Week3_Clean/Filter Data and Make

After retrieving web data and storing them using MongoDB (pymongo), we are considering to clean or format the data in a certain consistent data, filter the data using "pipeline", and make the plot using "chart" module. All the coding was performed in Jupyter Notebook.

1. Create a new collection, and transfer the retrieved data (.json format) to the new data collection and make a copy for that collections using either mongo shell or cmd:

hw3_1.png hw3_2.png

2. Below is the link for the code on how to show the top 3 posted categories in one selected zone:
   https://anaconda.org/tangli666/week3_hw_v2/notebook

hw3_3.png

3. Below is the link for the code on how to show the relationship between the item condition and the average price:
   https://anaconda.org/tangli666/week3_hw_v10/notebook
   Note: in order to filter and format the 'price', some modification was made and update to a the new collection:
   """
   for i in item_info.find():
   try:
   price = int(i['price'].split(' ')[0])
   except ValueError:
   price = 0
   item_info.update({'_id':i['_id']},{'$set':{'price':price}})
   """

hw3_4.png

4. Last, the command line for exporting the data collection to a csv file:

hw3_5.png