A step-by-step guide on how to apply a function to each cell of a Pandas DataFrame in multiple ways.
ApplyMap applies the function to every cell (being every intersection of row and column) so basically across the entire dataframe. Whereas .map just does it for a single row or a single column Keep other columns when using min() with groupby df = pd.DataFrame( {"AAA": [1, 1, 1, 2...
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在大多数简单的场景中,我们只需要将一整列元素视为一个元素,Pandas就会自动将该函数应用于每个单元格。(in most simple scenarios, what we need to do is just to treat a whole column ofelementsas an element, Pandas will automatically apply the function to each cell.) 3.矢量化带来最大提速的四个根...
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通过指定 `engine='numba'` 和 `engine_kwargs` 参数(`raw` 也必须设置为 `True`),可以使用 Numba 执行 apply 聚合。参见使用 Numba 提升性能以获取参数的一般用法和性能考虑。 Numba 将应用于可能的两个例程: 1. 如果 `func` 是标准 Python 函数,则引擎将[JIT](https://numba.pydata.org/numba-doc/...
(self, start, end, step) 2852 """ 2853 For an ordered MultiIndex, compute the slice locations for input 2854 labels. (...) 2900 sequence of such. 2901 """ 2902 # This function adds nothing to its parent implementation (the magic 2903 # happens in get_slice_bound method), but it ...
(self, key) 1189 maybe_callable = com.apply_if_callable(key, self.obj) 1190 maybe_callable = self._check_deprecated_callable_usage(key, maybe_callable) -> 1191 return self._getitem_axis(maybe_callable, axis=axis) File ~/work/pandas/pandas/pandas/core/indexing.py:1411, in _LocIndexer._...
(mean ± std. dev. of 1 run, 1 loop each) # Function is cached and performance will improve In [5]: %timeit roll.apply(f, engine='numba', raw=True) 188 ms ± 1.93 ms per loop (mean ± std. dev. of 7 runs, 10 loops each) In [6]: %timeit roll.apply(f, engine='cython...
apply(function) # 对某一列应用自定义函数 数据可视化 import matplotlib.pyplot as plt # 绘制柱状图 df[column_name].plot(kind="bar") # 绘制散点图 df.plot(x="column_name1", y="column_name2", kind="scatter") 数据分析 # 描述性统计分析 df.describe() # 相关性分析 df....