CODEWITHSUNDEEP
pythonpandasdictionarydataframeunix-timestamp
zsad512
zsad512

Reputation: 881

Extracting key,values from a dictionary as a dataframe

I have a dictionary that I have extracted from a json url that has 6 keys. My interest is only in the values for the key 'value'. The data is structured like this:

    [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}]

Where 'x' is the unix timestamp and 'y' is the value for that time. How can I extract the values from the 'value' dictionary and restructure them so that 'x' is labelled 'date' and structured in this format: 2011-09-13?

Upvotes: 3

Views: 2306

Answers (3)

Vico
Vico

Reputation: 579

if i understood you correctly, pandas should be able to convert it to a dataframe:

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

then you can use the to_datetime to convert it to yyyy/mm/dd format:

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

output:

    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

Upvotes: 2

Philip B.
Philip B.

Reputation: 637

I'm not sure why you would want a dictionary if all you want is the dates. You can do this and just get a list of the dates.

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

EDIT: if you want this in a similar dictionary format:

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

Upvotes: 0

MSeifert
MSeifert

Reputation: 152587

Assuming you assign what is kept in 'values' to a variable called lst (e.g. lst = data['value']) you can use this:

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]})

With:

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}]

This gives me this 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

Upvotes: 3

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