# Splits a DataFrame/Series logarithmically
```python
pip install a-pandas-ex-logsplit
```
```python
from a_pandas_ex_logsplit import pd_add_logsplit
pd_add_logsplit()
import pandas as pd
df = pd.read_csv("https://github.com/pandas-dev/pandas/raw/main/doc/data/titanic.csv")
df = df[:50]
for h in df.ds_logsplit(columns=["Cabin", "Fare"], includeindex=False):
print(h)
for h in df.ds_logsplit(columns="Cabin", includeindex=True):
print(h)
for h in df.ds_logsplit(columns="Cabin", includeindex=False):
print(h)
for h in df.Cabin.ds_logsplit(includeindex=True):
print(h)
[(nan, 7.25)]
[('C85', 71.2833), (nan, 7.925)]
[('C123', 53.1), (nan, 8.05), (nan, 8.4583)]
[('E46', 51.8625), (nan, 21.075), (nan, 11.1333), (nan, 30.0708)]
[('G6', 16.7), ('C103', 26.55), (nan, 8.05), (nan, 31.275), (nan, 7.8542)]
[(nan, 16.0), (nan, 29.125), (nan, 13.0), (nan, 18.0), (nan, 7.225), (nan, 26.0)]
[('D56', 13.0), (nan, 8.0292), ('A6', 35.5), (nan, 21.075), (nan, 31.3875), (nan, 7.225), ('C23 C25 C27', 263.0)]
[(nan, 7.8792), (nan, 7.8958), (nan, 27.7208), ('B78', 146.5208), (nan, 7.75), (nan, 10.5), (nan, 82.1708), (nan, 52.0)]
[(nan, 7.2292), (nan, 8.05), (nan, 18.0), (nan, 11.2417), (nan, 9.475), (nan, 21.0), (nan, 7.8958), (nan, 41.5792), (nan, 7.8792)]
[(nan, 8.05), (nan, 15.5), (nan, 7.75), (nan, 21.6792), (nan, 17.8)]
[(0, 1)]
[(1, 2), (2, 3)]
[(3, 4), (4, 5), (5, 6)]
[(6, 7), (7, 8), (8, 9), (9, 10)]
[(10, 11), (11, 12), (12, 13), (13, 14), (14, 15)]
[(15, 16), (16, 17), (17, 18), (18, 19), (19, 20), (20, 21)]
[(21, 22), (22, 23), (23, 24), (24, 25), (25, 26), (26, 27), (27, 28)]
[(28, 29), (29, 30), (30, 31), (31, 32), (32, 33), (33, 34), (34, 35), (35, 36)]
[(36, 37), (37, 38), (38, 39), (39, 40), (40, 41), (41, 42), (42, 43), (43, 44), (44, 45)]
[(45, 46), (46, 47), (47, 48), (48, 49), (49, 50)]
[nan]
['C85', nan]
['C123', nan, nan]
['E46', nan, nan, nan]
['G6', 'C103', nan, nan, nan]
[nan, nan, nan, nan, nan, nan]
['D56', nan, 'A6', nan, nan, nan, 'C23 C25 C27']
[nan, nan, nan, 'B78', nan, nan, nan, nan]
[nan, nan, nan, nan, nan, nan, nan, nan, nan]
[nan, nan, nan, nan, nan]
[(0, nan)]
[(1, 'C85'), (2, nan)]
[(3, 'C123'), (4, nan), (5, nan)]
[(6, 'E46'), (7, nan), (8, nan), (9, nan)]
[(10, 'G6'), (11, 'C103'), (12, nan), (13, nan), (14, nan)]
[(15, nan), (16, nan), (17, nan), (18, nan), (19, nan), (20, nan)]
[(21, 'D56'), (22, nan), (23, 'A6'), (24, nan), (25, nan), (26, nan), (27, 'C23 C25 C27')]
[(28, nan), (29, nan), (30, nan), (31, 'B78'), (32, nan), (33, nan), (34, nan), (35, nan)]
[(36, nan), (37, nan), (38, nan), (39, nan), (40, nan), (41, nan), (42, nan), (43, nan), (44, nan)]
[(45, nan), (46, nan), (47, nan), (48, nan), (49, nan)]
```
Raw data
{
"_id": null,
"home_page": "https://github.com/hansalemaos/a_pandas_ex_logsplit",
"name": "a-pandas-ex-logsplit",
"maintainer": "",
"docs_url": null,
"requires_python": "",
"maintainer_email": "",
"keywords": "log,logarithmically,logarithm,pandas,Series",
"author": "Johannes Fischer",
"author_email": "<aulasparticularesdealemaosp@gmail.com>",
"download_url": "https://files.pythonhosted.org/packages/a5/65/d2d5e42fc50d0a25af4bf3db4973018e56ce618437138c0f8bad644a1506/a_pandas_ex_logsplit-0.10.tar.gz",
"platform": null,
"description": "\n# Splits a DataFrame/Series logarithmically \n\n\n\n```python\n\npip install a-pandas-ex-logsplit\n\n```\n\n\n\n```python\n\nfrom a_pandas_ex_logsplit import pd_add_logsplit\n\npd_add_logsplit()\n\nimport pandas as pd\n\ndf = pd.read_csv(\"https://github.com/pandas-dev/pandas/raw/main/doc/data/titanic.csv\")\n\ndf = df[:50]\n\nfor h in df.ds_logsplit(columns=[\"Cabin\", \"Fare\"], includeindex=False):\n\n print(h)\n\nfor h in df.ds_logsplit(columns=\"Cabin\", includeindex=True):\n\n print(h)\n\nfor h in df.ds_logsplit(columns=\"Cabin\", includeindex=False):\n\n print(h)\n\nfor h in df.Cabin.ds_logsplit(includeindex=True):\n\n print(h)\n\n\t\n\n\t\n\n[(nan, 7.25)]\n\n[('C85', 71.2833), (nan, 7.925)]\n\n[('C123', 53.1), (nan, 8.05), (nan, 8.4583)]\n\n[('E46', 51.8625), (nan, 21.075), (nan, 11.1333), (nan, 30.0708)]\n\n[('G6', 16.7), ('C103', 26.55), (nan, 8.05), (nan, 31.275), (nan, 7.8542)]\n\n[(nan, 16.0), (nan, 29.125), (nan, 13.0), (nan, 18.0), (nan, 7.225), (nan, 26.0)]\n\n[('D56', 13.0), (nan, 8.0292), ('A6', 35.5), (nan, 21.075), (nan, 31.3875), (nan, 7.225), ('C23 C25 C27', 263.0)]\n\n[(nan, 7.8792), (nan, 7.8958), (nan, 27.7208), ('B78', 146.5208), (nan, 7.75), (nan, 10.5), (nan, 82.1708), (nan, 52.0)]\n\n[(nan, 7.2292), (nan, 8.05), (nan, 18.0), (nan, 11.2417), (nan, 9.475), (nan, 21.0), (nan, 7.8958), (nan, 41.5792), (nan, 7.8792)]\n\n[(nan, 8.05), (nan, 15.5), (nan, 7.75), (nan, 21.6792), (nan, 17.8)]\n\n\n\n\n\n[(0, 1)]\n\n[(1, 2), (2, 3)]\n\n[(3, 4), (4, 5), (5, 6)]\n\n[(6, 7), (7, 8), (8, 9), (9, 10)]\n\n[(10, 11), (11, 12), (12, 13), (13, 14), (14, 15)]\n\n[(15, 16), (16, 17), (17, 18), (18, 19), (19, 20), (20, 21)]\n\n[(21, 22), (22, 23), (23, 24), (24, 25), (25, 26), (26, 27), (27, 28)]\n\n[(28, 29), (29, 30), (30, 31), (31, 32), (32, 33), (33, 34), (34, 35), (35, 36)]\n\n[(36, 37), (37, 38), (38, 39), (39, 40), (40, 41), (41, 42), (42, 43), (43, 44), (44, 45)]\n\n[(45, 46), (46, 47), (47, 48), (48, 49), (49, 50)]\n\n\n\n\n\n[nan]\n\n['C85', nan]\n\n['C123', nan, nan]\n\n['E46', nan, nan, nan]\n\n['G6', 'C103', nan, nan, nan]\n\n[nan, nan, nan, nan, nan, nan]\n\n['D56', nan, 'A6', nan, nan, nan, 'C23 C25 C27']\n\n[nan, nan, nan, 'B78', nan, nan, nan, nan]\n\n[nan, nan, nan, nan, nan, nan, nan, nan, nan]\n\n[nan, nan, nan, nan, nan]\n\n\n\n\n\n[(0, nan)]\n\n[(1, 'C85'), (2, nan)]\n\n[(3, 'C123'), (4, nan), (5, nan)]\n\n[(6, 'E46'), (7, nan), (8, nan), (9, nan)]\n\n[(10, 'G6'), (11, 'C103'), (12, nan), (13, nan), (14, nan)]\n\n[(15, nan), (16, nan), (17, nan), (18, nan), (19, nan), (20, nan)]\n\n[(21, 'D56'), (22, nan), (23, 'A6'), (24, nan), (25, nan), (26, nan), (27, 'C23 C25 C27')]\n\n[(28, nan), (29, nan), (30, nan), (31, 'B78'), (32, nan), (33, nan), (34, nan), (35, nan)]\n\n[(36, nan), (37, nan), (38, nan), (39, nan), (40, nan), (41, nan), (42, nan), (43, nan), (44, nan)]\n\n[(45, nan), (46, nan), (47, nan), (48, nan), (49, nan)]\n\n\n\n```\n\n",
"bugtrack_url": null,
"license": "MIT",
"summary": "Splits a DataFrame/Series logarithmically",
"version": "0.10",
"split_keywords": [
"log",
"logarithmically",
"logarithm",
"pandas",
"series"
],
"urls": [
{
"comment_text": "",
"digests": {
"md5": "4e1bb62937afd37c1eec11a9a9cf727a",
"sha256": "f0f3189fadcfba072bd9d5e6bede5b0f89926aa32be9abb973dede2b9d946243"
},
"downloads": -1,
"filename": "a_pandas_ex_logsplit-0.10-py3-none-any.whl",
"has_sig": false,
"md5_digest": "4e1bb62937afd37c1eec11a9a9cf727a",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": null,
"size": 6492,
"upload_time": "2022-12-25T03:44:47",
"upload_time_iso_8601": "2022-12-25T03:44:47.833126Z",
"url": "https://files.pythonhosted.org/packages/1f/27/70b09b07fed06b904ca82028253100ed2d7f198b5a2a90d32db613b52cad/a_pandas_ex_logsplit-0.10-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"md5": "94bdf447f9347aa5df04fdca50ce9050",
"sha256": "adb1bab69923a519d13f857fdab6af84b9db2a6ced0293363352e7fb707287f6"
},
"downloads": -1,
"filename": "a_pandas_ex_logsplit-0.10.tar.gz",
"has_sig": false,
"md5_digest": "94bdf447f9347aa5df04fdca50ce9050",
"packagetype": "sdist",
"python_version": "source",
"requires_python": null,
"size": 4659,
"upload_time": "2022-12-25T03:44:49",
"upload_time_iso_8601": "2022-12-25T03:44:49.406055Z",
"url": "https://files.pythonhosted.org/packages/a5/65/d2d5e42fc50d0a25af4bf3db4973018e56ce618437138c0f8bad644a1506/a_pandas_ex_logsplit-0.10.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2022-12-25 03:44:49",
"github": true,
"gitlab": false,
"bitbucket": false,
"github_user": "hansalemaos",
"github_project": "a_pandas_ex_logsplit",
"travis_ci": false,
"coveralls": false,
"github_actions": false,
"requirements": [],
"lcname": "a-pandas-ex-logsplit"
}
JSON
