How do i convert python dictionary to pandas framework

December 22, 2022

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:

>Solution :

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')
df.reset_index(inplace=True, names="date")
print(df)

Gives:

                  date   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

The inplace argument makes sure you modify the dataframe instead of creating a new one (the default), and the names argument renames the column that used to be index.