Data visualization - White Snake 2: black snake robbery (2)

Catalog

Visual analysis

4、 Comments per day of the week

5、 Heat analysis of main roles

6、 Visualize map boundaries

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      The premise here is data visualization -《 White Snake 2： The green snake robbed 》（1） Has been run in , Put it here again , Easy to watch and use , See data visualization for details -《 White Snake 2： The green snake robbed 》（1） After the steps in, come and watch more continuously .

Toolkit introduction ： 

# Data processing 
import numpy as np
import pandas as pd
 
# visualization 
import matplotlib.pyplot as plt
import seaborn as sns
 
from pyecharts.charts import Map
from pyecharts import options as opts
from pyecharts.globals import ThemeType, SymbolType, ChartType

Read the form  

df = pd.read_excel(r"C:\Users\1\Desktop\ Data analysis \ White Snake ( Including provinces ) data .xlsx")

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Visual analysis

4、 Comments per day of the week

First, let's check , Comments per day in a week , The code is as follows ：

df[' Comment on time '].dt.dayofweek.value_counts()# Default 0 Monday ;1 Tuesday 

You can see Specific comments per day , among 0 The default is Monday ,1 The default is Tuesday, and so on .

Here, because the default above is different from our usual habits , So we add it 1 operation .

df[' week '] = df[' Comment on time '].dt.dayofweek+1
comment_week = df.groupby(' week ')[' Comment on '].count()

  Expand the

When making visual graphics , We can directly take the color in the system , The code is as follows

colors = sns.color_palette('husl')# You can directly take the color in the system 
colors
# The color patches in the following figure are from 0,1,2,3,4,5 Sort and index 

  Make one week_day_map Dictionary , Use... In the back map() Function to call

week_day_map = {
    1:' Monday ',2:' Tuesday ',3:' Wednesday ',4:' Thursday ',5:' Friday ',6:' Saturday ',7:' Sunday '
}
plt.figure(figsize=(12,5))#figure The pixel is 12×5
plt.plot(comment_week.index,comment_week.values,color=colors[5])
plt.bar(comment_week.index,comment_week.values,color=colors[0])
plt.ylim(0,5000)# Set up y The boundaries of the shaft 
_ = plt.xticks(comment_week.index,comment_week.index.map(week_day_map))
plt.title(' Comments per day of the week ')
plt.xlabel(' God ')
plt.ylabel(' Comment quantity ')

The results after running are as follows , We can see in a week Comments per day .

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5、 Heat analysis of main roles

Here we will list the main roles , And use join Connect . 

names = [' The small white ',' indigo plant ',' Xu Xian ',' Fahai ',' Sima ',' Sister sun ',' Tauren sect leader ',' Masked man ',' Baoqing workshop leader ',' Scholar ']

#join Splicing 
temp = ' and '.join(names)
temp

 # A string of count Method , Is to count this string , The number of times a substring appears
temp.count(' Small ')

Learned to join, We can now link comments , Use a dictionary to show their names and the number of comments received . Use histogram to show , Arranging . The code is as follows ：

content = ''.join(df[' Comment on '].values.tolist())

name_counts = []
for name in names:
    count = content.count(name)
    name_counts.append((name,count))
   

names_counter = pd.DataFrame(data=name_counts,columns=[' full name ',' frequency '])

# Arrange from large to small 
names_counter.sort_values(' frequency ',inplace=True,ascending=False)
plt.figure(figsize=(12,5))
plt.bar(names_counter[' full name '],names_counter[' frequency '])

plt.title(' Main role popularity evaluation ')
plt.xlabel(' Character name ')
plt.ylabel(' Comment frequency ')

  The running results clearly show that the majority of people comment on Xiaoqing , Scholar 、 Masked man 、 There are few Tauren sect leaders .

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6、 Visualize map boundaries

Calculate the comment volume of each province , adopt map To show the comments of various provinces , Title naming , So that users can quickly 、 See the result intuitively . The code is as follows ： 

data = df[' Province '].value_counts()

c = (
    Map(init_opts=opts.InitOpts())
    .add("",[list(z) for z in zip(data.index.tolist(),data.values.tolist())],"china")
    .set_global_opts(
        title_opts=opts.TitleOpts(title=" The geographical distribution of the number of comments "),
        visualmap_opts=opts.VisualMapOpts(max_=2209,min_=53,range_color=['pink','lightpink','blue'])
    )
)

c.render_notebook()

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