Q:

I have a problem reading the information from a json file

I save data from the car's computer into a json file. (For example, engine revolutions per minute.)
My file json looks like this:
{"Clock": "17:37:42", "Value": 801.0}
{"Clock": "17:37:43", "Value": 791.0}
{"Clock": "17:37:45", "Value": 766.0}
{"Clock": "17:37:46", "Value": 788.0}
{"Clock": "17:37:47", "Value": 792.0}
....

I have to read this information and put it in a graph.
Can you give me a hand?I would be very grateful.
I use the python language with the specific libraries: tkinter, matplotlib, etc
My code portion looks like this:
def animate(i):
    data=[]
    with open('rpm.json') as f:
        data=json.dumps(f)

    data = pb.DataFrame(data)

    clock=data[(data['Ora']=='%H:%M:%S')]
    clock["datestamp"]=np.array(clock['%H:%M:%S']).astype("datetime64[s]")
    dateClock=(clock["datesteam"]).tolist()

    value = data[(data['Value'] == rpm.value.magnitude)]
    value["datestamp"] = np.array(value[rpm.value.magnitude]).astype("datetime64[s]")
    datevValue = (value["datesteam"]).tolist()

    a.clear()
    a.plot_date(dateClock, clock["Clock"])
    a.plot_date(dateValue, value["Value"])

A:

As @Josh Gordon pointed out in the comments you do not have a valid json file. One solution would be to fix the format by enclosing the dicts as follows.  
{"data":[
  {"Clock": "17:37:42", "Value": 801.0},
  {"Clock": "17:37:43", "Value": 791.0},
  {"Clock": "17:37:45", "Value": 766.0},
  {"Clock": "17:37:46", "Value": 788.0},
  {"Clock": "17:37:47", "Value": 792.0}
]}

Another option would be to parse the dictionaries one by one. If you want a pandas dataframe in the end, you could use literal_eval function from the ast module to read each line.
import ast

data = pd.DataFrame()
f = open('rpm.json')
for line in f:
    data = data.append(ast.literal_eval(line), ignore_index=True)
f.close()

You should get a dataframe as below and from there it should be easy to plot the graphs.
In [1]: data
Out[1]: 
   Clock     Value
0  17:37:42  801.0
1  17:37:43  791.0
2  17:37:45  766.0
3  17:37:46  788.0
4  17:37:47  792.0

