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In [120]:

    
#!/usr/bin/env python
# coding=utf-8

import pandas as pa 
import numpy as np

import json
import os
import networkx as nx
import pygraphviz as gz
from networkx.drawing.nx_pydot import write_dot
import math

import matplotlib
import matplotlib.pyplot as plt
%matplotlib notebook

import itertools

import csv
from sqlalchemy import exists, func

from database import *

from matplotlib import pylab, pyplot
from matplotlib import dates

import statsmodels
import seaborn as sns
sns.set(color_codes=True)

current_palette = sns.color_palette()
first = current_palette[0]
second = current_palette[1]
sns.set_palette(
    [second, first] + current_palette[2:]
)

from scipy import stats
from scipy.stats import norm, normaltest, mannwhitneyu, ranksums

from datetime import datetime, timedelta, date

date_format = '%Y-%m-%dT%H:%M:%S.%fZ'
date_format2 = '%Y-%m-%d %H:%M:%S'

plt.style.use(['seaborn-paper'])
sns.set_style("whitegrid")
#plt.rc('font', family='serif', serif='Charter')
plt.rc('font', family='serif', serif='DejaVu Serif')

SMALL_SIZE = 8
MEDIUM_SIZE = 9
BIGGER_SIZE = 13

plt.rc('font', size=MEDIUM_SIZE)          # controls default text sizes
plt.rc('axes', titlesize=MEDIUM_SIZE)     # fontsize of the axes title
plt.rc('axes', labelsize=MEDIUM_SIZE)    # fontsize of the x and y labels
plt.rc('xtick', labelsize=MEDIUM_SIZE)    # fontsize of the tick labels
plt.rc('ytick', labelsize=MEDIUM_SIZE)    # fontsize of the tick labels
plt.rc('legend', fontsize=MEDIUM_SIZE)    # legend fontsize
plt.rc('figure', titlesize=MEDIUM_SIZE)  # fontsize of the figure title

x_width  = 6.8898
x_height = x_width / 1.618

s_width  = 3.4449
s_height = s_width / 1.618

def save_plot(name, fig, width, height):
    #fig.tight_layout()
    fig.set_size_inches(width, height)
    #f.subplots_adjust(top=0.86)

    fig.savefig(CDIR+'/'+name, bbox_inches="tight")
    #plt.savefig(CDIR+'/video_view_percentages.pdf', bbox_inches="tight")



In [121]:

    
DIR = '../../data/data_evaluation_3MONTHS_filtered'
CDIR = '../../data/data_evaluation_3MONTHS_filtered/charts'

db = YTDatabase()



In [122]:

    
# read data from channel statistics notebook
df_channel = pa.read_csv(DIR+r'/df_channel_statistics_first_day.txt', sep=str('\t'), encoding='utf-8')



In [123]:

    
# read data from collaboration notebook
#df_graph = pa.read_csv(DIR+r'/df_collabs.txt', sep=str('\t'), encoding='utf-8', index_col=0)
df_graph_filtered = pa.read_csv(DIR+r'/df_filtered_collabs.txt', sep=str('\t'), encoding='utf-8', index_col=0)


df_graph_filtered['from_cluster'] = df_graph_filtered['from_cluster'].apply(json.loads)
df_graph_filtered['to_cluster'] = df_graph_filtered['to_cluster'].apply(json.loads)
df_graph_filtered['videos'] = df_graph_filtered['videos'].apply(json.loads)

#print len(df_graph)
print len(df_graph_filtered)


df_graph_filtered.head()









    



1728






    Out[123]:






  
    
      
      cluster
      from
      from_category
      from_cluster
      from_network
      from_popularity
      from_topic
      to
      to_category
      to_cluster
      to_network
      to_popularity
      to_topic
      videos
      weight
    
  
  
    
      0
      4806
      UCw-hc7ZJummS0AvWyjUX56A
      Sports
      [3102, 4806, 12796]
      None
      3
      Fitness
      UCWYtZYH4kcbMm29liIOSGQQ
      Sports
      [2996, 3143, 5232, 7762]
      BroadbandTV
      3
      Sports
      [Q-Z7QYkqn2M]
      1
    
    
      1
      5556
      UClpEE-Led9ZK0GJQKvU--3Q
      Entertainment
      [5556, 5884, 7050]
      Maker Studios
      2
      Football
      UCIKF1msqN7lW9gplsifOPkQ
      Entertainment
      [12627]
      BroadbandTV
      3
      Music
      [On4GE5hAU8s]
      1
    
    
      2
      5556
      UClpEE-Led9ZK0GJQKvU--3Q
      Entertainment
      [5556, 5884, 7050]
      Maker Studios
      2
      Football
      UC3M4u8_WwqY-2xDbJXxo5eQ
      Gaming
      [4139, 7049]
      OmniaMediaCo
      3
      Sports game
      [4cEkXTIcvH4]
      1
    
    
      3
      6135
      UCMDz09-3zO1hm1pqRA-Er0A
      Entertainment
      [6135]
      BroadbandTV
      3
      Lifestyle
      UC3fxB7rF6T7wqymKUJxwmXA
      Entertainment
      
      BroadbandTV
      3
      Lifestyle
      [C7Waq5ZgqGU]
      1
    
    
      4
      1367
      UCUcBFGAfOzut1x4GSWa6Akg
      Entertainment
      [1367, 4731, 5365, 6760]
      Maker Studios
      3
      Music
      UCCk_JV7ar8HlxWZRXQKgS7w
      People & Blogs
      
      BroadbandTV
      2
      Lifestyle
      [RJir4ghyTDo]
      1



In [ ]:



In [124]:

    
# Number of actual collab videos
videos = []
for i, row in df_graph_filtered.iterrows():
    videos.extend(row['videos'])
    
print len(videos)



In [6]:

    
# get history statistscs from db of videos

with db._session_scope(False) as session:

    df_video_history = pa.read_sql(session.query(VideoHistory).statement, db.engine)



In [125]:

    
len(df_graph_filtered['to'].unique())









    Out[125]:





1114



In [10]:

    
df_video_history = df_video_history.set_index(['id'])

df_video_history['crawlTimestamp'] = df_video_history['crawlTimestamp'].apply(lambda x: datetime.strptime(x, date_format2))

df_video_history.head()









    Out[10]:






  
    
      
      videoID
      viewCount
      commentCount
      likeCount
      dislikeCount
      crawlTimestamp
    
    
      id
      
      
      
      
      
      
    
  
  
    
      1
      qBZKeIbNDcE
      9
      0
      1
      0
      2016-12-28 03:03:22
    
    
      2
      2A6NSm9aSVQ
      139
      5
      14
      2
      2016-12-28 03:03:23
    
    
      3
      0hAym2XYn9w
      60
      3
      9
      0
      2016-12-28 03:03:48
    
    
      4
      VGHx-lcdJps
      258
      21
      64
      2
      2016-12-28 03:03:49
    
    
      5
      xYi6uCXmCFo
      337
      8
      94
      1
      2016-12-28 03:03:52



In [12]:

    
# Video history grouping
video_groups = df_video_history.groupby(['videoID'])

print len(df_video_history), len(video_groups)

# filtering too young videos for half life study of 6 days
vcounts = video_groups['viewCount'].count()
mask = vcounts[vcounts > 11].index

df_video_history_filtered = df_video_history[df_video_history.videoID.isin(mask)]

video_groups_filtered = df_video_history_filtered.groupby(['videoID'])

print len(df_video_history_filtered), len(video_groups_filtered)









    



13035916 232879
12899364 207380



In [13]:

    
# video sample statistics

from sklearn import preprocessing

name = 'ISLuff63OlI'
group = video_groups.get_group(name)

print name
fig, axs = plt.subplots(ncols=4)
plt.title(name)
axs[0].plot_date(group['crawlTimestamp'], group['viewCount'], 'b-')
axs[0].axvline(group.iloc[0, 5] + timedelta(days=6), color='red', linewidth=.5)
axs[1].plot_date(group['crawlTimestamp'], group['commentCount'], 'b-')
axs[2].plot_date(group['crawlTimestamp'], group['likeCount'], 'b-')
axs[3].plot_date(group['crawlTimestamp'], group['dislikeCount'], 'r-')

    
print group['crawlTimestamp'].min()
print group['crawlTimestamp'].max()
print 'Age:', (group['crawlTimestamp'].max() - group['crawlTimestamp'].min()).days

# views percentage
# Create x, where x the scores columns values as floats
x = group['viewCount'].values.astype(float)

# Create a minimum and maximum processor object
min_max_scaler = preprocessing.MinMaxScaler()

# Create an object to transform the data to fit minmax processor
x_scaled = min_max_scaler.fit_transform(x)

# Run the normalizer on the dataframe
group['viewCount_norm'] = x_scaled

print 'First 6 days made', group[(group.crawlTimestamp <= group.iloc[0, 5] + timedelta(days=6))]['viewCount_norm'].max(), '%'

#group









    



ISLuff63OlI






    














    











    



2017-01-08 00:07:01
2017-04-25 11:06:00
Age: 107
First 6 days made 0.745408929895 %






    



/home/mlode/intel/intelpython27/lib/python2.7/site-packages/sklearn/preprocessing/data.py:321: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.
  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)
/home/mlode/intel/intelpython27/lib/python2.7/site-packages/sklearn/preprocessing/data.py:356: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.
  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)
/home/mlode/intel/intelpython27/lib/python2.7/site-packages/ipykernel/__main__.py:33: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy



In [14]:

    
# multi sample channel statistics
import random


sampled_df_i  = random.sample(video_groups.indices, 10)
df_list  = map(lambda df_i: (df_i, video_groups.get_group(df_i)), sampled_df_i)


for name, group in df_list:
    print name
    fig, axs = plt.subplots(ncols=4)
    plt.title(name)
    axs[0].plot_date(group['crawlTimestamp'], group['viewCount'], 'b-')
    axs[0].axvline(group.iloc[0, 5] + timedelta(days=6), color='red', linewidth=.5)
    axs[1].plot_date(group['crawlTimestamp'], group['commentCount'], 'b-')
    axs[2].plot_date(group['crawlTimestamp'], group['likeCount'], 'b-')
    axs[3].plot_date(group['crawlTimestamp'], group['dislikeCount'], 'r-')









    



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In [15]:

    
# difference between video stats of collab and non_collab videos of same channel
# are collab videos more popular than normal for channel?



In [16]:

    
# create boxplot per channel, collab noncollab videos compared
# comparing what?
# viewcount gradient?
# viewcount max, mean? -> max mean of collab, non_collab videos
# consider age of video? take only first 6 days into account



In [14]:

    
# age dist, for all videos

df_video_age = []

for name, group in video_groups:

    age = (group['crawlTimestamp'].max() - group['crawlTimestamp'].min()).days
    df_video_age.append((name, age))
    
df_video_age = pa.DataFrame(df_video_age, columns=['id', 'age'])









    



---------------------------------------------------------------------------
KeyboardInterrupt                         Traceback (most recent call last)
<ipython-input-14-2b334d46f8ad> in <module>()
      5 for name, group in video_groups:
      6 
----> 7     age = (group['crawlTimestamp'].max() - group['crawlTimestamp'].min()).days
      8     df_video_age.append((name, age))
      9 

/home/mlode/intel/intelpython27/lib/python2.7/site-packages/pandas/core/generic.pyc in stat_func(self, axis, skipna, level, numeric_only, **kwargs)
   5611                                       skipna=skipna)
   5612         return self._reduce(f, name, axis=axis, skipna=skipna,
-> 5613                             numeric_only=numeric_only)
   5614 
   5615     return set_function_name(stat_func, name, cls)

/home/mlode/intel/intelpython27/lib/python2.7/site-packages/pandas/core/series.pyc in _reduce(self, op, name, axis, skipna, numeric_only, filter_type, **kwds)
   2316                                           'numeric_only.'.format(name))
   2317             with np.errstate(all='ignore'):
-> 2318                 return op(delegate, skipna=skipna, **kwds)
   2319 
   2320         return delegate._reduce(op=op, name=name, axis=axis, skipna=skipna,

/home/mlode/intel/intelpython27/lib/python2.7/site-packages/pandas/core/nanops.pyc in f(values, axis, skipna, **kwds)
    103                         result = alt(values, axis=axis, skipna=skipna, **kwds)
    104                 else:
--> 105                     result = alt(values, axis=axis, skipna=skipna, **kwds)
    106             except Exception:
    107                 try:

/home/mlode/intel/intelpython27/lib/python2.7/site-packages/pandas/core/nanops.pyc in reduction(values, axis, skipna)
    433     def reduction(values, axis=None, skipna=True):
    434         values, mask, dtype, dtype_max = _get_values(
--> 435             values, skipna, fill_value_typ=fill_value_typ, )
    436 
    437         if ((axis is not None and values.shape[axis] == 0) or

/home/mlode/intel/intelpython27/lib/python2.7/site-packages/pandas/core/nanops.pyc in _get_values(values, skipna, fill_value, fill_value_typ, isfinite, copy)
    182         mask = _isfinite(values)
    183     else:
--> 184         mask = isnull(values)
    185 
    186     dtype = values.dtype

/home/mlode/intel/intelpython27/lib/python2.7/site-packages/pandas/types/missing.pyc in isnull(obj)
     43     pandas.notnull: boolean inverse of pandas.isnull
     44     """
---> 45     return _isnull(obj)
     46 
     47 

/home/mlode/intel/intelpython27/lib/python2.7/site-packages/pandas/types/missing.pyc in _isnull_new(obj)
     53         raise NotImplementedError("isnull is not defined for MultiIndex")
     54     elif isinstance(obj, (ABCSeries, np.ndarray, ABCIndexClass)):
---> 55         return _isnull_ndarraylike(obj)
     56     elif isinstance(obj, ABCGeneric):
     57         return obj._constructor(obj._data.isnull(func=isnull))

/home/mlode/intel/intelpython27/lib/python2.7/site-packages/pandas/types/missing.pyc in _isnull_ndarraylike(obj)
    143     elif needs_i8_conversion(obj):
    144         # this is the NaT pattern
--> 145         result = values.view('i8') == iNaT
    146     else:
    147         result = np.isnan(values)

KeyboardInterrupt:



In [118]:

    
df_video_age['date'] = df_video_age['age'].apply(lambda x: date(2016, 12, 28) + timedelta(days=x))
df_video_age['dayn'] = df_video_age['date'].apply(lambda x: x.strftime("%A"))

print df_video_age['age'].min()

#ax = df_video_age[['date']].groupby(df_video_age['date']).count().plot(kind='bar')
test = df_video_age[['dayn']].groupby(df_video_age['dayn']).count()
order = [u'Monday',u'Tuesday',u'Wednesday',u'Thursday',u'Friday',u'Saturday',u'Sunday']


fig, axs = plt.subplots(1, 2)

#fig, ax = plt.subplots()
ax = test.ix[order].plot(kind='bar',legend=False, ax=axs[1])
ax.set_xlabel('Weekday')
ax.set_ylabel('Videos')
fig.tight_layout()
axs[1].set_title('Videos per Weekday')
#save_plot('video_weekday_counts.pdf', fig, s_width, s_height)

#ax.set_xticks(ax.get_xticks()[::2])
#fig = plt.figure()
ax = sns.distplot(df_video_age['age'], bins=100, ax=axs[0])
ax.set_xlabel('Age (Days)')
ax.set_ylabel('Density')
fig.tight_layout()
axs[0].set_title('Video Age')

save_plot('video_age_and_days.pdf', fig, 2*s_width, s_height)
#ax.set_xticklabels(df_video_age['date'])
#age_groups = range(0, 70, 5)
#print age_groups
#for age in age_groups:
#    ax.axvline(age, color='red', linewidth=.5)



df_video_age.head()









    



0






    














    











    Out[118]:






  
    
      
      id
      age
      date
      dayn
    
  
  
    
      0
      --0s03JYIww
      27
      2017-01-24
      Tuesday
    
    
      1
      --1lgNZ9cvY
      14
      2017-01-11
      Wednesday
    
    
      2
      --4kDK_RBjg
      48
      2017-02-14
      Tuesday
    
    
      3
      --4n0eZ8XTQ
      1
      2016-12-29
      Thursday
    
    
      4
      --5mUkeFBmI
      56
      2017-02-22
      Wednesday



In [126]:

    
# get all videos
with db._session_scope(False) as session:

    df_videos = pa.read_sql(session.query(Video).statement, db.engine)
    

df_videos = df_videos.set_index(['id'])

df_videos['dateAdded'] = df_videos['dateAdded'].apply(lambda x: datetime.strptime(x, date_format))
df_videos['crawlTimestamp'] = df_videos['crawlTimestamp'].apply(lambda x: datetime.strptime(x, date_format2))


# filter videos only related to channel with collabs

df_videos_collabs = df_videos[ (df_videos.channelID.isin(df_graph_filtered['from'].tolist())) | (df_videos.channelID.isin(df_graph_filtered['to'].tolist()))]
df_videos_collabs_without_gaming = df_videos[ (df_videos.channelID.isin(df_graph_filtered['from'].tolist())) | (df_videos.channelID.isin(df_graph_filtered['to'].tolist()))]

#df_videos_collabs.head()

print 'All videos:', len(df_videos)
print 'Collab related videos:', len(df_videos_collabs)
print 'Actual collab videos:', len(videos)

df_videos_collabs.head()









    



All videos: 250420
Collab related videos: 81942
Actual collab videos: 3925






    Out[126]:






  
    
      
      channelID
      title
      description
      category
      dateAdded
      tags
      topicIds
      attribution
      duration
      crawlTimestamp
      deleted
    
    
      id
      
      
      
      
      
      
      
      
      
      
      
    
  
  
    
      __3e1lrV5c8
      UCajyfb5EUPd3f3deUk0dBEQ
      NEUE DM ZAHNBÜRSTEN PINSEL im LIVE TEST | Mami...
      Neu bei dm gibt es die gehypten Zahnbürsten Pi...
      26
      2017-02-19 11:30:00
      ["mamiseelen","dm zahnb\u00fcrsten pinsel","li...
      ["/m/0yxzwny","/m/012xff"]
      None
      PT7M41S
      2017-02-20 00:10:55
      None
    
    
      __3Pz5Hmm4c
      UCw6Ou-fRcPa23GvYq-VHEbA
      IS LONZO BALL'S $495 SIGNATURE SHOE THE ZO2 WO...
      ENJOY AND LEAVE A LIKE!\nPodcast Link - https:...
      26
      2017-05-06 04:12:59
      ["lonzo","ball","zo2","lonzo ball sneaker","bi...
      []
      None
      PT11M16S
      2017-05-07 00:12:42
      None
    
    
      __6aGbnZHXQ
      UCFdIIKy9ZQuIuRnpXLVabfQ
      Provando Doce  de 1 Dolar - Dollar Tree ? Vit...
                    ? Clique Gostei, Comente, INSCR...
      22
      2017-04-05 23:00:01
      ["vitoriademattia diario8765redfwhgy24","TAG V...
      []
      None
      PT21M56S
      2017-04-07 00:12:54
      None
    
    
      __9yzCbryRw
      UCBkZWl8s1Cg22Yw11O6A-PQ
      A Futile Effort!/Hope Incarnate! | Made By Son...
      READ THIS :D\nI really Love this Song a lot. I...
      23
      2017-02-12 19:32:47
      ["Hip Hop","Sonic The Ghetto-Hog productions",...
      ["/m/08b26_"]
      None
      PT4M10S
      2017-02-13 00:10:02
      None
    
    
      __a6tnmzoU4
      UCxL7KXCV5YW0KdPRV583VJQ
      NEW YEAR NEW BABY!?
      WHATS IN MY BAG? → http://bit.ly/2iO7QLz\nWATC...
      22
      2017-01-13 20:00:30
      ["new year","new years","baby","pregnant","new...
      []
      None
      PT7M32S
      2017-01-14 00:13:27
      None



In [127]:

    
with db._session_scope(False) as session:

    df_channel_history_collabs = pa.read_sql(session.query(ChannelHistory).filter((ChannelHistory.channelID.in_(df_graph_filtered['from'].tolist())) | (ChannelHistory.channelID.in_(df_graph_filtered['to'].tolist()))).statement, db.engine)

channel_collabs_groups = df_channel_history_collabs.groupby(['channelID'])

print len(df_channel_history_collabs), len(channel_collabs_groups)









    



191320 1599



In [261]:

    
print len(df_graph_filtered)

df_collab_channels = pa.Series([])

df_collab_channels = df_collab_channels.append(df_graph_filtered['to'])
df_collab_channels = df_collab_channels.append(df_graph_filtered['from'])

print len(df_collab_channels.unique())
df_collab_channels.head()









    



1728
1599






    Out[261]:





0    UCWYtZYH4kcbMm29liIOSGQQ
1    UCIKF1msqN7lW9gplsifOPkQ
2    UC3M4u8_WwqY-2xDbJXxo5eQ
3    UC3fxB7rF6T7wqymKUJxwmXA
4    UCCk_JV7ar8HlxWZRXQKgS7w
dtype: object



In [ ]:

    
# mark videos collab or non_collab
# create video collab list

df_videos_collabs['collab_window'] = 'Non_Collab'

for name, group in channel_collabs_groups:
    videos = []
    # own videos
    for index, row in df_graph_filtered[df_graph_filtered['to'] == name].iterrows():
        #print row.videos
        for vid in row['videos']:
            df_videos_collabs.ix[vid, 'collab_window'] = 'Collab'

    # external videos (hes in)
    for index, row in df_graph_filtered[df_graph_filtered['from'] == name].iterrows():
        #print row.videos
        for vid in row['videos']:
            df_videos_collabs.ix[vid, 'collab_window'] = 'Collab'
            
print len(df_videos_collabs)
df_videos_collabs.head()



In [133]:

    
# FIX read old collabs list, filter with new
# df_videos_collabs_without_gaming

df_videos_collabs = pa.read_csv(DIR+r'/df_video_collabs_stats.txt', sep=str('\t'), encoding='utf-8')
#df_videos_collabs=df_videos_collabs.set_index(['id'])
print len(df_videos_collabs)

print len(df_videos_collabs_without_gaming)
mask = df_videos_collabs_without_gaming.index

df_videos_collabs = df_videos_collabs[df_videos_collabs.id.isin(mask)]
df_videos_collabs=df_videos_collabs.set_index(['id'])
print len(df_videos_collabs)



In [134]:

    
print len(df_videos_collabs)

print len(df_videos_collabs[df_videos_collabs.collab_window=='Collab'])

print len(df_videos_collabs[df_videos_collabs.collab_window=='Non_Collab'])
#df_videos_collabs.to_csv(DIR+r'/df_videos_collab.txt', sep=str('\t'), encoding='utf-8')



In [ ]:

    
# calculate video view gradient like for channel, see if difference for collabs

#video_groups_filtered = df_video_history_filtered

print len(video_groups_filtered)
print len(df_videos_collabs)
mask = df_videos_collabs.index

df_video_history_collabs = df_video_history_filtered[df_video_history_filtered.videoID.isin(mask)]
video_history_collab_group = df_video_history_collabs.groupby('videoID')
print len(video_history_collab_group)

name = 'ISLuff63OlI'
group = video_groups_filtered.get_group(name)
group



In [ ]:

    
# fix zero entries

for name, group in video_history_collab_group:
    if 0 in group['viewCount'].values:
        print name
        subs = group['viewCount'].values.astype(float)
        subs[subs==0] = 1.0
        print subs
        df_video_history_collabs.ix[group.index, 'viewCount'] = subs



In [ ]:

    
# label every video history point either collab or non_collab
# calc gradient and p_gradient of viewcounts
# compare gradients then

df_video_history_collabs['gradient'] = np.nan
df_video_history_collabs['p_gradient'] = np.nan
df_video_history_collabs['collab_window'] = 'Non_Collab'

cnt=0

for name, group in video_history_collab_group:
    
    if len(group['viewCount']) < 2:
        cnt += 1
        continue
    
    subs = group['viewCount'].values
    group.drop(group.index[:1], inplace=True)
    df_video_history_collabs.ix[group.index, 'gradient'] = np.diff(subs)
    df_video_history_collabs.ix[group.index, 'p_gradient'] = [100.0 * a1 / a2 - 100 for a1, a2 in zip(subs[1:], subs)]

    # set collab or non collab
    df_video_history_collabs.ix[group.index, 'collab_window'] = df_videos_collabs.ix[name, 'collab_window']


print cnt

print 'Non_Collabs:'
print df_video_history_collabs[df_video_history_collabs.collab_window=='Non_Collab']['gradient'].describe()
print '\nCollabs:'
print df_video_history_collabs[df_video_history_collabs.collab_window=='Collab']['gradient'].describe()

print '\nNon_Collabs:'
print df_video_history_collabs[df_video_history_collabs.collab_window=='Non_Collab']['p_gradient'].describe()
print '\nCollabs:'
print df_video_history_collabs[df_video_history_collabs.collab_window=='Collab']['p_gradient'].describe()



In [135]:

    
print 'Non_Collabs:'
print df_video_history_collabs[df_video_history_collabs.collab_window=='Non_Collab']['gradient'].describe()
print '\nCollabs:'
print df_video_history_collabs[df_video_history_collabs.collab_window=='Collab']['gradient'].describe()

print '\nNon_Collabs:'
print df_video_history_collabs[df_video_history_collabs.collab_window=='Non_Collab']['p_gradient'].describe()
print '\nCollabs:'
print df_video_history_collabs[df_video_history_collabs.collab_window=='Collab']['p_gradient'].describe()









    



Non_Collabs:
count    8.048123e+06
mean     2.236156e+03
std      2.439116e+04
min     -9.052200e+04
25%      6.000000e+00
50%      5.300000e+01
75%      4.050000e+02
max      7.837219e+06
Name: gradient, dtype: float64

Collabs:
count    4.840720e+05
mean     3.034038e+03
std      2.935988e+04
min     -3.096500e+04
25%      9.000000e+00
50%      8.500000e+01
75%      6.900000e+02
max      5.809283e+06
Name: gradient, dtype: float64

Non_Collabs:
count    8.048123e+06
mean     4.044399e+01
std      2.812795e+04
min     -6.056647e+01
25%      6.521876e-02
50%      2.594559e-01
75%      8.672448e-01
max      5.090080e+07
Name: p_gradient, dtype: float64

Collabs:
count    4.840720e+05
mean     3.857606e+02
std      2.580275e+05
min     -8.309859e+01
25%      7.127940e-02
50%      2.492748e-01
75%      7.865941e-01
max      1.794914e+08
Name: p_gradient, dtype: float64



In [136]:

    
print len(df_video_history_collabs)

print len(df_video_history_collabs[df_video_history_collabs.gradient.notnull()])

df_video_history_collabs.to_csv(DIR+r'/df_video_history_collabs.txt', sep=str('\t'), encoding='utf-8')



In [137]:

    
# FIX
# Filter out the gaming videos

print len(df_video_history_collabs)
print len(df_videos_collabs)
mask = df_videos_collabs.index

df_video_history_collabs = df_video_history_collabs[df_video_history_collabs.videoID.isin(mask)]
video_history_collab_group = df_video_history_collabs.groupby('videoID')
print len(df_video_history_collabs)



In [138]:

    
print len(df_video_history_collabs)



In [141]:

    
df_video_history_collabs.head()









    Out[141]:






  
    
      
      videoID
      viewCount
      commentCount
      likeCount
      dislikeCount
      crawlTimestamp
      gradient
      p_gradient
      collab_window
    
    
      id
      
      
      
      
      
      
      
      
      
    
  
  
    
      1
      qBZKeIbNDcE
      9.0
      0
      1
      0
      2016-12-28 03:03:22
      NaN
      NaN
      Non_Collab
    
    
      2
      2A6NSm9aSVQ
      139.0
      5
      14
      2
      2016-12-28 03:03:23
      NaN
      NaN
      Non_Collab
    
    
      7
      H_PpwvS8hA8
      127.0
      5
      9
      0
      2016-12-28 03:04:08
      NaN
      NaN
      Non_Collab
    
    
      9
      lsN0rGSfXWw
      122.0
      4
      37
      0
      2016-12-28 03:04:36
      NaN
      NaN
      Non_Collab
    
    
      18
      Azqy_VVj1PI
      47536.0
      2072
      13325
      60
      2016-12-29 00:06:15
      NaN
      NaN
      Non_Collab



In [ ]:

    
#df_video_history_collabs = pa.read_csv(DIR+r'/df_video_history_collabs.txt', sep=str('\t'), encoding='utf-8')

#df_video_history_collabs['crawlTimestamp'] = df_video_history_collabs['crawlTimestamp'].apply(lambda x: datetime.strptime(x, date_format2))
#video_history_collab_group = df_video_history_collabs.groupby('videoID')



In [249]:

    
df_video_history_collabs_test = df_video_history_collabs[df_video_history_collabs.p_gradient<10000]
df_video_history_collabs_test.head()









    Out[249]:






  
    
      
      videoID
      viewCount
      commentCount
      likeCount
      dislikeCount
      crawlTimestamp
      gradient
      p_gradient
      collab_window
      day_after
    
    
      id
      
      
      
      
      
      
      
      
      
      
    
  
  
    
      1864
      qBZKeIbNDcE
      422.0
      27
      51
      2
      2016-12-29 00:11:56
      413.0
      4588.888889
      Non_Collab
      -1
    
    
      1865
      2A6NSm9aSVQ
      8883.0
      138
      409
      18
      2016-12-29 00:11:56
      8744.0
      6290.647482
      Non_Collab
      -1
    
    
      1870
      H_PpwvS8hA8
      10086.0
      107
      374
      12
      2016-12-29 00:11:56
      9959.0
      7841.732283
      Non_Collab
      -1
    
    
      1872
      lsN0rGSfXWw
      1477.0
      21
      437
      0
      2016-12-29 00:11:56
      1355.0
      1110.655738
      Non_Collab
      -1
    
    
      3932
      Azqy_VVj1PI
      115837.0
      3484
      30545
      135
      2016-12-30 00:12:13
      68301.0
      143.682683
      Non_Collab
      -1



In [58]:

    
df_video_history_collabs_test.crawlTimestamp.dtype









    Out[58]:





dtype('<M8[ns]')



In [250]:

    
len(df_video_history_collabs[df_video_history_collabs.p_gradient>10000])









    Out[250]:





645



In [144]:

    
fig, (ax1, ax2) = plt.subplots(ncols=2)
sns.violinplot(x="collab_window", y="gradient", data=df_video_history_collabs_test, ax=ax1, inner='box')
sns.barplot(x="collab_window", y="gradient", data=df_video_history_collabs_test, ci=99,errwidth=1., capsize=.1, ax=ax2)
plt.legend(["{}% CI".format(99)])
ax1.set_xlabel('')
ax2.set_xlabel('')
ax1.set_ylabel('Views')
ax2.set_ylabel('mean(Views)')
fig.suptitle('Video view-count')
fig.tight_layout()
fig.subplots_adjust(top=0.86)

fig, (ax1, ax2) = plt.subplots(ncols=2)
sns.violinplot(x="collab_window", y="p_gradient", data=df_video_history_collabs_test, ax=ax1, inner='box')
sns.barplot(x="collab_window", y="p_gradient", data=df_video_history_collabs_test, ci=99,errwidth=1., capsize=.1, ax=ax2)
plt.legend(["{}% CI".format(99)])
ax1.set_xlabel('')
ax2.set_xlabel('')
ax1.set_ylabel('Views %')
ax2.set_ylabel('mean(Views %)')
fig.suptitle('Video view-count %')
fig.tight_layout()
fig.subplots_adjust(top=0.86)









    














    











    



---------------------------------------------------------------------------
KeyboardInterrupt                         Traceback (most recent call last)
<ipython-input-144-0a5c1bc7d899> in <module>()
      1 fig, (ax1, ax2) = plt.subplots(ncols=2)
      2 sns.violinplot(x="collab_window", y="gradient", data=df_video_history_collabs_test, ax=ax1, inner='box')
----> 3 sns.barplot(x="collab_window", y="gradient", data=df_video_history_collabs_test, ci=99,errwidth=1., capsize=.1, ax=ax2)
      4 plt.legend(["{}% CI".format(99)])
      5 ax1.set_xlabel('')

/home/mlode/intel/intelpython27/lib/python2.7/site-packages/seaborn/categorical.pyc in barplot(x, y, hue, data, order, hue_order, estimator, ci, n_boot, units, orient, color, palette, saturation, errcolor, errwidth, capsize, ax, **kwargs)
   2897                           estimator, ci, n_boot, units,
   2898                           orient, color, palette, saturation,
-> 2899                           errcolor, errwidth, capsize)
   2900 
   2901     if ax is None:

/home/mlode/intel/intelpython27/lib/python2.7/site-packages/seaborn/categorical.pyc in __init__(self, x, y, hue, data, order, hue_order, estimator, ci, n_boot, units, orient, color, palette, saturation, errcolor, errwidth, capsize)
   1543                                  order, hue_order, units)
   1544         self.establish_colors(color, palette, saturation)
-> 1545         self.estimate_statistic(estimator, ci, n_boot)
   1546 
   1547         self.errcolor = errcolor

/home/mlode/intel/intelpython27/lib/python2.7/site-packages/seaborn/categorical.pyc in estimate_statistic(self, estimator, ci, n_boot)
   1449                     boots = bootstrap(stat_data, func=estimator,
   1450                                       n_boot=n_boot,
-> 1451                                       units=unit_data)
   1452                     confint.append(utils.ci(boots, ci))
   1453 

/home/mlode/intel/intelpython27/lib/python2.7/site-packages/seaborn/algorithms.pyc in bootstrap(*args, **kwargs)
     73     for i in range(int(n_boot)):
     74         resampler = rs.randint(0, n, n)
---> 75         sample = [a.take(resampler, axis=0) for a in args]
     76         boot_dist.append(func(*sample, **func_kwargs))
     77     return np.array(boot_dist)

KeyboardInterrupt:



In [274]:

    
# Boxplot viewcount gradient for collab videos 

fig, (ax1, ax2) = plt.subplots(ncols=2)
sns.boxplot(x="collab_window", y="gradient", data=df_video_history_collabs_test, ax=ax1, order=['Collab', 'Non_Collab'])
sns.barplot(x="collab_window", y="gradient", data=df_video_history_collabs_test, ci=99, errwidth=1., capsize=.1, ax=ax2, order=['Collab', 'Non_Collab'])
plt.legend(["{}% CI".format(99)])
ax1.set_xlabel('')
ax2.set_xlabel('')
ax1.set_ylabel('Views Gradient')
ax2.set_ylabel('mean(Views Gradient)')
ax1.set_xticklabels(['Collaboration', 'Non-Collaboration'])
ax2.set_xticklabels(['Collaboration', 'Non-Collaboration'])
fig.suptitle('Video Views Gradient')
fig.tight_layout()
fig.subplots_adjust(top=0.86)
ax1.set_ylim([-1600.0, 3500.0])
ax2.set_ylim([3100.0, 4400.0])
save_plot('collab_video_views_box_ci.pdf', fig, 2*s_width, 0.75*s_height)

# test, norm fitting
test = df_video_history_collabs_test[df_video_history_collabs_test["gradient"].notnull()]

# Distribution fitting
# norm.fit(data) returns a list of two parameters 
# (mean, parameters[0] and std, parameters[1]) via a MLE(Maximum-Likelihood) approach 
# to data, which should be in array form.
(mu, sigma) = stats.norm.fit(test['gradient'])

fig = plt.figure()
ax = sns.distplot(test['gradient'], fit=norm, kde=False, bins=1000, norm_hist=True)
#plt.legend(["$\mu=${0:.2g}, $\sigma=${1:.2f}".format(mu, sigma)])
#plt.legend(["Fitted normal dist"])
ax.set_xlabel('Views')
ax.set_ylabel('Density')
plt.title('Video Views')
ax.set_ylim([0.0, 1.5e-05])
ax.set_xlim([-260000.0, 260000.0])
ax.set_xticks(ax.get_xticks()[::2])
ax.set_yticks(ax.get_yticks()[::2])
plt.tight_layout()
plt.legend(["Fitted Gaussian"],prop={'size':8})
save_plot('collab_video_views_norm_fit.pdf', fig, s_width, s_height)
print 'Normal-dist-test:', normaltest(test['gradient'])

collabs_grads = test[test.collab_window == 'Collab']['gradient']
non_collabs_grads = test[test.collab_window == 'Non_Collab']['gradient']

manu = mannwhitneyu(x=collabs_grads, y=non_collabs_grads, alternative='two-sided')
ranks = ranksums(x=collabs_grads, y=non_collabs_grads)

print '\nMann-Whitney-test:', manu, '\np*2:', manu.pvalue*2
print 'Wilcoxon rank-sum-test:', ranks, '\np*2:', ranks.pvalue*2


print 'Non_Collabs:'
print df_video_history_collabs_test[df_video_history_collabs_test.collab_window=='Non_Collab']['gradient'].describe()
print '\nCollabs:'
print df_video_history_collabs_test[df_video_history_collabs_test.collab_window=='Collab']['gradient'].describe()









    














    











    














    











    



Normal-dist-test: NormaltestResult(statistic=13192493.868126784, pvalue=0.0)

Mann-Whitney-test: MannwhitneyuResult(statistic=609040441102.5, pvalue=0.0) 
p*2: 0.0
Wilcoxon rank-sum-test: RanksumsResult(statistic=67.132698369255181, pvalue=0.0) 
p*2: 0.0
Non_Collabs:
count    3.815349e+06
mean     3.305907e+03
std      3.059019e+04
min     -8.462700e+04
25%      1.300000e+01
50%      1.040000e+02
75%      7.620000e+02
max      7.837219e+06
Name: gradient, dtype: float64

Collabs:
count    2.973160e+05
mean     4.144535e+03
std      3.616439e+04
min     -3.096500e+04
25%      1.900000e+01
50%      1.580000e+02
75%      1.212000e+03
max      5.809283e+06
Name: gradient, dtype: float64



In [181]:

    
# Boxplot view count percental gradient for collab videos 

fig, (ax1, ax2) = plt.subplots(ncols=2)
sns.violinplot(x="collab_window", y="p_gradient", data=df_video_history_collabs_test, ax=ax1, order=['Collab', 'Non_Collab'])
sns.barplot(x="collab_window", y="p_gradient", data=df_video_history_collabs_test, ci=99, errwidth=1., capsize=.1, ax=ax2, order=['Collab', 'Non_Collab'])
plt.legend(["{}% CI".format(99)])
ax1.set_xlabel('')
ax2.set_xlabel('')
ax1.set_ylabel('Views Growth %')
ax2.set_ylabel('mean(Views Growth %)')
ax1.set_xticklabels(['Collaboration', 'Non-Collaboration'])
ax2.set_xticklabels(['Collaboration', 'Non-Collaboration'])
fig.suptitle('Video Views Growth %')
fig.tight_layout()
fig.subplots_adjust(top=0.86)
ax1.set_ylim([-120.0, 150.0])
ax2.set_ylim([0.0, 8.5])
save_plot('collab_video_views_box_ci_perc_violin.pdf', fig, 2*s_width, s_height)

# test, norm fitting
test = df_video_history_collabs_test[df_video_history_collabs_test["p_gradient"].notnull()]

# Distribution fitting
# norm.fit(data) returns a list of two parameters 
# (mean, parameters[0] and std, parameters[1]) via a MLE(Maximum-Likelihood) approach 
# to data, which should be in array form.
(mu, sigma) = stats.norm.fit(test['p_gradient'])

fig = plt.figure()
ax = sns.distplot(test['p_gradient'], fit=norm, kde=False, bins=1000, norm_hist=True)
#plt.legend(["$\mu=${0:.2g}, $\sigma=${1:.2f}".format(mu, sigma)])
#plt.legend(["Fitted normal dist"])
ax.set_xlabel('Views Growth %')
ax.set_ylabel('Density')
plt.title('Video Views Growth %')
ax.set_ylim([0.0, 0.006])
ax.set_xlim([-700.0, 700.0])
ax.set_xticks(ax.get_xticks()[::2])
ax.set_yticks(ax.get_yticks()[::2])
plt.tight_layout()
plt.legend(["Fitted Gaussian"],prop={'size':8})
#save_plot('collab_video_views_norm_fit.pdf', fig, s_width, s_height)
print 'Normal-dist-test:', normaltest(test['p_gradient'])

collabs_grads = test[test.collab_window == 'Collab']['p_gradient']
non_collabs_grads = test[test.collab_window == 'Non_Collab']['p_gradient']

manu = mannwhitneyu(x=collabs_grads, y=non_collabs_grads, alternative='two-sided')
ranks = ranksums(x=collabs_grads, y=non_collabs_grads)

print '\nMann-Whitney-test:', manu, '\np*2:', manu.pvalue*2
print 'Wilcoxon rank-sum-test:', ranks, '\np*2:', ranks.pvalue*2

print test[test.collab_window == 'Collab']['p_gradient'].describe()
print test[test.collab_window == 'Non_Collab']['p_gradient'].describe()

print test[test.collab_window == 'Collab']['p_gradient'].median()
print test[test.collab_window == 'Non_Collab']['p_gradient'].median()









    














    











    














    











    



Normal-dist-test: NormaltestResult(statistic=12255295.288119387, pvalue=0.0)

Mann-Whitney-test: MannwhitneyuResult(statistic=559694078142.5, pvalue=3.085456597251137e-33) 
p*2: 6.1709131945e-33
Wilcoxon rank-sum-test: RanksumsResult(statistic=-12.009439135899697, pvalue=3.1698755695281311e-33) 
p*2: 6.33975113906e-33
count    297316.000000
mean          6.310792
std         109.123101
min         -83.098592
25%           0.104556
50%           0.319975
75%           0.952141
max        9961.585366
Name: p_gradient, dtype: float64
count    3.815349e+06
mean     7.796696e+00
std      1.230656e+02
min     -5.923076e+01
25%      1.008007e-01
50%      3.407290e-01
75%      1.078746e+00
max      9.978182e+03
Name: p_gradient, dtype: float64
0.319975005511
0.340729001585



In [ ]:



In [ ]:

    
# get for all videos in df_videos_collabs the max popaurlty statiscs vlaue of the 12 days data


df_videos_collabs['12day_viewmax'] = np.nan
df_videos_collabs['12day_likemax'] = np.nan
df_videos_collabs['12day_dislikemax'] = np.nan
df_videos_collabs['12day_commentmax'] = np.nan

for index, group in video_history_collab_group:
    #print group.head()
    df_videos_collabs.ix[index, '12day_viewmax'] = group[(group.crawlTimestamp <= group.iloc[0, 5] + timedelta(days=12))]['viewCount'].max()
    df_videos_collabs.ix[index, '12day_likemax'] = group[(group.crawlTimestamp <= group.iloc[0, 5] + timedelta(days=12))]['likeCount'].max()
    df_videos_collabs.ix[index, '12day_dislikemax'] = group[(group.crawlTimestamp <= group.iloc[0, 5] + timedelta(days=12))]['dislikeCount'].max()
    df_videos_collabs.ix[index, '12day_commentmax'] = group[(group.crawlTimestamp <= group.iloc[0, 5] + timedelta(days=12))]['commentCount'].max()









    



/home/mlode/intel/intelpython27/lib/python2.7/site-packages/ipykernel/__main__.py:4: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
/home/mlode/intel/intelpython27/lib/python2.7/site-packages/ipykernel/__main__.py:5: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
/home/mlode/intel/intelpython27/lib/python2.7/site-packages/ipykernel/__main__.py:6: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
/home/mlode/intel/intelpython27/lib/python2.7/site-packages/ipykernel/__main__.py:7: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy



In [148]:

    
print len(df_videos_collabs)
print df_videos_collabs['12day_viewmax'].describe()
print len(df_videos_collabs[df_videos_collabs['12day_viewmax'].isnull()])
print df_videos_collabs['12day_likemax'].describe()
print len(df_videos_collabs[df_videos_collabs['12day_likemax'].isnull()])
print df_videos_collabs['12day_dislikemax'].describe()
print len(df_videos_collabs[df_videos_collabs['12day_dislikemax'].isnull()])
print df_videos_collabs['12day_commentmax'].describe()
print len(df_videos_collabs[df_videos_collabs['12day_commentmax'].isnull()])









    



81486
count    6.764800e+04
mean     1.728373e+05
std      5.010130e+05
min      1.000000e+00
25%      4.940000e+03
50%      2.772850e+04
75%      1.266482e+05
max      1.706475e+07
Name: 12day_viewmax, dtype: float64
13838
count    6.764800e+04
mean     9.233844e+03
std      3.951147e+04
min      0.000000e+00
25%      1.510000e+02
50%      9.080000e+02
75%      4.824250e+03
max      1.956001e+06
Name: 12day_likemax, dtype: float64
13838
count     67648.000000
mean        307.286084
std        3184.341032
min           0.000000
25%           6.000000
50%          32.000000
75%         149.000000
max      639683.000000
Name: 12day_dislikemax, dtype: float64
13838
count    6.764800e+04
mean     1.201489e+03
std      2.144439e+04
min      0.000000e+00
25%      2.900000e+01
50%      1.520000e+02
75%      6.570000e+02
max      5.423785e+06
Name: 12day_commentmax, dtype: float64
13838



In [149]:

    
# save to file
df_videos_collabs.to_csv(DIR+r'/df_video_collabs_stats.txt', sep=str('\t'), encoding='utf-8')
#df_videos_collabs = pa.read_csv(DIR+r'/df_video_collabs_stats.txt', sep=str('\t'), encoding='utf-8')
#df_videos_collabs=df_videos_collabs.set_index(['id'])
#df_videos_collabs.head()



In [182]:

    
# all viewcounts complete
# for all channel mixed not very good/accurate, as channel have different popularities

from scipy.stats import norm, normaltest, mannwhitneyu, ranksums

print len(df_videos_collabs[df_videos_collabs['collab_window']=='Collab'])

fig, (ax1, ax2) = plt.subplots(ncols=2)
sns.violinplot(x="collab_window", y="12day_viewmax", data=df_videos_collabs, ax=ax1, inner='box',order=['Collab', 'Non_Collab'])
sns.barplot(x="collab_window", y="12day_viewmax", data=df_videos_collabs, ci=95,errwidth=1., capsize=.1, ax=ax2,order=['Collab', 'Non_Collab'])
plt.legend(["{}% CI".format(95)])
ax1.set_xlabel('')
ax2.set_xlabel('')
ax1.set_ylabel('Views')
ax2.set_ylabel('mean(Views)')
fig.suptitle('Video Views')
ax1.set_xticklabels(['Collaboration', 'Non-Collaboration'])
ax2.set_xticklabels(['Collaboration', 'Non-Collaboration'])
fig.tight_layout()
fig.subplots_adjust(top=0.86)
ax1.set_ylim([-650000.0, 2000000.0])
save_plot('collab_video_12day_view_box_ci_violin.pdf', fig, 2*s_width, s_height)

# test, norm fitting
test = df_videos_collabs[df_videos_collabs["12day_viewmax"].notnull()]

# Distribution fitting
# norm.fit(data) returns a list of two parameters 
# (mean, parameters[0] and std, parameters[1]) via a MLE(Maximum-Likelihood) approach 
# to data, which should be in array form.
(mu, sigma) = stats.norm.fit(test['12day_viewmax'])

fig = plt.figure()
ax = sns.distplot(test['12day_viewmax'], fit=norm, kde=False, bins=1000, norm_hist=True)
#plt.legend(["normal dist. fit ($\mu=${0:.2g}, $\sigma=${1:.2f})".format(mu, sigma)])
plt.legend(["Fitted normal dist"])
ax.set_xlabel('Views')
ax.set_ylabel('Density')
plt.title('Video views')
plt.tight_layout()

print 'Normal-dist-test:', normaltest(test['12day_viewmax'])

collabs_grads = test[test.collab_window == 'Collab']['12day_viewmax']
non_collabs_grads = test[test.collab_window == 'Non_Collab']['12day_viewmax']

manu = mannwhitneyu(x=collabs_grads, y=non_collabs_grads, alternative='two-sided')
ranks = ranksums(x=collabs_grads, y=non_collabs_grads)

print '\nMann-Whitney-test:', manu, '\np*2:', manu.pvalue*2
print 'Wilcoxon rank-sum-test:', ranks, '\np*2:', ranks.pvalue*2


fig, (ax1, ax2) = plt.subplots(ncols=2)
sns.violinplot(x="collab_window", y="12day_likemax", data=df_videos_collabs, ax=ax1, inner='box',order=['Collab', 'Non_Collab'])
sns.barplot(x="collab_window", y="12day_likemax", data=df_videos_collabs, ci=95,errwidth=1., capsize=.1, ax=ax2,order=['Collab', 'Non_Collab'])
plt.legend(["{}% CI".format(95)])
ax1.set_xlabel('')
ax2.set_xlabel('')
ax1.set_ylabel('Likes')
ax2.set_ylabel('mean(Likes)')
fig.suptitle('Video Likes')
ax1.set_xticklabels(['Collaboration', 'Non-Collaboration'])
ax2.set_xticklabels(['Collaboration', 'Non-Collaboration'])
fig.tight_layout()
fig.subplots_adjust(top=0.86)
ax1.set_ylim([-44000.0, 123000.0])
ax2.set_ylim([0.0, 19000.0])
save_plot('collab_video_12day_like_box_ci_violin.pdf', fig, 2*s_width, s_height)

# test, norm fitting
test = df_videos_collabs[df_videos_collabs["12day_likemax"].notnull()]

# Distribution fitting
# norm.fit(data) returns a list of two parameters 
# (mean, parameters[0] and std, parameters[1]) via a MLE(Maximum-Likelihood) approach 
# to data, which should be in array form.
(mu, sigma) = stats.norm.fit(test['12day_likemax'])

fig = plt.figure()
ax = sns.distplot(test['12day_likemax'], fit=norm, kde=False, bins=1000, norm_hist=True)
#plt.legend(["normal dist. fit ($\mu=${0:.2g}, $\sigma=${1:.2f})".format(mu, sigma)])
plt.legend(["Fitted normal dist"])
ax.set_xlabel('Likes')
ax.set_ylabel('Density')
plt.title('Video likes')
plt.tight_layout()

print 'Normal-dist-test:', normaltest(test['12day_likemax'])

collabs_grads = test[test.collab_window == 'Collab']['12day_likemax']
non_collabs_grads = test[test.collab_window == 'Non_Collab']['12day_likemax']

manu = mannwhitneyu(x=collabs_grads, y=non_collabs_grads, alternative='two-sided')
ranks = ranksums(x=collabs_grads, y=non_collabs_grads)

print '\nMann-Whitney-test:', manu, '\np*2:', manu.pvalue*2
print 'Wilcoxon rank-sum-test:', ranks, '\np*2:', ranks.pvalue*2


fig, (ax1, ax2) = plt.subplots(ncols=2)
sns.violinplot(x="collab_window", y="12day_dislikemax", data=df_videos_collabs, ax=ax1, inner='box',order=['Collab', 'Non_Collab'])
sns.barplot(x="collab_window", y="12day_dislikemax", data=df_videos_collabs, ci=95,errwidth=1., capsize=.1, ax=ax2,order=['Collab', 'Non_Collab'])
plt.legend(["{}% CI".format(95)])
ax1.set_xlabel('')
ax2.set_xlabel('')
ax1.set_ylabel('Dislikes')
ax2.set_ylabel('mean(Dislikes)')
fig.suptitle('Video Dislikes')
ax1.set_xticklabels(['Collaboration', 'Non-Collaboration'])
ax2.set_xticklabels(['Collaboration', 'Non-Collaboration'])
fig.tight_layout()
fig.subplots_adjust(top=0.86)
ax1.set_ylim([-7000.0, 9000.0])
save_plot('collab_video_12day_dislike_box_ci_violin.pdf', fig, 2*s_width, s_height)

# test, norm fitting
test = df_videos_collabs[df_videos_collabs["12day_dislikemax"].notnull()]

# Distribution fitting
# norm.fit(data) returns a list of two parameters 
# (mean, parameters[0] and std, parameters[1]) via a MLE(Maximum-Likelihood) approach 
# to data, which should be in array form.
(mu, sigma) = stats.norm.fit(test['12day_dislikemax'])

fig = plt.figure()
ax = sns.distplot(test['12day_dislikemax'], fit=norm, kde=False, bins=1000, norm_hist=True)
#plt.legend(["normal dist. fit ($\mu=${0:.2g}, $\sigma=${1:.2f})".format(mu, sigma)])
plt.legend(["Fitted normal dist"])
ax.set_xlabel('Dislikes')
ax.set_ylabel('Density')
plt.title('Video dislikes')
plt.tight_layout()

print 'Normal-dist-test:', normaltest(test['12day_dislikemax'])

collabs_grads = test[test.collab_window == 'Collab']['12day_dislikemax']
non_collabs_grads = test[test.collab_window == 'Non_Collab']['12day_dislikemax']

manu = mannwhitneyu(x=collabs_grads, y=non_collabs_grads, alternative='two-sided')
ranks = ranksums(x=collabs_grads, y=non_collabs_grads)

print '\nMann-Whitney-test:', manu, '\np*2:', manu.pvalue*2
print 'Wilcoxon rank-sum-test:', ranks, '\np*2:', ranks.pvalue*2


fig, (ax1, ax2) = plt.subplots(ncols=2)
sns.violinplot(x="collab_window", y="12day_commentmax", data=df_videos_collabs, ax=ax1, inner='box',order=['Collab', 'Non_Collab'])
sns.barplot(x="collab_window", y="12day_commentmax", data=df_videos_collabs, ci=95,errwidth=1., capsize=.1, ax=ax2,order=['Collab', 'Non_Collab'])
plt.legend(["{}% CI".format(95)], loc=0)
ax1.set_xlabel('')
ax2.set_xlabel('')
ax1.set_ylabel('Comments')
ax2.set_ylabel('mean(Comments)')
fig.suptitle('Video Comments')
ax1.set_xticklabels(['Collaboration', 'Non-Collaboration'])
ax2.set_xticklabels(['Collaboration', 'Non-Collaboration'])
fig.tight_layout()
fig.subplots_adjust(top=0.86)
ax1.set_ylim([-12000.0, 14000.0])
save_plot('collab_video_12day_comment_box_ci_violin.pdf', fig, 2*s_width, s_height)

# test, norm fitting
test = df_videos_collabs[df_videos_collabs["12day_commentmax"].notnull()]

# Distribution fitting
# norm.fit(data) returns a list of two parameters 
# (mean, parameters[0] and std, parameters[1]) via a MLE(Maximum-Likelihood) approach 
# to data, which should be in array form.
(mu, sigma) = stats.norm.fit(test['12day_commentmax'])

fig = plt.figure()
ax = sns.distplot(test['12day_commentmax'], fit=norm, kde=False, bins=1000, norm_hist=True)
#plt.legend(["normal dist. fit ($\mu=${0:.2g}, $\sigma=${1:.2f})".format(mu, sigma)])
plt.legend(["Fitted normal dist"])
ax.set_xlabel('Comments')
ax.set_ylabel('Density')
plt.title('Video comments')
plt.tight_layout()

print 'Normal-dist-test:', normaltest(test['12day_commentmax'])

collabs_grads = test[test.collab_window == 'Collab']['12day_commentmax']
non_collabs_grads = test[test.collab_window == 'Non_Collab']['12day_commentmax']

manu = mannwhitneyu(x=collabs_grads, y=non_collabs_grads, alternative='two-sided')
ranks = ranksums(x=collabs_grads, y=non_collabs_grads)

print '\nMann-Whitney-test:', manu, '\np*2:', manu.pvalue*2
print 'Wilcoxon rank-sum-test:', ranks, '\np*2:', ranks.pvalue*2

#print 'Collab:\n', df_videos_collabs.ix[df_videos_collabs.collab_window=='Collab',:].describe()
#print '\nNon_Collab:\n',df_videos_collabs.ix[df_videos_collabs.collab_window=='Non_Collab',:].describe()









    



4234






    














    











    














    











    



Normal-dist-test: NormaltestResult(statistic=101917.2419282568, pvalue=0.0)

Mann-Whitney-test: MannwhitneyuResult(statistic=147421662.0, pvalue=9.3143387005482232e-27) 
p*2: 1.86286774011e-26
Wilcoxon rank-sum-test: RanksumsResult(statistic=10.70821234960807, pvalue=9.3142992794214324e-27) 
p*2: 1.86285985588e-26






    














    











    














    











    



Normal-dist-test: NormaltestResult(statistic=140872.45728523246, pvalue=0.0)

Mann-Whitney-test: MannwhitneyuResult(statistic=153109791.5, pvalue=4.695549889606518e-53) 
p*2: 9.39109977921e-53
Wilcoxon rank-sum-test: RanksumsResult(statistic=15.331611690477786, pvalue=4.7017430583714563e-53) 
p*2: 9.40348611674e-53






    














    











    














    











    



Normal-dist-test: NormaltestResult(statistic=294539.16440824792, pvalue=0.0)

Mann-Whitney-test: MannwhitneyuResult(statistic=148802470.5, pvalue=2.5642100414263072e-32) 
p*2: 5.12842008285e-32
Wilcoxon rank-sum-test: RanksumsResult(statistic=11.830554783754005, pvalue=2.7134241110749869e-32) 
p*2: 5.42684822215e-32






    














    











    














    











    



Normal-dist-test: NormaltestResult(statistic=348146.15695784736, pvalue=0.0)

Mann-Whitney-test: MannwhitneyuResult(statistic=149519667.0, pvalue=2.1979927274611458e-35) 
p*2: 4.39598545492e-35
Wilcoxon rank-sum-test: RanksumsResult(statistic=12.413503154633055, pvalue=2.2077730402106398e-35) 
p*2: 4.41554608042e-35



In [395]:

    
fig, (ax1, ax2) = plt.subplots(ncols=2)
sns.boxplot(x="collab_window", y="12day_viewmax", data=df_videos_collabs, ax=ax1,order=['Collab', 'Non_Collab'])
sns.barplot(x="collab_window", y="12day_viewmax", data=df_videos_collabs, ci=95,errwidth=1., capsize=.1, ax=ax2,order=['Collab', 'Non_Collab'])
plt.legend(["{}% CI".format(95)])
ax1.set_xlabel('')
ax2.set_xlabel('')
ax1.set_ylabel('Views')
ax2.set_ylabel('mean(Views)')
fig.suptitle('Video Views')
ax1.set_xticklabels(['Collaboration', 'Non-Collaboration'])
ax2.set_xticklabels(['Collaboration', 'Non-Collaboration'])
fig.tight_layout()
fig.subplots_adjust(top=0.86)
#ax1.set_ylim([-12000.0, 500000.0])
ax2.set_ylim([150000.0, 280000.0])
ax1.set_yscale('log')
ax2.set_yscale('log')
from matplotlib.ticker import FormatStrFormatter
from matplotlib.ticker import AutoMinorLocator
plt.tick_params(axis='y', which='minor')
ax2.yaxis.set_minor_formatter(FormatStrFormatter('%g'))
yloc = plt.MaxNLocator(5)
ylog = plt.LinearLocator(numticks=6)
ylog2 = plt.LinearLocator(numticks=6)
ax2.yaxis.set_major_locator(ylog)
ax2.yaxis.set_minor_locator(ylog2)
#ax2.set_yticks(ax2.get_yticks()[::2])
#ax2.set_yticklabels(ax2.get_yticklabels()[::8])
save_plot('collab_video_12day_view_box_ci_log.pdf', fig, 2*s_width, 0.75*s_height)

print 'Collab:\n', df_videos_collabs.ix[df_videos_collabs.collab_window=='Collab','12day_viewmax'].describe()
print '\nNon_Collab:\n',df_videos_collabs.ix[df_videos_collabs.collab_window=='Non_Collab','12day_viewmax'].describe()

print 'Collab:\n', df_videos_collabs.ix[df_videos_collabs.collab_window=='Collab','12day_viewmax'].median()
print '\nNon_Collab:\n',df_videos_collabs.ix[df_videos_collabs.collab_window=='Non_Collab','12day_viewmax'].median()









    














    











    



Collab:
count    4.234000e+03
mean     2.454193e+05
std      6.825585e+05
min      3.000000e+00
25%      6.969250e+03
50%      4.560500e+04
75%      1.874062e+05
max      1.407286e+07
Name: 12day_viewmax, dtype: float64

Non_Collab:
count    6.341400e+04
mean     1.679912e+05
std      4.861064e+05
min      1.000000e+00
25%      4.865000e+03
50%      2.675550e+04
75%      1.233325e+05
max      1.706475e+07
Name: 12day_viewmax, dtype: float64
Collab:
45605.0

Non_Collab:
26755.5



In [183]:

    
test = df_videos_collabs[df_videos_collabs["12day_viewmax"].notnull()]

# df_channel_history['viewCount_gradient']
fig = plt.figure()
ax = sns.barplot(x="collab_window", y="12day_viewmax", data=test, ci=99,errwidth=1., capsize=.1,order=['Collab', 'Non_Collab'])
plt.legend(["{}% CI".format(99)])
ax.set_xticklabels(['Collaboration', 'Non-Collaboration'])
ax.set_xlabel('')
ax.set_ylabel('mean(Views)')
plt.title('Video views comparision')
plt.tight_layout()


#fig = plt.figure()
#ax = sns.distplot(test['6day_viewmax'], kde=False)
#ax.set_yscale('log')
#ax.set_xscale('log')

from scipy.stats import norm, normaltest, mannwhitneyu, ranksums

# Distribution fitting
# norm.fit(data) returns a list of two parameters 
# (mean, parameters[0] and std, parameters[1]) via a MLE(Maximum-Likelihood) approach 
# to data, which should be in array form.
(mu, sigma) = stats.norm.fit(test['12day_viewmax'])

fig = plt.figure()
ax = sns.distplot(test['12day_viewmax'], fit=norm, kde=False, bins=100, norm_hist=True)
#plt.legend(["normal dist. fit ($\mu=${0:.2g}, $\sigma=${1:.2f})".format(mu, sigma)])
plt.legend(["Fitted normal dist"])
ax.set_xlabel('Views')
ax.set_ylabel('Density')
plt.title('Video views')
plt.tight_layout()


print 'Normal-dist-test:', normaltest(test['12day_viewmax'])

collabs_grads = test[test.collab_window == 'Collab']['12day_viewmax']
non_collabs_grads = test[test.collab_window == 'Non_Collab']['12day_viewmax']

print '\nMann-Whitney-test:', mannwhitneyu(x=collabs_grads, y=non_collabs_grads, alternative='two-sided')
print 'Wilcoxon rank-sum-test:', ranksums(x=collabs_grads, y=non_collabs_grads)









    














    











    














    











    



Normal-dist-test: NormaltestResult(statistic=101917.2419282568, pvalue=0.0)

Mann-Whitney-test: MannwhitneyuResult(statistic=147421662.0, pvalue=9.3143387005482232e-27)
Wilcoxon rank-sum-test: RanksumsResult(statistic=10.70821234960807, pvalue=9.3142992794214324e-27)



In [260]:

    
print len(df_videos_collabs)
print len(df_videos_collabs.groupby(['channelID']))



In [265]:

    
print len(df_videos_collabs)
print len(df_videos_collabs.groupby(['channelID']))
df_video_channel_gr = df_videos_collabs.groupby(['channelID', 'collab_window'], as_index=False)



In [264]:

    
# take every channel and get max/mean of collab, non_collab groups for views etc.
# create list with two entrys for every channel, collab, non_collab with mean value of above

# TODO check why number of channels with both types is so low

print len(df_videos_collabs)
print len(df_videos_collabs.groupby(['channelID']))
df_video_channel_mean = df_videos_collabs.groupby(['channelID', 'collab_window'], as_index=False).mean()
print len(df_video_channel_mean)
#print df_video_channel_mean.head()
video_channel_group = df_video_channel_mean.groupby('channelID')
print len(video_channel_group)

print df_videos_collabs['collab_window'].value_counts()
# calc growth of collab to non_collab per channel, only the two of same channel is comparable directly
df_collab_growth = []

#print video_channel_group.count()

for name, group in video_channel_group:
    if len(group) > 1:
        #print name
        #print group[['collab_window', '6day_viewmax']]
        c_v = group.iloc[0]['12day_viewmax']
        nc_v = group.iloc[1]['12day_viewmax']
        g = 100.0 * c_v / nc_v - 100
        #print g
        df_collab_growth.append((name, g))
    else:
        print name, group['collab_window']

df_collab_growth = pa.DataFrame(df_collab_growth)
print len(df_collab_growth)
#print df_collab_growth.head()
#df_collab_growth[1].mean()









    



 81486
1589
2705
1589
Non_Collab    77252
Collab         4234
Name: collab_window, dtype: int64
UC-PjJT0RVURj_L6D8D3WIww 12    Non_Collab
Name: collab_window, dtype: object
UC-SH7ZCar_m5Udkthm3j0Uw 15    Non_Collab
Name: collab_window, dtype: object
UC-SmWrDkILvnmfzE-43CdtA 18    Non_Collab
Name: collab_window, dtype: object
UC-V2ze2c4s7rV4neMd7ZS-w 21    Non_Collab
Name: collab_window, dtype: object
UC-q_5nCwFN3cUTW_wXlc7kg 34    Non_Collab
Name: collab_window, dtype: object
UC04ZT2MN2qF7tn2_JahRQRw 47    Non_Collab
Name: collab_window, dtype: object
UC0Juf8RX49w9_7FdvcjtNdA 50    Non_Collab
Name: collab_window, dtype: object
UC0PXqiud6dbwOAk8RvslgpQ 51    Non_Collab
Name: collab_window, dtype: object
UC0x8XBfvJoDzSqMzE56g2BQ 76    Non_Collab
Name: collab_window, dtype: object
UC19hDT4twVO-2553FiQmcXQ 83    Non_Collab
Name: collab_window, dtype: object
UC1C0x6WPqAavZoja1Qga-uw 86    Non_Collab
Name: collab_window, dtype: object
UC1H-sZk-cj-tDpfTCZ1f1YQ 89    Non_Collab
Name: collab_window, dtype: object
UC1J5R-2zsrrICAYmkt5g_6Q 92    Non_Collab
Name: collab_window, dtype: object
UC1NB1xUQ8ItM5VoA4101lUQ 93    Non_Collab
Name: collab_window, dtype: object
UC1Xga1RR7NpowZH40Yn48tQ 104    Non_Collab
Name: collab_window, dtype: object
UC1bM_rWa49vRd-aPKo1kUCQ 107    Non_Collab
Name: collab_window, dtype: object
UC1ga45T4B7LX4llKa3khq4g 112    Non_Collab
Name: collab_window, dtype: object
UC1mc5_VnbSnFTZQygWu2nqQ 113    Collab
Name: collab_window, dtype: object
UC1sVnwXAjct1g2oaFojIpnA 122    Non_Collab
Name: collab_window, dtype: object
UC2NyUeUIru1fwBEnZBTD3UQ 153    Non_Collab
Name: collab_window, dtype: object
UC2bYhAHyaqfWlPXWBVk4BcA 156    Non_Collab
Name: collab_window, dtype: object
UC2k2x0gsgTYxqIkxQGe8hmQ 161    Non_Collab
Name: collab_window, dtype: object
UC2wQ3publKlYOLZ2Ho4xv0Q 166    Non_Collab
Name: collab_window, dtype: object
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UCyCyTe_1bT2aIPwG_gxbyeg 2634    Non_Collab
Name: collab_window, dtype: object
UCyDE9oEVAZwBtIsqMV26SIQ 2635    Non_Collab
Name: collab_window, dtype: object
UCyE4KeQg54Bx34LxQB22xkw 2638    Non_Collab
Name: collab_window, dtype: object
UCyGFZ2SQkAyOQllKcDESYeg 2639    Non_Collab
Name: collab_window, dtype: object
UCyISjICIlDZQhEC-Y0jMViw 2644    Non_Collab
Name: collab_window, dtype: object
UCyP6Mnxv84diX2StFMSa_Gg 2645    Non_Collab
Name: collab_window, dtype: object
UCyb0T6J6nL5ibo0HLRMINKw 2652    Non_Collab
Name: collab_window, dtype: object
UCyf3pMjaBl5ola2zmkp5dWA 2655    Non_Collab
Name: collab_window, dtype: object
UCyiPo3XZZ2_XiC8y9mqAqig 2656    Non_Collab
Name: collab_window, dtype: object
UCysmiBQRJc97SbSuBuZeRUQ 2659    Non_Collab
Name: collab_window, dtype: object
UCz3KPXSncFWfDkWfdUe_79w 2672    Non_Collab
Name: collab_window, dtype: object
UCz6tAQuMTRij-zv-crzP8sQ 2675    Collab
Name: collab_window, dtype: object
UCzBd-289owXoR9jwcCau84Q 2678    Non_Collab
Name: collab_window, dtype: object
UCzJIliq68IHSn-Kwgjeg2AQ 2681    Non_Collab
Name: collab_window, dtype: object
UCzK_qU_rnQ_W9CTKq7XTe3A 2682    Non_Collab
Name: collab_window, dtype: object
UCzN_QwrNpAMWxVIjZKKSkPQ 2683    Non_Collab
Name: collab_window, dtype: object
UCzNq9i-DlDDBLjPerVzJW-A 2684    Non_Collab
Name: collab_window, dtype: object
UCzNtNcKsSe-IKyOtjUeoRNA 2685    Non_Collab
Name: collab_window, dtype: object
UCzPu_uL2lOoXfgYVhjMQfSw 2686    Collab
Name: collab_window, dtype: object
UCz_BJGTYrM4ntr7vsrsjfLw 2693    Non_Collab
Name: collab_window, dtype: object
UCz_cDc_2arKIb6SlJoqFT0w 2694    Non_Collab
Name: collab_window, dtype: object
UCzbsojogXsEBljxVAHC5YIg 2697    Non_Collab
Name: collab_window, dtype: object
UCzdX8HbZrbGRwBkikmvpAXw 2698    Non_Collab
Name: collab_window, dtype: object
UCzo4OXE8JxogJHWJ2SypiNg 2701    Non_Collab
Name: collab_window, dtype: object
UCzzCZoXYp5JH7QKhWadOQcA 2704    Non_Collab
Name: collab_window, dtype: object
1116



In [259]:

    
# average difference, or growth between collab and noncollab, in%
fig, (ax1, ax2) = plt.subplots(ncols=2)
sns.violinplot(data=df_collab_growth, ax=ax1,color=sns.color_palette()[1])
sns.barplot(data=df_collab_growth, ci=95,errwidth=1., capsize=.1, ax=ax2,color=sns.color_palette()[1])
plt.legend(["{}% CI".format(95)])
ax1.set_xlabel('')
ax2.set_xlabel('')
ax1.set_ylabel('Factor %')
ax2.set_ylabel('mean(Factor %)')
fig.suptitle('Video Views Growth Factor %')
ax1.set_xticklabels([''])
ax2.set_xticklabels([''])
fig.tight_layout()
fig.subplots_adjust(top=0.86)
ax1.set_ylim([-250.0, 600.0])
ax2.set_ylim([15.0, 55.0])

for bar in ax2.patches:
    x = bar.get_x()
    width = bar.get_width()
    centre = x+width/2.
    newwidth = width/2
    bar.set_x(centre-newwidth/2.)
    bar.set_width(newwidth)
    
save_plot('collab_video_12day_growth_factor_box_ci_violin.pdf', fig, 2*s_width, 0.75*s_height)

print df_collab_growth.describe()









    














    











    



                 1
count  1108.000000
mean     34.322166
std     272.959967
min     -99.701347
25%     -37.295373
50%      -6.731477
75%      32.477690
max    6376.281237



In [244]:

    
# average difference, or growth between collab and noncollab, in%
fig, (ax1, ax2) = plt.subplots(ncols=2)
sns.boxplot(data=df_collab_growth, ax=ax1,color=sns.color_palette()[1])
sns.barplot(data=df_collab_growth, ci=95,errwidth=1., capsize=.1, ax=ax2,color=sns.color_palette()[1])
plt.legend(["{}% CI".format(95)])
ax1.set_xlabel('')
ax2.set_xlabel('')
ax1.set_ylabel('Factor %')
ax2.set_ylabel('mean(Factor %)')
fig.suptitle('Video Views Growth Factor %')
ax1.set_xticklabels([''])
ax2.set_xticklabels([''])
fig.tight_layout()
fig.subplots_adjust(top=0.86)
ax1.set_ylim([-120.0, 160.0])
ax2.set_ylim([15.0, 55.0])

for bar in ax2.patches:
    x = bar.get_x()
    width = bar.get_width()
    centre = x+width/2.
    newwidth = width/2
    bar.set_x(centre-newwidth/2.)
    bar.set_width(newwidth)
    
save_plot('collab_video_12day_growth_factor_box_ci.pdf', fig, 2*s_width, 0.75*s_height)

print df_collab_growth.describe()









    














    











    



                 1
count  1108.000000
mean     34.322166
std     272.959967
min     -99.701347
25%     -37.295373
50%      -6.731477
75%      32.477690
max    6376.281237



In [154]:

    
df_channel = df_channel.set_index('id')
df_channel.head()









    Out[154]:






  
    
      
      topicIds
      network
      viewCount
      subscriberCount
      videoCount
      commentCount
      category
      popularity
    
    
      id
      
      
      
      
      
      
      
      
    
  
  
    
      UC__Pj66OeDibNZNN__L913g
      Music
      None
      3253022
      23029
      967
      0
      Entertainment
      2
    
    
      UC__PZLSRGtUQiTtvm3hPoEQ
      Movies
      BroadbandTV
      310896
      5878
      144
      0
      Entertainment
      1
    
    
      UC__rmdgxs3ZF0zK_he7Tmig
      Lifestyle
      None
      1291254
      8146
      294
      121
      How-to & Style
      1
    
    
      UC_-CxgsxX0tpnm24WO-797Q
      Lifestyle
      Maker Studios
      625545
      18990
      67
      101
      How-to & Style
      2
    
    
      UC_1FUFB6TlGeGOyDI4ikkzg
      Movies
      BroadbandTV
      89020205
      106760
      288
      0
      Entertainment
      3



In [155]:

    
# use above data and split data into groups further to compare
# popualrity classes
# category class

# todo add popualrity class to channel and category
# read from file from channel notebook?
#

df_collab_growth_ext = df_collab_growth.copy()
df_collab_growth_ext.columns= ['id', 'growth']
df_collab_growth_ext['category'] = 'NaN'
df_collab_growth_ext['popularity'] = np.nan
df_collab_growth_ext.set_index('id', inplace=True)

for i, row in df_collab_growth_ext.iterrows():
    df_collab_growth_ext.loc[i, 'category'] = df_channel.loc[i, 'category']
    df_collab_growth_ext.loc[i, 'popularity'] = df_channel.loc[i, 'popularity']
    
df_collab_growth_ext.head()









    Out[155]:






  
    
      
      growth
      category
      popularity
    
    
      id
      
      
      
    
  
  
    
      UC-4kjzuh4822B9yPSgpZQgA
      10.813472
      Entertainment
      4.0
    
    
      UC-63s9JLCZqIDlhXK6VHb7w
      -89.944618
      Entertainment
      2.0
    
    
      UC-A4oZF4AlOEdlyZWBCI0cQ
      -4.630593
      Entertainment
      4.0
    
    
      UC-CRXGRabkMD7x6V_Nk9F0A
      -89.551831
      Film & Animation
      1.0
    
    
      UC-NINtNMPM75eaqh07RCy_Q
      93.463303
      Education
      3.0



In [156]:

    
# investigate groups from classes
df_collab_growth_category_groups = df_collab_growth_ext.groupby(by='category')
df_collab_growth_popularity_groups = df_collab_growth_ext.groupby(by='popularity')

print df_collab_growth_category_groups.count()
print df_collab_growth_popularity_groups.count()









    



                        growth  popularity
category                                  
Cars & Vehicles              7           7
Comedy                     128         129
Education                   40          40
Entertainment              387         387
Film & Animation            54          54
Gaming                      33          33
How-to & Style              91          93
Music                       60          61
News & Politics             12          12
Non-profits & Activism       2           2
People & Blogs             218         222
Pets & Animals               3           3
Science & Technology        34          34
Sports                      33          33
Travel & Events              6           6
            growth  category
popularity                  
0.0             25        25
1.0             78        78
2.0            278       282
3.0            532       536
4.0            186       186
5.0              8         8
6.0              1         1



In [157]:

    
test = df_collab_growth_category_groups.filter(lambda x: len(x) > 30)
test = test.groupby(by='category')

test_0 = test.get_group('Entertainment')
test_1 = test.get_group('People & Blogs')



In [185]:

    
fig = plt.figure()
ax =sns.barplot(x='popularity', y='growth', data=df_collab_growth_ext, ci=95,errwidth=1., capsize=.1)
plt.legend(["{}% CI".format(95)])
ax.set_xlabel('Popularity')
ax.set_ylabel('Views Growth Factor %')
fig.suptitle('Views Growth Factor % per Popularity')
ax.set_xticklabels(range(7))
fig.tight_layout()
fig.subplots_adjust(top=0.92)
ax.set_ylim([-50.0, 450.0])
save_plot('collab_video_growth_factor_popularities.pdf', fig, s_width, 1.5*s_height)


fig = plt.figure()
ax = sns.barplot(x='category', y='growth', data=df_collab_growth_ext, ci=95,errwidth=1., capsize=.1)
plt.legend(["{}% CI".format(95)], loc=2)
ax.set_xlabel('Category')
ax.set_ylabel('Views Growth Factor %')
fig.suptitle('Views Growth Factor % per Category')
#ax1.set_xticklabels([''])
ax.set_xticklabels(ax.get_xticklabels(),rotation=45)
plt.setp(ax.xaxis.get_majorticklabels(), ha='right')
ax.set_ylim([-100.0, 650.0])
fig.tight_layout()
fig.subplots_adjust(top=0.92)

save_plot('collab_video_growth_factor_categories.pdf', fig, 2*s_width, 1.5*s_height)



In [216]:

    
fig, (ax1, ax2) = plt.subplots(ncols=2, gridspec_kw = {'width_ratios':[1, 2]})

sns.barplot(x='popularity', y='growth', data=df_collab_growth_ext, ci=95,errwidth=1., capsize=.1, ax=ax1)
plt.legend(["{}% CI".format(95)])
#ax1.set_xlabel('Popularity')
ax1.xaxis.label.set_visible(False)
ax1.set_ylabel('Views Growth Factor %')
ax1.set_title('Views Growth Factor % per Popularity')
ax1.set_xticklabels(range(7))
ax1.set_ylim([-50.0, 450.0])
#fig.tight_layout()
sns.barplot(x='category', y='growth', data=df_collab_growth_ext, ci=95,errwidth=1., capsize=.1, ax=ax2)
#plt.legend(["{}% CI".format(95)], loc=2)
#ax2.set_xlabel('Category')
ax2.xaxis.label.set_visible(False)
ax2.set_ylabel('Views Growth Factor %')
ax2.set_title('Views Growth Factor % per Category')
#ax1.set_xticklabels([''])
ax2.set_xticklabels(ax2.get_xticklabels(),rotation=45)
plt.setp(ax2.xaxis.get_majorticklabels(), ha='right')
ax2.set_ylim([-100.0, 650.0])

fig.subplots_adjust(top=0.92)
#fig.tight_layout()
save_plot('collab_video_growth_factor_combined.pdf', fig, 4*s_width, 1.5*s_height)



In [186]:

    
fig, (ax1, ax2) = plt.subplots(ncols=2)
sns.violinplot(x='popularity', y='growth', data=df_collab_growth_ext, ax=ax1)
sns.barplot(x='popularity', y='growth', data=df_collab_growth_ext, ci=95,errwidth=1., capsize=.1, ax=ax2)
plt.legend(["{}% CI".format(95)])
ax1.set_xlabel('Popularity')
ax2.set_xlabel('Popularity')
ax1.set_ylabel('Views Growth Factor %')
ax2.set_ylabel('mean(Views Growth Factor %)')
fig.suptitle('Views Growth Factor % per Popularity')
#ax1.set_xticklabels([''])
#ax2.set_xticklabels([''])
ax1.set_xticklabels(range(7))
ax2.set_xticklabels(range(7))
fig.tight_layout()
#fig.subplots_adjust(top=0.86)
#ax1.set_ylim([-250.0, 800.0])
save_plot('collab_video_growth_factor_popularities_violin_bar.pdf', fig, 2.5*s_width, 1.5*s_height)



In [187]:

    
fig, (ax1, ax2) = plt.subplots(ncols=2)
sns.violinplot(x='category', y='growth', data=df_collab_growth_ext, ax=ax1,scale='width')
sns.barplot(x='category', y='growth', data=df_collab_growth_ext, ci=95,errwidth=1., capsize=.1, ax=ax2)
plt.legend(["{}% CI".format(95)])
#ax1.set_xlabel('')
#ax2.set_xlabel('')
ax1.set_ylabel('Views Growth Factor %')
ax2.set_ylabel('mean(Views Growth Factor %)')
fig.suptitle('Views Growth Factor % per Category')
#ax1.set_xticklabels([''])
#ax2.set_xticklabels([''])
ax1.set_xticklabels(ax1.get_xticklabels(),rotation=90)
ax2.set_xticklabels(ax2.get_xticklabels(),rotation=90)
fig.tight_layout()
#fig.subplots_adjust(top=0.86)
#ax1.set_ylim([-250.0, 800.0])
save_plot('collab_video_growth_factor_categories_violin_bar.pdf', fig, 3.5*s_width, 1.5*s_height)



In [171]:

    
# make plots for views etc. x days after collab
# mark history data with x day after
print df_video_history_collabs['crawlTimestamp'].dtype
df_video_history_collabs.head()









    



datetime64[ns]






    Out[171]:






  
    
      
      videoID
      viewCount
      commentCount
      likeCount
      dislikeCount
      crawlTimestamp
      gradient
      p_gradient
      collab_window
      day_after
    
    
      id
      
      
      
      
      
      
      
      
      
      
    
  
  
    
      1
      qBZKeIbNDcE
      9.0
      0
      1
      0
      2016-12-28 03:03:22
      NaN
      NaN
      Non_Collab
      -1
    
    
      2
      2A6NSm9aSVQ
      139.0
      5
      14
      2
      2016-12-28 03:03:23
      NaN
      NaN
      Non_Collab
      -1
    
    
      7
      H_PpwvS8hA8
      127.0
      5
      9
      0
      2016-12-28 03:04:08
      NaN
      NaN
      Non_Collab
      -1
    
    
      9
      lsN0rGSfXWw
      122.0
      4
      37
      0
      2016-12-28 03:04:36
      NaN
      NaN
      Non_Collab
      -1
    
    
      18
      Azqy_VVj1PI
      47536.0
      2072
      13325
      60
      2016-12-29 00:06:15
      NaN
      NaN
      Non_Collab
      -1



In [173]:

    
df_video_history_collabs['day_after'] = -1

cnt=0

for name, group in video_history_collab_group:
    
    if df_videos_collabs.ix[name, 'collab_window'] == 'Collab':
        date = group.iloc[0, 5]
        df_video_history_collabs.ix[group.index, 'day_after']  = group['crawlTimestamp'].apply(lambda x: (x-date).days)

df_video_history_collabs.head()









    Out[173]:






  
    
      
      videoID
      viewCount
      commentCount
      likeCount
      dislikeCount
      crawlTimestamp
      gradient
      p_gradient
      collab_window
      day_after
    
    
      id
      
      
      
      
      
      
      
      
      
      
    
  
  
    
      1
      qBZKeIbNDcE
      9.0
      0
      1
      0
      2016-12-28 03:03:22
      NaN
      NaN
      Non_Collab
      -1
    
    
      2
      2A6NSm9aSVQ
      139.0
      5
      14
      2
      2016-12-28 03:03:23
      NaN
      NaN
      Non_Collab
      -1
    
    
      7
      H_PpwvS8hA8
      127.0
      5
      9
      0
      2016-12-28 03:04:08
      NaN
      NaN
      Non_Collab
      -1
    
    
      9
      lsN0rGSfXWw
      122.0
      4
      37
      0
      2016-12-28 03:04:36
      NaN
      NaN
      Non_Collab
      -1
    
    
      18
      Azqy_VVj1PI
      47536.0
      2072
      13325
      60
      2016-12-29 00:06:15
      NaN
      NaN
      Non_Collab
      -1



In [203]:

    
df_video_history_collabs_test = df_video_history_collabs[(df_video_history_collabs.day_after < 6)]
df_video_history_collabs_test = df_video_history_collabs_test[(df_video_history_collabs_test.p_gradient < 10000)]
print df_video_history_collabs.day_after.unique()
print df_video_history_collabs_test.day_after.unique()









    



[ -1   0   1   2   3   4   5   6   7   8   9  10  11  12  13  14  15  16
  17  18  19  20  21  22  23  24  25  26  27  28  29  30  31  32  33  34
  35  36  37  38  39  40  41  42  43  44  45  46  47  48  49  50  51  52
  53  54  55  56  57  58  59  60  61  62  63  64  65  66  67  68  69  70
  71  72  73  74  75  76  77  78  79  80  81  82  83  84  85  86  87  88
  89  90  91  92  93  94  95  96  97  98  99 100 101 102 103 104 105 106
 107 108 109 110 111 112 113 114 115 116 117]
[-1  1  2  3  4  5  0]



In [275]:

    
fig = plt.figure()
ax = sns.boxplot(x=df_video_history_collabs_test["day_after"], y=df_video_history_collabs_test["gradient"], order=[0, 1, 2, 3, 4, 5, -1])
ax.set_xticklabels(['Day 0', 'Day 1', 'Day 2', 'Day 3', 'Day 4','Day 5', 'Non-Collab'])
ax.set_xlabel('Days')
ax.set_ylabel('Views')
ax.set_ylim([-6000.0, 120000.0])
save_plot('collab_video_6days_sep_views_box_s_zero.pdf', fig, 1.5*s_width, 1.5*s_height)



In [204]:

    
fig = plt.figure()
ax = sns.boxplot(x=df_video_history_collabs_test["day_after"], y=df_video_history_collabs_test["gradient"], order=[1, 2, 3, 4, 5, -1])
ax.set_xticklabels(['Day 1', 'Day 2', 'Day 3', 'Day 4','Day 5', 'Non-Collab'])
ax.set_xlabel('Days')
ax.set_ylabel('Views')
ax.set_ylim([-6000.0, 120000.0])
save_plot('collab_video_6days_sep_views_box_s.pdf', fig, 1.5*s_width, 1.5*s_height)

fig = plt.figure()
ax = sns.violinplot(x=df_video_history_collabs_test["day_after"], y=df_video_history_collabs_test["gradient"], order=[1, 2, 3, 4, 5, -1])
ax.set_xticklabels(['Day 1', 'Day 2', 'Day 3', 'Day 4','Day 5', 'Non-Collab'])
ax.set_xlabel('Days')
ax.set_ylabel('Views')
ax.set_ylim([-6000.0, 190000.0])
save_plot('collab_video_6days_sep_views_box_violin_s.pdf', fig, 1.5*s_width, 1.5*s_height)

fig = plt.figure()
ax1 = sns.barplot(x="day_after", y="gradient", data=df_video_history_collabs_test, ci=99, errwidth=1., capsize=.1, order=[1, 2, 3, 4, 5, -1])
ax1.set_xticklabels(['Day 1', 'Day 2', 'Day 3', 'Day 4','Day 5', 'Non-Collab'])
plt.legend(["{}% CI".format(99)])
ax1.set_ylabel('View Gradient')
ax2.set_ylabel('mean(View Gradient)')
save_plot('collab_video_6days_sep_views_ci_s.pdf', fig, 1.5*s_width, 1.5*s_height)

fig, (ax1, ax2) = plt.subplots(ncols=2)
sns.violinplot(x="collab_window", y="gradient", data=df_video_history_collabs_test, ax=ax1, order=['Collab', 'Non_Collab'])
ax1 = sns.barplot(x="collab_window", y="gradient", data=df_video_history_collabs_test,ax=ax2, ci=99, errwidth=1., capsize=.1, order=[1, 2, 3, 4, 5, -1])

plt.legend(["{}% CI".format(99)])
ax1.set_xlabel('')
ax2.set_xlabel('')
ax1.set_ylabel('View Gradient')
ax2.set_ylabel('mean(View Gradient)')
ax1.set_xticklabels(['Collaboration', 'Non-Collaboration'])
ax2.set_xticklabels(['Collaboration', 'Non-Collaboration'])
fig.suptitle('Video-based Channel View Growth (12 Days Window)')
fig.subplots_adjust(top=0.91)
fig.tight_layout()
ax1.set_ylim([-50000.0, 250000.0])
save_plot('collab_video_6days_sep_views_box_ci_violin.pdf', fig, 2*s_width, s_height)

print 'Non_Collabs:'
print df_video_history_collabs[df_video_history_collabs.collab_window=='Non_Collab']['gradient'].describe()
print '\nCollabs:'
print df_video_history_collabs[df_video_history_collabs.collab_window=='Collab']['gradient'].describe()









    














    











    














    











    














    











    














    











    



Non_Collabs:
count    3.815970e+06
mean     3.328708e+03
std      3.080674e+04
min     -8.462700e+04
25%      1.300000e+01
50%      1.040000e+02
75%      7.630000e+02
max      7.837219e+06
Name: gradient, dtype: float64

Collabs:
count    2.973410e+05
mean     4.159084e+03
std      3.633765e+04
min     -3.096500e+04
25%      1.900000e+01
50%      1.580000e+02
75%      1.212000e+03
max      5.809283e+06
Name: gradient, dtype: float64



In [202]:

    
len(df_video_history_collabs_test[df_video_history_collabs_test.p_gradient > 10000])









    Out[202]:





645



In [258]:

    
fig = plt.figure()
ax1 = sns.barplot(x="day_after", y="gradient", data=df_video_history_collabs_test, ci=99, errwidth=1., capsize=.1, order=[1, 2, 3, 4, 5, -1])
ax1.set_xticklabels(['Day 1', 'Day 2', 'Day 3', 'Day 4','Day 5', 'Non-Collab'])
plt.legend(["{}% CI".format(99)])
ax1.set_ylabel('View Gradient')
#ax1.set_ylim([-20.0, 750.0])
ax1.xaxis.label.set_visible(False)
ax1.set_yscale('log')
plt.title('Video Views Gradient')
save_plot('collab_video_6days_sep_views_ci_s.pdf', fig, 1.5*s_width, s_height)

fig = plt.figure()
ax1 = sns.barplot(x="day_after", y="p_gradient", data=df_video_history_collabs_test, ci=99, errwidth=1., capsize=.1, order=[1, 2, 3, 4, 5, -1])
ax1.set_xticklabels(['Day 1', 'Day 2', 'Day 3', 'Day 4','Day 5', 'Non-Collab'])
plt.legend(["{}% CI".format(99)])
ax1.set_ylabel('View Growth %')
ax1.xaxis.label.set_visible(False)
ax1.set_yscale('log')
plt.title('Video Views Growth %')
#ax.set_ylim([-20.0, 750.0])
save_plot('collab_video_6days_sep_views_ci_perc_s.pdf', fig, 1.5*s_width, s_height)



In [206]:

    
fig = plt.figure()
ax = sns.boxplot(x=df_video_history_collabs_test["day_after"], y=df_video_history_collabs_test["p_gradient"], order=[1, 2, 3, 4, 5, -1])
ax.set_xticklabels(['Day 1', 'Day 2', 'Day 3', 'Day 4','Day 5', 'Non-Collab'])
ax.set_xlabel('Days')
ax.set_ylabel('Views')
#ax.set_ylim([-20.0, 750.0])
save_plot('collab_video_6days_sep_views_box_perc_s.pdf', fig, 1.5*s_width, 1.5*s_height)

fig = plt.figure()
ax = sns.violinplot(x=df_video_history_collabs_test["day_after"], y=df_video_history_collabs_test["p_gradient"], order=[1, 2, 3, 4, 5, -1])
ax.set_xticklabels(['Day 1', 'Day 2', 'Day 3', 'Day 4','Day 5', 'Non-Collab'])
ax.set_xlabel('Days')
ax.set_ylabel('Views')
#ax.set_ylim([-20.0, 750.0])
save_plot('collab_video_6days_sep_views_box_perc_violin_s.pdf', fig, 1.5*s_width, 1.5*s_height)

fig = plt.figure()
ax1 = sns.barplot(x="day_after", y="p_gradient", data=df_video_history_collabs_test, ci=99, errwidth=1., capsize=.1, order=[1, 2, 3, 4, 5, -1])
ax1.set_xticklabels(['Day 1', 'Day 2', 'Day 3', 'Day 4','Day 5', 'Non-Collab'])
plt.legend(["{}% CI".format(99)])
ax.set_xlabel('Days')
ax.set_ylabel('Views')
save_plot('collab_video_6days_sep_views_ci_perc_s.pdf', fig, 1.5*s_width, 1.5*s_height)

fig, (ax1, ax2) = plt.subplots(ncols=2)
sns.violinplot(x="collab_window", y="p_gradient", data=df_video_history_collabs_test, ax=ax1, order=['Collab', 'Non_Collab'])
sns.barplot(x="collab_window", y="p_gradient", data=df_video_history_collabs_test, ci=99, errwidth=1., capsize=.1, ax=ax2, order=['Collab', 'Non_Collab'])
plt.legend(["{}% CI".format(99)])
ax1.set_xlabel('')
ax2.set_xlabel('')
ax1.set_ylabel('View Growth %')
ax2.set_ylabel('mean(View Growth %)')
ax1.set_xticklabels(['Collaboration', 'Non-Collaboration'])
ax2.set_xticklabels(['Collaboration', 'Non-Collaboration'])
fig.suptitle('Video-based Channel View Growth % (12 Days Window)')
fig.tight_layout()
fig.subplots_adjust(top=0.86)
#ax1.set_ylim([-400.0, 1000.0])
save_plot('collab_video_6days_sep_views_box_ci_perc_violin.pdf', fig, 2*s_width, s_height)

print 'Non_Collabs:'
print df_video_history_collabs[df_video_history_collabs.collab_window=='Non_Collab']['p_gradient'].describe()
print '\nCollabs:'
print df_video_history_collabs[df_video_history_collabs.collab_window=='Collab']['p_gradient'].describe()









    














    











    














    











    














    











    














    











    



Non_Collabs:
count    3.815970e+06
mean     5.027533e+01
std      3.373144e+04
min     -5.923076e+01
25%      1.008216e-01
50%      3.408586e-01
75%      1.079559e+00
max      5.090080e+07
Name: p_gradient, dtype: float64

Collabs:
count    2.973410e+05
mean     6.125236e+02
std      3.291674e+05
min     -8.309859e+01
25%      1.045746e-01
50%      3.200044e-01
75%      9.523810e-01
max      1.794914e+08
Name: p_gradient, dtype: float64



In [ ]:

	cluster	from	from_category	from_cluster	from_network	from_popularity	from_topic	to	to_category	to_cluster	to_network	to_popularity	to_topic	videos	weight
0	4806	UCw-hc7ZJummS0AvWyjUX56A	Sports	[3102, 4806, 12796]	None	3	Fitness	UCWYtZYH4kcbMm29liIOSGQQ	Sports	[2996, 3143, 5232, 7762]	BroadbandTV	3	Sports	[Q-Z7QYkqn2M]	1
1	5556	UClpEE-Led9ZK0GJQKvU--3Q	Entertainment	[5556, 5884, 7050]	Maker Studios	2	Football	UCIKF1msqN7lW9gplsifOPkQ	Entertainment	[12627]	BroadbandTV	3	Music	[On4GE5hAU8s]	1
2	5556	UClpEE-Led9ZK0GJQKvU--3Q	Entertainment	[5556, 5884, 7050]	Maker Studios	2	Football	UC3M4u8_WwqY-2xDbJXxo5eQ	Gaming	[4139, 7049]	OmniaMediaCo	3	Sports game	[4cEkXTIcvH4]	1
3	6135	UCMDz09-3zO1hm1pqRA-Er0A	Entertainment	[6135]	BroadbandTV	3	Lifestyle	UC3fxB7rF6T7wqymKUJxwmXA	Entertainment		BroadbandTV	3	Lifestyle	[C7Waq5ZgqGU]	1
4	1367	UCUcBFGAfOzut1x4GSWa6Akg	Entertainment	[1367, 4731, 5365, 6760]	Maker Studios	3	Music	UCCk_JV7ar8HlxWZRXQKgS7w	People & Blogs		BroadbandTV	2	Lifestyle	[RJir4ghyTDo]	1

	videoID	viewCount	commentCount	likeCount	dislikeCount	crawlTimestamp
id
1	qBZKeIbNDcE	9	0	1	0	2016-12-28 03:03:22
2	2A6NSm9aSVQ	139	5	14	2	2016-12-28 03:03:23
3	0hAym2XYn9w	60	3	9	0	2016-12-28 03:03:48
4	VGHx-lcdJps	258	21	64	2	2016-12-28 03:03:49
5	xYi6uCXmCFo	337	8	94	1	2016-12-28 03:03:52

	id	age	date	dayn
0	--0s03JYIww	27	2017-01-24	Tuesday
1	--1lgNZ9cvY	14	2017-01-11	Wednesday
2	--4kDK_RBjg	48	2017-02-14	Tuesday
3	--4n0eZ8XTQ	1	2016-12-29	Thursday
4	--5mUkeFBmI	56	2017-02-22	Wednesday

	channelID	title	description	category	dateAdded	tags	topicIds	attribution	duration	crawlTimestamp	deleted
id
__3e1lrV5c8	UCajyfb5EUPd3f3deUk0dBEQ	NEUE DM ZAHNBÜRSTEN PINSEL im LIVE TEST \| Mami...	Neu bei dm gibt es die gehypten Zahnbürsten Pi...	26	2017-02-19 11:30:00	["mamiseelen","dm zahnb\u00fcrsten pinsel","li...	["/m/0yxzwny","/m/012xff"]	None	PT7M41S	2017-02-20 00:10:55	None
__3Pz5Hmm4c	UCw6Ou-fRcPa23GvYq-VHEbA	IS LONZO BALL'S $495 SIGNATURE SHOE THE ZO2 WO...	ENJOY AND LEAVE A LIKE!\nPodcast Link - https:...	26	2017-05-06 04:12:59	["lonzo","ball","zo2","lonzo ball sneaker","bi...	[]	None	PT11M16S	2017-05-07 00:12:42	None
__6aGbnZHXQ	UCFdIIKy9ZQuIuRnpXLVabfQ	Provando Doce de 1 Dolar - Dollar Tree ? Vit...	? Clique Gostei, Comente, INSCR...	22	2017-04-05 23:00:01	["vitoriademattia diario8765redfwhgy24","TAG V...	[]	None	PT21M56S	2017-04-07 00:12:54	None
__9yzCbryRw	UCBkZWl8s1Cg22Yw11O6A-PQ	A Futile Effort!/Hope Incarnate! \| Made By Son...	READ THIS :D\nI really Love this Song a lot. I...	23	2017-02-12 19:32:47	["Hip Hop","Sonic The Ghetto-Hog productions",...	["/m/08b26_"]	None	PT4M10S	2017-02-13 00:10:02	None
__a6tnmzoU4	UCxL7KXCV5YW0KdPRV583VJQ	NEW YEAR NEW BABY!?	WHATS IN MY BAG? → http://bit.ly/2iO7QLz\nWATC...	22	2017-01-13 20:00:30	["new year","new years","baby","pregnant","new...	[]	None	PT7M32S	2017-01-14 00:13:27	None

	videoID	viewCount	commentCount	likeCount	dislikeCount	crawlTimestamp	gradient	p_gradient	collab_window
id
1	qBZKeIbNDcE	9.0	0	1	0	2016-12-28 03:03:22	NaN	NaN	Non_Collab
2	2A6NSm9aSVQ	139.0	5	14	2	2016-12-28 03:03:23	NaN	NaN	Non_Collab
7	H_PpwvS8hA8	127.0	5	9	0	2016-12-28 03:04:08	NaN	NaN	Non_Collab
9	lsN0rGSfXWw	122.0	4	37	0	2016-12-28 03:04:36	NaN	NaN	Non_Collab
18	Azqy_VVj1PI	47536.0	2072	13325	60	2016-12-29 00:06:15	NaN	NaN	Non_Collab

	videoID	viewCount	commentCount	likeCount	dislikeCount	crawlTimestamp	gradient	p_gradient	collab_window	day_after
id
1864	qBZKeIbNDcE	422.0	27	51	2	2016-12-29 00:11:56	413.0	4588.888889	Non_Collab	-1
1865	2A6NSm9aSVQ	8883.0	138	409	18	2016-12-29 00:11:56	8744.0	6290.647482	Non_Collab	-1
1870	H_PpwvS8hA8	10086.0	107	374	12	2016-12-29 00:11:56	9959.0	7841.732283	Non_Collab	-1
1872	lsN0rGSfXWw	1477.0	21	437	0	2016-12-29 00:11:56	1355.0	1110.655738	Non_Collab	-1
3932	Azqy_VVj1PI	115837.0	3484	30545	135	2016-12-30 00:12:13	68301.0	143.682683	Non_Collab	-1

	topicIds	network	viewCount	subscriberCount	videoCount	commentCount	category	popularity
id
UC__Pj66OeDibNZNN__L913g	Music	None	3253022	23029	967	0	Entertainment	2
UC__PZLSRGtUQiTtvm3hPoEQ	Movies	BroadbandTV	310896	5878	144	0	Entertainment	1
UC__rmdgxs3ZF0zK_he7Tmig	Lifestyle	None	1291254	8146	294	121	How-to & Style	1
UC_-CxgsxX0tpnm24WO-797Q	Lifestyle	Maker Studios	625545	18990	67	101	How-to & Style	2
UC_1FUFB6TlGeGOyDI4ikkzg	Movies	BroadbandTV	89020205	106760	288	0	Entertainment	3

	growth	category	popularity
id
UC-4kjzuh4822B9yPSgpZQgA	10.813472	Entertainment	4.0
UC-63s9JLCZqIDlhXK6VHb7w	-89.944618	Entertainment	2.0
UC-A4oZF4AlOEdlyZWBCI0cQ	-4.630593	Entertainment	4.0
UC-CRXGRabkMD7x6V_Nk9F0A	-89.551831	Film & Animation	1.0
UC-NINtNMPM75eaqh07RCy_Q	93.463303	Education	3.0