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Avro schema for weather data
#!/usr/bin/env python
import avro.schema
from avro.datafile import DataFileWriter
from import DatumWriter
import csv
import calendar
import time
schema = avro.schema.parse(open("weather.avsc").read())
writer_null = DataFileWriter(open("weather_data_null.avro", "wb"), DatumWriter(), schema, codec="null")
writer_deflate = DataFileWriter(open("weather_data_deflate.avro", "wb"), DatumWriter(), schema, codec="deflate")
writer_snappy = DataFileWriter(open("weather_data_snappy.avro", "wb"), DatumWriter(), schema, codec="snappy")
# Header: Date/Time,Temp (C),Dew Point Temp (C),Rel Hum (%),Wind Spd (km/h),Visibility (km),Stn Press (kPa),Weather
fields = "time temp dew_point_temp humidity wind_speed visibility pressure weather".split()
headers = dict([(v,i) for i,v in enumerate(fields)])
with open("weather_2012.csv") as csvfile:
reader = csv.reader(csvfile) # skip header
for boring_row in reader:
row = dict(zip(fields, boring_row))
# convert fields to right type
row["time"] = int(time.mktime((time.strptime(row["time"], "%Y-%m-%d %H:%M:%S"))))
for int_field in "humidity wind_speed".split():
row[int_field] = int(row[int_field])
for float_field in "temp dew_point_temp visibility pressure".split():
row[float_field] = float(row[float_field])
"namespace": "",
"type": "record",
"name": "Reading",
"doc": "Weather reading at a point in time",
"fields": [
{"name": "time", "type": "int", "doc": "Seconds since epoch"},
{"name": "temp", "type": "float", "doc": "Temperature in Celsius"},
{"name": "dew_point_temp", "type": "float", "doc": "Dew point temperature in Celsius"},
{"name": "humidity", "type": "int", "doc": "Relative humidity %"},
{"name": "wind_speed", "type": "float", "doc": "Wind speed in km/h"},
{"name": "visibility", "type": "float", "doc": "Visibility in km"},
{"name": "pressure", "type": "float", "doc": "Atmospheric pressure in kPa"},
{"name": "weather", "type": "string", "doc": "Weather summary"}
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ghost commented Mar 5, 2019

Will you explain the code ?

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