In [3]:

    

# import requests
from __future__ import print_function    # (at top of module)
import time
import sys
import os
import pandas as pd
import numpy as np

PROJ_ROOT = os.path.join(os.getcwd(), os.pardir)

%load_ext watermark
%watermark -a "Thomas Turner" -d -t -v -p numpy,pandas -g


    

    




The watermark extension is already loaded. To reload it, use:
  %reload_ext watermark
Thomas Turner 07/31/2016 11:04:48 

CPython 3.5.2
IPython 5.0.0

numpy 1.11.1
pandas 0.18.1
Git hash: 23ee99f8aa682e9b4da872442de9df32b7a22b7a

In [4]:

    

# load environment variables from .env file using dotenv.
from dotenv import load_dotenv
dotenv_path = os.path.join(PROJ_ROOT, '.env')
load_dotenv(dotenv_path)

# Load the "autoreload" extension
%load_ext autoreload

# always reload modules marked with "%aimport"
%autoreload 1

# add the 'src' directory as one where we can import modules
src_dir = os.path.join(PROJ_ROOT, 'src')
sys.path.append(src_dir)

# import my methods from the source code
%aimport data.spotipy_functions
from data.spotipy_functions import get_tracks_from_playlist
from data.spotipy_functions import get_features_for_tracks


    

    




The autoreload extension is already loaded. To reload it, use:
  %reload_ext autoreload

In [5]:

    

# This line tells the notebook to show plots inside of the notebook
%matplotlib inline

import matplotlib.pyplot as plt
import seaborn as sb


    

In [6]:

    

playlists = pd.read_csv(os.path.join(PROJ_ROOT,'data','raw','spotify_playlists.csv'))


    

In [7]:

    

playlists


    

    
Out[7]:






  

    

      

      
playlist_id
      
playlist_name
      
user_id
    
  
  

    

      
0
      
2Ejbdf2DC9JGr5qdT0Z852
      
Old School Hip Hop House Party
      
spotify_uk_
    
    

      
1
      
7wUUwoxU2S6BRKA2bDPYKD
      
Massive dance hits
      
spotify_uk_
    
    

      
2
      
2aUKPl1SatBDloOMrR1hAp
      
Indie Roadtrip
      
spotify_uk_
    
    

      
3
      
5O2ERf8kAYARVVdfCKZ9G7
      
Coffee Table Jazz
      
spotify
    
    

      
4
      
6CRNGyV2hoQDyYpUPjHrkz
      
Top Classical
      
spotify
    
    

      
5
      
4EMB7RVcDBWTJDzsr1jXMe
      
Spin Deep House 40
      
goldjr
    
  

In [10]:

    

data_array = []
for i in range(1):
    print(i)
    table = get_features_for_tracks(get_tracks_from_playlist(playlists.ix[i,2],playlists.ix[i,0]))
    table['playlist_id'] = playlists.ix[i,0]
    table['playlist_name'] = playlists.ix[i,1]
    table['data_set'] = i
    data_array.append(table)
master_dataset = pd.concat(data_array)


    

    




0

In [11]:

    

master_dataset.shape


    

    
Out[11]:





(100, 21)

In [12]:

    

feature_subset = master_dataset[['playlist_name','acousticness','danceability','energy','speechiness','tempo','valence']]
sb.pairplot(feature_subset.dropna(), hue='playlist_name')


    

    
Out[12]:





<seaborn.axisgrid.PairGrid at 0x10e663710>






    

In [12]:

    

sb.violinplot(x='data_set', y='tempo', data=feature_subset)


    

    
Out[12]:





<matplotlib.axes._subplots.AxesSubplot at 0x111faa160>






    

In [14]:

    

tempo_vs_danceable = master_dataset[['playlist_name','danceability','tempo',]]
sb.pairplot(tempo_vs_danceable.dropna(), hue='playlist_name')


    

    
Out[14]:





<seaborn.axisgrid.PairGrid at 0x1118496d8>






    

In [94]:

    

sb.violinplot?


    

In [ ]: