Q:

Convert json dict to row in pandas df

I have pulled JSON data from a url. The result is a dictionary. How can I transform this dictionary so that each key is a column, and the timestamp is the index for each row- where the dict values correspond to the row entries each time the url is called?
Here is the data:
with urllib.request.urlopen('https://api.blockchain.info/stats') as url:
    block_data = json.loads(url.read().decode())

# Convert to Pandas
block_df = pd.DataFrame(block_data)

I tried: 
block_df = pd.DataFrame(block_data)
block_df = pd.DataFrame(block_data, index = 'timestamp')
block_df = pd.DataFrame.from_dict(block_data)
block_df = pd.DataFrame.from_dict(block_data, orient = 'columns')

But all attempts give different errors:

ValueError: If using all scalar values, you must pass an index

and 

TypeError: Index(...) must be called with a collection of some kind, 'timestamp' was passed

A:

Wrap the block_data in a list
pd.DataFrame([block_data]).set_index('timestamp')

               blocks_size    difficulty  estimated_btc_sent  estimated_transaction_volume_usd     hash_rate  market_price_usd  miners_revenue_btc  miners_revenue_usd  minutes_between_blocks  n_blocks_mined  n_blocks_total   n_btc_mined    n_tx  nextretarget   total_btc_sent  total_fees_btc           totalbc  trade_volume_btc  trade_volume_usd
timestamp                                                                                                                                                                                                                                                                                                                                                
1504121943000    167692649  888171856257      24674767461479                      1.130867e+09  7.505715e+09           4583.09                2540         11645247.85                    7.92             170          482689  212500000000  281222        483839  174598204968248     41591624963  1653361250000000          43508.93      1.994054e+08

With datetime index.
df = pd.DataFrame([block_data]).set_index('timestamp')
df.index = pd.to_datetime(df.index, unit='ms')
df

                     blocks_size    difficulty  estimated_btc_sent  estimated_transaction_volume_usd     hash_rate  market_price_usd  miners_revenue_btc  miners_revenue_usd  minutes_between_blocks  n_blocks_mined  n_blocks_total   n_btc_mined    n_tx  nextretarget   total_btc_sent  total_fees_btc           totalbc  trade_volume_btc  trade_volume_usd
timestamp                                                                                                                                                                                                                                                                                                                                                      
2017-08-30 19:39:03    167692649  888171856257      24674767461479                      1.130867e+09  7.505715e+09           4583.09                2540         11645247.85                    7.92             170          482689  212500000000  281222        483839  174598204968248     41591624963  1653361250000000          43508.93      1.994054e+08

