World Population Trend

World Population Trend Analysis

Download population data from data.gov

https://openei.org/doe-opendata/dataset/a7fea769-691d-4536-8ed3-471e993a2445/resource/86c50aa8-e40f-4859-b52e-29bb10166456/download/populationbycountry19802010millions.csv

Download population data from data.gov¶

https://openei.org/doe-opendata/dataset/a7fea769-691d-4536-8ed3-471e993a2445/resource/86c50aa8-e40f-4859-b52e-29bb10166456/download/populationbycountry19802010millions.csv

In [40]:

## Read data
##
import pandas as pd
pop=pd.read_csv("populationbycountry19802010millions.csv")

Cleanup¶

Fill na, rename and convert data type

In [41]:

pop=pop.fillna(0)
pop=pop.replace('--',0)
pop=pop.rename(columns={"Unnamed: 0": "geo_location"})
##
for i in range(2008,2011):
    pop[str(i)]=pop[str(i)].astype('float')

Select top 20 geo locations based on 2010 population¶

In [42]:

pop=pop.nlargest(10, '2010').reset_index(drop=True)
pop.head()

Out[42]:

	geo_location	1980	1981	1982	1983	1984	1985	1986	1987	1988	...	2001	2002	2003	2004	2005	2006	2007	2008	2009	2010
0	World	4451.32679	4534.01064	4613.94102	4694.9362	4775.05313	4856.52116	4940.1947	5026.35348	5113.1907	...	6165.32364	6241.71768	6317.4264	6393.12094	6469.12974	6545.88444	6623.57196	6700.76588	6776.91747	6853.01941
1	Asia & Oceania	2469.81743	2518.13689	2563.52187	2610.10261	2655.88059	2702.75408	2751.06581	2802.31491	2853.88173	...	3450.06017	3490.97008	3531.28903	3570.71937	3609.62956	3648.16942	3686.3589	3724.32335	3762.16099	3799.67028
2	China	984.73646	997.00072	1012.49049	1028.35653	1042.75605	1058.00772	1074.52256	1093.72571	1112.86641	...	1270.74423	1277.59472	1284.30332	1291.0018	1297.76532	1304.26188	1310.58354	1317.06568	1323.59158	1330.14129
3	India	684.8877	699.15377	713.71094	728.52155	744.01697	759.61221	775.06324	790.64043	806.3794	...	1023.29508	1040.28482	1057.25112	1074.15902	1090.97303	1107.62435	1124.1348	1140.56621	1156.89777	1173.10802
4	Africa	478.96479	493.63386	508.69882	523.66026	538.6777	553.7385	569.05927	584.06285	599.18529	...	823.10298	843.06171	863.1923	883.59024	904.41715	925.68403	947.55885	969.97221	992.58225	1015.47842

5 rows × 32 columns

Plotting¶

In [43]:

%matplotlib inline
import seaborn as sns
import matplotlib.pyplot as plt

2010 population comparison¶

In [44]:

plt.figure( figsize = (20,6))
#set style of plots
sns.set_style('white')
sns.plt.xlabel('geo_location',weight='bold',size=22)
sns.plt.xticks(weight='bold',size=15,rotation=-45,color='blue')
sns.plt.yticks(weight='bold',size=15,color='blue')
#####################################
g=sns.barplot( y = '2010',
          x = 'geo_location',
          data = pop)
##
g.set_title('World Population 2010',size=40)
g.set_ylabel('Population in Millions',size=22,weight='bold')
g.set_xlabel('Geographic Region',size=22,weight='bold')

Out[44]:

<matplotlib.text.Text at 0x9d31e10>

Population Trend Using Point plot¶

In [87]:

v=pop.columns.drop('geo_location')
popM=pd.melt(pop, id_vars='geo_location', value_vars=v)
popM['value']=popM['value'].astype('float')
#popM=popM[popM['geo_location']in('World')]
popM=popM.set_index('geo_location')
popM=popM.loc[['World','Asia & Oceania','Africa','China','India','United States']].reset_index()
popM.head()

Out[87]:

	geo_location	variable	value
0	World	1980	4451.32679
1	World	1981	4534.01064
2	World	1982	4613.94102
3	World	1983	4694.93620
4	World	1984	4775.05313

In [88]:

plt.figure( figsize = (20,8))
#set style of plots
sns.set_style('white')
sns.plt.xlabel('geo_location',weight='bold',size=22)
sns.plt.xticks(weight='bold',size=15,rotation=-90,color='blue')
sns.plt.yticks(weight='bold',size=15,color='blue')
#####################################
g=sns.pointplot(x='variable', y='value', hue='geo_location',
          data = popM)
##
g.set_title('Population Growth Trend (1980 - 2010)',size=30)
g.set_ylabel('Population in Millions',size=22,weight='bold')
g.set_xlabel('Year',size=22,weight='bold')
##
plt.setp(g.get_legend().get_texts(), fontsize='15') # for legend text
plt.setp(g.get_legend().get_title(), fontsize='18') 

Out[88]:

[None, None]

Population Trend Using Box plot¶

In [89]:

plt.figure( figsize = (20,10))
#set style of plots
sns.set_style('white')
sns.plt.xlabel('geo_location',weight='bold',size=22)
sns.plt.xticks(weight='bold',size=15,rotation=-90,color='blue')
sns.plt.yticks(weight='bold',size=15,color='blue')
#####################################
g=sns.boxplot( x = popM['geo_location'],y = popM['value'])
##
g.set_title('Population Growth Trend (1980 - 2010)',size=30)
g.set_ylabel('Population in Millions',size=22,weight='bold')
g.set_xlabel('Year',size=22,weight='bold')
##

Out[89]:

<matplotlib.text.Text at 0x21c82a58>