Coverage for /pythoncovmergedfiles/medio/medio/usr/local/lib/python3.11/site-packages/pandas/__init__.py: 100%
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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 NamedFunc,
104 array,
105 Categorical,
106 set_eng_float_format,
107 Series,
108 DataFrame,
109)
110from pandas.core.col import col
112from pandas.core.dtypes.dtypes import SparseDtype
114from pandas.tseries.api import infer_freq
115from pandas.tseries import offsets
117from pandas.core.computation.api import eval
119from pandas.core.reshape.api import (
120 concat,
121 lreshape,
122 melt,
123 wide_to_long,
124 merge,
125 merge_asof,
126 merge_ordered,
127 crosstab,
128 pivot,
129 pivot_table,
130 get_dummies,
131 from_dummies,
132 cut,
133 qcut,
134)
136from pandas import api, arrays, errors, io, plotting, tseries
137from pandas import testing
138from pandas.util._print_versions import show_versions
140from pandas.io.api import (
141 # excel
142 ExcelFile,
143 ExcelWriter,
144 read_excel,
145 # parsers
146 read_csv,
147 read_fwf,
148 read_table,
149 # pickle
150 read_pickle,
151 to_pickle,
152 # pytables
153 HDFStore,
154 read_hdf,
155 # sql
156 read_sql,
157 read_sql_query,
158 read_sql_table,
159 # misc
160 read_clipboard,
161 read_parquet,
162 read_orc,
163 read_feather,
164 read_html,
165 read_xml,
166 read_json,
167 read_stata,
168 read_sas,
169 read_spss,
170 read_iceberg,
171)
173from pandas.io.json._normalize import json_normalize
175from pandas.util._tester import test
177# use the closest tagged version if possible
178from pandas._version_meson import ( # pyright: ignore [reportMissingImports]
179 __version__,
180 __git_version__,
181)
184# module level doc-string
185__doc__ = """
186pandas - a powerful data analysis and manipulation library for Python
187=====================================================================
189**pandas** is a Python package providing fast, flexible, and expressive data
190structures designed to make working with "relational" or "labeled" data both
191easy and intuitive. It aims to be the fundamental high-level building block for
192doing practical, **real world** data analysis in Python. Additionally, it has
193the broader goal of becoming **the most powerful and flexible open source data
194analysis / manipulation tool available in any language**. It is already well on
195its way toward this goal.
197Main Features
198-------------
199Here are just a few of the things that pandas does well:
201 - Easy handling of missing data in floating point as well as non-floating
202 point data.
203 - Size mutability: columns can be inserted and deleted from DataFrame and
204 higher dimensional objects
205 - Automatic and explicit data alignment: objects can be explicitly aligned
206 to a set of labels, or the user can simply ignore the labels and let
207 `Series`, `DataFrame`, etc. automatically align the data for you in
208 computations.
209 - Powerful, flexible group by functionality to perform split-apply-combine
210 operations on data sets, for both aggregating and transforming data.
211 - Make it easy to convert ragged, differently-indexed data in other Python
212 and NumPy data structures into DataFrame objects.
213 - Intelligent label-based slicing, fancy indexing, and subsetting of large
214 data sets.
215 - Intuitive merging and joining data sets.
216 - Flexible reshaping and pivoting of data sets.
217 - Hierarchical labeling of axes (possible to have multiple labels per tick).
218 - Robust IO tools for loading data from flat files (CSV and delimited),
219 Excel files, databases, and saving/loading data from the ultrafast HDF5
220 format.
221 - Time series-specific functionality: date range generation and frequency
222 conversion, moving window statistics, date shifting and lagging.
223"""
225# Use __all__ to let type checkers know what is part of the public API.
226# Pandas is not (yet) a py.typed library: the public API is determined
227# based on the documentation.
228__all__ = [
229 "NA",
230 "ArrowDtype",
231 "BooleanDtype",
232 "Categorical",
233 "CategoricalDtype",
234 "CategoricalIndex",
235 "DataFrame",
236 "DateOffset",
237 "DatetimeIndex",
238 "DatetimeTZDtype",
239 "ExcelFile",
240 "ExcelWriter",
241 "Flags",
242 "Float32Dtype",
243 "Float64Dtype",
244 "Grouper",
245 "HDFStore",
246 "Index",
247 "IndexSlice",
248 "Int8Dtype",
249 "Int16Dtype",
250 "Int32Dtype",
251 "Int64Dtype",
252 "Interval",
253 "IntervalDtype",
254 "IntervalIndex",
255 "MultiIndex",
256 "NaT",
257 "NamedAgg",
258 "NamedFunc",
259 "Period",
260 "PeriodDtype",
261 "PeriodIndex",
262 "RangeIndex",
263 "Series",
264 "SparseDtype",
265 "StringDtype",
266 "Timedelta",
267 "TimedeltaIndex",
268 "Timestamp",
269 "UInt8Dtype",
270 "UInt16Dtype",
271 "UInt32Dtype",
272 "UInt64Dtype",
273 "__git_version__",
274 "__version__",
275 "api",
276 "array",
277 "arrays",
278 "bdate_range",
279 "col",
280 "concat",
281 "crosstab",
282 "cut",
283 "date_range",
284 "describe_option",
285 "errors",
286 "eval",
287 "factorize",
288 "from_dummies",
289 "get_dummies",
290 "get_option",
291 "infer_freq",
292 "interval_range",
293 "io",
294 "isna",
295 "isnull",
296 "json_normalize",
297 "lreshape",
298 "melt",
299 "merge",
300 "merge_asof",
301 "merge_ordered",
302 "notna",
303 "notnull",
304 "offsets",
305 "option_context",
306 "options",
307 "period_range",
308 "pivot",
309 "pivot_table",
310 "plotting",
311 "qcut",
312 "read_clipboard",
313 "read_csv",
314 "read_excel",
315 "read_feather",
316 "read_fwf",
317 "read_hdf",
318 "read_html",
319 "read_iceberg",
320 "read_json",
321 "read_orc",
322 "read_parquet",
323 "read_pickle",
324 "read_sas",
325 "read_spss",
326 "read_sql",
327 "read_sql_query",
328 "read_sql_table",
329 "read_stata",
330 "read_table",
331 "read_xml",
332 "reset_option",
333 "set_eng_float_format",
334 "set_option",
335 "show_versions",
336 "test",
337 "testing",
338 "timedelta_range",
339 "to_datetime",
340 "to_numeric",
341 "to_pickle",
342 "to_timedelta",
343 "tseries",
344 "unique",
345 "wide_to_long",
346]