How to convert list object to Pandas Dataframe with defined format?

Question

Printed list has this output (Jupyter):

(u'variable',    price  threshold
0   13.5      100.0
1   10.0        NaN)
(u'standard',    price  threshold
0   12.5      300.0
1   11.0        NaN)
(u'fixed',    price  threshold
0   14.5      250.0
1   10.1      200.0
2    9.0        NaN)
(u'standing-charge',    price
0      9)

Converted to Pandas Dataframe:

    0               1
0   variable        price threshold 0 13.5 100.0 1 10...
1   standard        price threshold 0 12.5 300.0 1 11...
2   fixed           price threshold 0 14.5 250.0 1 10...
3   standing-charge price 0 9

I need:

type       price  threshold
variable    13.5   100
variable    10.0   NaN
...

Please, help ;)

Oh, and the index of you DataFrame has to be unique, so your I need is not possible. — MaxNoe
– MaxNoe, Commented May 26, 2016 at 19:55
It's output from parsing json file: rows_list = [] for index, row in ds.iterrows(): a = {} a = row['plan'], json_normalize(row['rates']) rows_list.append(a) print rows_list — Jan Rock
– Jan Rock, Commented May 26, 2016 at 19:57
How to parse this format? Haven't seen any similar format yet. — Jan Rock
– Jan Rock, Commented May 26, 2016 at 20:02

MaxNoe · Accepted Answer · 2016-05-26 20:06:41Z

1

After you clearified what is in you list, you could do something like this:

df = pd.DataFrame()
for key, df2 in row_list:
    df2['index'] = key
    df = df.append(df2, ignore_index=True)

answered May 26, 2016 at 20:06

MaxNoe

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