AppliedPlotting-Assignment 4 Solved

This assignment requires that you to find at least two datasets on the web which are related, and that you visualize these datasets to answer a question with the broad topic of economic activity or measures (see below) for the region of Ann Arbor, Michigan, United States, or United States more broadly.

You can merge these datasets with data from different regions if you like! For instance, you might want to compare Ann Arbor, Michigan, United States to Ann Arbor, USA. In that case at least one source file must be about Ann Arbor, Michigan, United States.

You are welcome to choose datasets at your discretion, but keep in mind they will be shared with your peers, so choose appropriate datasets. Sensitive, confidential, illicit, and proprietary materials are not good choices for datasets for this assignment. You are welcome to upload datasets of your own as well, and link to them using a third party repository such as github, bitbucket, pastebin, etc. Please be aware of the Coursera terms of service with respect to intellectual property.

Also, you are welcome to preserve data in its original language, but for the purposes of grading you should provide english translations. You are welcome to provide multiple visuals in different languages if you would like!

As this assignment is for the whole course, you must incorporate principles discussed in the first week, such as having as high data-ink ratio (Tufte) and aligning with Cairo’s principles of truth, beauty, function, and insight.

Here are the assignment instructions:

•     State the region and the domain category that your data sets are about (e.g., Ann Arbor, Michigan, United States and economic activity or measures).

•     You must state a question about the domain category and region that you identified as being interesting.

•     You must provide at least two links to available datasets. These could be links to files such as CSV or Excel files, or links to websites which might have data in tabular form, such as Wikipedia pages.

•     You must upload an image which addresses the research question you stated. In addition to addressing the question, this visual should follow Cairo’s principles of truthfulness, functionality, beauty, and insightfulness.

•     You must contribute a short (1-2 paragraph) written justification of how your visualization addresses your stated research question.

What do we mean by economic activity or measures? For this category you might look at the inputs or outputs to the given economy, or major changes in the economy compared to other regions.

1.1        Tips
•     Wikipedia is an excellent source of data, and I strongly encourage you to explore it for new data sources.

•     Many governments run open data initiatives at the city, region, and country levels, and these are wonderful resources for localized data sources.

•     Several international agencies, such as the United Nations, the World Bank, the Global Open Data Index are other great places to look for data.

•     This assignment requires you to convert and clean datafiles. Check out the discussion forums for tips on how to do this from various sources, and share your successes with your fellow students!

1.2        Example
Looking for an example? Here’s what our course assistant put together for the Ann Arbor, MI,

USA area using sports and athletics as the topic. Example Solution File

In [1]: import pandas as pd

import matplotlib.pyplot as plt

url_births='https://raw.githubusercontent.com/hamzaelanssari/dataset_birth_ df_births=pd.read_csv(url_births) url_deaths='https://raw.githubusercontent.com/hamzaelanssari/dataset_birth_ df_deaths=pd.read_csv(url_deaths)

In [12]: ''' World Arab df_ARB_births=df_births[df_births['Country Code']=='ARB'] df_ARB_deaths=df_deaths[df_deaths['Country Code']=='ARB']

# Caribbean Countries df_CSS_births=df_births[df_births['Country Code']=='CSS'] df_CSS_deaths=df_deaths[df_deaths['Country Code']=='CSS']

# Central Europe and the Baltics df_CEB_births=df_births[df_births['Country Code']=='CEB'] df_CEB_deaths=df_deaths[df_deaths['Country Code']=='CEB']

#East Asia & Pacific

df_EAS_births=df_births[df_births['Country Code']=='EAS'] df_EAS_deaths=df_deaths[df_deaths['Country Code']=='EAS']

#European Union df_EUU_births=df_births[df_births['Country Code']=='EUU'] df_EUU_deaths=df_deaths[df_deaths['Country Code']=='EUU']

#Latin America & Caribbean df_LCN_births=df_births[df_births['Country Code']=='LCN'] df_LCN_deaths=df_deaths[df_deaths['Country Code']=='LCN']

#North America df_NAC_births=df_births[df_births['Country Code']=='NAC'] df_NAC_deaths=df_deaths[df_deaths['Country Code']=='NAC'] '''

Out[12]: "\nWorld Arab\ndf_ARB_births=df_births[df_births['Country Code']=='ARB']\n

In [13]: df_births.rename(columns={'Value': 'Value_Births'},inplace=True) df_deaths.rename(columns={'Value': 'Value_Deaths'},inplace=True)

In [14]: #Check empty Birth_Data df_births.isnull().sum()

#Other method df_birth.isnull().values.any()

#Check empty Deaths_Data df_deaths.isnull().sum()

Out[14]: Country Name                           0

                              Country Code                0

                            Year                                     0

Value_Deaths         0 dtype: int64

In [17]: #USA df_USA_births=df_births[df_births['Country Code']=='USA'] df_USA_deaths=df_deaths[df_deaths['Country Code']=='USA']

#CHINA df_CHN_births=df_births[df_births['Country Code']=='CHN'] df_CHN_deaths=df_deaths[df_deaths['Country Code']=='CHN']

#INDIA df_IND_births=df_births[df_births['Country Code']=='IND'] df_IND_deaths=df_deaths[df_deaths['Country Code']=='IND']

In [5]:

In [48]: # merge data of births with data of deaths

# USA

df_USA=pd.merge(df_USA_births,df_USA_deaths,on=['Year','Country Code','Cou df_USA.set_index('Year',inplace=True)

# CHINA

df_CHN=pd.merge(df_CHN_births,df_CHN_deaths,on=['Year','Country Code','Cou df_CHN.set_index('Year',inplace=True)

# INDIA

df_IND=pd.merge(df_IND_births,df_IND_deaths,on=['Year','Country Code','Cou df_IND.set_index('Year',inplace=True)

# Set Axis

axis=df_USA.index.tolist() df_CHN

Out[48]:                                            Country Name Country Code Value_Births Value_Deaths

Year

1960                                     China        CHN       20.86     25.43

1961                                     China        CHN       18.02     14.24

1962                                     China        CHN       37.01     10.02

1963                                     China        CHN       43.37     10.04

1964                                     China        CHN       39.14     11.50

1965                                     China        CHN       37.88     9.50

1966                                     China        CHN       35.05     8.83

1967                                     China        CHN       33.96     8.43

1968                                     China        CHN       35.59     8.21

1969                                     China        CHN       34.11     8.03

1970                                     China        CHN       33.43     7.60

1971                                     China        CHN       30.65     7.32

1972                                     China        CHN       29.77     7.61

1973                                     China        CHN       27.93     7.04

1974                                     China        CHN       24.82     7.34

1975                                     China        CHN       23.01     7.32

1976                                     China        CHN       19.91     7.25

1977                                     China        CHN       18.93     6.87

1978                                     China        CHN       18.25     6.25

1979                                     China        CHN       17.82     6.21

1980                                     China        CHN       18.21     6.34

1981                                     China        CHN       20.91     6.36

1982                                     China        CHN       22.28     6.60

1983                                     China        CHN       20.19     6.90

1984                                     China        CHN       19.90     6.82

1985                                     China        CHN       21.04     6.78

1986                                     China        CHN       22.43     6.86

1987                                     China        CHN       23.33     6.72

1988                                     China        CHN       22.37     6.64

1989                                     China        CHN       21.58     6.54

1990                                     China        CHN       21.06     6.67

1991                                     China        CHN       19.68     6.70

1992                                     China        CHN       18.27     6.64

1993                                     China        CHN       18.09     6.64

1994                                     China        CHN       17.70     6.49

1995                                     China        CHN       17.12     6.57

1996                                     China        CHN       16.98     6.56

1997                                     China        CHN       16.57     6.51

1998                                     China        CHN       15.64     6.50

1999                                     China        CHN       14.64     6.46

2000                                     China        CHN       14.03     6.45

2001                                     China        CHN       13.38     6.43

2002                                     China        CHN       12.86     6.41

2003                                     China        CHN       12.41     6.40

2004                                     China        CHN       12.29     6.42

2005                                     China        CHN       12.40     6.51

2006                                     China        CHN       12.09     6.81

2007                                     China        CHN       12.10     6.93

2008                                     China        CHN       12.14     7.06

2009                                     China        CHN       12.13     7.08

2010                                     China        CHN       11.90     7.11

2011                                     China        CHN       11.93     7.14

2012                                     China        CHN       12.10     7.15

2013                                     China        CHN       12.08     7.16

2014                                     China        CHN       12.37     7.16

2015                                     China        CHN       12.07     7.11

2016                                     China        CHN       12.00     7.30

In [47]: fig, ax = plt.subplots(1, figsize=(10, 7))

#colors = ['green', 'red']

#ax.axis(ymin=0,ymax=100)

# USA ax.plot(axis,df_USA['Value_Births'].tolist(),alpha = 0.8, label = 'USA bir ax.plot(axis,df_USA['Value_Deaths'].tolist(),alpha = 0.8, label = 'USA dea

# CHINA ax.plot(axis,df_CHN['Value_Births'].tolist(),alpha = 0.8, label = 'China b ax.plot(axis,df_CHN['Value_Deaths'].tolist(),alpha = 0.8, label = 'China d

# INDIA ax.plot(axis,df_IND['Value_Births'].tolist(),alpha = 0.8, label = 'India b ax.plot(axis,df_IND['Value_Deaths'].tolist(),alpha = 0.8, label = 'India d ax.legend(loc ='best', frameon=False,fontsize=13) ax.set_xlabel('Years',fontsize=15) ax.set_ylabel('Birth and death rate per 1000 people ',fontsize=15) fig.suptitle('Births Vs Deaths between 1960-2016',fontsize=17) plt.show()