How do i convert python dictionary to pandas framework

Question

I Have a JSON type dictionary in Python like this:

d = {'2022-12-21 20:00:00': {'1. open': '135.5900',
                             '2. high': '135.7300',
                             '3. low': '135.5900',
                             '4. close': '135.6700',
                             '5. volume': '18031'},
     '2022-12-21 19:45:00': {'1. open': '135.5700',
                             '2. high': '135.6000',
                             '3. low': '135.5500',
                             '4. close': '135.5700',
                             '5. volume': '4253'}}

and I would like to convert in into a pandas dataframe like:

  |timestamp           |open |high |low  |close|volume 
1 |2022-12-21 20:00:00 | 135 |135  | 135 | 135 |18031
2 |2022-12-21 19:45:00 | 134 | 112 | 123 | 231 |24124

I tried to do it with:

df = pd.DataFrame.from_dict(d, orient='index')

but after this the dates become the indexing column and I want an indexing column as well:

Try this: df.reset_index().rename(columns={'index': 'timestamp'}) — r-beginners
– r-beginners, Commented Dec 22, 2022 at 12:51

Tomerikoo · Accepted Answer · 2022-12-22 13:09:35Z

The dates become the index by default. You can reset the index and rename the dates column using the reset_index method:

import pandas as pd

d = {'2022-12-21 20:00:00': {'1. open': '135.5900',
                             '2. high': '135.7300',
                             '3. low': '135.5900',
                             '4. close': '135.6700',
                             '5. volume': '18031'},
     '2022-12-21 19:45:00': {'1. open': '135.5700',
                             '2. high': '135.6000',
                             '3. low': '135.5500',
                             '4. close': '135.5700',
                             '5. volume': '4253'}}

df = pd.DataFrame.from_dict(d, orient='index').reset_index(names="timestamp")
print(df)

The names argument renames the column that used to be the index (new in version 1.5.0).

For older versions, use rename:

df = pd.DataFrame.from_dict(d, orient='index').reset_index().rename(columns={'index': 'timestamp'})

Gives:

             timestamp   1. open   2. high    3. low  4. close 5. volume
0  2022-12-21 20:00:00  135.5900  135.7300  135.5900  135.6700     18031
1  2022-12-21 19:45:00  135.5700  135.6000  135.5500  135.5700      4253

jmendes16 · Accepted Answer · 2022-12-22 12:52:51Z

0

Create a new column df['new_index']=range(1, len(df) + 1) and then set that as the new index of the dataframe df.set_index('new_index'). That should definitely work.

I would also be interested to find out what happens if you try df.reset_index().

You may need to set the name of the index to "timestamp" first df.index.name="timestamp".

edited Dec 22, 2022 at 12:52

answered Dec 22, 2022 at 12:49

jmendes16

335 bronze badges

1 Comment

Spartex Over a year ago

i tried df.reset_index() too it doesnt change anything after that. any other way to index it rather than adding rows because some rows are going to get deleted after some operations

Sagun Devkota · Accepted Answer · 2022-12-22 12:55:16Z

0

reset the index

self.stocks[s]= pd.DataFrame.from_dict(self.stocks[s],orient ='index').reset_index()

answered Dec 22, 2022 at 12:55

Sagun Devkota

5053 silver badges11 bronze badges

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