本文介绍了计算上面行的平方和的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！
问题描述
我有一个数据集，如下所示：
Value Type X_sq
-1.975767 Weather
-0.540979 Fruits
-2.359127 Fruits
-2.815604 Corona
-0.929755 Weather
例如，在第一行，上面没有任何东西。仅如此(-1.975767 x-1.975767)。在第二行中，它的上方没有水果行，所以它将是-0.540979 x-0.540979。然而，在第三行中，当我们扫描前面的所有行时，我们应该发现水果已经在那里了。所以我们应该吃最后一颗果子……X_sq值并计算新的平方和。
Value Type X_sq
-1.975767 Weather -1.975767 * -1.975767 = x
-0.540979 Fruits -0.540979 * -0.540979 = y
-2.359127 Fruits y + ( -2.359127 x -2.359127)
-2.815604 Corona -2.815604 * -2.815604
-0.929755 Weather x + (-0.929755 * -0.929755)
执行此操作的有效方法是什么？
def updateSS(X_sq, X_new):
return X_sq + X_new**2
编辑：
----> 1 df['sumOfSquares'] = df['avg_country_tone'].pow(2).groupby(['themes', 'suppliers_country']).cumsum()
File /usr/local/Cellar/ipython/8.0.1/libexec/lib/python3.10/site-packages/pandas/core/series.py:1929, in Series.groupby(self, by, axis, level, as_index, sort, group_keys, squeeze, observed, dropna)
1925 axis = self._get_axis_number(axis)
1927 # error: Argument "squeeze" to "SeriesGroupBy" has incompatible type
1928 # "Union[bool, NoDefault]"; expected "bool"
-> 1929 return SeriesGroupBy(
1930 obj=self,
1931 keys=by,
1932 axis=axis,
1933 level=level,
1934 as_index=as_index,
1935 sort=sort,
1936 group_keys=group_keys,
1937 squeeze=squeeze, # type: ignore[arg-type]
1938 observed=observed,
1939 dropna=dropna,
1940 )
File /usr/local/Cellar/ipython/8.0.1/libexec/lib/python3.10/site-packages/pandas/core/groupby/groupby.py:882, in GroupBy.__init__(self, obj, keys, axis, level, grouper, exclusions, selection, as_index, sort, group_keys, squeeze, observed, mutated, dropna)
879 if grouper is None:
880 from pandas.core.groupby.grouper import get_grouper
--> 882 grouper, exclusions, obj = get_grouper(
883 obj,
884 keys,
885 axis=axis,
886 level=level,
887 sort=sort,
888 observed=observed,
889 mutated=self.mutated,
890 dropna=self.dropna,
891 )
893 self.obj = obj
894 self.axis = obj._get_axis_number(axis)
File /usr/local/Cellar/ipython/8.0.1/libexec/lib/python3.10/site-packages/pandas/core/groupby/grouper.py:882, in get_grouper(obj, key, axis, level, sort, observed, mutated, validate, dropna)
880 in_axis, level, gpr = False, gpr, None
881 else:
--> 882 raise KeyError(gpr)
883 elif isinstance(gpr, Grouper) and gpr.key is not None:
884 # Add key to exclusions
885 exclusions.add(gpr.key)
KeyError: 'themes'
even though themes is there. Themes = type
推荐答案
使用：
df['X_sq'] = df['Value'].pow(2).groupby(df['Type']).cumsum()
print(df)
# Output
Value Type X_sq
0 -1.975767 Weather 3.903655
1 -0.540979 Fruits 0.292658
2 -2.359127 Fruits 5.858138
3 -2.815604 Corona 7.927626
4 -0.929755 Weather 4.768100
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