Fast convert JSON column into Pandas dataframe

json_normalize takes an already processed json string or a pandas series of such strings.

pd.io.json.json_normalize(df.data.apply(json.loads))

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setup

import pandas as pd
import json

df = pd.read_csv('http://pastebin.com/raw/7L86m9R2', \
                 header=None, index_col=0, names=['data'])

I think you can first convert string column data to dict, then create list of numpy arrays by values and last DataFrame.from_records:

df = pd.read_csv('http://pastebin.com/raw/7L86m9R2', \
                 header=None, index_col=0, names=['data'])

a = df.data.apply(json.loads).values.tolist() 
print (pd.DataFrame.from_records(a))

Another idea:

 df = pd.json_normalize(df['data'])

data = { "events":[
{
"timemillis":1563467463580, "date":"18.7.2019", "time":"18:31:03,580", "name":"Player is loading", "data":"" }, {
"timemillis":1563467463668, "date":"18.7.2019", "time":"18:31:03,668", "name":"Player is loaded", "data":"5" } ] }

from pandas.io.json import json_normalize
result = json_normalize(data,'events')
print(result)