Public account: You and the cabin by: Peter Editor: Peter

Hello, I’m Peter

The national tourist city has finally been updated! Today brings is: there is heaven, there is Suzhou hangzhou: Suzhou to 😄 finally squeezed out the time to suzhou’s tourist attractions and food guide finished writing.

Peter: I visited Suzhou once many years ago, so I still remember it. Up to now, I still remember going to several scenic spots: Guanqian Street, Lingering Garden, Suzhou Museum…

Personally feel suzhou this city or pretty good, is really representative of jiangnan city. Arrive suzhou station, see suzhou moat, river street adjacent; Go to the city, the house low eaves, white walls black tiles, old streets and gardens everywhere; Seeing all kinds of gardens and historic sites, the sense of history is thick; I heard the local people speak softly and softly.

I will definitely visit Suzhou again sometime in the future

suzhou

Suzhou is a highly developed area in China’s economy. It is the economic center, industrial and commercial center and logistics center of Jiangsu Province, as well as an important center of finance, culture, art, education and transportation. The map below, from Wikipedia, shows the county-level cities and districts that make up Suzhou. Consolidate your geography

The data source

There are two data in this paper: Suzhou cuisine and Suzhou scenic spots. The data is obtained by crawler, and there are data acquisition methods at the end of this article.

Solemnly declare: The data in this paper is only used for data analysis and visualization, not for other purposes; If reproduced, please indicate the source ~

Effect of data

We present the data analysis of scenic spots in Suzhou. The highlight of this article is that all the graphics use the visualization library: Pyecharts, which is a domestic visualization library

Suzhou attractions

The data analysis of Suzhou scenic spots is mainly carried out from the following aspects:

Import libraries

import pandas as pd
import re

Display all columns
# pd.set_option('display.max_columns', None)

# display all rows
# pd.set_option('display.max_rows', None)

# set the display length of value to 100, default to 50
# pd.set_option('max_colwidth',100)

# Drawing correlation
import jieba
import matplotlib.pyplot as plt
from pyecharts.globals import CurrentConfig, OnlineHostType   # Import in advance, prevent the graph
from pyecharts import options as opts  # configuration items
from pyecharts.charts import Bar, Pie, Line, HeatMap, Funnel, WordCloud, Grid, Page  # Class of each graph
from pyecharts.commons.utils import JsCode   
from pyecharts.globals import ThemeType,SymbolType
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Omit the relevant data import and data exploration sections and focus on seeing the results of the data analysis. The data of scenic spots in Suzhou is 2000*8

Attractions distribution

The number of scenic spots in various places of Suzhou is shown here:

c = (
    Pie(init_opts=opts.InitOpts(theme=ThemeType.CHALK))
    .add(""[list(z) for z in zip(df2["location"].tolist(), df2["number"].tolist())])
    .set_global_opts(title_opts=opts.TitleOpts(title="Scenic Spots in Suzhou"),
                    legend_opts=opts.LegendOpts(pos_left="80%", orient="vertical"))
    .set_series_opts(label_opts=opts.LabelOpts(formatter="{b}: {c}"))
)

c.render_notebook()
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Top 10 places to visit

A lot of scenic spots have tourists write a guide number, we can take the guide number as a reference:

Scenic spot comments

Percentage of travelers

The percentage of travelers represents the approximate number of tourists who have visited the same spot:

Garden city

Suzhou is a garden city. We can see from the data that there are 457 scenic spots related to gardens in Suzhou. More famous is:

  • Humble administrator’s garden
  • Lingering garden
  • Master of the nets garden

The ancient city streets

Suzhou is also a city with a heavy sense of history. The data shows that there are 106 streets:

  • Shan street,
  • Pingjiang Road Historical District
  • Guanqian street

temple

The figures also show that suzhou has 96 temples, the most famous of which is Hanshan Temple

Moon wu Ti frost all over the sky, jiangfeng fishing fire to sorrow sleep, outside the city of Gusu Hanshan Temple, midnight bell to the passenger ship

Scenic spot word cloud map

The Chinese names and profiles of scenic spots we obtained are displayed in the word cloud:

1, all word cloud display

2. Cut the top 50 high-frequency words

Through the display of two kinds of word cloud maps, we found that among the scenic spots in Suzhou:

  1. There are more parks
  2. It is also rich in museums, such as museums, exhibition halls, art galleries and so on
  3. Rich in culture: culture, conservation, architecture, etc

Suzhou cuisine

Suzhou food data has 2000 items and 6 fields.

Score distribution

The distribution of the score field. A score of 0.0 means no score

From the picture above, we can see that many stores have no ratings. Next, let’s take a look at the top 10 stores with a score of 5.

  • steak
  • Well-known hotel
  • The coffee shop

Average price of consumption

Statistical analysis of field “average price”, classification rules:

def price(x) :
    if x < 20:
        return "It's cheap."
    if x < 50:
        return "Affordable"
    if x < 100:
        return "Acceptable"
    if x < 200:
        return "Higher per capita consumption"
    else:
        return "Fancy Restaurant"
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Use the following code to draw the pie chart:

c = (
    Pie(init_opts=opts.InitOpts(theme=ThemeType.CHALK))
    .add(""[list(z) for z in zip(df5["Classification"].tolist(), df5["The number"].tolist())])
    .set_global_opts(title_opts=opts.TitleOpts(title="Proportion Distribution of the average Price of gourmet food Stores in Suzhou",subtitle="Note: Excluding stores without average price"),
                    legend_opts=opts.LegendOpts(pos_left="80%", orient="vertical"))
    .set_series_opts(label_opts=opts.LabelOpts(formatter="{b}: {c}"))
)

c.render_notebook()
Copy the code

Expensive restaurant

Through the descending order of the average price field, let’s see where are the high consumption places in Suzhou?

The hotel

According to the data, there are 86 hotels in Suzhou, among which the famous hotels are as follows:

  • Intercontinental Suzhou
  • Jinling Hotel Garden Hotel
  • Shilla Hotel Suzhou Jinji Lake

Noodle shop

There are 117 noodle restaurants in total, and if you’re a noodle lover, these are the places you should not miss:

  • Old Tung Wu Noodle Restaurant (Mooli Road Shop)
  • The focal plane
  • Tong Dexing Boutique Noodle Shop (Kwun Qian Street Store)

Gourmet shop statistics

From the noodle shop, barbecue point, hotel, hot pot string of 8 different aspects of the statistics of food shops:

Food word cloud

The word cloud map of Suzhou is mainly to display the field of “recommended dishes” in the data to see what local people like.

1. All word clouds

2. Top 50 words

As can be seen from the word cloud map, suzhou people’s taste is still very light. Among the tourist recommendations:

  • Beef is the main dish
  • Dishes: braised (meat), steamed and roasted
  • Love shrimp: shrimp, shrimp, river shrimp and so on
  • Like seafood: especially fish, squid, salmon and so on

Suzhou is located in Taihu Lake, so you can’t miss hairy crabs.

conclusion

After watching the above analysis of suzhou’s tourist attractions and cuisines, the must-see places to visit if you visit Suzhou:

  1. Gardens: Humble Administrator’s Garden and Lingering Garden
  2. Temples: Hanshan Temple, Zhongyuan Temple
  3. Street: Shantang Street, Pingjiang Road historical Block, Guanqian Street
  4. Suzhou Museum

What you can’t miss:

  1. Suzhou beef (beef powder, beef crisp, etc.) can be tasted
  2. Shrimp and crab: There are many aquatic products in taihu area, such as shrimp and hairy crab
  3. Pasta: You can’t miss wonton or fried bun. Peter: Yes, I had a small wonton named “Le Hui”. It was very delicious

Welfare to come up: data analysis source code access, pay attention to you and hut, reply to Suzhou can obtain the following information, has been packaged:

1, 2: Data analysis code of suzhou scenic spots and delicacies

3: The list of stop words Peter has been using is also being added

4, 5: Raw data of scenic spots and delicacies