Sawbo notebook for N' Pai

So here we load useful library from python

import pandas as pd
import numpy as np
from datetime import datetime

You can use pd.read_csv to read csv file. They have function to read SQL table or excel too.

content = pd.read_csv('content.csv', header=None, names=['content_id', 'video_name'])
content.head()

This is another table. We put column names in a list with argument called names

share_content = pd.read_csv('sharecontentviabluetoothevent.csv', header=None, 
                            names=['id', 'time', 'ip', 'alt', 'lat', 'lon', 'device_id', 'peer_id', 'content_id', 'app_id'])
share_content.head()

Here is how you sort by the columns.

share_content_sorted  = share_content.sort('device_id')
share_content_sorted.head()

event = pd.read_csv('sharesawboevent.csv', header=None, 
                    names=['id', 'time', 'ip', 'alt', 'lat', 'lon', 'device_id', 'peer_id', 'app_id'])
event.head()

Cross two table by using this

share_content_merged = share_content.merge(content, on='content_id')

Python also has datetime library to convert time stamp to datetime format.

date = datetime.fromtimestamp(share_content_merged.time[0]/1000)

Notice that I can select column i.e. share_content_merged['time'] and map python function to it.

share_content_merged['time_proc']= share_content_merged['time'].map(lambda x: datetime.fromtimestamp(x/1000))
share_content_merged.ip = share_content_merged.ip.fillna('')

To access each row use share_content_merged.iloc[0] then you can pull each row value i.e. share_content_merged.iloc[0].time (this will give you datetime)

titipata/sawbo_example.md