def test_string_nonconversion():
t, n = 3, 1000
string = np.random.choice(['a', 'b', 'c'], (t, n))
num = np.random.randn(t, n)
p = pd.Panel({'a': string, 'b': num})
panel = PanelData(p, var_name='OtherEffect', convert_dummies=False)
assert is_string_dtype(panel.dataframe['a'].dtype)
assert np.all(panel.dataframe['a'] == string.T.ravel())
def convert_columns(s, drop_first):
if is_string_dtype(s.dtype) and s.map(lambda v: is_string_like(v)).all():
s = s.astype('category')
if is_categorical(s):
out = pd.get_dummies(s, drop_first=drop_first)
out.columns = [str(s.name) + '.' + str(c) for c in out]
return out
return s
def test_string_nonconversion():
t, n = 3, 1000
string = np.random.choice(['a', 'b', 'c'], (t, n))
num = np.random.randn(t, n)
time = date_range('1-1-2000', periods=t)
entities = ['entity.{0}'.format(i) for i in range(n)]
p = panel_to_frame(None, items=['a', 'b'], major_axis=time, minor_axis=entities,
swap=True)
p['a'] = string.T.ravel()
p['b'] = num.T.ravel()
panel = PanelData(p, var_name='OtherEffect', convert_dummies=False)
assert is_string_dtype(panel.dataframe['a'].dtype)
assert np.all(panel.dataframe['a'] == string.T.ravel())
def __init__(self, x, var_name='x', nobs=None, convert_dummies=True, drop_first=True):
if isinstance(x, IVData):
self.__dict__.update(copy.deepcopy(x.__dict__))
return
if x is None and nobs is not None:
x = np.empty((nobs, 0))
elif x is None:
raise ValueError('nobs required when x is None')
self.original = x
xndim = x.ndim
if xndim > 2:
raise ValueError(dim_err.format(var_name, xndim))
if isinstance(x, np.ndarray):
x = x.astype(dtype=np.float64)
if xndim == 1:
x.shape = (x.shape[0], -1)
self._ndarray = x.astype(np.float64)
index = list(range(x.shape[0]))
if x.shape[1] == 1:
cols = [var_name]
else:
cols = [var_name + '.{0}'.format(i) for i in range(x.shape[1])]
self._pandas = pd.DataFrame(x, index=index, columns=cols)
self._labels = {0: index, 1: cols}
elif isinstance(x, (pd.Series, pd.DataFrame)):
if isinstance(x, pd.Series):
name = var_name if not x.name else x.name
x = pd.DataFrame({name: x})
copied = False
columns = list(x.columns)
if len(set(columns)) != len(columns):
raise ValueError('DataFrame contains duplicate column names. '
'All column names must be distinct')
all_numeric = True
for col in x:
c = x[col]
if is_string_dtype(c.dtype) and \
c.map(lambda v: is_string_like(v)).all():
c = c.astype('category')
if not copied:
x = x.copy()
copied = True
x[col] = c
dt = c.dtype
all_numeric = all_numeric and is_numeric_dtype(dt)
if not (is_numeric_dtype(dt) or is_categorical_dtype(dt)):
raise ValueError('Only numeric, string or categorical '
'data permitted')
if convert_dummies:
x = expand_categoricals(x, drop_first)
self._pandas = x
self._ndarray = self._pandas.values
if all_numeric or convert_dummies:
self._ndarray = self._ndarray.astype(np.float64)
self._labels = {i: list(label) for i, label in zip(range(x.ndim), x.axes)}
else:
try:
import xarray as xr
except ImportError:
raise TypeError(type_err)
if isinstance(x, xr.DataArray):
if x.ndim == 1:
x = xr.concat([x], dim=var_name).transpose()
index = list(x.coords[x.dims[0]].values)
cols = x.coords[x.dims[1]].values
if is_numeric_dtype(cols.dtype):
cols = [var_name + '.{0}'.format(i) for i in range(x.shape[1])]
cols = list(cols)
self._ndarray = x.values.astype(np.float64)
self._pandas = pd.DataFrame(self._ndarray, columns=cols,
index=index)
self._labels = {0: index, 1: cols}
else:
raise TypeError(type_err)
if nobs is not None:
if self._ndarray.shape[0] != nobs:
msg = 'Array required to have {nobs} obs, has ' \
'{act}'.format(nobs=nobs, act=self._ndarray.shape[0])
raise ValueError(msg)
