如何在 Python Pandas 中使用字典顺序切片选择数据子集?
简介
Pandas 具有双重选择功能,可以使用索引位置或索引标签选择数据子集。在这篇文章中,我将向您展示如何"使用字典顺序切片选择数据子集"。
Google 上有很多数据集。在 kaggle.com 中搜索电影数据集。这篇文章使用了来自 kaggle 的电影数据集。
如何操作
仅导入本示例所需的列的电影数据集。
import pandas as pd import numpy as np movies = pd.read_csv("https://raw.githubusercontent.com/sasankac/TestDataSet/master/movies_data.csv",index_col="title", usecols=["title","budget","vote_average","vote_count"]) movies.sample(n=5)
budget | vote_average | vote_count | |
---|---|---|---|
titile | |||
Little Voice | 0 | 6.6 | 61 |
Grown Ups 2 | 80000000 | 5.8 | 1155 |
The Best Years of Our Lives | 2100000 | 7.6 | 143 |
Tusk | 2800000 | 5.1 | 366 |
Operation Chromite | 0 | 5.8 | 29 |
我始终建议对索引进行排序,特别是如果索引由字符串组成。如果您在索引排序后处理大量数据集,您会注意到差异。
如果我不对索引进行排序会怎样?
没问题,您的代码将永远运行。开个玩笑,如果索引标签未排序,那么熊猫必须逐个遍历所有标签以匹配您的查询。想象一下没有索引页的牛津词典,你要做什么?索引排序后,您可以快速跳转到要提取的标签,Pandastoo 也是如此。
让我们首先检查我们的索引是否已排序。
# 检查索引是否已排序? movies.index.is_monotonic
False
显然,索引未排序。我们将尝试选择以 A% 开头的电影。这就像写
select * from movies where title like'A%'
movies.loc["Aa":"Bb"]
select * from movies where title like 'A%' --------------------------------------------------------------------------- ValueErrorTraceback (most recent call last) ~\anaconda3\lib\site-packages\pandas\core\indexes\base.py in get_slice_bound(self, labe l, side, kind) 4844try: -> 4845return self._searchsorted_monotonic(label, side) 4846except ValueError: ~\anaconda3\lib\site-packages\pandas\core\indexes\base.py in _searchsorted_monotonic(se lf, label, side) 4805 -> 4806raise ValueError("index must be monotonic increasing or decreasing") 4807 ValueError: index must be monotonic increasing or decreasing During handling of the above exception, another exception occurred: KeyErrorTraceback (most recent call last) in ----> 1 movies.loc["Aa": "Bb"] ~\anaconda3\lib\site-packages\pandas\core\indexing.py in getitem (self, key) 1766 1767maybe_callable = com.apply_if_callable(key, self.obj) -> 1768return self._getitem_axis(maybe_callable, axis=axis) 1769 1770def _is_scalar_access(self, key: Tuple): ~\anaconda3\lib\site-packages\pandas\core\indexing.py in _getitem_axis(self, key, axis) 1910if isinstance(key, slice): 1911self._validate_key(key, axis) -> 1912return self._get_slice_axis(key, axis=axis) 1913elif com.is_bool_indexer(key): 1914return self._getbool_axis(key, axis=axis) ~\anaconda3\lib\site-packages\pandas\core\indexing.py in _get_slice_axis(self, slice_ob j, axis) 1794 1795labels = obj._get_axis(axis) -> 1796indexer = labels.slice_indexer( 1797slice_obj.start, slice_obj.stop, slice_obj.step, kind=self.name 1798) ~\anaconda3\lib\site-packages\pandas\core\indexes\base.py in slice_indexer(self, start, end, step, kind) 4711slice(1, 3) 4712""" -> 4713start_slice, end_slice = self.slice_locs(start, end, step=step, kind=ki nd) 4714 4715# return a slice ~\anaconda3\lib\site-packages\pandas\core\indexes\base.py in slice_locs(self, start, en d, step, kind) 4924start_slice = None 4925if start is not None: -> 4926start_slice = self.get_slice_bound(start, "left", kind) 4927if start_slice is None: 4928start_slice = 0 ~\anaconda3\lib\site-packages\pandas\core\indexes\base.py in get_slice_bound(self, labe l, side, kind) 4846except ValueError: 4847# raise the original KeyError -> 4848raise err 4849 4850if isinstance(slc, np.ndarray): ~\anaconda3\lib\site-packages\pandas\core\indexes\base.py in get_slice_bound(self, labe l, side, kind) 4840# we need to look up the label 4841try: -> 4842slc = self.get_loc(label) 4843except KeyError as err: 4844try: ~\anaconda3\lib\site-packages\pandas\core\indexes\base.py in get_loc(self, key, method, tolerance) 2646return self._engine.get_loc(key) 2647except KeyError: -> 2648return self._engine.get_loc(self._maybe_cast_indexer(key)) 2649indexer = self.get_indexer([key], method=method, tolerance=tolerance) 2650if indexer.ndim > 1 or indexer.size > 1: pandas\_libs\index.pyx in pandas._libs.index.IndexEngine.get_loc() pandas\_libs\index.pyx in pandas._libs.index.IndexEngine.get_loc() pandas\_libs\index.pyx in pandas._libs.index.IndexEngine._get_loc_duplicates() pandas\_libs\index.pyx in pandas._libs.index.IndexEngine._maybe_get_bool_indexer() KeyError: 'Aa'
按升序对索引进行排序,然后尝试使用相同的命令来利用排序进行字典切片。
True
现在我们的数据已设置好,可以进行字典切片了。现在让我们选择以字母 A 开头到字母 B 的所有电影标题。
budget | vote_average | vote_count | |
---|---|---|---|
title | |||
Abandon | 25000000 | 4.6 | 45 |
Abandoned | 0 | 5.8 | 27 |
Abduction | 35000000 | 5.6 | 961 |
Aberdeen | 0 | 7.0 | 6 |
About Last Night | 12500000 | 6.0 | 210 |
... | ... | ... | ... |
Battle for the Planet of the Apes | 1700000 | 5.5 | 215 |
Battle of the Year | 20000000 | 5.9 | 88 |
Battle: Los Angeles | 70000000 | 5.5 | 1448 |
Battlefield Earth | 44000000 | 3.0 | 255 |
Battleship | 209000000 | 5.5 | 2114 |
292 rows × 3 columns
True
title | budget | vote_average | vote_count |
---|---|---|---|
Æon Flux | 62000000 | 5.4 | 703 |
xXx: State of the Union | 60000000 | 4.7 | 549 |
xXx | 70000000 | 5.8 | 1424 |
eXistenZ | 15000000 | 6.7 | 475 |
[REC]² | 5600000 | 6.4 | 489 |
budget vote_average vote_count title
This is a no brainer to see the empty DataFrame as the data is sorted in reverse order. Let us reverse the letters and run this again.
title | budget | vote_average | vote_count |
---|---|---|---|
B-Girl | 0 | 5.5 | 7 |
Ayurveda: Art of Being | 300000 | 5.5 | 3 |
Away We Go | 17000000 | 6.7 | 189 |
Awake | 86000000 | 6.3 | 395 |
Avengers: Age of Ultron | 280000000 | 7.3 | 6767 |
... | ... | ... | ... |
About Last Night | 12500000 | 6.0 | 210 |
Aberdeen | 0 | 7.0 | 6 |
Abduction | 35000000 | 5.6 | 961 |
Abandoned | 0 | 5.8 | 27 |
Abandon | 25000000 | 4.6 | 45 |
228 rows × 3 columns