Extracting data from a dictionary object to csv using Python 3.4

Posted on 2015-02-04
Last Modified: 2015-02-16
Hi, I'd like some help with the syntax to extract  data from a dictionary object into csv. Below is the code so far and the required csv output. I can use the script to print the data but just want to modify it now to export the data.

 Required csv output

import urllib.request
import json
import sqlite3
import decimal
import csv

decimal.getcontext().prec = 2

HOST = ""
KEY = '...................................'

# Data categories
VAL = "val"             # Location-specific data
TEXT = "txt"            # Textual data
IMAGE = "image"         # Stand-alone imagery
LAYER = "layer"         # Map overlay imagery

# Resource type: forecast or observation
FORECAST = "wxfcs"

# Field
ALL = "all"             # Can also be used instead of a location ID

DATA_TYPE = "json"      # Easier to work with than the XML alternative

# Time steps
DAILY = "daily"
THREE_HOURLY = "3hourly"
HOURLY = "hourly"

#Location ID
LOCATION = "3002"

url = (HOST + '/' + VAL + '/' + OBSERVATIONS + '/' + ALL + '/' + DATA_TYPE + '/' + LOCATION + '?' + 'res=' + HOURLY + '&key=' + KEY)

print (url)

response = urllib.request.urlopen(url)
FCData =
FCDataStr = FCData.decode('utf-8')

#Converts JSON data to a dictionary object
FCData_Dic = json.loads(FCDataStr)

#The following are examples of extracting data from the dictionary object.
#The JSON data is heavily nested.
#Each [] goes one level down, usually defined with {} in the JSON data.
dataDate = (FCData_Dic['SiteRep']['DV']['dataDate'])
print('dataDate =',dataDate)

#Get the Location info
LocationID = (FCData_Dic['SiteRep']['DV']['Location'].get('i',''))
print('LocationID =',LocationID)

Lat = (FCData_Dic['SiteRep']['DV']['Location'].get('lat',''))
print('Lat =',Lat)

Lon = (FCData_Dic['SiteRep']['DV']['Location'].get('lon',''))
print('Lon =',Lon)

Name = (FCData_Dic['SiteRep']['DV']['Location'].get('name',''))
print('Name =',Name)

Country = (FCData_Dic['SiteRep']['DV']['Location'].get('country',''))
print('Country =',Country)

Continent = (FCData_Dic['SiteRep']['DV']['Location'].get('continent',''))
print('Continent =',Continent)

Elevation = (FCData_Dic['SiteRep']['DV']['Location'].get('elevation',''))
print('Elevation =',Elevation)

#There are also [] in the JSON data, which are referenced with integers, 
# starting from [0]
#Here, the [0] refers to the first day's block of data defined with [].
DateDay0 = (FCData_Dic['SiteRep']['DV']['Location']['Period'][0]['value'])
print('DateDay0 =',DateDay0)

#The second [0] picks out each of the first day's forecast data, in this case the time, referenced by '$'
TimeOfFC = (FCData_Dic['SiteRep']['DV']['Location']['Period'][0]['Rep'][0]['$'])
print('TimeOfFC =',TimeOfFC)

#Ditto for the temperature.    
Temperature = decimal.Decimal((FCData_Dic['SiteRep']['DV']['Location']['Period'][0]['Rep'][0].get('T','')))
print('Temperature =',Temperature)

#Ditto for the screen relative humidity.    
ScreenRelativeHumidity = decimal.Decimal((FCData_Dic['SiteRep']['DV']['Location']['Period'][0]['Rep'][0].get('H','')))
print('ScreenRelativeHumidity =',ScreenRelativeHumidity)

#Ditto for the weather Type (a code number).
WeatherType = decimal.Decimal((FCData_Dic['SiteRep']['DV']['Location']['Period'][0]['Rep'][0].get('W','')))
print('WeatherType =',WeatherType)

#Ditto for the wind gust.
WindGust = ((FCData_Dic['SiteRep']['DV']['Location']['Period'][0]['Rep'][0].get('G','')))
print('WindGust =',WindGust)

#Ditto for the pressure.    
Pressure = decimal.Decimal((FCData_Dic['SiteRep']['DV']['Location']['Period'][0]['Rep'][0].get('P','')))
print('Pressure =',Pressure)

#Ditto for the visibility.    
Visibility = decimal.Decimal((FCData_Dic['SiteRep']['DV']['Location']['Period'][0]['Rep'][0].get('V','')))
print('Visibility =',Visibility)

#Ditto for the wind direction.    
WindDirection = ((FCData_Dic['SiteRep']['DV']['Location']['Period'][0]['Rep'][0].get('D','')))
print('WindDirection =',WindDirection)

#Ditto for the wind speed.    
WindSpeed = decimal.Decimal((FCData_Dic['SiteRep']['DV']['Location']['Period'][0]['Rep'][0].get('S','')))
print('WindSpeed =',WindSpeed)

#Ditto for the pressure tendency.    
PressureTendency = ((FCData_Dic['SiteRep']['DV']['Location']['Period'][0]['Rep'][0].get('Pt','')))
print('PressureTendency =',PressureTendency)

#Ditto for the dew point.    
DewPoint = decimal.Decimal((FCData_Dic['SiteRep']['DV']['Location']['Period'][0]['Rep'][0].get('Dp','')))
print('DewPoint =',DewPoint)

# write the csv
OUTPUT_FILE = "C:\Temp\example_data.csv"
# Open the otput file for writing in binary mode.
with open(OUTPUT_FILE, 'w', newline='') as outcsv:
    # Wrap the file object by the writer.
    writer = csv.writer(outcsv)
    # Write the header line to the output if needed.

    # Loop through the dictionary and get the tuples.
    # Need to sort it somehow. Not shown here.
    for t in LocationID:

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Question by:crompnk
  • 2
LVL 28

Accepted Solution

pepr earned 500 total points
ID: 40590952
You need to form the row (list) of values between the lines 156 and 157 (or earlier, when collecting the data). The writer.writerow(row) expect the iterable structure (usually a list). You get the LocationID and put it as a single element of the row. This is wrong. You have to get the wanted elements for the LocationID, build the list of them in the order to be present in the CSV file -- basically the same way you do it for the CSV heading (before the loop).

Author Comment

ID: 40592330
Hi, thanks, this works great, but why is the data exported 4 times i.e. 4 rows of the same data:

 # Loop through the dictionary and get the tuples.
    # Need to sort it somehow. Not shown here.
    for t in LocationID:

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LVL 28

Assisted Solution

pepr earned 500 total points
ID: 40592462
This is wrong again. Apparently, you get four t's when iterating through the LocationID in the loop. However, you form the row from the variable values collected before the loop. Because of that you get four times the same rows.

You have to form the row based on the t value inside the final-loop body.

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