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1from __future__ import annotations
3__docformat__ = "restructuredtext"
5# Let users know if they're missing any of our hard dependencies
6# except tzdata (see https://github.com/pandas-dev/pandas/issues/63264)
7_hard_dependencies = ("numpy", "dateutil")
9for _dependency in _hard_dependencies:
10 try:
11 __import__(_dependency)
12 except ImportError as _e: # pragma: no cover
13 raise ImportError(
14 f"Unable to import required dependency {_dependency}. "
15 "Please see the traceback for details."
16 ) from _e
18del _hard_dependencies, _dependency
20try:
21 # numpy compat
22 from pandas.compat import (
23 is_numpy_dev as _is_numpy_dev, # pyright: ignore[reportUnusedImport] # noqa: F401
24 )
25except ImportError as _err: # pragma: no cover
26 _module = _err.name
27 raise ImportError(
28 f"C extension: {_module} not built. If you want to import "
29 "pandas from the source directory, you may need to run "
30 "'python -m pip install -ve . --no-build-isolation -Ceditable-verbose=true' "
31 "to build the C extensions first."
32 ) from _err
34from pandas._config import (
35 get_option,
36 set_option,
37 reset_option,
38 describe_option,
39 option_context,
40 options,
41)
43# let init-time option registration happen
44import pandas.core.config_init # pyright: ignore[reportUnusedImport] # noqa: F401
46from pandas.core.api import (
47 # dtype
48 ArrowDtype,
49 Int8Dtype,
50 Int16Dtype,
51 Int32Dtype,
52 Int64Dtype,
53 UInt8Dtype,
54 UInt16Dtype,
55 UInt32Dtype,
56 UInt64Dtype,
57 Float32Dtype,
58 Float64Dtype,
59 CategoricalDtype,
60 PeriodDtype,
61 IntervalDtype,
62 DatetimeTZDtype,
63 StringDtype,
64 BooleanDtype,
65 # missing
66 NA,
67 isna,
68 isnull,
69 notna,
70 notnull,
71 # indexes
72 Index,
73 CategoricalIndex,
74 RangeIndex,
75 MultiIndex,
76 IntervalIndex,
77 TimedeltaIndex,
78 DatetimeIndex,
79 PeriodIndex,
80 IndexSlice,
81 # tseries
82 NaT,
83 Period,
84 period_range,
85 Timedelta,
86 timedelta_range,
87 Timestamp,
88 date_range,
89 bdate_range,
90 Interval,
91 interval_range,
92 DateOffset,
93 # conversion
94 to_numeric,
95 to_datetime,
96 to_timedelta,
97 # misc
98 Flags,
99 Grouper,
100 factorize,
101 unique,
102 NamedAgg,
103 array,
104 Categorical,
105 set_eng_float_format,
106 Series,
107 DataFrame,
108)
109from pandas.core.col import col
111from pandas.core.dtypes.dtypes import SparseDtype
113from pandas.tseries.api import infer_freq
114from pandas.tseries import offsets
116from pandas.core.computation.api import eval
118from pandas.core.reshape.api import (
119 concat,
120 lreshape,
121 melt,
122 wide_to_long,
123 merge,
124 merge_asof,
125 merge_ordered,
126 crosstab,
127 pivot,
128 pivot_table,
129 get_dummies,
130 from_dummies,
131 cut,
132 qcut,
133)
135from pandas import api, arrays, errors, io, plotting, tseries
136from pandas import testing
137from pandas.util._print_versions import show_versions
139from pandas.io.api import (
140 # excel
141 ExcelFile,
142 ExcelWriter,
143 read_excel,
144 # parsers
145 read_csv,
146 read_fwf,
147 read_table,
148 # pickle
149 read_pickle,
150 to_pickle,
151 # pytables
152 HDFStore,
153 read_hdf,
154 # sql
155 read_sql,
156 read_sql_query,
157 read_sql_table,
158 # misc
159 read_clipboard,
160 read_parquet,
161 read_orc,
162 read_feather,
163 read_html,
164 read_xml,
165 read_json,
166 read_stata,
167 read_sas,
168 read_spss,
169 read_iceberg,
170)
172from pandas.io.json._normalize import json_normalize
174from pandas.util._tester import test
176# use the closest tagged version if possible
177_built_with_meson = False
178try:
179 from pandas._version_meson import ( # pyright: ignore [reportMissingImports]
180 __version__,
181 __git_version__,
182 )
184 _built_with_meson = True
185except ImportError:
186 from pandas._version import get_versions
188 v = get_versions()
189 __version__ = v.get("closest-tag", v["version"])
190 __git_version__ = v.get("full-revisionid")
191 del get_versions, v
194# module level doc-string
195__doc__ = """
196pandas - a powerful data analysis and manipulation library for Python
197=====================================================================
199**pandas** is a Python package providing fast, flexible, and expressive data
200structures designed to make working with "relational" or "labeled" data both
201easy and intuitive. It aims to be the fundamental high-level building block for
202doing practical, **real world** data analysis in Python. Additionally, it has
203the broader goal of becoming **the most powerful and flexible open source data
204analysis / manipulation tool available in any language**. It is already well on
205its way toward this goal.
207Main Features
208-------------
209Here are just a few of the things that pandas does well:
211 - Easy handling of missing data in floating point as well as non-floating
212 point data.
213 - Size mutability: columns can be inserted and deleted from DataFrame and
214 higher dimensional objects
215 - Automatic and explicit data alignment: objects can be explicitly aligned
216 to a set of labels, or the user can simply ignore the labels and let
217 `Series`, `DataFrame`, etc. automatically align the data for you in
218 computations.
219 - Powerful, flexible group by functionality to perform split-apply-combine
220 operations on data sets, for both aggregating and transforming data.
221 - Make it easy to convert ragged, differently-indexed data in other Python
222 and NumPy data structures into DataFrame objects.
223 - Intelligent label-based slicing, fancy indexing, and subsetting of large
224 data sets.
225 - Intuitive merging and joining data sets.
226 - Flexible reshaping and pivoting of data sets.
227 - Hierarchical labeling of axes (possible to have multiple labels per tick).
228 - Robust IO tools for loading data from flat files (CSV and delimited),
229 Excel files, databases, and saving/loading data from the ultrafast HDF5
230 format.
231 - Time series-specific functionality: date range generation and frequency
232 conversion, moving window statistics, date shifting and lagging.
233"""
235# Use __all__ to let type checkers know what is part of the public API.
236# Pandas is not (yet) a py.typed library: the public API is determined
237# based on the documentation.
238__all__ = [
239 "NA",
240 "ArrowDtype",
241 "BooleanDtype",
242 "Categorical",
243 "CategoricalDtype",
244 "CategoricalIndex",
245 "DataFrame",
246 "DateOffset",
247 "DatetimeIndex",
248 "DatetimeTZDtype",
249 "ExcelFile",
250 "ExcelWriter",
251 "Flags",
252 "Float32Dtype",
253 "Float64Dtype",
254 "Grouper",
255 "HDFStore",
256 "Index",
257 "IndexSlice",
258 "Int8Dtype",
259 "Int16Dtype",
260 "Int32Dtype",
261 "Int64Dtype",
262 "Interval",
263 "IntervalDtype",
264 "IntervalIndex",
265 "MultiIndex",
266 "NaT",
267 "NamedAgg",
268 "Period",
269 "PeriodDtype",
270 "PeriodIndex",
271 "RangeIndex",
272 "Series",
273 "SparseDtype",
274 "StringDtype",
275 "Timedelta",
276 "TimedeltaIndex",
277 "Timestamp",
278 "UInt8Dtype",
279 "UInt16Dtype",
280 "UInt32Dtype",
281 "UInt64Dtype",
282 "api",
283 "array",
284 "arrays",
285 "bdate_range",
286 "col",
287 "concat",
288 "crosstab",
289 "cut",
290 "date_range",
291 "describe_option",
292 "errors",
293 "eval",
294 "factorize",
295 "from_dummies",
296 "get_dummies",
297 "get_option",
298 "infer_freq",
299 "interval_range",
300 "io",
301 "isna",
302 "isnull",
303 "json_normalize",
304 "lreshape",
305 "melt",
306 "merge",
307 "merge_asof",
308 "merge_ordered",
309 "notna",
310 "notnull",
311 "offsets",
312 "option_context",
313 "options",
314 "period_range",
315 "pivot",
316 "pivot_table",
317 "plotting",
318 "qcut",
319 "read_clipboard",
320 "read_csv",
321 "read_excel",
322 "read_feather",
323 "read_fwf",
324 "read_hdf",
325 "read_html",
326 "read_iceberg",
327 "read_json",
328 "read_orc",
329 "read_parquet",
330 "read_pickle",
331 "read_sas",
332 "read_spss",
333 "read_sql",
334 "read_sql_query",
335 "read_sql_table",
336 "read_stata",
337 "read_table",
338 "read_xml",
339 "reset_option",
340 "set_eng_float_format",
341 "set_option",
342 "show_versions",
343 "test",
344 "testing",
345 "timedelta_range",
346 "to_datetime",
347 "to_numeric",
348 "to_pickle",
349 "to_timedelta",
350 "tseries",
351 "unique",
352 "wide_to_long",
353]