from __future__ import annotations
|
|
import re
|
from typing import (
|
TYPE_CHECKING,
|
Hashable,
|
)
|
|
import numpy as np
|
|
from pandas.util._decorators import Appender
|
|
from pandas.core.dtypes.common import (
|
is_extension_array_dtype,
|
is_list_like,
|
)
|
from pandas.core.dtypes.concat import concat_compat
|
from pandas.core.dtypes.missing import notna
|
|
import pandas.core.algorithms as algos
|
from pandas.core.arrays import Categorical
|
import pandas.core.common as com
|
from pandas.core.indexes.api import (
|
Index,
|
MultiIndex,
|
)
|
from pandas.core.reshape.concat import concat
|
from pandas.core.reshape.util import tile_compat
|
from pandas.core.shared_docs import _shared_docs
|
from pandas.core.tools.numeric import to_numeric
|
|
if TYPE_CHECKING:
|
from pandas._typing import AnyArrayLike
|
|
from pandas import DataFrame
|
|
|
@Appender(_shared_docs["melt"] % {"caller": "pd.melt(df, ", "other": "DataFrame.melt"})
|
def melt(
|
frame: DataFrame,
|
id_vars=None,
|
value_vars=None,
|
var_name=None,
|
value_name: Hashable = "value",
|
col_level=None,
|
ignore_index: bool = True,
|
) -> DataFrame:
|
# If multiindex, gather names of columns on all level for checking presence
|
# of `id_vars` and `value_vars`
|
if isinstance(frame.columns, MultiIndex):
|
cols = [x for c in frame.columns for x in c]
|
else:
|
cols = list(frame.columns)
|
|
if value_name in frame.columns:
|
raise ValueError(
|
f"value_name ({value_name}) cannot match an element in "
|
"the DataFrame columns."
|
)
|
|
if id_vars is not None:
|
if not is_list_like(id_vars):
|
id_vars = [id_vars]
|
elif isinstance(frame.columns, MultiIndex) 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 "
|
f"in the DataFrame: {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, MultiIndex) 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 "
|
f"the DataFrame: {list(missing)}"
|
)
|
if col_level is not None:
|
idx = frame.columns.get_level_values(col_level).get_indexer(
|
id_vars + value_vars
|
)
|
else:
|
idx = algos.unique(frame.columns.get_indexer_for(id_vars + value_vars))
|
frame = frame.iloc[:, idx]
|
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, MultiIndex):
|
if len(frame.columns.names) == len(set(frame.columns.names)):
|
var_name = frame.columns.names
|
else:
|
var_name = [f"variable_{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: dict[Hashable, AnyArrayLike] = {}
|
for col in id_vars:
|
id_data = frame.pop(col)
|
if is_extension_array_dtype(id_data):
|
if K > 0:
|
id_data = concat([id_data] * K, ignore_index=True)
|
else:
|
# We can't concat empty list. (GH 46044)
|
id_data = type(id_data)([], name=id_data.name, dtype=id_data.dtype)
|
else:
|
# error: Incompatible types in assignment (expression has type
|
# "ndarray[Any, dtype[Any]]", variable has type "Series")
|
id_data = np.tile(id_data._values, K) # type: ignore[assignment]
|
mdata[col] = id_data
|
|
mcolumns = id_vars + var_name + [value_name]
|
|
if frame.shape[1] > 0:
|
mdata[value_name] = concat(
|
[frame.iloc[:, i] for i in range(frame.shape[1])]
|
).values
|
else:
|
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)
|
|
result = frame._constructor(mdata, columns=mcolumns)
|
|
if not ignore_index:
|
result.index = tile_compat(frame.index, K)
|
|
return result
|
|
|
def lreshape(data: DataFrame, groups, dropna: bool = True) -> DataFrame:
|
"""
|
Reshape wide-format data to long. Generalized inverse of DataFrame.pivot.
|
|
Accepts a dictionary, ``groups``, in which each key is a new column name
|
and each value is a list of old column names that will be "melted" under
|
the new column name as part of the reshape.
|
|
Parameters
|
----------
|
data : DataFrame
|
The wide-format DataFrame.
|
groups : dict
|
{new_name : list_of_columns}.
|
dropna : bool, default True
|
Do not include columns whose entries are all NaN.
|
|
Returns
|
-------
|
DataFrame
|
Reshaped DataFrame.
|
|
See Also
|
--------
|
melt : Unpivot a DataFrame from wide to long format, optionally leaving
|
identifiers set.
|
pivot : Create a spreadsheet-style pivot table as a DataFrame.
|
DataFrame.pivot : Pivot without aggregation that can handle
|
non-numeric data.
|
DataFrame.pivot_table : Generalization of pivot that can handle
|
duplicate values for one index/column pair.
|
DataFrame.unstack : Pivot based on the index values instead of a
|
column.
|
wide_to_long : Wide panel to long format. Less flexible but more
|
user-friendly than melt.
|
|
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
|
"""
|
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)
|
|
|
def wide_to_long(
|
df: DataFrame, stubnames, i, j, sep: str = "", suffix: str = r"\d+"
|
) -> DataFrame:
|
r"""
|
Unpivot a DataFrame from wide to long format.
|
|
Less flexible but more user-friendly than melt.
|
|
With stubnames ['A', 'B'], this function expects to find one or more
|
group of columns with format
|
A-suffix1, A-suffix2,..., B-suffix1, B-suffix2,...
|
You specify what you want to call this suffix in the resulting long format
|
with `j` (for example `j='year'`)
|
|
Each row of these wide variables are assumed to be uniquely identified by
|
`i` (can be a single column name or a list of column names)
|
|
All remaining variables in the data frame are left intact.
|
|
Parameters
|
----------
|
df : DataFrame
|
The wide-format DataFrame.
|
stubnames : str or list-like
|
The stub name(s). The wide format variables are assumed to
|
start with the stub names.
|
i : str or list-like
|
Column(s) to use as id variable(s).
|
j : str
|
The name of the sub-observation variable. What you wish to name your
|
suffix in the long format.
|
sep : str, default ""
|
A character indicating the separation of the variable names
|
in the wide format, to be stripped from the names in the long format.
|
For example, if your column names are A-suffix1, A-suffix2, you
|
can strip the hyphen by specifying `sep='-'`.
|
suffix : str, default '\\d+'
|
A regular expression capturing the wanted suffixes. '\\d+' captures
|
numeric suffixes. Suffixes with no numbers could be specified with the
|
negated character class '\\D+'. You can also further disambiguate
|
suffixes, for example, if your wide variables are of the form A-one,
|
B-two,.., and you have an unrelated column A-rating, you can ignore the
|
last one by specifying `suffix='(!?one|two)'`. When all suffixes are
|
numeric, they are cast to int64/float64.
|
|
Returns
|
-------
|
DataFrame
|
A DataFrame that contains each stub name as a variable, with new index
|
(i, j).
|
|
See Also
|
--------
|
melt : Unpivot a DataFrame from wide to long format, optionally leaving
|
identifiers set.
|
pivot : Create a spreadsheet-style pivot table as a DataFrame.
|
DataFrame.pivot : Pivot without aggregation that can handle
|
non-numeric data.
|
DataFrame.pivot_table : Generalization of pivot that can handle
|
duplicate values for one index/column pair.
|
DataFrame.unstack : Pivot based on the index values instead of a
|
column.
|
|
Notes
|
-----
|
All extra variables are left untouched. This simply uses
|
`pandas.melt` under the hood, but is hard-coded to "do the right thing"
|
in a typical case.
|
|
Examples
|
--------
|
>>> np.random.seed(123)
|
>>> df = pd.DataFrame({"A1970" : {0 : "a", 1 : "b", 2 : "c"},
|
... "A1980" : {0 : "d", 1 : "e", 2 : "f"},
|
... "B1970" : {0 : 2.5, 1 : 1.2, 2 : .7},
|
... "B1980" : {0 : 3.2, 1 : 1.3, 2 : .1},
|
... "X" : dict(zip(range(3), np.random.randn(3)))
|
... })
|
>>> df["id"] = df.index
|
>>> df
|
A1970 A1980 B1970 B1980 X id
|
0 a d 2.5 3.2 -1.085631 0
|
1 b e 1.2 1.3 0.997345 1
|
2 c f 0.7 0.1 0.282978 2
|
>>> pd.wide_to_long(df, ["A", "B"], i="id", j="year")
|
... # doctest: +NORMALIZE_WHITESPACE
|
X A B
|
id year
|
0 1970 -1.085631 a 2.5
|
1 1970 0.997345 b 1.2
|
2 1970 0.282978 c 0.7
|
0 1980 -1.085631 d 3.2
|
1 1980 0.997345 e 1.3
|
2 1980 0.282978 f 0.1
|
|
With multiple id columns
|
|
>>> df = pd.DataFrame({
|
... 'famid': [1, 1, 1, 2, 2, 2, 3, 3, 3],
|
... 'birth': [1, 2, 3, 1, 2, 3, 1, 2, 3],
|
... 'ht1': [2.8, 2.9, 2.2, 2, 1.8, 1.9, 2.2, 2.3, 2.1],
|
... 'ht2': [3.4, 3.8, 2.9, 3.2, 2.8, 2.4, 3.3, 3.4, 2.9]
|
... })
|
>>> df
|
famid birth ht1 ht2
|
0 1 1 2.8 3.4
|
1 1 2 2.9 3.8
|
2 1 3 2.2 2.9
|
3 2 1 2.0 3.2
|
4 2 2 1.8 2.8
|
5 2 3 1.9 2.4
|
6 3 1 2.2 3.3
|
7 3 2 2.3 3.4
|
8 3 3 2.1 2.9
|
>>> l = pd.wide_to_long(df, stubnames='ht', i=['famid', 'birth'], j='age')
|
>>> l
|
... # doctest: +NORMALIZE_WHITESPACE
|
ht
|
famid birth age
|
1 1 1 2.8
|
2 3.4
|
2 1 2.9
|
2 3.8
|
3 1 2.2
|
2 2.9
|
2 1 1 2.0
|
2 3.2
|
2 1 1.8
|
2 2.8
|
3 1 1.9
|
2 2.4
|
3 1 1 2.2
|
2 3.3
|
2 1 2.3
|
2 3.4
|
3 1 2.1
|
2 2.9
|
|
Going from long back to wide just takes some creative use of `unstack`
|
|
>>> w = l.unstack()
|
>>> w.columns = w.columns.map('{0[0]}{0[1]}'.format)
|
>>> w.reset_index()
|
famid birth ht1 ht2
|
0 1 1 2.8 3.4
|
1 1 2 2.9 3.8
|
2 1 3 2.2 2.9
|
3 2 1 2.0 3.2
|
4 2 2 1.8 2.8
|
5 2 3 1.9 2.4
|
6 3 1 2.2 3.3
|
7 3 2 2.3 3.4
|
8 3 3 2.1 2.9
|
|
Less wieldy column names are also handled
|
|
>>> np.random.seed(0)
|
>>> df = pd.DataFrame({'A(weekly)-2010': np.random.rand(3),
|
... 'A(weekly)-2011': np.random.rand(3),
|
... 'B(weekly)-2010': np.random.rand(3),
|
... 'B(weekly)-2011': np.random.rand(3),
|
... 'X' : np.random.randint(3, size=3)})
|
>>> df['id'] = df.index
|
>>> df # doctest: +NORMALIZE_WHITESPACE, +ELLIPSIS
|
A(weekly)-2010 A(weekly)-2011 B(weekly)-2010 B(weekly)-2011 X id
|
0 0.548814 0.544883 0.437587 0.383442 0 0
|
1 0.715189 0.423655 0.891773 0.791725 1 1
|
2 0.602763 0.645894 0.963663 0.528895 1 2
|
|
>>> pd.wide_to_long(df, ['A(weekly)', 'B(weekly)'], i='id',
|
... j='year', sep='-')
|
... # doctest: +NORMALIZE_WHITESPACE
|
X A(weekly) B(weekly)
|
id year
|
0 2010 0 0.548814 0.437587
|
1 2010 1 0.715189 0.891773
|
2 2010 1 0.602763 0.963663
|
0 2011 0 0.544883 0.383442
|
1 2011 1 0.423655 0.791725
|
2 2011 1 0.645894 0.528895
|
|
If we have many columns, we could also use a regex to find our
|
stubnames and pass that list on to wide_to_long
|
|
>>> stubnames = sorted(
|
... set([match[0] for match in df.columns.str.findall(
|
... r'[A-B]\(.*\)').values if match != []])
|
... )
|
>>> list(stubnames)
|
['A(weekly)', 'B(weekly)']
|
|
All of the above examples have integers as suffixes. It is possible to
|
have non-integers as suffixes.
|
|
>>> df = pd.DataFrame({
|
... 'famid': [1, 1, 1, 2, 2, 2, 3, 3, 3],
|
... 'birth': [1, 2, 3, 1, 2, 3, 1, 2, 3],
|
... 'ht_one': [2.8, 2.9, 2.2, 2, 1.8, 1.9, 2.2, 2.3, 2.1],
|
... 'ht_two': [3.4, 3.8, 2.9, 3.2, 2.8, 2.4, 3.3, 3.4, 2.9]
|
... })
|
>>> df
|
famid birth ht_one ht_two
|
0 1 1 2.8 3.4
|
1 1 2 2.9 3.8
|
2 1 3 2.2 2.9
|
3 2 1 2.0 3.2
|
4 2 2 1.8 2.8
|
5 2 3 1.9 2.4
|
6 3 1 2.2 3.3
|
7 3 2 2.3 3.4
|
8 3 3 2.1 2.9
|
|
>>> l = pd.wide_to_long(df, stubnames='ht', i=['famid', 'birth'], j='age',
|
... sep='_', suffix=r'\w+')
|
>>> l
|
... # doctest: +NORMALIZE_WHITESPACE
|
ht
|
famid birth age
|
1 1 one 2.8
|
two 3.4
|
2 one 2.9
|
two 3.8
|
3 one 2.2
|
two 2.9
|
2 1 one 2.0
|
two 3.2
|
2 one 1.8
|
two 2.8
|
3 one 1.9
|
two 2.4
|
3 1 one 2.2
|
two 3.3
|
2 one 2.3
|
two 3.4
|
3 one 2.1
|
two 2.9
|
"""
|
|
def get_var_names(df, stub: str, sep: str, suffix: str) -> list[str]:
|
regex = rf"^{re.escape(stub)}{re.escape(sep)}{suffix}$"
|
pattern = re.compile(regex)
|
return [col for col in df.columns if pattern.match(col)]
|
|
def melt_stub(df, stub: str, i, j, value_vars, sep: str):
|
newdf = melt(
|
df,
|
id_vars=i,
|
value_vars=value_vars,
|
value_name=stub.rstrip(sep),
|
var_name=j,
|
)
|
newdf[j] = Categorical(newdf[j])
|
newdf[j] = newdf[j].str.replace(re.escape(stub + sep), "", regex=True)
|
|
# GH17627 Cast numerics suffixes to int/float
|
newdf[j] = to_numeric(newdf[j], errors="ignore")
|
|
return newdf.set_index(i + [j])
|
|
if not is_list_like(stubnames):
|
stubnames = [stubnames]
|
else:
|
stubnames = list(stubnames)
|
|
if any(col in stubnames for col in df.columns):
|
raise ValueError("stubname can't be identical to a column name")
|
|
if not is_list_like(i):
|
i = [i]
|
else:
|
i = list(i)
|
|
if df[i].duplicated().any():
|
raise ValueError("the id variables need to uniquely identify each row")
|
|
value_vars = [get_var_names(df, stub, sep, suffix) for stub in stubnames]
|
|
value_vars_flattened = [e for sublist in value_vars for e in sublist]
|
id_vars = list(set(df.columns.tolist()).difference(value_vars_flattened))
|
|
_melted = [melt_stub(df, s, i, j, v, sep) for s, v in zip(stubnames, value_vars)]
|
melted = _melted[0].join(_melted[1:], how="outer")
|
|
if len(i) == 1:
|
new = df[id_vars].set_index(i).join(melted)
|
return new
|
|
new = df[id_vars].merge(melted.reset_index(), on=i).set_index(i + [j])
|
|
return new
|
