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Analysis on the principle of video barrage crawling of Bingbing station B
The data analysis
import pandas as pd
data = pd.read_excel(r"bingbing.xlsx")data.head()
Copy the codeThe user
gender
level
comments
give a like
0
Food being original
male
6
[stay][Stay][Stay] There you are hey!
158457
1
Never put off till tomorrow what you can guide THU
male
6
I am one of the only 3 followers of Bingbing
148439
2
Hello, teacher. My name is He
male
6
[add knowledge]
89634
3
Cctv.com look
A secret
6
Ice bing is coming! Are we gonna lose our jobs? [doge][doge]
118370
4
Xiamen university,
A secret
5
Welcome bingbing!!
66196
The original link
Data preprocessing
Data description
data.describe()
Copy the codelevel
give a like
count
1180.000000
1180.000000
mean
4.481356
2200.617797
std
1.041379
10872.524850
min
2.000000
1.000000
25%
4.000000
4.000000
50%
5.000000
9.000000
75%
5.000000
203.750000
max
6.000000
158457.000000
To delete a null value
data.dropna()
Copy the codeThe user
gender
level
comments
give a like
0
Food being original
male
6
[stay][Stay][Stay] There you are hey!
158457
1
Never put off till tomorrow what you can guide THU
male
6
I am one of the only 3 followers of Bingbing
148439
2
Hello, teacher. My name is He
male
6
[add knowledge]
89634
3
Cctv.com look
A secret
6
Ice bing is coming! Are we gonna lose our jobs? [doge][doge]
118370
4
Xiamen university,
A secret
5
Welcome bingbing!!
66196
.
.
.
.
.
.
1175
Black swordfish
A secret
5
A million dollars in 11 hours. How fast.
5
1176
It’s your pleasure
male
6
Bingbing fans rise rate: 1.073,000 in 11 hours, 97,500 in average per hour, 1,625 per minute, per second…
5
1177
Fast boy Treaster
male
4
I went to the bathroom during military training and forgot which team it was. After watching the video of UP, I realized that it was triple [doge]
5
1178
Very serious big bear
male
5
I think bingbing hosting the Spring Festival Gala should not be a problem. [OK]
5
1179
Fly slippers and roar
A secret
5
How a Tier 2 player became a Top 100 player on the last day of 2020
5
1180 rows × 5 columns
To delete a null value
data.drop_duplicates()
Copy the codeThe user
gender
level
comments
give a like
0
Food being original
male
6
[stay][Stay][Stay] There you are hey!
158457
1
Never put off till tomorrow what you can guide THU
male
6
I am one of the only 3 followers of Bingbing
148439
2
Hello, teacher. My name is He
male
6
[add knowledge]
89634
3
Cctv.com look
A secret
6
Ice bing is coming! Are we gonna lose our jobs? [doge][doge]
118370
4
Xiamen university,
A secret
5
Welcome bingbing!!
66196
.
.
.
.
.
.
1175
Black swordfish
A secret
5
A million dollars in 11 hours. How fast.
5
1176
It’s your pleasure
male
6
Bingbing fans rise rate: 1.073,000 in 11 hours, 97,500 in average per hour, 1,625 per minute, per second…
5
1177
Fast boy Treaster
male
4
I went to the bathroom during military training and forgot which team it was. After watching the video of UP, I realized that it was triple [doge]
5
1178
Very serious big bear
male
5
I think bingbing hosting the Spring Festival Gala should not be a problem. [OK]
5
1179
Fly slippers and roar
A secret
5
How a Tier 2 player became a Top 100 player on the last day of 2020
5
1179 rows × 5 columns
visualization
Thumb up TOP20
Df1 = data.sort_values(by=" like ", Ascending =False). Head (20) from Pyecharts import options as optsfrom Pyecharts.charts Import Barfrom pyecharts.faker import faker c1 = (Bar().add_xaxis(df1[" comment "].to_list()).add_yaxis(" likes ", Df1 [" like "].to_list(), color= faker.rand_color ()).set_global_opts(title_opts= opts.titleopts (title=" comment hot Top20"), datazoom_opts=[opts.DataZoomOpts(), opts.DataZoomOpts(type_="inside")], ) .render_notebook())c1Copy the code
Grade distribution
Value_counts ().sort_index(Ascending =False) 6 1655 5024 3123 1382 63Name: level, dtype: int64 from pyecharts import options as optsfrom pyecharts.charts import Piefrom pyecharts.faker import Faker c2 = ( Pie (), add (" ", [the list (z) for z in zip ([STR (I) for I in range (2, 7)], [63138312502165])], the radius = (" 40% ", "75%"), Title_opts = opts.titleopts (title_opts= opts.titleopts (title=" distribution "), legend_opts= opts.legendopts (Orient ="vertical", pos_top="15%", pos_left="2%"), ) .set_series_opts(label_opts=opts.LabelOpts(formatter="{b}: {c}")) .render_notebook())c2Copy the code
Gender distribution
Value_counts ().sort_index(Ascending =False) from Pyecharts import options as optsfrom Pyecharts.charts import Piefrom pyecharts. Faker import faker c4 = (Pie().add("", [list(z) for z in zip([" male "," male "," secret "], [" 404 ", '103', '673']], the radius = "40%", "75%"],). Set_global_opts (title_opts = opts. TitleOpts (title = "gender distribution"), legend_opts=opts.LegendOpts(orient="vertical", pos_top="15%", pos_left="2%"), ) .set_series_opts(label_opts=opts.LabelOpts(formatter="{b}: {c}")) .render_notebook() )c4Copy the code
Draw word cloud map
from wordcloud import WordCloudimport jiebafrom tkinter import _flattenfrom matplotlib.pyplot import imreadfrom PIL import Image, ImageDraw, ImageFontimport matplotlib.pyplot as pltwith open('stoplist.txt', 'r', encoding='utf-8') as f: StopWords = f.read()with open('.txt','r',encoding=' utF-8 ') as t: stopWord = t.read()total = stopWord.split() + stopWords.split()def my_word_cloud(data=None, stopWords=None, img=None): DataCut = data.apply(jieba.lcut) # 分词 dataAfter = datacut. apply(lambda x: WordFre = pd.series (_flatten(list(dataAfter))).value_counts() # = plt.imread(img) plt.figure(figsize=(20,20)) wc = WordCloud(scale=10,font_path='C:/Windows/Fonts/STXINGKA.TTF',mask=mask,background_color="white",) wc.fit_words(wordFre) Plt.imshow (wc) plt.axis('off')my_word_cloud(data=data[" comments "],stopWords=stopWords,img="1.jpeg")Copy the code
It’s so cold, I’m all ice – ice
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