, Range):
ncol = colkey.length()
else:
ncol = len(colkey)
if len(rowkey) == 1 and ncol == 1:
if isinstance(colkey, Range):
return self._dataframe.getValue(rowkey[0], colkey.first())
else:
return self._dataframe.getValue(rowkey[0], colkey[0])
if rkeys is None:
r = self._dataframe.select(rowkey, colkey)
else:
if not isinstance(rkeys, list):
rkeys = [rkeys]
r = self._dataframe.select(rkeys, rowkey, colkey)
if r is None:
return None
if isinstance(r, MISeries):
r = series.Series(series=r)
else:
r = DataFrame(dataframe=r)
return r
def _setitem_loc(self, key, value):
if isinstance(value, datetime.datetime):
value = [miutil.jdatetime(value)]
if isinstance(value, (list, tuple)):
if isinstance(value[0], datetime.datetime):
value = miutil.jdatetime(value)
#value = np.array(value)
if isinstance(value, np.NDArray):
value = value._array
self._dataframe.setRow(key, value)
def _getitem_iloc(self, key):
if not isinstance(key, tuple):
key = (key, None)
if isinstance(key[0], int) and isinstance(key[1], int):
return self._dataframe.getValue(key[0], key[1])
k = key[0]
if isinstance(k, int):
if k < 0:
k = self.shape[0] + k
rowkey = k
elif isinstance(k, slice):
sidx = 0 if k.start is None else k.start
if sidx < 0:
sidx = self.shape[0] + sidx
eidx = self.shape[0] - 1 if k.stop is None else k.stop - 1
if eidx < 0:
eidx = self.shape[0] + eidx
step = 1 if k.step is None else k.step
rowkey = Range(sidx, eidx, step)
elif isinstance(k, list):
rowkey = k
elif isinstance(k, np.NDArray):
rowkey = k.aslist()
else:
return None
k = key[1]
if k is None:
colkey = Range(0, self.shape[1] - 1, 1)
else:
if isinstance(k, int):
sidx = k
if sidx < 0:
sidx = self.shape[1] + sidx
eidx = sidx
step = 1
colkey = Range(sidx, eidx, step)
elif isinstance(k, slice):
sidx = 0 if k.start is None else k.start
if sidx < 0:
sidx = self.shape[1] + sidx
eidx = self.shape[1] - 1 if k.stop is None else k.stop - 1
if eidx < 0:
eidx = self.shape[1] + eidx
step = 1 if k.step is None else k.step
colkey = Range(sidx, eidx, step)
elif isinstance(k, list):
colkey = k
elif isinstance(k, np.NDArray):
colkey = k.aslist()
else:
return None
r = self._dataframe.select(rowkey, colkey)
if r is None:
return None
if isinstance(r, MISeries):
r = series.Series(series=r)
else:
r = DataFrame(dataframe=r)
return r
def _getitem_at(self, key):
ridx = key[0]
cidx = key[1]
ridx = self._index.index(ridx)
if ridx < 0:
raise KeyError(key)
cidx = self.columns.indexOfName(cidx)
if cidx < 0:
raise KeyError(key)
return self._dataframe.getValue(ridx, cidx)
def _getitem_iat(self, key):
ridx = key[0]
cidx = key[1]
if ridx < 0:
ridx = self.shape[0] + ridx
if cidx < 0:
cidx = self.shape[1] + cidx
return self._dataframe.getValue(ridx, cidx)
def __getkey(self, key):
if isinstance(key, basestring):
rkey = self.index.get_indices(key)
ikey = rkey[0]
rindex = rkey[1]
if len(ikey) == 1:
ikey = ikey[0]
elif len(ikey) > 1:
ikey = list(ikey)
else:
raise KeyError(key)
return ikey, rindex
elif isinstance(key, (list, tuple, np.NDArray)) and isinstance(
key[0], basestring):
if isinstance(key, (np.NDArray)):
key = key.asarray()
rkey = self.index.get_indices(key)
ikey = rkey[0]
rindex = rkey[1]
rdata = rkey[2]
rrindex = rkey[3]
if len(ikey) == 0:
raise KeyError()
else:
ikey = list(ikey)
return ikey, rindex, rdata, rrindex
else:
return key, None
def __iter__(self):
"""
provide iteration over the values of the Series
"""
#return iter(self.data)
#return zip(iter(self.index), iter(self.data))
return iter(self.index)
def iteritems(self):
"""
Lazily iterate over (index, value) tuples
"""
return zip(iter(self.index), iter(self))
def __len__(self):
return self.shape[0]
def __str__(self):
return self._dataframe.toString()
def __repr__(self):
return self._dataframe.toString()
def head(self, n=5):
'''
Get top rows
:param n: (*int*) row number.
:returns: Top rows
'''
print self._dataframe.head(n)
def tail(self, n=5):
'''
Get bottom rows
:param n: (*int*) row number.
:returns: Bottom rows
'''
print self._dataframe.tail(n)
def transpose(self):
'''
Transpose data frame.
:returns: Transposed data frame.
'''
r = self._dataframe.transpose()
return DataFrame(dataframe=r)
T = property(transpose)
def insert(self, loc, column, value):
'''
Insert column into DataFrame at specified location.
:param loc: (*int*) Insertation index.
:param column: (*string*) Label of inserted column.
:param value: (*array_like*) Column values.
'''
if isinstance(value, datetime.datetime):
value = miutil.jdatetime(value)
if isinstance(value, (list, tuple)):
if isinstance(value[0], datetime.datetime):
value = miutil.jdatetime(value)
value = np.array(value)
if isinstance(value, Index):
if isinstance(value[0], datetime.datetime):
value = miutil.jdatetime(value.data)
else:
value = value.data
value = np.array(value)
if isinstance(value, np.NDArray):
value = value._array
self._dataframe.addColumn(loc, column, value)
def drop(self, columns=None):
'''
Drop specified labels from rows or columns.
:param columns: (*list like*) Column labels.
'''
if isinstance(columns, basestring):
columns = [columns]
r = self._dataframe.drop(columns)
return DataFrame(dataframe=r)
def append(self, other):
'''
Append another data frame.
:param other: (*DataFrame, dict, list*) Other data frame or row data.
:returns: (*DataFrame*) Appended data frame.
'''
if isinstance(other, DataFrame):
r = self._dataframe.append(other._dataframe)
return DataFrame(dataframe=r)
else:
self._dataframe.append(other)
return self
def describe(self):
'''
Generates descriptive statistics that summarize the central tendency, dispersion and shape of a
dataset’s distribution, excluding NaN values.
:returns: Describe DataFrame.
'''
r = self._dataframe.describe()
return DataFrame(dataframe=r)
def sort_index(self, axis=0, ascending=True):
'''
Sort by the index along either axis
:param axis: (*int*) Axis to be sorted {0 or ‘index’, 1 or ‘columns’}, default 0
:param ascending: (*boolean*) Sort ascending vs. descending.
:returns: Sorted DataFrame
'''
df = self._dataframe.sortByIndex(ascending)
return DataFrame(dataframe=df)
def sort_values(self, by, axis=0, ascending=True):
'''
Sort by the values along either axis
:param by: (*string or list of string*) Name or list of names to sort by.
:param axis: (*int*) Axis to be sorted {0 or ‘index’, 1 or ‘columns’}, default 0
:param ascending: (*boolean*) Sort ascending vs. descending. Specify list for multiple sort orders.
If this is a list of bools, must match the length of the by.
:returns: Sorted DataFrame
'''
if isinstance(by, basestring):
by = [by]
if isinstance(ascending, bool):
ascending = [ascending] * len(by)
df = self._dataframe.sortBy(by, ascending)
return DataFrame(dataframe=df)
def groupby(self, by):
'''
Group DataFrame.
:param by: Period string.
:returns: GroupBy object.
'''
if isinstance(by, basestring):
by = [by]
gb = self._dataframe.groupBy(by)
return groupby.GroupBy(gb)
def resample(self, by):
'''
Group DataFrame by date time index.
:param by: Used to determine the groups for the groupby.
:returns: GroupBy object
'''
gb = self._dataframe.groupByIndex(by)
return groupby.GroupBy(gb)
def count(self):
'''
Return the count of the values for the requested axis
'''
return DataFrame(dataframe=self._dataframe.count())
def sum(self):
'''
Return the sum of the values for the requested axis
'''
return DataFrame(dataframe=self._dataframe.sum())
def mean(self):
'''
Return the mean of the values for the requested axis
'''
return DataFrame(dataframe=self._dataframe.mean())
def min(self):
'''
Return the minimum of the values for the requested axis
'''
return DataFrame(dataframe=self._dataframe.min())
def max(self):
'''
Return the maximum of the values for the requested axis
'''
return DataFrame(dataframe=self._dataframe.max())
def median(self):
'''
Return the median of the values for the requested axis
'''
return DataFrame(dataframe=self._dataframe.median())
def std(self):
'''
Return the standard deviation of the values for the requested axis
'''
return DataFrame(dataframe=self._dataframe.stdDev())
@classmethod
def read_table(cls, filepath, **kwargs):
'''
Create DataFrame by reading column oriented data from a file.
:param filepath: (*string*) File path for reading.
:param delimiter: (*string*) Variable delimiter character. Default is ``None``, means space or tab
delimiter.
:param format: (*string*) Colomn format of the file. Default is ``None``, means all columns were
read as string variable. ``%s``: string; ``%i``: integer; ``%f``: float; ``%{yyyyMMdd...}D``:
date time.
:param skiprows: (*int*) Lines to skip at beginning of the file. Default is ``0``.
:param skipfooter: (*int*) Number of lines at bottom of file to skip.
:param encoding: (*string*) Character encoding scheme associated with the file. Default is ``UTF8``.
:param names: (*array_like*) List of column names to use. If file contains no header row, then you should
explicitly pass header=None. Default is None.
:param header: (*int*) Row number to use as the column names. If column names are passed explicitly
then the behavior is identical to ``header=None``.
:param index_col: (*int*) Column to use as the row labels (index) of the DataFrame.
:param index_format: (*string*) Index column format.
:param usecols: (*list*) Return a subset of the columns. If list-like, all elements
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).
:returns: (*DataFrame*) The DataFrame.
'''
delimiter = kwargs.pop('delimiter', None)
format = kwargs.pop('format', None)
skiprows = kwargs.pop('skiprows', 0)
skipfooter = kwargs.pop('skipfooter', 0)
encoding = kwargs.pop('encoding', 'UTF8')
names = kwargs.pop('names', None)
header = kwargs.pop('header', 0)
index_col = kwargs.pop('index_col', -1)
index_format = kwargs.pop('index_format', None)
usecols = kwargs.pop('usecols', None)
if usecols is None:
midf = MIDataFrame.readTable(filepath, delimiter, skiprows, format,
encoding, index_col, index_format,
names, header, skipfooter)
else:
midf = MIDataFrame.readTable(filepath, delimiter, skiprows, format,
encoding, index_col, index_format,
names, header, skipfooter, usecols)
return DataFrame(dataframe=midf)
def to_csv(self, filepath, delimiter=',', format=None, date_format=None, \
float_format=None, index=True):
'''
Save the data to an csv file.
:param filename: (*string*) The file name.
:param delimiter: (*string*) Field delimiter character. Default is ``,``.
:param format: (*string*) Format string.
:param date_format: (*string*) Date format string. i.e. 'yyyyMMddHH'.
:param float_format: (*string*) Float format string. i.e. '%.2f'.
:param index: (*boolean*) Write index or not.
'''
self._dataframe.saveCSV(filepath, delimiter, date_format, float_format,
index)
#################################################################
class DataFrame(object):
'''
Two-dimensional size-mutable, potentially heterogeneous tabular data structure with
labeled axes (rows and columns). Arithmetic operations align on both row and column
labels. Can be thought of as a dict-like container for Series objects.
:param data: (*array_like*) Two-dimensional array data or list of one-dimensional arrays.
:param index: (*list*) Data index list. Values must be unique and hashable, same length as data.
:param columns: (*list*) Column labels to use for resulting frame. Will default to
arange(n) if no column labels are provided
'''
def __init__(self, data=None, index=None, columns=None, dataframe=None):
if dataframe is None:
if not data is None:
if isinstance(data, dict):
columns = data.keys()
dlist = []
n = 1
for v in data.values():
if isinstance(v, (list, tuple)):
n = len(v)
v = minum.array(v)
elif isinstance(v, MIArray):
n = len(v)
dlist.append(v)
for i in range(len(dlist)):
d = dlist[i]
if not isinstance(d, MIArray):
d = [d] * n
d = minum.array(d)
dlist[i] = d
data = dlist
if isinstance(data, MIArray):
n = len(data)
data = data.array
else:
dlist = []
n = len(data[0])
for dd in data:
dlist.append(dd.array)
data = dlist
if index is None:
index = range(0, n)
else:
if n != len(index):
raise ValueError('Wrong length of index!')
if isinstance(index, (MIArray, DimArray)):
index = index.tolist()
if isinstance(index, Index):
self._index = index
else:
self._index = Index.factory(index)
if data is None:
self._dataframe = MIDataFrame(self._index._index)
else:
self._dataframe = MIDataFrame(data, self._index._index, columns)
else:
self._dataframe = dataframe
self._index = Index.factory(index=self._dataframe.getIndex())
#---- index property
def get_index(self):
return self._index
def set_index(self, value):
if isinstance(value, series.Series):
value = value.values
self._index = Index.factory(value, self._index.name)
self._dataframe.setIndex(self._index._index)
index = property(get_index, set_index)
#---- data property
def get_data(self):
r = self._dataframe.getData()
if isinstance(r, Array):
r = MIArray(r)
else:
rr = []
for d in r:
rr.append(MIArray(d))
r = rr
return r
def set_data(self, value):
value = minum.array(value)
self._dataframe.setData(value.array)
values = property(get_data, set_data)
#---- columns property
def get_columns(self):
return self._dataframe.getColumns()
def set_columns(self, value):
self._dataframe.setColumns(value)
columns = property(get_columns, set_columns)
#---- shape property
def get_shape(self):
s = self._dataframe.getShape()
s1 = []
for i in range(len(s)):
s1.append(s[i])
return tuple(s1)
shape = property(get_shape)
#---- dtypes property
def get_dtypes(self):
colnames = list(self.columns.getNames())
datatypes = list(self.columns.getDataTypes())
r = series.Series(datatypes, colnames, 'DataTypes')
return r
dtypes = property(get_dtypes)
@property
def loc(self):
'''
Access a group of rows and columns by label(s) or a boolean array.
'''
return LocIndexer(self)
@property
def iloc(self):
'''
Purely integer-location based indexing for selection by position.
'''
return ILocIndexer(self)
@property
def at(self):
'''
Access a single value for a row/column label pair.
'''
return AtIndexer(self)
@property
def iat(self):
'''
Access a single value for a row/column pair by integer position.
'''
return IAtIndexer(self)
def __getitem__(self, key):
if isinstance(key, basestring):
data = self._dataframe.getColumnData(key)
if data is None:
return data
idx = self._index[:]
r = series.Series(MIArray(data), idx, key)
return r
hascolkey = True
if isinstance(key, tuple):
ridx = key[0]
cidx = key[1]
if isinstance(ridx, int) and isinstance(cidx, int):
if ridx < 0:
ridx = self.shape[0] + ridx
if cidx < 0:
cidx = self.shape[1] + cidx
return self._dataframe.getValue(ridx, cidx)
elif isinstance(ridx, int) and isinstance(cidx, basestring):
if ridx < 0:
ridx = self.shape[0] + ridx
return self._dataframe.getValue(ridx, cidx)
else:
key = (key, slice(None))
hascolkey = False
k = key[0]
if isinstance(k, Index):
k = k.data
if isinstance(k, int):
if k < 0:
k = self.shape[0] + k
rowkey = k
elif isinstance(k, basestring):
sidx = self._index.index(k)
if sidx < 0:
return None
eidx = sidx
step = 1
rowkey = Range(sidx, eidx, step)
elif isinstance(k, slice):
if isinstance(k.start, basestring):
sidx = self._index.index(k.start)
if sidx < 0:
sidx = 0
else:
sidx = 0 if k.start is None else k.start
if sidx < 0:
sidx = self.shape[0] + sidx
if isinstance(k.stop, basestring):
eidx = self._index.index(k.stop)
if eidx < 0:
eidx = self.shape[0] + eidx
else:
eidx = self.shape[0] - 1 if k.stop is None else k.stop - 1
if eidx < 0:
eidx = self.shape[0] + eidx
step = 1 if k.step is None else k.step
rowkey = Range(sidx, eidx, step)
elif isinstance(k, (list,tuple,MIArray)):
if isinstance(k[0], int):
rowkey = k
else:
tlist = []
for tstr in k:
idx = self._index.index(tstr)
if idx >= 0:
tlist.append(idx)
rowkey = tlist
else:
rowkey = self._index.get_loc(k)
if not hascolkey:
colkey = Range(0, self.shape[1] - 1, 1)
else:
k = key[1]
if isinstance(k, int):
sidx = k
if sidx < 0:
sidx = self.shape[1] + sidx
eidx = sidx
step = 1
colkey = Range(sidx, eidx, step)
elif isinstance(k, slice):
sidx = 0 if k.start is None else k.start
if sidx < 0:
sidx = self.shape[1] + sidx
eidx = self.shape[1] - 1 if k.stop is None else k.stop - 1
if eidx < 0:
eidx = self.shape[1] + eidx
step = 1 if k.step is None else k.step
colkey = Range(sidx, eidx, step)
elif isinstance(k, list):
if isinstance(k[0], int):
colkey = k
else:
colkey = self.columns.indexOfName(k)
elif isinstance(k, basestring):
col = self.columns.indexOf(k)
colkey = Range(col, col + 1, 1)
else:
return None
r = self._dataframe.select(rowkey, colkey)
if r is None:
return None
if isinstance(r, MISeries):
r = series.Series(series=r)
else:
r = DataFrame(dataframe=r)
return r
def __setitem__(self, key, value):
if isinstance(value, datetime.datetime):
value = miutil.jdatetime(value)
if isinstance(value, (list, tuple)):
if isinstance(value[0], datetime.datetime):
value = miutil.jdatetime(value)
value = minum.array(value)
if isinstance(value, MIArray):
value = value.array
if isinstance(key, basestring):
if isinstance(value, series.Series):
value = value.values.array
self._dataframe.setColumn(key, value)
return
hascolkey = True
if isinstance(key, tuple):
ridx = key[0]
cidx = key[1]
if isinstance(ridx, int) and isinstance(cidx, int):
if ridx < 0:
ridx = self.shape[0] + ridx
if cidx < 0:
cidx = self.shape[1] + cidx
self._dataframe.setValue(ridx, cidx, value)
return
elif isinstance(ridx, int) and isinstance(cidx, basestring):
if ridx < 0:
ridx = self.shape[0] + ridx
self._dataframe.setValue(ridx, cidx, value)
return
else:
key = (key, slice(None))
hascolkey = False
k = key[0]
if isinstance(k, int):
if k < 0:
k = self.shape[0] + k
rowkey = k
elif isinstance(k, basestring):
sidx = self._index.index(k)
if sidx < 0:
return None
eidx = sidx
step = 1
rowkey = Range(sidx, eidx, step)
elif isinstance(k, slice):
if isinstance(k.start, basestring):
sidx = self._index.index(k.start)
if sidx < 0:
sidx = 0
else:
sidx = 0 if k.start is None else k.start
if sidx < 0:
sidx = self.shape[0] + sidx
if isinstance(k.stop, basestring):
eidx = self._index.index(k.stop)
if eidx < 0:
eidx = self.shape[0] + eidx
else:
eidx = self.shape[0] - 1 if k.stop is None else k.stop - 1
if eidx < 0:
eidx = self.shape[0] + eidx
step = 1 if k.step is None else k.step
rowkey = Range(sidx, eidx, step)
elif isinstance(k, list):
if isinstance(k[0], int):
rowkey = k
else:
tlist = []
for tstr in k:
idx = self._index.index(tstr)
if idx >= 0:
tlist.append(idx)
rowkey = tlist
else:
return
if not hascolkey:
colkey = Range(0, self.shape[1] - 1, 1)
else:
k = key[1]
if isinstance(k, int):
sidx = k
if sidx < 0:
sidx = self.shape[1] + sidx
eidx = sidx
step = 1
colkey = Range(sidx, eidx, step)
elif isinstance(k, slice):
sidx = 0 if k.start is None else k.start
if sidx < 0:
sidx = self.shape[1] + sidx
eidx = self.shape[1] - 1 if k.stop is None else k.stop - 1
if eidx < 0:
eidx = self.shape[1] + eidx
step = 1 if k.step is None else k.step
colkey = Range(sidx, eidx, step)
elif isinstance(k, list):
if isinstance(k[0], int):
colkey = k
else:
colkey = self.columns.indexOfName(k)
elif isinstance(k, basestring):
col = self.columns.indexOf(k)
colkey = Range(col, col + 1, 1)
else:
return
self._dataframe.setValues(rowkey, colkey, value)
def _getitem_loc(self, key):
if not isinstance(key, tuple):
key = (key, None)
k = key[0]
rkeys = key[0]
if isinstance(k, slice):
sidx = 0 if k.start is None else self._index.index(k.start)
if sidx < 0:
raise KeyError(key)
eidx = self.shape[0] - 1 if k.stop is None else self._index.index(k.stop)
if eidx < 0:
raise KeyError(key)
step = 1 if k.step is None else k.step
rowkey = Range(sidx, eidx, step)
else:
if isinstance(k, (list, tuple, MIArray)):
rowkey, rkeys = self._index.get_loc(k, outkeys=True)
else:
rowkey = self._index.index(k)
if rowkey < 0:
raise KeyError(key)
k = key[1]
if k is None:
colkey = Range(0, self.shape[1] - 1, 1)
else:
if isinstance(k, slice):
sidx = 0 if k.start is None else self.columns.indexOfName(k.start)
if sidx < 0:
raise KeyError(key)
eidx = self.shape[1] - 1 if k.stop is None else self.columns.indexOfName(k.stop)
if eidx < 0:
raise KeyError(key)
step = 1 if k.step is None else k.step
colkey = Range(sidx, eidx, step)
elif isinstance(k, list):
colkey = self.columns.indexOfName(k)
elif isinstance(k, basestring):
col = self.columns.indexOfName(k)
if col < 0:
raise KeyError(key)
colkey = [col]
else:
return None
if isinstance(rowkey, (int, Range)):
r = self._dataframe.select(rowkey, colkey)
else:
if isinstance(colkey, Range):
ncol = colkey.length()
else:
ncol = len(colkey)
if len(rowkey) == 1 and ncol == 1:
return self._dataframe.getValue(rowkey[0], colkey[0])
if not isinstance(rkeys, list):
rkeys = [rkeys]
r = self._dataframe.select(rkeys, rowkey, colkey)
if r is None:
return None
if isinstance(r, MISeries):
r = series.Series(series=r)
else:
r = DataFrame(dataframe=r)
return r
def _getitem_iloc(self, key):
if not isinstance(key, tuple):
key = (key, None)
if isinstance(key[0], int) and isinstance(key[1], int):
return self._dataframe.getValue(key[0], key[1])
k = key[0]
if isinstance(k, int):
if k < 0:
k = self.shape[0] + k
rowkey = k
elif isinstance(k, slice):
sidx = 0 if k.start is None else k.start
if sidx < 0:
sidx = self.shape[0] + sidx
eidx = self.shape[0] - 1 if k.stop is None else k.stop - 1
if eidx < 0:
eidx = self.shape[0] + eidx
step = 1 if k.step is None else k.step
rowkey = Range(sidx, eidx, step)
elif isinstance(k, list):
rowkey = k
else:
return None
k = key[1]
if k is None:
colkey = Range(0, self.shape[1] - 1, 1)
else:
if isinstance(k, int):
sidx = k
if sidx < 0:
sidx = self.shape[1] + sidx
eidx = sidx
step = 1
colkey = Range(sidx, eidx, step)
elif isinstance(k, slice):
sidx = 0 if k.start is None else k.start
if sidx < 0:
sidx = self.shape[1] + sidx
eidx = self.shape[1] - 1 if k.stop is None else k.stop - 1
if eidx < 0:
eidx = self.shape[1] + eidx
step = 1 if k.step is None else k.step
colkey = Range(sidx, eidx, step)
elif isinstance(k, list):
colkey = k
else:
return None
r = self._dataframe.select(rowkey, colkey)
if r is None:
return None
if isinstance(r, MISeries):
r = series.Series(series=r)
else:
r = DataFrame(dataframe=r)
return r
def _getitem_at(self, key):
ridx = key[0]
cidx = key[1]
ridx = self._index.index(ridx)
if ridx < 0:
raise KeyError(key)
cidx = self.columns.indexOfName(cidx)
if cidx < 0:
raise KeyError(key)
return self._dataframe.getValue(ridx, cidx)
def _getitem_iat(self, key):
ridx = key[0]
cidx = key[1]
if ridx < 0:
ridx = self.shape[0] + ridx
if cidx < 0:
cidx = self.shape[1] + cidx
return self._dataframe.getValue(ridx, cidx)
def __getkey(self, key):
if isinstance(key, basestring):
rkey = self.index.get_indices(key)
ikey = rkey[0]
rindex = rkey[1]
if len(ikey) == 1:
ikey = ikey[0]
elif len(ikey) > 1:
ikey = list(ikey)
else:
raise KeyError(key)
return ikey, rindex
elif isinstance(key, (list, tuple, MIArray, DimArray)) and isinstance(key[0], basestring):
if isinstance(key, (MIArray, DimArray)):
key = key.asarray()
rkey = self.index.get_indices(key)
ikey = rkey[0]
rindex = rkey[1]
rdata = rkey[2]
rrindex = rkey[3]
if len(ikey) == 0:
raise KeyError()
else:
ikey = list(ikey)
return ikey, rindex, rdata, rrindex
else:
return key, None
def __iter__(self):
"""
provide iteration over the values of the Series
"""
#return iter(self.data)
#return zip(iter(self.index), iter(self.data))
return iter(self.index)
def iteritems(self):
"""
Lazily iterate over (index, value) tuples
"""
return zip(iter(self.index), iter(self))
def __len__(self):
return self.shape[0]
def __str__(self):
return self._dataframe.toString()
def __repr__(self):
return self._dataframe.toString()
def head(self, n=5):
'''
Get top rows
:param n: (*int*) row number.
:returns: Top rows
'''
print self._dataframe.head(n)
def tail(self, n=5):
'''
Get bottom rows
:param n: (*int*) row number.
:returns: Bottom rows
'''
print self._dataframe.tail(n)
def transpose(self):
'''
Transpose data frame.
:returns: Transposed data frame.
'''
r = self._dataframe.transpose()
return DataFrame(dataframe=r)
T = property(transpose)
def insert(self, loc, column, value):
'''
Insert column into DataFrame at specified location.
:param loc: (*int*) Insertation index.
:param column: (*string*) Label of inserted column.
:param value: (*array_like*) Column values.
'''
if isinstance(value, datetime.datetime):
value = miutil.jdatetime(value)
if isinstance(value, (list, tuple)):
if isinstance(value[0], datetime.datetime):
value = miutil.jdatetime(value)
value = minum.array(value)
if isinstance(value, MIArray):
value = value.array
self._dataframe.addColumn(loc, column, value)
def drop(self, columns=None):
'''
Drop specified labels from rows or columns.
:param columns: (*list like*) Column labels.
'''
if isinstance(columns, basestring):
columns = [columns]
r = self._dataframe.drop(columns)
return DataFrame(dataframe=r)
def append(self, other):
'''
Append another data frame.
:param other: (*DataFrame*) Other data frame.
:returns: (*DataFrame*) Appended data frame.
'''
r = self._dataframe.append(other._dataframe)
return DataFrame(dataframe=r)
def describe(self):
'''
Generates descriptive statistics that summarize the central tendency, dispersion and shape of a
dataset’s distribution, excluding NaN values.
:returns: Describe DataFrame.
'''
r = self._dataframe.describe()
return DataFrame(dataframe=r)
def sort_index(self, axis=0, ascending=True):
'''
Sort by the index along either axis
:param axis: (*int*) Axis to be sorted {0 or ‘index’, 1 or ‘columns’}, default 0
:param ascending: (*boolean*) Sort ascending vs. descending.
:returns: Sorted DataFrame
'''
df = self._dataframe.sortByIndex(ascending)
return DataFrame(dataframe=df)
def sort_values(self, by, axis=0, ascending=True):
'''
Sort by the values along either axis
:param by: (*string or list of string*) Name or list of names to sort by.
:param axis: (*int*) Axis to be sorted {0 or ‘index’, 1 or ‘columns’}, default 0
:param ascending: (*boolean*) Sort ascending vs. descending. Specify list for multiple sort orders.
If this is a list of bools, must match the length of the by.
:returns: Sorted DataFrame
'''
if isinstance(by, basestring):
by = [by]
if isinstance(ascending, bool):
ascending = [ascending] * len(by)
df = self._dataframe.sortBy(by, ascending)
return DataFrame(dataframe=df)
def groupby(self, by):
'''
Group DataFrame.
:param by: Period string.
:returns: GroupBy object.
'''
if isinstance(by, basestring):
by = [by]
gb = self._dataframe.groupBy(by)
return groupby.GroupBy(gb)
def resample(self, by):
'''
Group DataFrame by date time index.
:param by: Used to determine the groups for the groupby.
:returns: GroupBy object
'''
gb = self._dataframe.groupByIndex(by)
return groupby.GroupBy(gb)
def count(self):
'''
Return the count of the values for the requested axis
'''
return DataFrame(dataframe=self._dataframe.count())
def sum(self):
'''
Return the sum of the values for the requested axis
'''
return DataFrame(dataframe=self._dataframe.sum())
def mean(self):
'''
Return the mean of the values for the requested axis
'''
return DataFrame(dataframe=self._dataframe.mean())
def min(self):
'''
Return the minimum of the values for the requested axis
'''
return DataFrame(dataframe=self._dataframe.min())
def max(self):
'''
Return the maximum of the values for the requested axis
'''
return DataFrame(dataframe=self._dataframe.max())
def median(self):
'''
Return the median of the values for the requested axis
'''
return DataFrame(dataframe=self._dataframe.median())
@classmethod
def read_table(cls, filepath, **kwargs):
'''
Create DataFrame by reading column oriented data from a file.
:param filepath: (*string*) File path for reading.
:param delimiter: (*string*) Variable delimiter character. Default is ``None``, means space or tab
delimiter.
:param format: (*string*) Colomn format of the file. Default is ``None``, means all columns were
read as string variable. ``%s``: string; ``%i``: integer; ``%f``: float; ``%{yyyyMMdd...}D``:
date time.
:param skiprows: (*int*) Lines to skip at beginning of the file. Default is ``0``.
:param skipfooter: (*int*) Number of lines at bottom of file to skip.
:param encoding: (*string*) Character encoding scheme associated with the file. Default is ``UTF8``.
:param names: (*array_like*) List of column names to use. If file contains no header row, then you should
explicitly pass header=None. Default is None.
:param header: (*int*) Row number to use as the column names. If column names are passed explicitly
then the behavior is identical to ``header=None``.
:param index_col: (*int*) Column to use as the row labels (index) of the DataFrame.
:param index_format: (*string*) Index column format.
:param usecols: (*list*) Return a subset of the columns. If list-like, all elements
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).
:returns: (*DataFrame*) The DataFrame.
'''
delimiter = kwargs.pop('delimiter', None)
format = kwargs.pop('format', None)
skiprows = kwargs.pop('skiprows', 0)
skipfooter = kwargs.pop('skipfooter', 0)
encoding = kwargs.pop('encoding', 'UTF8')
names = kwargs.pop('names', None)
header = kwargs.pop('header', 0)
index_col = kwargs.pop('index_col', -1)
index_format = kwargs.pop('index_format', None)
usecols = kwargs.pop('usecols', None)
if usecols is None:
midf = MIDataFrame.readTable(filepath, delimiter, skiprows, format, encoding,
index_col, index_format, names, header, skipfooter)
else:
midf = MIDataFrame.readTable(filepath, delimiter, skiprows, format, encoding,
index_col, index_format, names, header, skipfooter, usecols)
return DataFrame(dataframe=midf)
def to_csv(self, filepath, delimiter=',', format=None, date_format=None, \
float_format=None, index=True):
'''
Save the data to an csv file.
:param filename: (*string*) The file name.
:param delimiter: (*string*) Field delimiter character. Default is ``,``.
:param format: (*string*) Format string.
:param date_format: (*string*) Date format string. i.e. 'yyyyMMddHH'.
:param float_format: (*string*) Float format string. i.e. '%.2f'.
:param index: (*boolean*) Write index or not.
'''
self._dataframe.saveCSV(filepath, delimiter, date_format, float_format, index)
#################################################################