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January 24, 2018 16:50
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load-polar-hr-data-to-python-pandas-dataframe
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Name,Sport,Date,Start time,Duration,Total distance (km),Average heart rate (bpm),Average speed (km/h),Max speed (km/h),Average pace (min/km),Max pace (min/km),Calories,Fat percentage of calories(%),Average cadence (rpm),Average stride length (cm),Running index,Training load,Ascent (m),Descent (m),Notes,Height (cm),Weight (kg),HR max,HR sit,VO2max, | |
MTA,OTHER_INDOOR,23-06-2017,13:43:56,00:08:40,0.00,77,,,,,17,71,,,,,,,,170.0,64.0,,,, | |
Sample rate,Time,HR (bpm),Speed (km/h),Pace (min/km),Cadence,Altitude (m),Stride length (m),Distances (m),Temperatures (C),Power (W), | |
1,00:00:00,75,,,,,,,,, | |
,00:00:01,75,,,,,,,,, | |
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import pandas as pd | |
import datetime | |
import time | |
row=None | |
with open("hr.csv") as fp: | |
for i, line in enumerate(fp): | |
if i == 1: | |
print(line) | |
row=line | |
elif i > 1: | |
break | |
splits=row.split(',') | |
print(splits) | |
date=splits[2] | |
mtime=splits[3] | |
mdatetime = date+' '+mtime | |
starting_ts = pd.to_datetime(mdatetime) | |
dates=[] | |
hrs=[] | |
with open("data/hr.csv") as fp: | |
for i, line in enumerate(fp): | |
if i < 3: | |
continue #skip headers | |
else: | |
splits=line.split(',') | |
rate=splits[1] | |
hr=splits[2] | |
#print(hr) | |
x = time.strptime(rate.split(',')[0],'%H:%M:%S') | |
seconds = datetime.timedelta(hours=x.tm_hour,minutes=x.tm_min,seconds=x.tm_sec).total_seconds() | |
#print(seconds) | |
ndate= starting_ts + pd.to_timedelta(seconds, unit='s') | |
#print(ndate) | |
dates.append(ndate) | |
hrs.append(hr) | |
data={'date':dates, 'hr':hrs} | |
df = pd.DataFrame(data=data, index=dates) | |
df.head() | |
df.to_csv('hr_df.csv') | |
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