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feat (accessors): Import accessors implicitly (#45)
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@coroa
coroa authored Dec 12, 2023
1 parent 01e570e commit 82b57d9
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8 changes: 6 additions & 2 deletions CHANGELOG.rst
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Changelog
=========

* Fix type hints of function :func:`~core.aggregatelevel`
* Switch from black to ruff for formatting and update pre-commit versions
v0.4.0 (2023-12-12)
------------------------------------------------------------
* **BREAKING** :mod:`accessors` is imported implicitly. User code does not need to
import it any longer :pull:`45`
* Fix type hints of function :func:`~core.aggregatelevel` :pull:`44`
* Switch from black to ruff for formatting and update pre-commit versions :pull:`43`

v0.3.1 (2023-09-18)
------------------------------------------------------------
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109 changes: 75 additions & 34 deletions docs/notebooks/introduction.ipynb
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"source": [
"from pandas_indexing.datasets import remindhighre_power\n",
"\n",
"\n",
"df = remindhighre_power()\n",
"df.head()"
]
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"2. convenience accessors that are hooking into pandas as extensions, like\n",
" \n",
" ```python\n",
" import pandas_indexing.accessors\n",
" df.pix.assign(unit=\"Mt CO2e/yr)\n",
" ```\n",
"\n",
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"source": [
"from pandas_indexing.core import describelevel\n",
"\n",
"\n",
"describelevel(df) # or: df.idx"
]
},
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}
],
"source": [
"import pandas_indexing.accessors\n",
"\n",
"df.idx"
]
},
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"metadata": {},
"outputs": [],
"source": [
"from pandas_indexing import ismatch, isin # no .idx equivalents"
"from pandas_indexing import isin, ismatch # no .idx equivalents"
]
},
{
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"source": [
"from pandas_indexing import extractlevel, formatlevel\n",
"\n",
"\n",
"splitdf = extractlevel(df, variable=\"Secondary Energy|{carrier}|{fuel}\", drop=True)\n",
"# or: df.pix.extract(variable=\"Secondary Energy|{carrier}|{fuel}\")\n",
"splitdf"
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"source": [
"from pandas_indexing import semijoin\n",
"\n",
"\n",
"semijoin(\n",
" fossil_series, idx, how=\"right\"\n",
") # or: fossil_series.pix.semijoin(idx, how=\"right\")"
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"source": [
"from pandas_indexing import projectlevel\n",
"\n",
"\n",
"simple_fossil_series = projectlevel(fossil_series, [\"variable\", \"year\"])\n",
"# or: fossil_series.pix.project([\"variable\", \"year\"])\n",
"simple_fossil_series"
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"source": [
"from pandas_indexing import assignlevel\n",
"\n",
"\n",
"assignlevel(df, variable=\"Updated|\" + projectlevel(df.index, \"variable\"), unit=\"bla\")\n",
"# or: df.pix.assign(variable=df.index.pix.project(\"variable\"), unit=\"bla\")"
]
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"source": [
"from pandas_indexing import formatlevel\n",
"\n",
"\n",
"formatlevel(df, variable=\"Updated|{variable}\", unit=\"bla\")\n",
"# or: df.pix.format(variable=...)"
]
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"source": [
"from pandas_indexing import uniquelevel\n",
"\n",
"\n",
"uniquelevel(df, \"variable\")\n",
"# or: df.pix.unique(\"variable\")\n",
"# or in vanilla pandas: df.index.unique(\"variable\")"
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{
"data": {
"text/plain": [
"variable year \n",
"Coal 2030 182.0149\n",
" 2040 1.0729\n",
" 2050 0.5029\n",
" 2060 0.1836\n",
"Gas 2030 1584.4033\n",
" 2040 1289.4777\n",
" 2050 562.8482\n",
" 2060 349.9091\n",
"Nuclear 2030 275.5920\n",
" 2040 214.4376\n",
" 2050 156.7766\n",
" 2060 92.0667\n",
"2030 Coal 182.0149\n",
"2040 Coal 1.0729\n",
"2050 Coal 0.5029\n",
"2060 Coal 0.1836\n",
"2030 Gas 1584.4033\n",
"2040 Gas 1289.4777\n",
"2050 Gas 562.8482\n",
"2060 Gas 349.9091\n",
"2030 Nuclear 275.5920\n",
"2040 Nuclear 214.4376\n",
"2050 Nuclear 156.7766\n",
"2060 Nuclear 92.0667\n",
"Coal 2030 182.0149\n",
" 2040 1.0729\n",
" 2050 0.5029\n",
" 2060 0.1836\n",
"Gas 2030 1584.4033\n",
" 2040 1289.4777\n",
" 2050 562.8482\n",
" 2060 349.9091\n",
"Nuclear 2030 275.5920\n",
" 2040 214.4376\n",
" 2050 156.7766\n",
" 2060 92.0667\n",
"2030 Coal 182.0149\n",
"2040 Coal 1.0729\n",
"2050 Coal 0.5029\n",
"2060 Coal 0.1836\n",
"2030 Gas 1584.4033\n",
"2040 Gas 1289.4777\n",
"2050 Gas 562.8482\n",
"2060 Gas 349.9091\n",
"2030 Nuclear 275.5920\n",
"2040 Nuclear 214.4376\n",
"2050 Nuclear 156.7766\n",
"2060 Nuclear 92.0667\n",
"dtype: float64"
]
},
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"execution_count": 31,
"id": "ef1d54c6",
"metadata": {},
"outputs": [],
"outputs": [
{
"data": {
"text/plain": [
"variable year\n",
"Coal 2030 182.0149\n",
" 2040 1.0729\n",
" 2050 0.5029\n",
" 2060 0.1836\n",
"Gas 2030 1584.4033\n",
" 2040 1289.4777\n",
" 2050 562.8482\n",
" 2060 349.9091\n",
"Nuclear 2030 275.5920\n",
" 2040 214.4376\n",
" 2050 156.7766\n",
" 2060 92.0667\n",
"Coal 2030 182.0149\n",
" 2040 1.0729\n",
" 2050 0.5029\n",
" 2060 0.1836\n",
"Gas 2030 1584.4033\n",
" 2040 1289.4777\n",
" 2050 562.8482\n",
" 2060 349.9091\n",
"Nuclear 2030 275.5920\n",
" 2040 214.4376\n",
" 2050 156.7766\n",
" 2060 92.0667\n",
"dtype: float64"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from pandas_indexing import concat\n",
"\n",
"\n",
"concat([simple_fossil_series, simple_fossil_series.swaplevel()])"
]
},
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},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 32,
"id": "5b7cb7db",
"metadata": {},
"outputs": [
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" names=['model', 'scenario', 'variable'])"
]
},
"execution_count": 33,
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
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},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 33,
"id": "5b7cb7db",
"metadata": {},
"outputs": [
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"11 REMIND-MAgPIE 2.1-4.3 DeepElec_SSP2_HighRE_Budg900 Nuclear"
]
},
"execution_count": 34,
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
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2 changes: 1 addition & 1 deletion src/pandas_indexing/__init__.py
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from importlib.metadata import version as _version

from . import core, datasets
from . import accessors, core, datasets
from .arithmetics import add, divide, multiply, subtract
from .core import (
aggregatelevel,
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2 changes: 0 additions & 2 deletions tests/test_accessors.py
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from pandas import DataFrame, Index, MultiIndex
from pandas.testing import assert_frame_equal, assert_index_equal, assert_series_equal

import pandas_indexing.accessors # noqa: F401


def test_assign_index(midx: MultiIndex):
"""
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