Data visualization experiment 2
preface
Through pandas in python, the experiment is completed with bar graph, bar graph and line graph.
Title Requirements
1. "Tourism annual data. xls" and "tourism statistical data. csv" are tourism statistical data from 2009 to 2018, including the number of domestic tourists, rate income, per capita consumption and other data. Please show the data according to the following requirements and analyze the trend of relevant data over time from the figure. (data visualization tools can be python, R, AI)
(1) Use line chart to draw the total cost data of travel agency and domestic tourism.
(2) Use the histogram to draw the per capita consumption of domestic tourism, urban and rural tourism.
(3) The number of inbound tourists, foreign tourists, Hong Kong and Macao compatriots and Taiwan compatriots' inbound tourists are drawn with bar chart.
(4) Draw the data of domestic tourists, urban and rural tourists with bar chart.
Source data
- Annual tourism data
| Database: annual data | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Time: last 10 years | ||||||||||
| index | 2018 | 2017 | 2016 | 2015 | 2014 | 2013 | 2012 | 2011 | 2010 | 2009 |
| Number of travel agencies | 27939 | 27621 | 26650 | 26054 | 24944 | 23690 | 22784 | 20399 | ||
| Total number of star hotels | 11685 | 12327 | 12803 | 13293 | 12807 | 13513 | 13991 | 14237 | ||
| Inbound tourists (10000 person times) | 14120 | 13948.24 | 13844.38 | 13382.04 | 12849.83 | 12907.78 | 13240.53 | 13542.35 | 13376.22 | 12647.59 |
| Foreign inbound tourists (10000 person times) | 2916.53 | 2815.12 | 2598.54 | 2636.08 | 2629.03 | 2719.16 | 2711.2 | 2612.69 | 2193.75 | |
| Inbound tourists from Hong Kong and Macao compatriots (10000 person times) | 10444.59 | 10456.26 | 10233.64 | 9677.16 | 9762.5 | 9987.35 | 10304.85 | 10249.48 | 10005.44 | |
| Inbound tourists of Taiwan compatriots (10000 person times) | 587.13 | 573 | 549.86 | 536.59 | 516.25 | 534.02 | 526.3 | 514.06 | 448.4 | |
| Inbound overnight tourists (10000 person times) | 6073.84 | 5926.73 | 5688.57 | 5562.2 | 5568.59 | 5772.49 | 5758.07 | 5566.45 | 5087.52 | |
| Number of domestic residents leaving the country (10000 person times) | 16199 | 14272.74 | 13513 | 12786 | 11659.32 | 9818.52 | 8318.17 | 7025 | 5738.65 | 4765.62 |
| Number of domestic residents leaving the country for private reasons (10000 person times) | 13581.56 | 12850 | 12172 | 11002.91 | 9197.08 | 7705.51 | 6411.79 | 5150.79 | 4220.97 | |
| Domestic tourists (10000 person times) | 554000 | 500100 | 444000 | 400000 | 361100 | 326200 | 295700 | 264100 | 210300 | 190200 |
| Foreign exchange income from international tourism (USD million) | 127100 | 123417 | 120000 | 113650 | 56913 | 51664 | 50028 | 48464 | 45814 | 39675 |
| Total cost of domestic tourism (100 million yuan) | 51278 | 45660.77 | 39390 | 34195.1 | 30311.86 | 26276.1 | 22706.2 | 19305.39 | 12579.77 | 10183.7 |
| Note: number of border people entering China in 2016 | ||||||||||
| Data source: National Bureau of Statistics |
- Tourism statistics
| Database: annual data | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Time: last 10 years | ||||||||||
| index | 2018 | 2017 | 2016 | 2015 | 2014 | 2013 | 2012 | 2011 | 2010 | 2009 |
| Domestic tourists (million) | 5540 | 5001 | 4440 | 4000 | 3611 | 3262 | 2957 | 2641 | 2103 | 1902 |
| Domestic tourists of urban residents (million person times) | 3677 | 3195 | 2802 | 2483 | 2186 | 1933 | 1687 | 1065 | 903 | |
| Domestic tourists of rural residents (million person times) | 1324 | 1240 | 1188 | 1128 | 1076 | 1024 | 954 | 1038 | 999 | |
| Total cost of domestic tourism (100 million yuan) | 51278 | 45660.8 | 39390 | 34195.1 | 30311.9 | 26276.1 | 22706.2 | 19305.4 | 12579.8 | 10183.7 |
| Total domestic tourism expenditure of urban residents (100 million yuan) | 37673 | 32241.3 | 27610.9 | 24219.8 | 20692.6 | 17678 | 14808.6 | 9403.8 | 7233.8 | |
| Total domestic tourism expenditure of rural residents (100 million yuan) | 7987.7 | 7147.8 | 6584.2 | 6092.1 | 5583.5 | 5028.2 | 4496.8 | 3176 | 2949.9 | |
| Per capita cost of domestic tourism (yuan) | 913 | 888.2 | 857 | 839.7 | 805.5 | 767.9 | 731 | 598.2 | 535.4 | |
| Per capita cost of domestic tourism for urban residents (yuan) | 1024.6 | 1009.1 | 985.5 | 975.4 | 946.6 | 914.5 | 877.8 | 883 | 801.1 | |
| Per capita cost of domestic tourism for rural residents (yuan) | 603.3 | 576.4 | 554.2 | 540.2 | 518.9 | 491 | 471.4 | 306 | 295.3 | |
| Data source: National Bureau of Statistics |
Coding implementation
import pandas as pd import matplotlib.pyplot as plt import os # Data root data_path = r'data/Experiment 2' def dataSet(filename, header=2, nrows=12, index_col=0, usercols=None, file_type="excel", encoding="utf-8"): if file_type == "excel": data = pd.read_excel(os.path.join(data_path, filename), header=header, nrows=nrows, index_col=index_col, usecols=usercols, encoding=encoding) elif file_type == "csv": data = pd.read_csv(os.path.join(data_path, filename), header=header, nrows=nrows, index_col=index_col, usecols=usercols, encoding=encoding) else: print("Error ") return None return data def showBarh(data, im_name, im_title,xlabel="xlabel", ylabel="ylabel"): ax = data.plot(kind='barh', title=im_title) ax.set_xlabel(xlabel) ax.set_ylabel(ylabel) plt.show() # preservation fig = ax.get_figure() fig.savefig(os.path.join(data_path, im_name + ".jpg")) def showBar(data, im_name, im_title,xlabel="xlabel", ylabel="ylabel"): ax = data.plot(kind='bar', title=im_title) ax.set_xlabel(xlabel) ax.set_ylabel(ylabel) plt.show() # preservation fig = ax.get_figure() fig.savefig(os.path.join(data_path, im_name + ".jpg")) def showCumsum(data, im_name, im_title, xlabel="xlabel", ylabel="ylabel"): ax = data.plot(title=im_title) ax.set_xlabel(xlabel) ax.set_ylabel(ylabel) plt.show() # preservation fig = ax.get_figure() fig.savefig(os.path.join(data_path, im_name + ".jpg")) if __name__ == "__main__": """ //Read data """ data1 = dataSet(filename="Annual tourism data.xls") data2 = dataSet(filename="Tourism statistics.csv", header=2, nrows=9, file_type="csv", encoding="GBK") data2 = data2.T data1 = data1.T # print(data1 ["total domestic tourism cost (100 million yuan)", "number of travel agencies]]]) """ //Experiment 2-1-1: draw the total cost data of travel agencies and domestic tourism with line chart """ showCumsum( data=data1[["Total cost of domestic tourism(100 million yuan)", "Number of travel agencies(individual)"]], im_name="Experiment 2-1-1", im_title="Total travel agency and domestic travel cost data (line chart)", xlabel="particular year", ylabel="numerical value" ) """ //Experiment 2-1-2: draw the per capita consumption of domestic tourism, urban and rural tourism with histogram. """ showBar( data2[["Per capita cost of domestic tourism(element)","Per capita cost of domestic tourism for urban residents(element)","Per capita cost of domestic tourism for rural residents(element)"]], im_name="Experiment 2-1-2", im_title="Per capita consumption (bar chart)", xlabel="particular year", ylabel="numerical value" ) """ //Experiment 2-1-3: draw the number of inbound tourists, foreigners, Hong Kong and Macao compatriots, and Taiwan compatriots. """ showBar( data1[["Inbound tourists(10000 person times)","Foreign tourists(10000 person times)","Inbound tourists from Hong Kong and Macao compatriots(10000 person times)","Inbound tourists from Taiwan compatriots(10000 person times)"]], im_name="Experiment 2-1-3", im_title="Number of inbound tourists (bar chart)", xlabel="particular year", ylabel="numerical value" ) """ //Experiment 2-1-4: draw the data of domestic tourists, urban and rural tourists with bar graph. """ showBarh( data2[["Domestic tourists(Million people)","Urban residents domestic tourists(Million people)","Rural residents domestic tourists(Million people)"]], im_name="Experiment 2-1-4", im_title="Number of inbound tourists (bar chart)", xlabel="particular year", ylabel="numerical value" ) plt.show()