def _chop(self, sdata: DataFrame, slice_obj: slice) -> DataFrame:
# Fastpath equivalent to:
# if self.axis == 0:
# return sdata.iloc[slice_obj]
# else:
# return sdata.iloc[:, slice_obj]
mgr = sdata._mgr.get_slice(slice_obj, axis=1 - self.axis)
df = sdata._constructor(mgr)
return df.__finalize__(sdata, method="groupby")
def _chop(self, sdata: DataFrame, slice_obj: slice) -> DataFrame:
# Fastpath equivalent to:
# if self.axis == 0:
# return sdata.iloc[slice_obj]
# else:
# return sdata.iloc[:, slice_obj]
mgr = sdata._mgr.get_slice(slice_obj, axis=1 - self.axis)
# __finalize__ not called here, must be applied by caller if applicable
return sdata._constructor(mgr)
def melt(
frame: DataFrame,
id_vars=None,
value_vars=None,
var_name=None,
value_name="value",
col_level=None,
) -> DataFrame:
# TODO: what about the existing index?
# If multiindex, gather names of columns on all level for checking presence
# of `id_vars` and `value_vars`
if isinstance(frame.columns, ABCMultiIndex):
cols = [x for c in frame.columns for x in c]
else:
cols = list(frame.columns)
if id_vars is not None:
if not is_list_like(id_vars):
id_vars = [id_vars]
elif isinstance(frame.columns,
ABCMultiIndex) and not isinstance(id_vars, list):
raise ValueError(
"id_vars must be a list of tuples when columns are a MultiIndex"
)
else:
# Check that `id_vars` are in frame
id_vars = list(id_vars)
missing = Index(com.flatten(id_vars)).difference(cols)
if not missing.empty:
raise KeyError("The following 'id_vars' are not present"
" in the DataFrame: {missing}"
"".format(missing=list(missing)))
else:
id_vars = []
if value_vars is not None:
if not is_list_like(value_vars):
value_vars = [value_vars]
elif isinstance(frame.columns,
ABCMultiIndex) and not isinstance(value_vars, list):
raise ValueError(
"value_vars must be a list of tuples when columns are a MultiIndex"
)
else:
value_vars = list(value_vars)
# Check that `value_vars` are in frame
missing = Index(com.flatten(value_vars)).difference(cols)
if not missing.empty:
raise KeyError("The following 'value_vars' are not present in"
" the DataFrame: {missing}"
"".format(missing=list(missing)))
frame = frame.loc[:, id_vars + value_vars]
else:
frame = frame.copy()
if col_level is not None: # allow list or other?
# frame is a copy
frame.columns = frame.columns.get_level_values(col_level)
if var_name is None:
if isinstance(frame.columns, ABCMultiIndex):
if len(frame.columns.names) == len(set(frame.columns.names)):
var_name = frame.columns.names
else:
var_name = [
"variable_{i}".format(i=i)
for i in range(len(frame.columns.names))
]
else:
var_name = [
frame.columns.name
if frame.columns.name is not None else "variable"
]
if isinstance(var_name, str):
var_name = [var_name]
N, K = frame.shape
K -= len(id_vars)
mdata = {}
for col in id_vars:
id_data = frame.pop(col)
if is_extension_array_dtype(id_data):
id_data = concat([id_data] * K, ignore_index=True)
else:
id_data = np.tile(id_data.values, K)
mdata[col] = id_data
mcolumns = id_vars + var_name + [value_name]
mdata[value_name] = frame.values.ravel("F")
for i, col in enumerate(var_name):
# asanyarray will keep the columns as an Index
mdata[col] = np.asanyarray(
frame.columns._get_level_values(i)).repeat(N)
return frame._constructor(mdata, columns=mcolumns)
def lreshape(data: DataFrame,
groups,
dropna: bool = True,
label=None) -> DataFrame:
"""
Reshape long-format data to wide. Generalized inverse of DataFrame.pivot
Parameters
----------
data : DataFrame
groups : dict
{new_name : list_of_columns}
dropna : boolean, default True
Examples
--------
>>> data = pd.DataFrame({'hr1': [514, 573], 'hr2': [545, 526],
... 'team': ['Red Sox', 'Yankees'],
... 'year1': [2007, 2007], 'year2': [2008, 2008]})
>>> data
hr1 hr2 team year1 year2
0 514 545 Red Sox 2007 2008
1 573 526 Yankees 2007 2008
>>> pd.lreshape(data, {'year': ['year1', 'year2'], 'hr': ['hr1', 'hr2']})
team year hr
0 Red Sox 2007 514
1 Yankees 2007 573
2 Red Sox 2008 545
3 Yankees 2008 526
Returns
-------
reshaped : DataFrame
"""
if isinstance(groups, dict):
keys = list(groups.keys())
values = list(groups.values())
else:
keys, values = zip(*groups)
all_cols = list(set.union(*[set(x) for x in values]))
id_cols = list(data.columns.difference(all_cols))
K = len(values[0])
for seq in values:
if len(seq) != K:
raise ValueError("All column lists must be same length")
mdata = {}
pivot_cols = []
for target, names in zip(keys, values):
to_concat = [data[col].values for col in names]
mdata[target] = concat_compat(to_concat)
pivot_cols.append(target)
for col in id_cols:
mdata[col] = np.tile(data[col].values, K)
if dropna:
mask = np.ones(len(mdata[pivot_cols[0]]), dtype=bool)
for c in pivot_cols:
mask &= notna(mdata[c])
if not mask.all():
mdata = {k: v[mask] for k, v in mdata.items()}
return data._constructor(mdata, columns=id_cols + pivot_cols)
