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I have a list data like bellow which has 6 things in each element in list

"timestamp , open , high , low , close, volume"

['1606470300000,244.25,244.25,244.00,244.00,489269', 
 '1606470360000,244.05,244.30,244.00,244.15,452854', 
 '1606470420000,244.15,244.20,244.00,244.10,403043', 
 '1606470480000,244.15,244.15,243.95,244.00,370182', 
 '1606470540000,244.05,244.20,244.00,244.15,445286', 
 '1606470600000,244.15,244.25,244.05,244.20,473342', 
 '1606470660000,244.25,244.35,244.00,244.05,491117', 
 '1606470720000,244.05,244.20,244.00,244.20,298261', 
 '1606470780000,244.20,244.25,244.10,244.25,344172', 
 '1606470840000,244.20,244.35,244.20,244.30,347080', 
 '1606470900000,244.30,244.40,244.25,244.30,447630', 
 '1606470960000,244.30,244.30,244.00,244.00,360666', 
 '1606471020000,244.05,244.15,243.95,243.95,467724',
 '1606471080000,243.95,244.10,243.70,244.00,386080', 
 '1606471140000,244.00,244.20,243.70,244.20,166559']

this is a 1 minute data and I want to convert it in 15 minute data

please help

ombk
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Amit Sharma
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1 Answers1

1

Here is one way to do it using pandas.

import pandas as pd
list_data = ['1606470300000,244.25,244.25,244.00,244.00,489269',
 '1606470360000,244.05,244.30,244.00,244.15,452854',
 '1606470420000,244.15,244.20,244.00,244.10,403043',
 '1606470480000,244.15,244.15,243.95,244.00,370182',
 '1606470540000,244.05,244.20,244.00,244.15,445286',
 '1606470600000,244.15,244.25,244.05,244.20,473342',
 '1606470660000,244.25,244.35,244.00,244.05,491117',
 '1606470720000,244.05,244.20,244.00,244.20,298261',
 '1606470780000,244.20,244.25,244.10,244.25,344172',
 '1606470840000,244.20,244.35,244.20,244.30,347080',
 '1606470900000,244.30,244.40,244.25,244.30,447630',
 '1606470960000,244.30,244.30,244.00,244.00,360666',
 '1606471020000,244.05,244.15,243.95,243.95,467724',
 '1606471080000,243.95,244.10,243.70,244.00,386080',
 '1606471140000,244.00,244.20,243.70,244.20,166559']

# Convert to usable a dataframe  
df = pd.DataFrame(list_data)\
         .loc[:, 0]\
         .str.rsplit(",", expand=True)
df.columns = ["timestamp" , "open" , "high" , "low" , "close", "volume"]
df.loc[:, "timestamp"] = pd.to_datetime(df.timestamp.astype(int)*1e6)
df = df.set_index("timestamp")
df = df.astype(float)

# That is how it looks like
df.head()
                       open    high     low   close    volume
timestamp                                                    
2020-11-27 09:45:00  244.25  244.25  244.00  244.00  489269.0
2020-11-27 09:46:00  244.05  244.30  244.00  244.15  452854.0
2020-11-27 09:47:00  244.15  244.20  244.00  244.10  403043.0
2020-11-27 09:48:00  244.15  244.15  243.95  244.00  370182.0
2020-11-27 09:49:00  244.05  244.20  244.00  244.15  445286.0


# resample to 15 min data
df.resample("15min").mean()
Alex
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  • excellent job. I was too lazy to write it all tbh – ombk Nov 29 '20 at 14:38
  • thank you so much @Alex everything is fine but its giving error OverflowError: Python int too large to convert to C long could you please help – Amit Sharma Nov 29 '20 at 14:42
  • ok thank you all guys and specialy @Alex for your answer error was solved with changing int to 'int64' thank you again – Amit Sharma Nov 29 '20 at 14:51
  • thank you @Alex one more thing this time stamp starts from 15.15 and ends on 15.29 but in this solution it is in different could you tell me reason or solution for that – Amit Sharma Nov 29 '20 at 18:18
  • @AmitSharma There was a mistake in the answer. It is not `resample("15m")` (which would mean 15 months) but `resample("15min")`. – Alex Nov 29 '20 at 19:00