从字典中提取键值作为数据框

Extracting key,values from a dictionary as a dataframe

我有一本从 json url 中提取的字典,它有 6 个键。我只对键 'value' 的值感兴趣。数据结构如下:

    [in] print(data)
    [out] ...'values': [{'x': 1230940800, 'y': 0}, 
{'x': 1231113600, 'y': 0}, 
{'x': 1231286400, 'y': 0}, 
{'x': 1231459200, 'y': 0}, 
{'x': 1231632000, 'y': 0}, 
{'x': 1231804800, 'y': 0}, 
{'x': 1231977600, 'y': 0}, 
{'x': 1232150400, 'y': 0}, 
{'x': 1232323200, 'y': 0}, 
{'x': 1232496000, 'y': 0}, 
{'x': 1232668800, 'y': 0}, 
{'x': 1232841600, 'y': 0}, 
{'x': 1233014400, 'y': 0}, 
{'x': 1233187200, 'y': 0}, 
{'x': 1233360000, 'y': 0}] 

其中 'x' 是 unix 时间戳,'y' 是那个时间的值。 我如何从 'value' 字典中提取值并重组它们,以便 'x' 被标记为 'date' 并按以下格式构建:2011-09-13?

如果我理解正确,pandas 应该能够将其转换为数据帧:

df = pd.DataFrame(values_dictionary).rename(columns={'x':'Date'})

然后你可以使用to_datetime将其转换为yyyy/mm/dd格式:

df['Date'] = pd.to_datetime(df['Date'].astype(str), unit='s')

输出:

    Date        y
0   2009-01-03  0
1   2009-01-05  0
2   2009-01-07  0
3   2009-01-09  0
4   2009-01-11  0
5   2009-01-13  0
6   2009-01-15  0
7   2009-01-17  0
8   2009-01-19  0
9   2009-01-21  0
10  2009-01-23  0
11  2009-01-25  0
12  2009-01-27  0
13  2009-01-29  0
14  2009-01-31  0

如果您只需要日期,我不确定您为什么需要字典。您可以执行此操作并获得日期列表。

import datetime
dates = [datetime.datetime.fromtimestamp(xydict['x']).strftime("%Y-%m-%d") for xydict in values]

编辑:如果你想用类似的字典格式:

import datetime 
dates = [{'date' : datetime.datetime.fromtimestamp(xydict['x']).strftime("%Y-%m-%d")} for xydict in values]

假设您将 'values' 中保留的内容分配给名为 lst 的变量(例如 lst = data['value']),您可以使用此:

import pandas as pd
import numpy as np

df = pd.DataFrame({'Date': np.array([subdct['x'] for subdct in lst], dtype='datetime64[s]'),
                   'y': [subdct['y'] for subdct in lst]})

有:

lst = [{'x': 1230940800, 'y': 0}, 
       {'x': 1231113600, 'y': 0}, 
       {'x': 1231286400, 'y': 0}, 
       {'x': 1231459200, 'y': 0}, 
       {'x': 1231632000, 'y': 0}, 
       {'x': 1231804800, 'y': 0}, 
       {'x': 1231977600, 'y': 0}, 
       {'x': 1232150400, 'y': 0}, 
       {'x': 1232323200, 'y': 0}, 
       {'x': 1232496000, 'y': 0}, 
       {'x': 1232668800, 'y': 0}, 
       {'x': 1232841600, 'y': 0}, 
       {'x': 1233014400, 'y': 0}, 
       {'x': 1233187200, 'y': 0}, 
       {'x': 1233360000, 'y': 0}]

这给了我这个 df:

         Date  y
0  2009-01-03  0
1  2009-01-05  0
2  2009-01-07  0
3  2009-01-09  0
4  2009-01-11  0
5  2009-01-13  0
6  2009-01-15  0
7  2009-01-17  0
8  2009-01-19  0
9  2009-01-21  0
10 2009-01-23  0
11 2009-01-25  0
12 2009-01-27  0
13 2009-01-29  0
14 2009-01-31  0