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python - 转换 pandas.DataFrame 中的元组列表

问题描述

我有三个元组列表，这些列表的第一个元素是一年，如下所示。

list1 = [
    ('2010', 1783675.0), ('2011', 1815815.0), ('2012', 1633258.0), ('2013', 1694062.0), ('2014', 1906527.0), 
    ('2015', 1908661.0), ('2016', 2492979.0), ('2017', 2846997.0), ('2018', 2930313.0), ('2019', 2654724.0)
]

list2 = [
    ('2010', 302816.0), ('2011', 229549.0), ('2012', 323063.0), ('2013', 285066.0), ('2014', 282003.0), 
    ('2015', 354500.0), ('2016', 275383.0), ('2017', 322074.0), ('2018', 366909.0), ('2019', 297942.0)
]

list3 =[
    ('2010', 149036.0), ('2011', 144112.0), ('2012', 173944.0), ('2013', 205724.0), ('2014', 214019.0), 
    ('2015', 261462.0), ('2016', 260646.0), ('2017', 279267.0), ('2018', 288120.0), ('2019', 277106.0)
]

我想使用这些列表创建一个 pandas.DataFrame ，将年份设置为行索引：

          list1     list2     list3
2010  1783675.0  302816.0  149036.0
2011  1815815.0  229549.0  144112.0
2012  1633258.0  323063.0  173944.0
2013  1694062.0  285066.0  205724.0
2014  1906527.0  282003.0  214019.0
2015  1908661.0  354500.0  261462.0
2016  2492979.0  275383.0  260646.0
2017  2846997.0  322074.0  279267.0
2018  2930313.0  366909.0  288120.0
2019  2654724.0  297942.0  277106.0

标签： pythonpandasdataframe

解决方案

已提供答案的另一种选择：python 的defaultdict可以简化在将数据读入数据帧之前将数据集中到一个字典中的过程：

 from collections import defaultdict
 from itertools import chain

 #chain the lists into one, then
 #get all the similar values into one list:

 d = defaultdict(list)

 for k, v in chain(list1,list2,list3):
     d[k].append(v)

 #read the data into a pandas dataframe:

 df = pd.DataFrame.from_dict(d, orient='index', columns=['list1','list2','list3'])

          list1      list2       list3
2010    1783675.0   302816.0    149036.0
2011    1815815.0   229549.0    144112.0
2012    1633258.0   323063.0    173944.0
2013    1694062.0   285066.0    205724.0
2014    1906527.0   282003.0    214019.0
2015    1908661.0   354500.0    261462.0
2016    2492979.0   275383.0    260646.0
2017    2846997.0   322074.0    279267.0
2018    2930313.0   366909.0    288120.0
2019    2654724.0   297942.0    277106.0

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