阅读＆＃34; .db＆＃34;字典类型文件到pandas DataFrame

Question

如何将包含以下数据的文件导入到pandas DataFrame中？它保存为＆＃34; data.db＆＃34;，这是我不熟悉的格式。

{"hostname":"136.243.73.66","ip":"136.243.73.66","port":16600,"TCPPort":15600,"UDPPort":14600,"seen":1,"connected":0,"tried":0,"weight":1,"dateTried":null,"dateLastConnected":null,"dateCreated":{"$$date":1514997141045},"isTrusted":true,"key":"5d301e5f46bb6e0db9d379c19c451cc6905c09885e5529c7a3c5d2750674db5ab8c3e714edd4fd53fee86499db4f93f8","remoteKey":null,"lastConnections":[],"_id":"FTpfNM4c4OuXAS6d"}
    {"hostname":"45.77.187.45","ip":"45.77.187.45","port":16600,"TCPPort":15600,"UDPPort":14600,"seen":1,"connected":0,"tried":0,"weight":1,"dateTried":null,"dateLastConnected":null,"dateCreated":{"$$date":1514997141046},"isTrusted":true,"key":"c752b75fbc74eaba10745937d50abd2decf71c509aff49db6662a180ba76fa3f74e5118ad905adb3b6873c250270f85f","remoteKey":null,"lastConnections":[],"_id":"f6Gn2xXyoeMrSvi8"}
    {"hostname":"mainnet.deviota.com","ip":null,"port":16600,"TCPPort":15600,"UDPPort":14600,"seen":1,"connected":0,"tried":0,"weight":1,"dateTried":null,"dateLastConnected":null,"dateCreated":{"$$date":1514997141048},"isTrusted":true,"key":"a923372977f65fe08f472916f671a1749963cea36701682761307af8537c52d4e2414f4e5b471898ef84a0957b5deec3","remoteKey":null,"lastConnections":[],"_id":"oVKsMubQ5rtAhfpq"}
    {"hostname":"mainnet2.deviota.com","ip":null,"port":16600,"TCPPort":15600,"UDPPort":14600,"seen":1,"connected":0,"tried":0,"weight":1,"dateTried":null,"dateLastConnected":null,"dateCreated":{"$$date":1514997141049},"isTrusted":true,"key":"9aae219149f088c9295de31125fb1f39060dd4fe1540c048f2bd097375298703c43d49dc48bb609f708f8b6e2578f7f2","remoteKey":null,"lastConnections":[],"_id":"rkQpS6BimYvfDIZU"}

这是我能得到的尽可能接近，但目前标签仍然在表格内。

file_path = "path_to_file/data.db"

def read_data():
    with open(file_path) as f:
        return [x.split(',') for x in f.readlines()]

a = read_data()

pd.DataFrame(a)

Answer 1

.db不是特定的文件类型，尽管它经常用于sqlite文件。然而，这似乎只是一系列JSON文档，每行一个。

with open(file_path) as f:
    return [json.loads(x) for x in f]

Answer 2

您可以使用read_json，因为它是带有参数json的{​​{1}}文件：

lines=True

如果要在df = pd.read_json('sample.db', lines=True) print (df) TCPPort UDPPort _id connected dateCreated \ 0 15600 14600 FTpfNM4c4OuXAS6d 0 {'$$date': 1514997141045} 1 15600 14600 f6Gn2xXyoeMrSvi8 0 {'$$date': 1514997141046} 2 15600 14600 oVKsMubQ5rtAhfpq 0 {'$$date': 1514997141048} 3 15600 14600 rkQpS6BimYvfDIZU 0 {'$$date': 1514997141049} dateLastConnected dateTried hostname ip \ 0 NaN NaN 136.243.73.66 136.243.73.66 1 NaN NaN 45.77.187.45 45.77.187.45 2 NaN NaN mainnet.deviota.com None 3 NaN NaN mainnet2.deviota.com None isTrusted key \ 0 True 5d301e5f46bb6e0db9d379c19c451cc6905c09885e5529... 1 True c752b75fbc74eaba10745937d50abd2decf71c509aff49... 2 True a923372977f65fe08f472916f671a1749963cea3670168... 3 True 9aae219149f088c9295de31125fb1f39060dd4fe1540c0... lastConnections port remoteKey seen tried weight 0 [] 16600 NaN 1 0 1 1 [] 16600 NaN 1 0 1 2 [] 16600 NaN 1 0 1 3 [] 16600 NaN 1 0 1 列中添加dateCreated的词典中的解析值：

apply