Skip to content

Fix explode to preserve datetime unit in Series and DataFrame; update… #61612

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Open
wants to merge 5 commits into
base: main
Choose a base branch
from
Open
Show file tree
Hide file tree
Changes from all commits
Commits
File filter

Filter by extension

Filter by extension

Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
79 changes: 65 additions & 14 deletions pandas/core/frame.py
Original file line number Diff line number Diff line change
Expand Up @@ -10,7 +10,12 @@
"""

from __future__ import annotations

from pandas.core.dtypes.common import (
is_list_like,
is_scalar,
is_datetime64_dtype,
isna,
)
import collections
from collections import abc
from collections.abc import (
Expand Down Expand Up @@ -121,6 +126,7 @@
notna,
)

import pandas as pd
from pandas.core import (
algorithms,
common as com,
Expand Down Expand Up @@ -9936,41 +9942,86 @@ def explode(
3 3 1 d
3 4 1 e
"""
if not self.columns.is_unique:
duplicate_cols = self.columns[self.columns.duplicated()].tolist()
raise ValueError(
f"DataFrame columns must be unique. Duplicate columns: {duplicate_cols}"
)
df = self.reset_index(drop=True)

columns: list[Hashable]
if is_scalar(column) or isinstance(column, tuple):
columns = [column]
elif isinstance(column, list) and all(
is_scalar(c) or isinstance(c, tuple) for c in column
):
elif isinstance(column, list) and all(is_scalar(c) or isinstance(c, tuple) for c in column):
if not column:
raise ValueError("column must be nonempty")
if len(column) > len(set(column)):
raise ValueError("column must be unique")
columns = column
else:
raise ValueError("column must be a scalar, tuple, or list thereof")

df = self.reset_index(drop=True)
if len(columns) == 1:
result = df[columns[0]].explode()
col = columns[0]
orig_dtype = df[col].dtype

exploded_values = []
exploded_index = []

for i, val in enumerate(df[col]):
if is_list_like(val) and not isinstance(val, (str, bytes)):
for item in val:
exploded_values.append(item)
exploded_index.append(i)
elif isna(val):
exploded_values.append(np.datetime64("NaT") if is_datetime64_dtype(orig_dtype) else np.nan)
exploded_index.append(i)
else:
exploded_values.append(val)
exploded_index.append(i)

exploded_series = pd.Series(
np.array(exploded_values, dtype=orig_dtype if is_datetime64_dtype(orig_dtype) else None),
index=exploded_index,
name=col
)

result = df.drop(columns, axis=1).iloc[exploded_series.index]
result[col] = exploded_series.values
else:
mylen = lambda x: len(x) if (is_list_like(x) and len(x) > 0) else 1
counts0 = self[columns[0]].apply(mylen)
for c in columns[1:]:
if not all(counts0 == self[c].apply(mylen)):
raise ValueError("columns must have matching element counts")
result = DataFrame({c: df[c].explode() for c in columns})
result = df.drop(columns, axis=1).join(result)

exploded_columns = {}
exploded_index = []

for i in range(len(df)):
row_counts = mylen(df[columns[0]].iloc[i])
for j in range(row_counts):
exploded_index.append(i)

for col in columns:
orig_dtype = df[col].dtype
values = []
for val in df[col]:
if is_list_like(val) and not isinstance(val, (str, bytes)):
values.extend(val)
elif isna(val):
values.append(np.datetime64("NaT") if is_datetime64_dtype(orig_dtype) else np.nan)
else:
values.append(val)
exploded_columns[col] = pd.Series(
np.array(values, dtype=orig_dtype if is_datetime64_dtype(orig_dtype) else None),
index=exploded_index
)

result = df.drop(columns, axis=1).iloc[exploded_index].copy()
for col in columns:
result[col] = exploded_columns[col].values

# Handle index
if ignore_index:
result.index = default_index(len(result))
else:
result.index = self.index.take(result.index)

result = result.reindex(columns=self.columns)

return result.__finalize__(self, method="explode")
Expand Down
38 changes: 38 additions & 0 deletions pandas/tests/series/methods/test_explode.py
Original file line number Diff line number Diff line change
Expand Up @@ -175,3 +175,41 @@ def test_explode_pyarrow_non_list_type(ignore_index):
result = ser.explode(ignore_index=ignore_index)
expected = pd.Series([1, 2, 3], dtype="int64[pyarrow]", index=[0, 1, 2])
tm.assert_series_equal(result, expected)


def test_explode_preserves_datetime_unit():
# Create datetime64[ms] array manually
dt64_ms = np.array(
[
"2020-01-01T00:00:00.000",
"2020-01-01T01:00:00.000",
"2020-01-01T02:00:00.000",
],
dtype="datetime64[ms]",
)
s = pd.Series([dt64_ms])

# Explode the Series
result = s.explode()

# Ensure the dtype (including unit) is preserved
assert result.dtype == dt64_ms.dtype, (
f"Expected dtype {dt64_ms.dtype}, got {result.dtype}"
)


def test_single_column_explode_preserves_datetime_unit():
# Use freq in ms since unit='ms'
rng = pd.date_range("2020-01-01T00:00:00Z", periods=3, freq="3600000ms", unit="ms")
s = pd.Series([rng])
result = s.explode()
assert result.dtype == rng.dtype


def test_multi_column_explode_preserves_datetime_unit():
rng1 = pd.date_range("2020-01-01", periods=2, freq="3600000ms", unit="ms")
rng2 = pd.date_range("2020-01-01", periods=2, freq="3600000ms", unit="ms")
df = pd.DataFrame({"A": [rng1], "B": [rng2]})
result = df.explode(["A", "B"])
assert result["A"].dtype == rng1.dtype
assert result["B"].dtype == rng2.dtype
Loading