将json字典转换为熊猫行df
问题描述:
我从一个url中提取了JSON数据。结果是一本字典。如何转换此字典以便每个键都是列,并且时间戳是每行的索引 - 每次调用url时字典values
对应于行条目?将json字典转换为熊猫行df
下面是数据:
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)
我想:
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')
但所有尝试给不同的错误:
ValueError: If using all scalar values, you must pass an index
和
TypeError: Index(...) must be called with a collection of some kind, 'timestamp' was passed
答
裹在列表
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
随着datetime
指数block_data
。
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
谢谢,这太棒了。还有一个问题 - 如何在设置后将unix timstamp索引转换为datetime? – zsad512
我已更新我的帖子。 – piRSquared