Question

现在我不得不对dataframe_one进行计算，然后在dataframe_two上创建一个新列并填充结果。 dataframe_one是多索引的，而第二个不是，但有些列与dataframe_one中的索引匹配。

这就是我目前正在做的事情：将pandas导入为pd 导入numpy为np

dataframe_two = {}
dataframe_two['project_id'] = [1, 2]
dataframe_two['scenario'] = ['hgh', 'low']
dataframe_two = pd.DataFrame(dataframe_two)
dataframe_one = {}
dataframe_one['ts_project_id'] = [1, 1, 1, 1, 1, 2, 2, 2, 2, 2]
dataframe_one['ts_scenario'] = ['hgh', 'hgh', 'hgh', 'hgh', 'hgh', 'low', 'low', 'low', 'low', 'low']
dataframe_one['ts_economics_atcf'] = [-2, 2, -3, 4, 5 , -6, 3, -3, 4, 5]
dataframe_one = pd.DataFrame(dataframe_one)
dataframe_one.index = [dataframe_one['ts_project_id'], dataframe_one['ts_scenario']]

project_scenario = zip(dataframe_two['project_id'], dataframe_two['scenario'])
dataframe_two['econ_irr'] = np.zeros(len(dataframe_two.index))
i = 0
for project, scenario in project_scenario:
    # Grabs corresponding series from dataframe_one
    atcf = dataframe_one.ix[project].ix[scenario]['ts_economics_atcf']
    irr = np.irr(atcf.values)
    dataframe_two['econ_irr'][i] = irr
    i = i + 1

print dataframe_two

有更简单的方法吗？

干杯！

Answer 1

如果我理解正确，您需要与SQL group_by和聚合函数等效的pandas。它们与DataFrame的groupby方法和groupby.SeriesGroupBy对象的aggregate方法基本相同。

>>> dataframe_one['ts_economics_atcf'].groupby(level=[0,1]).aggregate(np.irr)
ts_project_id  ts_scenario
1              hgh            0.544954
2              low            0.138952
dtype: float64

Pandas：在Dataframe中填写一个新列，其中两列匹配

1 个答案: