notebook.community

Edit and run



In [1]:

    
import decimal
import random
round(random.uniform(0, 1), 3)









    Out[1]:





0.022



In [2]:

    
for n in range(20):
    print(round(random.uniform(0, 1), 2))



In [3]:

    
from random import randint
print(randint(0,100))



In [4]:

    
import decimal
import random
random.seed(9001)
for n in range(10):
    temp = round(random.uniform(0, 1), 3)
    print(temp)



In [5]:

    
import networkx as nx
import matplotlib.pyplot as plt
import numpy as np
import warnings
from custom import custom_funcs as cf
from datetime import datetime

warnings.filterwarnings('ignore')

%load_ext autoreload
%autoreload 2
%matplotlib inline
G = nx.read_gpickle('Synthetic Social Network.pkl')



In [6]:

    
nx.draw(G)



In [7]:

    
Z = nx.gnp_random_graph (20 , 0.6)



In [8]:

    
nx.draw(Z)



In [9]:

    
Z.nodes()









    Out[9]:





[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19]



In [10]:

    
Z.edges(data=True)









    Out[10]:





[(0, 19, {}),
 (0, 17, {}),
 (0, 18, {}),
 (0, 3, {}),
 (0, 4, {}),
 (0, 5, {}),
 (0, 6, {}),
 (0, 10, {}),
 (0, 12, {}),
 (0, 14, {}),
 (0, 15, {}),
 (1, 2, {}),
 (1, 3, {}),
 (1, 4, {}),
 (1, 6, {}),
 (1, 7, {}),
 (1, 10, {}),
 (1, 11, {}),
 (1, 13, {}),
 (1, 14, {}),
 (1, 15, {}),
 (1, 16, {}),
 (1, 18, {}),
 (1, 19, {}),
 (2, 18, {}),
 (2, 19, {}),
 (2, 4, {}),
 (2, 5, {}),
 (2, 7, {}),
 (2, 9, {}),
 (2, 17, {}),
 (2, 12, {}),
 (2, 14, {}),
 (3, 19, {}),
 (3, 5, {}),
 (3, 6, {}),
 (3, 17, {}),
 (3, 11, {}),
 (3, 12, {}),
 (3, 13, {}),
 (3, 14, {}),
 (4, 6, {}),
 (4, 7, {}),
 (4, 8, {}),
 (4, 9, {}),
 (4, 10, {}),
 (4, 12, {}),
 (4, 14, {}),
 (4, 15, {}),
 (4, 17, {}),
 (4, 18, {}),
 (5, 17, {}),
 (5, 6, {}),
 (5, 7, {}),
 (5, 8, {}),
 (5, 10, {}),
 (5, 13, {}),
 (5, 14, {}),
 (5, 15, {}),
 (6, 7, {}),
 (6, 8, {}),
 (6, 9, {}),
 (6, 10, {}),
 (6, 12, {}),
 (6, 13, {}),
 (6, 14, {}),
 (6, 16, {}),
 (6, 17, {}),
 (6, 18, {}),
 (6, 19, {}),
 (7, 10, {}),
 (7, 11, {}),
 (7, 13, {}),
 (7, 14, {}),
 (7, 16, {}),
 (7, 17, {}),
 (7, 19, {}),
 (8, 16, {}),
 (8, 17, {}),
 (8, 19, {}),
 (8, 9, {}),
 (8, 11, {}),
 (8, 14, {}),
 (8, 15, {}),
 (9, 16, {}),
 (9, 17, {}),
 (9, 19, {}),
 (9, 10, {}),
 (9, 11, {}),
 (9, 14, {}),
 (9, 15, {}),
 (10, 11, {}),
 (10, 16, {}),
 (10, 17, {}),
 (10, 18, {}),
 (10, 19, {}),
 (11, 16, {}),
 (11, 17, {}),
 (11, 13, {}),
 (12, 16, {}),
 (12, 18, {}),
 (12, 19, {}),
 (12, 13, {}),
 (12, 14, {}),
 (12, 15, {}),
 (13, 16, {}),
 (13, 18, {}),
 (14, 17, {}),
 (14, 18, {}),
 (14, 19, {}),
 (15, 18, {}),
 (15, 19, {}),
 (15, 16, {}),
 (16, 17, {}),
 (16, 18, {})]



In [11]:

    
Z2 = Z.to_directed()
Z2.edges()









    Out[11]:





[(0, 19),
 (0, 17),
 (0, 18),
 (0, 3),
 (0, 4),
 (0, 5),
 (0, 6),
 (0, 10),
 (0, 12),
 (0, 14),
 (0, 15),
 (1, 2),
 (1, 3),
 (1, 4),
 (1, 6),
 (1, 7),
 (1, 10),
 (1, 11),
 (1, 13),
 (1, 14),
 (1, 15),
 (1, 16),
 (1, 18),
 (1, 19),
 (2, 1),
 (2, 18),
 (2, 19),
 (2, 4),
 (2, 5),
 (2, 7),
 (2, 9),
 (2, 17),
 (2, 12),
 (2, 14),
 (3, 0),
 (3, 1),
 (3, 19),
 (3, 5),
 (3, 6),
 (3, 17),
 (3, 11),
 (3, 12),
 (3, 13),
 (3, 14),
 (4, 0),
 (4, 1),
 (4, 2),
 (4, 6),
 (4, 7),
 (4, 8),
 (4, 9),
 (4, 10),
 (4, 12),
 (4, 14),
 (4, 15),
 (4, 17),
 (4, 18),
 (5, 0),
 (5, 17),
 (5, 2),
 (5, 3),
 (5, 6),
 (5, 7),
 (5, 8),
 (5, 10),
 (5, 13),
 (5, 14),
 (5, 15),
 (6, 0),
 (6, 1),
 (6, 3),
 (6, 4),
 (6, 5),
 (6, 7),
 (6, 8),
 (6, 9),
 (6, 10),
 (6, 12),
 (6, 13),
 (6, 14),
 (6, 16),
 (6, 17),
 (6, 18),
 (6, 19),
 (7, 1),
 (7, 2),
 (7, 4),
 (7, 5),
 (7, 6),
 (7, 10),
 (7, 11),
 (7, 13),
 (7, 14),
 (7, 16),
 (7, 17),
 (7, 19),
 (8, 16),
 (8, 17),
 (8, 19),
 (8, 4),
 (8, 5),
 (8, 6),
 (8, 9),
 (8, 11),
 (8, 14),
 (8, 15),
 (9, 16),
 (9, 17),
 (9, 2),
 (9, 19),
 (9, 4),
 (9, 6),
 (9, 8),
 (9, 10),
 (9, 11),
 (9, 14),
 (9, 15),
 (10, 0),
 (10, 1),
 (10, 4),
 (10, 5),
 (10, 6),
 (10, 7),
 (10, 9),
 (10, 11),
 (10, 16),
 (10, 17),
 (10, 18),
 (10, 19),
 (11, 16),
 (11, 1),
 (11, 3),
 (11, 17),
 (11, 7),
 (11, 8),
 (11, 9),
 (11, 10),
 (11, 13),
 (12, 0),
 (12, 16),
 (12, 2),
 (12, 3),
 (12, 4),
 (12, 6),
 (12, 15),
 (12, 13),
 (12, 18),
 (12, 14),
 (12, 19),
 (13, 16),
 (13, 1),
 (13, 18),
 (13, 3),
 (13, 5),
 (13, 6),
 (13, 7),
 (13, 11),
 (13, 12),
 (14, 0),
 (14, 1),
 (14, 2),
 (14, 3),
 (14, 4),
 (14, 5),
 (14, 6),
 (14, 7),
 (14, 8),
 (14, 9),
 (14, 12),
 (14, 17),
 (14, 18),
 (14, 19),
 (15, 0),
 (15, 1),
 (15, 18),
 (15, 19),
 (15, 4),
 (15, 5),
 (15, 16),
 (15, 8),
 (15, 9),
 (15, 12),
 (16, 1),
 (16, 6),
 (16, 7),
 (16, 8),
 (16, 9),
 (16, 10),
 (16, 11),
 (16, 12),
 (16, 13),
 (16, 15),
 (16, 17),
 (16, 18),
 (17, 0),
 (17, 2),
 (17, 3),
 (17, 4),
 (17, 5),
 (17, 6),
 (17, 7),
 (17, 8),
 (17, 9),
 (17, 10),
 (17, 11),
 (17, 14),
 (17, 16),
 (18, 0),
 (18, 1),
 (18, 2),
 (18, 4),
 (18, 6),
 (18, 10),
 (18, 15),
 (18, 12),
 (18, 13),
 (18, 14),
 (18, 16),
 (19, 0),
 (19, 1),
 (19, 2),
 (19, 3),
 (19, 6),
 (19, 7),
 (19, 8),
 (19, 9),
 (19, 10),
 (19, 12),
 (19, 14),
 (19, 15)]



In [12]:

    
Z = nx.gnp_random_graph (50 , 0.10, seed=5001)



In [13]:

    
Z = Z.to_directed()
Z.edges()









    Out[13]:





[(0, 42),
 (0, 27),
 (0, 13),
 (1, 3),
 (1, 19),
 (1, 47),
 (2, 40),
 (2, 26),
 (3, 1),
 (3, 37),
 (3, 49),
 (3, 8),
 (3, 28),
 (3, 26),
 (3, 44),
 (3, 14),
 (4, 32),
 (4, 19),
 (4, 20),
 (4, 30),
 (4, 40),
 (4, 9),
 (4, 28),
 (4, 46),
 (4, 36),
 (5, 16),
 (5, 32),
 (5, 45),
 (6, 19),
 (6, 22),
 (6, 7),
 (6, 9),
 (6, 26),
 (6, 27),
 (6, 30),
 (7, 33),
 (7, 11),
 (7, 6),
 (7, 38),
 (7, 41),
 (8, 3),
 (8, 34),
 (8, 19),
 (8, 24),
 (8, 9),
 (8, 30),
 (9, 49),
 (9, 4),
 (9, 21),
 (9, 6),
 (9, 8),
 (9, 37),
 (10, 32),
 (10, 33),
 (10, 44),
 (10, 46),
 (11, 42),
 (11, 35),
 (11, 44),
 (11, 7),
 (12, 17),
 (12, 18),
 (12, 37),
 (12, 22),
 (12, 24),
 (12, 25),
 (12, 43),
 (13, 0),
 (13, 34),
 (13, 42),
 (13, 30),
 (14, 40),
 (14, 18),
 (14, 3),
 (14, 28),
 (15, 33),
 (15, 35),
 (15, 36),
 (15, 44),
 (16, 34),
 (16, 35),
 (16, 5),
 (16, 26),
 (16, 45),
 (16, 31),
 (17, 19),
 (17, 37),
 (17, 42),
 (17, 43),
 (17, 12),
 (17, 26),
 (18, 31),
 (18, 12),
 (18, 37),
 (18, 14),
 (18, 39),
 (19, 1),
 (19, 4),
 (19, 6),
 (19, 17),
 (19, 8),
 (19, 39),
 (20, 26),
 (20, 4),
 (20, 34),
 (20, 22),
 (21, 9),
 (21, 26),
 (21, 31),
 (21, 49),
 (22, 12),
 (22, 34),
 (22, 20),
 (22, 6),
 (22, 36),
 (23, 39),
 (23, 47),
 (24, 8),
 (24, 48),
 (24, 12),
 (25, 35),
 (25, 43),
 (25, 12),
 (25, 31),
 (26, 16),
 (26, 17),
 (26, 2),
 (26, 3),
 (26, 20),
 (26, 21),
 (26, 6),
 (26, 45),
 (27, 0),
 (27, 49),
 (27, 34),
 (27, 36),
 (27, 6),
 (27, 28),
 (28, 48),
 (28, 3),
 (28, 35),
 (28, 4),
 (28, 27),
 (28, 14),
 (29, 40),
 (29, 42),
 (29, 36),
 (30, 48),
 (30, 4),
 (30, 6),
 (30, 8),
 (30, 42),
 (30, 13),
 (31, 16),
 (31, 18),
 (31, 21),
 (31, 39),
 (31, 25),
 (31, 41),
 (32, 48),
 (32, 4),
 (32, 5),
 (32, 10),
 (32, 44),
 (32, 37),
 (32, 47),
 (33, 10),
 (33, 7),
 (33, 15),
 (34, 16),
 (34, 27),
 (34, 20),
 (34, 22),
 (34, 8),
 (34, 43),
 (34, 13),
 (34, 47),
 (35, 16),
 (35, 48),
 (35, 49),
 (35, 25),
 (35, 11),
 (35, 28),
 (35, 46),
 (35, 15),
 (36, 43),
 (36, 4),
 (36, 22),
 (36, 40),
 (36, 27),
 (36, 29),
 (36, 15),
 (37, 32),
 (37, 17),
 (37, 18),
 (37, 3),
 (37, 38),
 (37, 9),
 (37, 12),
 (38, 41),
 (38, 7),
 (38, 45),
 (38, 37),
 (39, 40),
 (39, 23),
 (39, 18),
 (39, 19),
 (39, 31),
 (40, 2),
 (40, 4),
 (40, 39),
 (40, 36),
 (40, 29),
 (40, 14),
 (41, 7),
 (41, 45),
 (41, 38),
 (41, 31),
 (42, 0),
 (42, 17),
 (42, 48),
 (42, 11),
 (42, 29),
 (42, 13),
 (42, 30),
 (42, 47),
 (43, 17),
 (43, 34),
 (43, 36),
 (43, 49),
 (43, 25),
 (43, 12),
 (44, 32),
 (44, 3),
 (44, 10),
 (44, 11),
 (44, 46),
 (44, 15),
 (45, 16),
 (45, 48),
 (45, 5),
 (45, 38),
 (45, 41),
 (45, 26),
 (45, 46),
 (45, 47),
 (46, 4),
 (46, 10),
 (46, 35),
 (46, 44),
 (46, 45),
 (47, 32),
 (47, 1),
 (47, 34),
 (47, 23),
 (47, 42),
 (47, 45),
 (48, 32),
 (48, 35),
 (48, 24),
 (48, 42),
 (48, 28),
 (48, 45),
 (48, 30),
 (49, 27),
 (49, 35),
 (49, 3),
 (49, 21),
 (49, 9),
 (49, 43)]



In [14]:

    
len(Z.edges())









    Out[14]:





270



In [15]:

    
nx.draw(Z)



In [16]:

    
import networkx as nx
import matplotlib.pyplot as plt
import numpy as np
import warnings
from custom import custom_funcs as cf
from datetime import datetime

warnings.filterwarnings('ignore')

%load_ext autoreload
%autoreload 2
%matplotlib inline









    



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



In [17]:

    
G = nx.gnp_random_graph(20 , 0.1, seed=5001)
G = G.to_directed()



In [18]:

    
nx.draw(G, with_labels=True)



In [19]:

    
sorted([(n,G.neighbors(n)) for n in G.nodes()], key=lambda x: len(x[1]), reverse=True)









    Out[19]:





[(16, [17, 2, 3, 7]),
 (12, [11, 17, 19]),
 (17, [16, 12, 14]),
 (19, [8, 18, 12]),
 (2, [16, 7]),
 (7, [16, 2]),
 (14, [17, 10]),
 (0, [13]),
 (1, [9]),
 (3, [16]),
 (5, [15]),
 (8, [19]),
 (9, [1]),
 (10, [14]),
 (11, [12]),
 (13, [0]),
 (15, [5]),
 (18, [19]),
 (4, []),
 (6, [])]



In [20]:

    
visited_nodes = list()
queue = [16]
pardaught = dict()
exclu = list()
path = set()
visited_nodes.extend(queue)
for node in queue:
    path.add(node)
    #print(node)
    neighbors = G.neighbors(node)
    exclu = [n for n in neighbors if n not in visited_nodes]
    print(exclu)
    pardaught[node] = exclu
    visited_nodes.extend(exclu)
    #queue.remove(node)
    queue.extend(exclu)









    



[17, 2, 3, 7]
[12, 14]
[]
[]
[]
[11, 19]
[10]
[]
[8, 18]
[]
[]
[]



In [21]:

    
newG = G.subgraph(path)
nx.draw(newG, with_labels=True)



In [22]:

    
pardaught









    Out[22]:





{2: [],
 3: [],
 7: [],
 8: [],
 10: [],
 11: [],
 12: [11, 19],
 14: [10],
 16: [17, 2, 3, 7],
 17: [12, 14],
 18: [],
 19: [8, 18]}



In [23]:

    
search = [11,19,10,17,2,3,7,12,14,8,18]
for n in range(len(search)):
    for key, values in pardaught.items():
        if search[n] in values:
            print(key)



In [24]:

    
import random
class MyRand(object):
    def __init__(self):
        self.last = None

    def __call__(self):
        r = random.randint(0, 100)
        while r == self.last:
            r = random.randint(0, 100)
        self.last = r
        return r

randint = MyRand()
lst = list()
for i in range(100):
    y = randint()
    lst.append(y)
print(lst)









    



[56, 12, 50, 30, 94, 83, 70, 10, 5, 36, 50, 20, 11, 65, 92, 40, 46, 99, 56, 51, 22, 67, 4, 96, 38, 12, 70, 5, 90, 17, 12, 24, 62, 19, 31, 34, 77, 0, 7, 64, 41, 11, 64, 42, 1, 72, 20, 55, 8, 44, 7, 95, 14, 17, 85, 67, 52, 7, 2, 69, 21, 23, 18, 74, 71, 42, 13, 12, 28, 84, 80, 23, 86, 40, 15, 26, 33, 52, 25, 100, 10, 33, 41, 48, 60, 30, 67, 40, 90, 43, 5, 28, 65, 99, 32, 65, 46, 89, 94, 65]



In [25]:

    
import pandas as pd
import numpy as np
df = pd.read_excel("Video_Prop.xlsx")
df.head()









    Out[25]:






  
    
      
      video ID
      uploader
      age
      category
      length
      views
      video rating(on a scale of 5)
      number of ratings
      number of comments
      Pi
    
  
  
    
      0
      jfACnjzXc3o
      DaMagicGenie
      1236
      Comedy
      284
      565
      2.92
      13
      13
      0.012813
    
    
      1
      uWAxLCkpPW4
      SpeedUpVideos
      1257
      Comedy
      182
      2084
      4.67
      49
      31
      0.047259
    
    
      2
      zzAFEtwIoAw
      djsvideodiarys
      1248
      Comedy
      266
      936
      2.13
      39
      13
      0.021226
    
    
      3
      3iguSOqYAVg
      techhomo
      1257
      Comedy
      180
      447
      5.00
      1
      13
      0.010137
    
    
      4
      sA2kG2p9INU
      Zombiesaurus666
      1257
      Comedy
      364
      80
      1.00
      1
      3
      0.001814



In [26]:

    
pdf = df.copy(deep = False)



In [27]:

    
pdf.drop(pdf.columns[[1,2,3,4,8,9]], axis=1, inplace=True)



In [28]:

    
pdf.head()









    Out[28]:






  
    
      
      video ID
      views
      video rating(on a scale of 5)
      number of ratings
    
  
  
    
      0
      jfACnjzXc3o
      565
      2.92
      13
    
    
      1
      uWAxLCkpPW4
      2084
      4.67
      49
    
    
      2
      zzAFEtwIoAw
      936
      2.13
      39
    
    
      3
      3iguSOqYAVg
      447
      5.00
      1
    
    
      4
      sA2kG2p9INU
      80
      1.00
      1



In [29]:

    
df.head()









    Out[29]:






  
    
      
      video ID
      uploader
      age
      category
      length
      views
      video rating(on a scale of 5)
      number of ratings
      number of comments
      Pi
    
  
  
    
      0
      jfACnjzXc3o
      DaMagicGenie
      1236
      Comedy
      284
      565
      2.92
      13
      13
      0.012813
    
    
      1
      uWAxLCkpPW4
      SpeedUpVideos
      1257
      Comedy
      182
      2084
      4.67
      49
      31
      0.047259
    
    
      2
      zzAFEtwIoAw
      djsvideodiarys
      1248
      Comedy
      266
      936
      2.13
      39
      13
      0.021226
    
    
      3
      3iguSOqYAVg
      techhomo
      1257
      Comedy
      180
      447
      5.00
      1
      13
      0.010137
    
    
      4
      sA2kG2p9INU
      Zombiesaurus666
      1257
      Comedy
      364
      80
      1.00
      1
      3
      0.001814



In [30]:

    
rdf = df.copy()



In [31]:

    
rdf.drop(rdf.columns[[1,2,3,4,8,9]], axis=1, inplace=True)



In [32]:

    
rdf.head()









    Out[32]:






  
    
      
      video ID
      views
      video rating(on a scale of 5)
      number of ratings
    
  
  
    
      0
      jfACnjzXc3o
      565
      2.92
      13
    
    
      1
      uWAxLCkpPW4
      2084
      4.67
      49
    
    
      2
      zzAFEtwIoAw
      936
      2.13
      39
    
    
      3
      3iguSOqYAVg
      447
      5.00
      1
    
    
      4
      sA2kG2p9INU
      80
      1.00
      1



In [33]:

    
df









    Out[33]:






  
    
      
      video ID
      uploader
      age
      category
      length
      views
      video rating(on a scale of 5)
      number of ratings
      number of comments
      Pi
    
  
  
    
      0
      jfACnjzXc3o
      DaMagicGenie
      1236
      Comedy
      284
      565
      2.92
      13
      13
      0.012813
    
    
      1
      uWAxLCkpPW4
      SpeedUpVideos
      1257
      Comedy
      182
      2084
      4.67
      49
      31
      0.047259
    
    
      2
      zzAFEtwIoAw
      djsvideodiarys
      1248
      Comedy
      266
      936
      2.13
      39
      13
      0.021226
    
    
      3
      3iguSOqYAVg
      techhomo
      1257
      Comedy
      180
      447
      5.00
      1
      13
      0.010137
    
    
      4
      sA2kG2p9INU
      Zombiesaurus666
      1257
      Comedy
      364
      80
      1.00
      1
      3
      0.001814
    
    
      5
      MZX0VXLq54w
      SpeedUpVideos
      1250
      Comedy
      28
      2755
      4.00
      27
      31
      0.062476
    
    
      6
      PawWL2Hml4I
      AniGamesCosplay
      1258
      Comedy
      240
      168
      5.00
      4
      2
      0.003810
    
    
      7
      7qM-_ez_5kY
      RandomCrazyChris
      1257
      People & Blogs
      52
      158
      0.00
      0
      0
      0.003583
    
    
      8
      0Ci_GcBeJpU
      ZombieDude323
      1257
      Comedy
      102
      74
      5.00
      2
      1
      0.001678
    
    
      9
      Wpgeu_dy8zw
      wiskeymikey
      1252
      Comedy
      54
      1914
      2.33
      6
      8
      0.043404
    
    
      10
      U0_CuZHtq_Y
      blubblub8
      1251
      Comedy
      25
      187
      1.00
      2
      0
      0.004241
    
    
      11
      Jwzoi05VDts
      brawlman14365
      1257
      Comedy
      44
      44
      0.00
      0
      7
      0.000998
    
    
      12
      xqdjb-XkzG4
      evithebest18
      1252
      Entertainment
      178
      314
      1.80
      5
      3
      0.007121
    
    
      13
      JYLectcOOi0
      demcity
      1250
      Comedy
      49
      151
      5.00
      1
      3
      0.003424
    
    
      14
      PCHG2tIt2RY
      lahlehluh
      1258
      Comedy
      364
      27
      0.00
      0
      0
      0.000612
    
    
      15
      v1qkmQPJIcU
      pirracingvids
      1251
      Comedy
      49
      234
      2.00
      4
      2
      0.005306
    
    
      16
      aeGBf5lnhhY
      figment1988
      987
      Entertainment
      30
      6570
      4.94
      31
      67
      0.148990
    
    
      17
      vYVGslS0itE
      AVGNVidz
      1258
      Comedy
      5
      52
      0.00
      0
      3
      0.001179
    
    
      18
      8DlZjt76Gnc
      SushieBoy
      976
      Autos & Vehicles
      246
      2323
      3.91
      32
      23
      0.052679
    
    
      19
      m4WPt9AZY9c
      AVGNVidz
      1257
      Comedy
      638
      914
      1.14
      44
      19
      0.020727
    
    
      20
      uQdqMfw9iuU
      tr0d
      493
      Entertainment
      153
      20125
      4.62
      29
      24
      0.456380
    
    
      21
      5rm-FVsBVcI
      tr0d
      493
      Entertainment
      138
      7653
      4.85
      27
      23
      0.173549
    
    
      22
      Lhj1IoVgjn8
      tr0d
      493
      Entertainment
      153
      9723
      4.79
      28
      20
      0.220491
    
    
      23
      Au0eBx05Zxc
      freedomanddemocracy
      1257
      News & Politics
      401
      602
      4.11
      9
      17
      0.013652
    
    
      24
      4yU2-LTFLf0
      JohnMcCaindotcom
      1241
      News & Politics
      64
      8646
      3.91
      138
      249
      0.196068
    
    
      25
      9-HfSY6LEqY
      votevets
      1254
      News & Politics
      38
      44097
      4.56
      143
      256
      1.000000
    
    
      26
      2EDOgG-GdPw
      campaigntvads
      1249
      News & Politics
      32
      105
      3.00
      2
      0
      0.002381
    
    
      27
      K2X8SVZNUrY
      tpmtv
      1257
      News & Politics
      114
      1255
      4.24
      21
      34
      0.028460
    
    
      28
      Fw2KxSWs0hA
      BullnBearAnalytics
      1221
      People & Blogs
      581
      203
      0.00
      0
      6
      0.004603
    
    
      29
      gO35AwPtn9o
      PatriotMcCain
      1144
      News & Politics
      242
      1407
      2.92
      12
      9
      0.031907
    
    
      30
      f2bGDgfHZvQ
      ForaTv
      1218
      News & Politics
      236
      1207
      5.00
      27
      26
      0.027371
    
    
      31
      gMTSIWXZ8ts
      JohnMcCainCampaign
      1124
      Entertainment
      294
      119
      0.00
      0
      0
      0.002699
    
    
      32
      mTSuHweJXxc
      campaigntvads
      1257
      News & Politics
      32
      38
      0.00
      0
      0
      0.000862
    
    
      33
      kD3LTtYXl2A
      BullnBearAnalytics
      1221
      People & Blogs
      243
      75
      0.00
      0
      1
      0.001701
    
    
      34
      HqlyqG6tNh0
      gangshabi
      1251
      News & Politics
      33
      708
      2.71
      7
      7
      0.016056
    
    
      35
      9_u-sWwyWI8
      ddaeghhehetar
      1258
      Pets & Animals
      238
      423
      5.00
      1
      2
      0.009592
    
    
      36
      T6jPQ6Ftesg
      thetoastedrawr
      1257
      People & Blogs
      80
      762
      3.85
      13
      22
      0.017280
    
    
      37
      rEycXsyVu_U
      jardine64
      1258
      People & Blogs
      19
      97
      5.00
      1
      2
      0.002200
    
    
      38
      SQ_HHV8vdAE
      AllisonRhodespzxwu
      1258
      People & Blogs
      175
      7
      0.00
      0
      0
      0.000159
    
    
      39
      htpulp7ZbWE
      HelenaMN
      1257
      People & Blogs
      79
      259
      3.88
      8
      6
      0.005873
    
    
      40
      7_4xqQffK8k
      weirdlover
      932
      People & Blogs
      114
      360
      4.00
      3
      4
      0.008164
    
    
      41
      CT5tGAu-rPs
      BleedingLlama
      1072
      People & Blogs
      414
      280
      4.75
      4
      3
      0.006350
    
    
      42
      jk1DSWkCa-U
      kirstykannibal
      1258
      People & Blogs
      150
      129
      5.00
      1
      3
      0.002925
    
    
      43
      MCyxepQKBUM
      aammbbeerr9
      1252
      Comedy
      30
      292
      0.00
      0
      3
      0.006622
    
    
      44
      gb0eVsg50xM
      shezzno
      1233
      Comedy
      81
      192
      1.67
      3
      2
      0.004354
    
    
      45
      saSNRofAFVE
      Calvin111117
      1170
      Education
      464
      8940
      3.67
      49
      240
      0.202735
    
    
      46
      s8wt91rs550
      giddyupgroudon
      1230
      Comedy
      52
      79
      0.00
      0
      2
      0.001792
    
    
      47
      5kC4F86fejc
      Cuso6
      1205
      Comedy
      67
      1047
      1.33
      6
      11
      0.023743
    
    
      48
      ywUYymEQALo
      lightanddarklove
      1140
      People & Blogs
      579
      330
      5.00
      1
      3
      0.007484
    
    
      49
      5uc9WMwzaiY
      MusicalMadlibs
      1040
      Entertainment
      340
      1731
      4.92
      13
      2
      0.039254



In [34]:

    
#sorted([row['views'] for index, row in df.iterrows()], key=lambda x: x[1], reverse=True)
vids3 = sorted([(index,row['views']) for index, row in df.iterrows()], key=lambda x: x[1], reverse=True)
vids3









    Out[34]:





[(25, 44097),
 (20, 20125),
 (22, 9723),
 (45, 8940),
 (24, 8646),
 (21, 7653),
 (16, 6570),
 (5, 2755),
 (18, 2323),
 (1, 2084),
 (9, 1914),
 (49, 1731),
 (29, 1407),
 (27, 1255),
 (30, 1207),
 (47, 1047),
 (2, 936),
 (19, 914),
 (36, 762),
 (34, 708),
 (23, 602),
 (0, 565),
 (3, 447),
 (35, 423),
 (40, 360),
 (48, 330),
 (12, 314),
 (43, 292),
 (41, 280),
 (39, 259),
 (15, 234),
 (28, 203),
 (44, 192),
 (10, 187),
 (6, 168),
 (7, 158),
 (13, 151),
 (42, 129),
 (31, 119),
 (26, 105),
 (37, 97),
 (4, 80),
 (46, 79),
 (33, 75),
 (8, 74),
 (17, 52),
 (11, 44),
 (32, 38),
 (14, 27),
 (38, 7)]



In [35]:

    
lst = list()
lst = [b for a,b in vids3]
lst



In [36]:

    
krange = list(range(1,len(lst) + 1))
print(krange)









    



[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]



In [37]:

    
import matplotlib.pyplot as plt
%matplotlib inline
plt.plot(krange,lst)
plt.xlabel('Rank')
plt.ylabel('Number of views')









    Out[37]:





<matplotlib.text.Text at 0x2bef81c6c18>



In [41]:

    
import numpy as np
x = np.random.randn(1,100)
print(x)









    



[[ 0.94729684  0.46909183  1.00115453 -0.61092595  0.92170068 -0.05816396
   1.40857548 -2.66626627 -2.60580196  1.70645506  0.0598769   0.58695552
   1.95258386 -0.58434851  0.05227812 -0.0271746  -0.87848271 -0.17056802
   0.38946828 -1.20282083  0.39153321 -0.75419866 -1.43372805 -0.82690073
  -0.5132132   0.65046054 -0.77968724  0.05294815  0.44574308 -0.02649773
   0.77866493  0.02541286  0.14700094  0.7429194  -0.81105169 -1.77753523
  -0.19225271  0.1213036  -0.48875408 -1.03465145  0.77332018  1.20635718
  -0.07757698  0.08924921  1.66056549  0.2845944  -1.34159463 -1.67160067
  -1.31347646 -0.34864679  0.23036932 -0.65969956 -1.28685391  0.12806982
   1.30327969  0.676675    0.5902247  -0.35708839 -1.59475668 -0.83244971
   0.53106679 -0.9214789  -0.25576786 -1.96184117 -1.46243881 -0.27714077
  -0.30989363 -0.07662866  0.48565302  1.03299212 -0.24838862  0.02786055
  -0.32477735  0.6992962  -0.42289343 -0.09306609 -0.01258436  0.74099279
  -0.53242132  0.46907114  1.01168721  0.01012836  0.17324436  1.42620656
   0.49350766 -0.2556211   0.68297439 -0.62317906 -0.36468559  0.92255132
   0.36889287 -1.20631449  0.684765   -0.16585046 -0.72872202  0.57842877
   0.54482401  0.91351162  0.10329881  0.14555765]]



In [50]:

    
plt.plot(np.linspace(0,1, len(krange)), lst)









    Out[50]:





[<matplotlib.lines.Line2D at 0x2bef7b14438>]



In [1]:

    
import random
lst = list()
for i in range(1000):
    lst.append(random.randint(0,999))
lst



In [6]:

    
lst.sort()
len(set(lst))









    Out[6]:





622



In [7]:

    
lst2 = list()
for i in range(1000):
    temp = random.randint(0,999)
    while temp in lst2:
        temp = random.randint(0,999)
    lst2.append(temp)
lst2



In [9]:

    
len(set(lst2))









    Out[9]:





1000



In [10]:

    
lst3 = list()
lsttemp = list()
for i in range(1000):
    temp = random.randint(0,999)
    while temp in lsttemp:
        temp = random.randint(0,999)
    lsttemp.append(temp)
    if len(lsttemp) == 50:
        lst3.extend(lsttemp)
        del lsttemp[:]
lst3



In [11]:

    
len(set(lst3))









    Out[11]:





623



In [61]:

    
lsttemp = list(range(10))



In [62]:

    
lsttemp









    Out[62]:





[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]



In [63]:

    
lsttemp.reverse()



In [64]:

    
lsttemp









    Out[64]:





[9, 8, 7, 6, 5, 4, 3, 2, 1, 0]



In [65]:

    
sum=0
for i in range(len(lsttemp)):
    temp = lsttemp[i]
    lsttemp[i] = lsttemp[i] + sum
    sum += temp



In [66]:

    
lsttemp









    Out[66]:





[9, 17, 24, 30, 35, 39, 42, 44, 45, 45]



In [58]:

    
import pandas as pd
import numpy as np
df = pd.read_excel("lastlast.xlsx")
df.head()









    Out[58]:






  
    
      
      id
      age
      length
      nviews
      rating
      nratings
      ncomments
    
  
  
    
      0
      IoHxJX92FEg
      1132
      217
      1157
      3.6
      5
      3
    
    
      1
      P7GBN4FiNDg
      1031
      426
      667
      4.0
      4
      4
    
    
      2
      GAP-8FZw3uw
      1131
      237
      1063
      0.0
      0
      1
    
    
      3
      O3Xp3-RYSu8
      1130
      294
      274
      1.0
      1
      2
    
    
      4
      gujUKcGpQEo
      1072
      109
      48
      5.0
      2
      1



In [59]:

    
#df['age'] =df['age'].astype(int)
#df['length'] =df['length'].astype(int)
#df['nviews'] =df['nviews'].astype(int)
#df['nratings'] =df['nratings'].astype(int)
#df['ncomments'] =df['ncomments'].astype(int)



In [60]:

    
df.ix[800]









    Out[60]:





id           lFptybbietQ
age                 1247
length               176
nviews              2199
rating              4.93
nratings             113
ncomments             65
Name: 800, dtype: object



In [61]:

    
#df.drop(df.columns[[7,8]], axis=1, inplace=True)



In [62]:

    
df.ix[800]









    Out[62]:





id           lFptybbietQ
age                 1247
length               176
nviews              2199
rating              4.93
nratings             113
ncomments             65
Name: 800, dtype: object



In [63]:

    
vidsdf = [(index,row['nviews'],row['nratings'],row['ncomments'],row['rating']) for index, row in df.iterrows()]



In [64]:

    
#print(vidsdf)



In [65]:

    
#from operator import itemgetter
#vidsdfsorted = sorted(vidsdf, key=lambda x: x[1], reverse=True)
vidsdf.sort(key = lambda ele : ele[1], reverse=True)



In [66]:

    
#print(vidsdfsorted)
#print(vidsdf)



In [67]:

    
#nviewslst = [b for a,b,c,d in vidsdfsorted]
nviewslst = [b for a,b,c,d,e in vidsdf]



In [68]:

    
#nviewslst.sort(reverse=True)
#print(nviewslst)



In [69]:

    
#nratingslst = [c for a,b,c,d in vidsdfsorted]
nratingslst = [c for a,b,c,d,e in vidsdf]
#print(nratingslst)



In [70]:

    
#ncommentslst = [d for a,b,c,d in vidsdfsorted]
ncommentslst = [d for a,b,c,d,e in vidsdf]
#print(ncommentslst)



In [71]:

    
ratingslst = [e for a,b,c,d,e in vidsdf]
#print(ratingslst)



In [72]:

    
%autosave 10









    














    



Autosaving every 10 seconds



In [73]:

    
krange = list(range(len(nviewslst)))
#print(krange)



In [74]:

    
t = np.linspace(0,1, len(krange))



In [75]:

    
from numpy import *
import math
import matplotlib.pyplot as plt



In [76]:

    
nviewslst[0]









    Out[76]:





3281256



In [77]:

    
plt.plot(t,nviewslst,'r', label='Number of views')
plt.legend(loc='upper right')
plt.ylim(0, 500000)
plt.show()



In [78]:

    
nratingslst[0]









    Out[78]:





3076



In [79]:

    
plt.plot(t,nratingslst,'b', label='Number of ratings')
plt.legend(loc='upper right')
plt.ylim(0, 6000)
plt.show()



In [80]:

    
ncommentslst[0]









    Out[80]:





1266



In [81]:

    
plt.plot(t,ncommentslst,'g', label='Number of comments')
plt.legend(loc='upper right')
plt.ylim(0, 6000)
plt.show()



In [88]:

    
print(ratingslst)









    



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In [2]:

    
import matplotlib.pyplot as plt
import matplotlib.patches as patches
x = [6,9,4,7,3]
y = [4,9,3,5,3]
# build a rectangle in axes coords
left, width = .25, .5
bottom, height = .25, .5
right = left + width
top = bottom + height

fig = plt.figure()
ax = fig.add_axes([0,0,1,1])

# axes coordinates are 0,0 is bottom left and 1,1 is upper right
p = patches.Rectangle(
    (left, bottom), width, height,
    fill=False, transform=ax.transAxes, clip_on=False
    )

ax.add_patch(p)

ax.text(right, top, 'right top',
        horizontalalignment='right',
        verticalalignment='top',
        transform=ax.transAxes)
ax.set_axis_off()
plt.show()



In [3]:

    
import numpy as np
import matplotlib.pyplot as plt
x = [6,9,4,7,3]
y = [4,9,3,5,3]
plt.text(0.5, 0.25, 'Brackmard minimum')
plt.plot(x, y, c = 'k')
plt.show()



In [4]:

    
import numpy as np
import matplotlib.pyplot as plt
X = np.linspace(-4, 4, 1024)
Y = .25 * (X + 4.) * (X + 1.) * (X - 2.)
plt.text(-0.5, -0.25, 'Brackmard minimum')
plt.plot(X, Y, c = 'k')
plt.show()



In [9]:

    
plt.text(10.75, 10.75, 'PP=0.87 \n SC=0.76')
plt.scatter(x, y, c = 'k')
plt.show()



In [ ]:

	video ID	uploader	age	category	length	views	video rating(on a scale of 5)	number of ratings	number of comments	Pi
0	jfACnjzXc3o	DaMagicGenie	1236	Comedy	284	565	2.92	13	13	0.012813
1	uWAxLCkpPW4	SpeedUpVideos	1257	Comedy	182	2084	4.67	49	31	0.047259
2	zzAFEtwIoAw	djsvideodiarys	1248	Comedy	266	936	2.13	39	13	0.021226
3	3iguSOqYAVg	techhomo	1257	Comedy	180	447	5.00	1	13	0.010137
4	sA2kG2p9INU	Zombiesaurus666	1257	Comedy	364	80	1.00	1	3	0.001814

	id	age	length	nviews	rating	nratings	ncomments
0	IoHxJX92FEg	1132	217	1157	3.6	5	3
1	P7GBN4FiNDg	1031	426	667	4.0	4	4
2	GAP-8FZw3uw	1131	237	1063	0.0	0	1
3	O3Xp3-RYSu8	1130	294	274	1.0	1	2
4	gujUKcGpQEo	1072	109	48	5.0	2	1