What will we cover
- You want to map data from an HTML table to an interactive map.
- The data is not clean and it is difficult to map data to countries, as they are often called different.
Step 1: Using Pandas to read the data
We will look at a table of data from wikipedia.org. In this example we will look at the data from average human heights by country.
Inspecting the first few columns you see a few issues already. There is some data missing and some countries are represented more than once.
To simplify our exercise we will only look at Average male height.
Let’s use pandas to read the content and inspect it. If you are new to pandas I can recommend the this post.
To read the content you can use the read_html(url) call from the pandas library. You need to instal lxml as well, see this post of details.
import pandas as pd
# The URL we will read our data from
url = 'https://en.wikipedia.org/wiki/Average_human_height_by_country'
# read_html returns a list of tables from the URL
tables = pd.read_html(url)
# The data is in the first table
table = tables[0]
print(table[:20])
Which will result in the following output.
Country/Region Average male height ... Year Source
0 Albania 174.0 cm (5 ft 8 1⁄2 in) ... 2008–2009 [11][12]
1 Argentina NaN ... 2004–2005 [13]
2 Argentina 174.46 cm (5 ft 8 1⁄2 in) ... 1998–2001 [14]
3 Armenia NaN ... 2005 [15]
4 Australia 175.6 cm (5 ft 9 in) ... 2011–2012 [16]
5 Austria 179 cm (5 ft 10 1⁄2 in) ... 2006 [17]
6 Azerbaijan 171.8 cm (5 ft 7 1⁄2 in) ... 2005 [18]
7 Bahrain 165.1 cm (5 ft 5 in) ... 2002 [19]
8 Bahrain 171.0 cm (5 ft 7 1⁄2 in) ... 2009 [20][21]
9 Bangladesh NaN ... 2007 [15]
10 Country/Region Average male height ... Year Source
11 Belgium 178.6 cm (5 ft 10 1⁄2 in) ... 2001 [22]
12 Benin NaN ... 2006 [15]
13 Bolivia NaN ... 2003 [15]
14 Bolivia 160.0 cm (5 ft 3 in) ... 1970 [23]
15 Bosnia and Herzegovina 183.9 cm (6 ft 0 in) ... 2014 [24]
16 Brazil 170.7 cm (5 ft 7 in) ... 2009 [25][26]
17 Brazil – Urban 173.5 cm (5 ft 8 1⁄2 in) ... 2009 [25]
18 Brazil – Rural 170.9 cm (5 ft 7 1⁄2 in) ... 2009 [25]
19 Bulgaria 175.2 cm (5 ft 9 in) ... 2010 [27]
Where you by inspection of line 10 see a line of input that needs to be cleaned.
Step 2: Some basic cleaning of the data
By inspection of the data you see that every 10 lines (or something) an line repeats the column names.
While this is practical if you inspect the data as a user, this seems to be annoying for us when we want to use the raw data.
Luckily this is easy to clean up using pandas.
import pandas as pd
# The URL we will read our data from
url = 'https://en.wikipedia.org/wiki/Average_human_height_by_country'
# read_html returns a list of tables from the URL
tables = pd.read_html(url)
# The data is in the first table
table = tables[0]
# To avoid writing it all the time
AVG_MH = 'Average male height'
# Remove duplicate rows with 'Average male height'
table = table.loc[table[AVG_MH] != AVG_MH].copy()
print(table[:20])
Where you can see the data is has cleaned up these columns.
Country/Region Average male height ... Year Source
0 Albania 174.0 cm (5 ft 8 1⁄2 in) ... 2008–2009 [11][12]
1 Argentina NaN ... 2004–2005 [13]
2 Argentina 174.46 cm (5 ft 8 1⁄2 in) ... 1998–2001 [14]
3 Armenia NaN ... 2005 [15]
4 Australia 175.6 cm (5 ft 9 in) ... 2011–2012 [16]
5 Austria 179 cm (5 ft 10 1⁄2 in) ... 2006 [17]
6 Azerbaijan 171.8 cm (5 ft 7 1⁄2 in) ... 2005 [18]
7 Bahrain 165.1 cm (5 ft 5 in) ... 2002 [19]
8 Bahrain 171.0 cm (5 ft 7 1⁄2 in) ... 2009 [20][21]
9 Bangladesh NaN ... 2007 [15]
11 Belgium 178.6 cm (5 ft 10 1⁄2 in) ... 2001 [22]
12 Benin NaN ... 2006 [15]
13 Bolivia NaN ... 2003 [15]
14 Bolivia 160.0 cm (5 ft 3 in) ... 1970 [23]
15 Bosnia and Herzegovina 183.9 cm (6 ft 0 in) ... 2014 [24]
16 Brazil 170.7 cm (5 ft 7 in) ... 2009 [25][26]
17 Brazil – Urban 173.5 cm (5 ft 8 1⁄2 in) ... 2009 [25]
18 Brazil – Rural 170.9 cm (5 ft 7 1⁄2 in) ... 2009 [25]
19 Bulgaria 175.2 cm (5 ft 9 in) ... 2010 [27]
20 Burkina Faso NaN ... 2003 [15]
Step 3: Convert data to floats
Inspecting the data that we need (Average male height) it is represented as a string with both the cm and ft/in figure. As I live in Denmark and we use the metric system and have never really understood any benefit of the US customary units (feel free to enlighten me).
Hence, we want to convert the strings in the column Average male height to a float representing the height in cm.
Notice, that some are NaN, while the rest are having the first number as the length in cm.
We can exploit that and convert it with a lambda function. If you are new to lambda functions you can see this tutorial.
import pandas as pd
import numpy as np
# The URL we will read our data from
url = 'https://en.wikipedia.org/wiki/Average_human_height_by_country'
# read_html returns a list of tables from the URL
tables = pd.read_html(url)
# The data is in the first table
table = tables[0]
# To avoid writing it all the time
AVG_MH = 'Average male height'
AMH_F = 'Aveage male height (float)'
# Remove duplicate rows with 'Average male height'
table = table.loc[table[AVG_MH] != AVG_MH].copy()
# Clean up data to have height in cm
table[AMH_F] = table.apply(lambda row: float(row[AVG_MH].split(' ')[0]) if row[AVG_MH] is not np.nan else np.nan,
axis=1)
print(table[:20])
Resulting in the following.
Country/Region ... Aveage male height (float)
0 Albania ... 174.00
1 Argentina ... NaN
2 Argentina ... 174.46
3 Armenia ... NaN
4 Australia ... 175.60
5 Austria ... 179.00
6 Azerbaijan ... 171.80
7 Bahrain ... 165.10
8 Bahrain ... 171.00
9 Bangladesh ... NaN
11 Belgium ... 178.60
12 Benin ... NaN
13 Bolivia ... NaN
14 Bolivia ... 160.00
15 Bosnia and Herzegovina ... 183.90
16 Brazil ... 170.70
17 Brazil – Urban ... 173.50
18 Brazil – Rural ... 170.90
19 Bulgaria ... 175.20
20 Burkina Faso ... NaN
Notice that np.nan is also a float and hence, the full column Average male height (float) are floats.
Step 4: Merge two sets of data with different representations of countries
To make the map in the end we will use the geopandas library, which has a nice low resolution dataset used to color countries. While the data by geopandas is represented as a DataFrame it is difficult to merge it as the DataFrame we have created from the htm_read call to pandas has varying names.
Example can be United States in the one we created and United States of America in the geopandas. Hence, we need some means to map them to the same representation.
For this purpose we can use the library pycountry.
Hence, applying that to both DataFrames we can merge them.
import pandas as pd
import numpy as np
import geopandas
import pycountry
# Helper function to map country names to alpha_3 representation - though some are not known by library
def lookup_country_code(country):
try:
return pycountry.countries.lookup(country).alpha_3
except LookupError:
return country
# The URL we will read our data from
url = 'https://en.wikipedia.org/wiki/Average_human_height_by_country'
# read_html returns a list of tables from the URL
tables = pd.read_html(url)
# The data is in the first table
table = tables[0]
# To avoid writing it all the time
AVG_MH = 'Average male height'
CR = 'Country/Region'
COUNTRY = 'Country'
AMH_F = 'Aveage male height (float)'
A3 = 'alpha3'
# Remove duplicate rows with 'Average male height'
table = table.loc[table[AVG_MH] != AVG_MH].copy()
# Clean up data to have height in cm
table[AMH_F] = table.apply(lambda row: float(row[AVG_MH].split(' ')[0]) if row[AVG_MH] is not np.nan else np.nan,
axis=1)
# Clean up the names if used a dash before
table[COUNTRY] = table.apply(
lambda row: row[CR].split(' – ')[0] if ' – ' in row[CR] else row[CR],
axis=1)
# Map the country name to the alpha3 representation
table[A3] = table.apply(lambda row: lookup_country_code(row[COUNTRY]), axis=1)
# Read the geopandas dataset
world = geopandas.read_file(geopandas.datasets.get_path('naturalearth_lowres'))
# Do the same mapping to alpha3
world[A3] = world.apply(lambda row: lookup_country_code(row['name']), axis=1)
# Merge the data
table = world.merge(table, how="left", left_on=[A3], right_on=[A3])
# Remove countries with no data
table = table.dropna(subset=[AMH_F])
# These lines are just used to get the full data
pd.set_option('display.max_rows', 300)
pd.set_option('display.max_columns', 50)
pd.set_option('display.width', 1000)
print(table)
Which will result in the following.
pop_est continent name iso_a3 gdp_md_est geometry alpha3 Country/Region Average male height Average female height Stature ratio(male to female) Sample population / age range Share ofpop. over 18covered[9][10][c] Methodology Year Source Aveage male height (float) Country
3 35623680 North America Canada CAN 1674000.0 MULTIPOLYGON (((-122.84000 49.00000, -122.9742... CAN Canada 175.1 cm (5 ft 9 in) 162.3 cm (5 ft 4 in) 1.08 18–79 94.7% Measured 2007–2009 [29] 175.10 Canada
4 326625791 North America United States of America USA 18560000.0 MULTIPOLYGON (((-122.84000 49.00000, -120.0000... USA United States 175.3 cm (5 ft 9 in) 161.5 cm (5 ft 3 1⁄2 in) 1.09 All Americans, 20+ (N= m:5,232 f:5,547, Median... 69% Measured 2011–2014 [132] 175.30 United States
5 326625791 North America United States of America USA 18560000.0 MULTIPOLYGON (((-122.84000 49.00000, -120.0000... USA United States – African Americans 175.5 cm (5 ft 9 in) 162.6 cm (5 ft 4 in) 1.08 African Americans, 20–39 (N= m:532 f:612, Medi... 3.4%[133] Measured 2015-2016 [134] 175.50 United States
6 326625791 North America United States of America USA 18560000.0 MULTIPOLYGON (((-122.84000 49.00000, -120.0000... USA United States – Hispanic and Latino Americans 169.5 cm (5 ft 6 1⁄2 in) 156.7 cm (5 ft 1 1⁄2 in) 1.08 Hispanic/Latin-Americans, 20–39 (N= m:745 f:91... 4.4%[133] Measured 2015–2016 [134] 169.50 United States
7 326625791 North America United States of America USA 18560000.0 MULTIPOLYGON (((-122.84000 49.00000, -120.0000... USA United States – Mexican Americans 168.8 cm (5 ft 6 1⁄2 in) 156.1 cm (5 ft 1 1⁄2 in) 1.09 Mexican Americans, 20–39 (N= m:429 f:511, Medi... 2.8%[133] Measured 2015–2016 [134] 168.80 United States
8 326625791 North America United States of America USA 18560000.0 MULTIPOLYGON (((-122.84000 49.00000, -120.0000... USA United States – Asian Americans 169.7 cm (5 ft 7 in) 156.2 cm (5 ft 1 1⁄2 in) 1.09 Non-Hispanic Asians, 20–39 (N= m:323 f:326, Me... 1.3%[133] Measured 2015–2016 [134] 169.70 United States
9 326625791 North America United States of America USA 18560000.0 MULTIPOLYGON (((-122.84000 49.00000, -120.0000... USA United States – Non-Hispanic whites 177.0 cm (5 ft 9 1⁄2 in) 163.3 cm (5 ft 4 1⁄2 in) 1.08 Non-Hispanic White Americans, 20–39 (N= m:892 ... 17.1%[133] Measured 2015–2016 [134] 177.00 United States
13 260580739 Asia Indonesia IDN 3028000.0 MULTIPOLYGON (((141.00021 -2.60015, 141.01706 ... IDN Indonesia 158 cm (5 ft 2 in) 147 cm (4 ft 10 in) 1.07 50+ (N= m:2,041 f:2,396, Median= m:158 cm (5 f... 22.5% Self-reported 1997 [59] 158.00 Indonesia
15 44293293 South America Argentina ARG 879400.0 MULTIPOLYGON (((-68.63401 -52.63637, -68.25000... ARG Argentina 174.46 cm (5 ft 8 1⁄2 in) 161.01 cm (5 ft 3 1⁄2 in) 1.08 Healthy, 18 (N= m:90 f:97, SD= m:7.43 cm (3 in... 2.9% Measured 1998–2001 [14] 174.46 Argentina
16 17789267 South America Chile CHL 436100.0 MULTIPOLYGON (((-68.63401 -52.63637, -68.63335... CHL Chile 169.6 cm (5 ft 7 in) 156.1 cm (5 ft 1 1⁄2 in) 1.09 15+ 107.2% Measured 2009–2010 [30] 169.60 Chile
19 47615739 Africa Kenya KEN 152700.0 POLYGON ((39.20222 -4.67677, 37.76690 -3.67712... KEN Kenya 169.6 cm (5 ft 7 in) NaN NaN 25–49 (N= f:1,600, SD= f:6.3 cm (2 1⁄2 in)) 53.7% Summary 2016 [69] 169.60 Kenya
20 47615739 Africa Kenya KEN 152700.0 POLYGON ((39.20222 -4.67677, 37.76690 -3.67712... KEN Kenya 169.6 cm (5 ft 7 in) 158.2 cm (5 ft 2 1⁄2 in) NaN 25–49 (N= f:4,856, SD= f:7.3 cm (3 in)) 52.5% Survey 2016 [15][69] 169.60 Kenya
25 142257519 Europe Russia RUS 3745000.0 MULTIPOLYGON (((178.72530 71.09880, 180.00000 ... Russia Russia 171.1 cm (5 ft 7 1⁄2 in) 158.2 cm (5 ft 2 1⁄2 in) 1.08 44-69 (N= m: 3892 f: 4643) 38.5% Measured 2007 [93] 171.10 Russia
26 142257519 Europe Russia RUS 3745000.0 MULTIPOLYGON (((178.72530 71.09880, 180.00000 ... Russia Russia 177.2 cm (5 ft 10 in) 164.1 cm (5 ft 4 1⁄2 in) 1.08 24 1.9% Measured 2004 [21][98] 177.20 Russia
29 5320045 Europe Norway -99 364700.0 MULTIPOLYGON (((15.14282 79.67431, 15.52255 80... NOR Norway 179.7 cm (5 ft 10 1⁄2 in) 167.1 cm (5 ft 6 in) 1.09 Conscripts, 18–44 (N= m:30,884 f:28,796) 35.3% Measured 2012 [88] 179.70 Norway
30 5320045 Europe Norway -99 364700.0 MULTIPOLYGON (((15.14282 79.67431, 15.52255 80... NOR Norway 179.7 cm (5 ft 10 1⁄2 in) 167 cm (5 ft 5 1⁄2 in) 1.08 20–85 (N= m:1534 f:1743) 93.6% Self-reported 2008–2009 [9][26][89] 179.70 Norway
34 54841552 Africa South Africa ZAF 739100.0 POLYGON ((16.34498 -28.57671, 16.82402 -28.082... ZAF South Africa 168 cm (5 ft 6 in) 159 cm (5 ft 2 1⁄2 in) 1.06 19 (N= m:121 f:118) 3.6% Measured 2003 [110] 168.00 South Africa
36 124574795 North America Mexico MEX 2307000.0 POLYGON ((-117.12776 32.53534, -115.99135 32.6... MEX Mexico 172 cm (5 ft 7 1⁄2 in) 159 cm (5 ft 2 1⁄2 in) 1.08 20–65 62.0% Measured 2014 [83] 172.00 Mexico
37 3360148 South America Uruguay URY 73250.0 POLYGON ((-57.62513 -30.21629, -56.97603 -30.1... URY Uruguay 170 cm (5 ft 7 in) 158 cm (5 ft 2 in) 1.08 Adults (N= m:2,249 f:2,114) NaN Measured 1990 [135] 170.00 Uruguay
38 207353391 South America Brazil BRA 3081000.0 POLYGON ((-53.37366 -33.76838, -53.65054 -33.2... BRA Brazil 170.7 cm (5 ft 7 in) 158.8 cm (5 ft 2 1⁄2 in) 1.07 18+ (N= m:62,037 f:65,696) 100.0% Measured 2009 [25][26] 170.70 Brazil
39 207353391 South America Brazil BRA 3081000.0 POLYGON ((-53.37366 -33.76838, -53.65054 -33.2... BRA Brazil – Urban 173.5 cm (5 ft 8 1⁄2 in) 161.6 cm (5 ft 3 1⁄2 in) 1.07 20–24 (N= m:6,360 f:6,305) 10.9% Measured 2009 [25] 173.50 Brazil
40 207353391 South America Brazil BRA 3081000.0 POLYGON ((-53.37366 -33.76838, -53.65054 -33.2... BRA Brazil – Rural 170.9 cm (5 ft 7 1⁄2 in) 158.9 cm (5 ft 2 1⁄2 in) 1.07 20–24 (N= m:1,939 f:1,633) 2.1% Measured 2009 [25] 170.90 Brazil
42 11138234 South America Bolivia BOL 78350.0 POLYGON ((-69.52968 -10.95173, -68.78616 -11.0... BOL Bolivia 160.0 cm (5 ft 3 in) 142.2 cm (4 ft 8 in) 1.13 Aymara, 20–29 NaN Measured 1970 [23] 160.00 Bolivia
43 31036656 South America Peru PER 410400.0 POLYGON ((-69.89364 -4.29819, -70.79477 -4.251... PER Peru 164 cm (5 ft 4 1⁄2 in) 151 cm (4 ft 11 1⁄2 in) 1.09 20+ 0.011509% Measured 2005 [90] 164.00 Peru
44 47698524 South America Colombia COL 688000.0 POLYGON ((-66.87633 1.25336, -67.06505 1.13011... COL Colombia 170.6 cm (5 ft 7 in) 158.7 cm (5 ft 2 1⁄2 in) 1.07 18–22 (N= m:1,528,875 f:1,468,110) 14.1% Measured 2002 [33] 170.60 Colombia
56 67106161 Europe France -99 2699000.0 MULTIPOLYGON (((-51.65780 4.15623, -52.24934 3... FRA France 175.6 cm (5 ft 9 in) 162.5 cm (5 ft 4 in) 1.08 18–70 (N= m/f:11,562) 85.9% Measured 2003–2004 [45][46] 175.60 France
57 67106161 Europe France -99 2699000.0 MULTIPOLYGON (((-51.65780 4.15623, -52.24934 3... FRA France 174.1 cm (5 ft 8 1⁄2 in) 161.9 cm (5 ft 3 1⁄2 in) 1.08 20+ 96.6% Measured 2001 [7] 174.10 France
58 16290913 South America Ecuador ECU 182400.0 POLYGON ((-75.37322 -0.15203, -75.23372 -0.911... ECU Ecuador 167.1 cm (5 ft 6 in) 154.2 cm (5 ft 1⁄2 in) 1.08 NaN NaN Measured 2014 [40] 167.10 Ecuador
60 2990561 North America Jamaica JAM 25390.0 POLYGON ((-77.56960 18.49053, -76.89662 18.400... JAM Jamaica 171.8 cm (5 ft 7 1⁄2 in) 160.8 cm (5 ft 3 1⁄2 in) 1.07 25–74 71.4% Measured 1994–1996 [66] 171.80 Jamaica
61 11147407 North America Cuba CUB 132900.0 POLYGON ((-82.26815 23.18861, -81.40446 23.117... CUB Cuba – Urban 168 cm (5 ft 6 in) 156 cm (5 ft 1 1⁄2 in) 1.08 15+ 79.2% Measured 1999 [35] 168.00 Cuba
66 17885245 Africa Mali MLI 38090.0 POLYGON ((-11.51394 12.44299, -11.46790 12.754... MLI Mali – Southern Mali 171.3 cm (5 ft 7 1⁄2 in) 160.4 cm (5 ft 3 in) 1.07 Rural adults (N= m:121 f:320, SD= m:6.6 cm (2 ... NaN Measured 1992 [81] 171.30 Mali
70 190632261 Africa Nigeria NGA 1089000.0 POLYGON ((2.69170 6.25882, 2.74906 7.87073, 2.... NGA Nigeria 163.8 cm (5 ft 4 1⁄2 in) 157.8 cm (5 ft 2 in) 1.04 18–74 98.6% Measured 1994–1996 [66] 163.80 Nigeria
71 190632261 Africa Nigeria NGA 1089000.0 POLYGON ((2.69170 6.25882, 2.74906 7.87073, 2.... NGA Nigeria 167.2 cm (5 ft 6 in) 160.3 cm (5 ft 3 in) 1.04 20–29 (N= m:139 f:76, SD= m:6.5 cm (2 1⁄2 in) ... 33.2% Measured 2011 [87] 167.20 Nigeria
72 24994885 Africa Cameroon CMR 77240.0 POLYGON ((14.49579 12.85940, 14.89336 12.21905... CMR Cameroon – Urban 170.6 cm (5 ft 7 in) 161.3 cm (5 ft 3 1⁄2 in) 1.06 15+ (N= m:3,746 f:5,078) 53.6% Measured 2003 [28] 170.60 Cameroon
75 27499924 Africa Ghana GHA 120800.0 POLYGON ((0.02380 11.01868, -0.04978 10.70692,... GHA Ghana 169.5 cm (5 ft 6 1⁄2 in) 158.5 cm (5 ft 2 1⁄2 in) 1.07 25–29 14.7% Measured 1987–1989 [49] 169.50 Ghana
87 19196246 Africa Malawi MWI 21200.0 POLYGON ((32.75938 -9.23060, 33.73972 -9.41715... MWI Malawi – Urban 166 cm (5 ft 5 1⁄2 in) 155 cm (5 ft 1 in) 1.07 16–60 (N= m:583 f:315, SD= m:6.0 cm (2 1⁄2 in)... 101.1% Measured 2000 [78] 166.00 Malawi
92 8299706 Asia Israel ISR 297000.0 POLYGON ((35.71992 32.70919, 35.54567 32.39399... ISR Israel 177 cm (5 ft 9 1⁄2 in) 166 cm (5 ft 5 1⁄2 in) 1.07 18–21 9.7% Measured 2010 [64] 177.00 Israel
96 2051363 Africa Gambia GMB 3387.0 POLYGON ((-16.71373 13.59496, -15.62460 13.623... GMB Gambia – Rural 168.0 cm (5 ft 6 in) 157.8 cm (5 ft 2 in) 1.06 21–49 (N= m:9,559 f:13,160, SD= m:6.7 cm (2 1⁄... NaN Measured 1950–1974 [47] 168.00 Gambia
100 6072475 Asia United Arab Emirates ARE 667200.0 POLYGON ((51.57952 24.24550, 51.75744 24.29407... ARE United Arab Emirates 173.4 cm (5 ft 8 1⁄2 in) 156.4 cm (5 ft 1 1⁄2 in) 1.11 NaN NaN NaN NaN [128] 173.40 United Arab Emirates
101 2314307 Asia Qatar QAT 334500.0 POLYGON ((50.81011 24.75474, 50.74391 25.48242... QAT Qatar 170.8 cm (5 ft 7 in) 161.1 cm (5 ft 3 1⁄2 in) 1.06 18 1.9% Measured 2005 [21][96] 170.80 Qatar
103 39192111 Asia Iraq IRQ 596700.0 POLYGON ((39.19547 32.16101, 38.79234 33.37869... IRQ Iraq – Baghdad 165.4 cm (5 ft 5 in) 155.8 cm (5 ft 1 1⁄2 in) 1.06 18–44 (N= m:700 f:800, SD= m:5.6 cm (2 in) f:1... 76.3% Measured 1999–2000 [61] 165.40 Iraq
107 68414135 Asia Thailand THA 1161000.0 POLYGON ((105.21878 14.27321, 104.28142 14.416... THA Thailand 170.3 cm (5 ft 7 in) 159 cm (5 ft 2 1⁄2 in) 1.07 STOU students, 15–19 (N= m:839 f:1,636, SD= m:... 0.2%[122] Self-reported 2005 [123] 170.30 Thailand
110 96160163 Asia Vietnam VNM 594900.0 POLYGON ((104.33433 10.48654, 105.19991 10.889... VNM Vietnam 162.1 cm (5 ft 4 in) 152.2 cm (5 ft 0 in) 1.07 25–29 (SD= m:5.39 cm (2 in) f:5.39 cm (2 in)) 15.9% Measured 1992–1993 [49] 162.10 Vietnam
111 96160163 Asia Vietnam VNM 594900.0 POLYGON ((104.33433 10.48654, 105.19991 10.889... VNM Vietnam 165.7 cm (5 ft 5 in) 155.2 cm (5 ft 1 in) 1.07 Students, 20–25 (N= m:1,000 f:1,000, SD= m:6.5... 2.0%[136] Measured 2006–2007 [137] 165.70 Vietnam
112 25248140 Asia North Korea PRK 40000.0 MULTIPOLYGON (((130.78000 42.22001, 130.78000 ... North Korea North Korea 165.6 cm (5 ft 5 in) 154.9 cm (5 ft 1 in) 1.07 Defectors, 20–39 (N= m/f:1,075) 46.4% Measured 2005 [70] 165.60 North Korea
113 51181299 Asia South Korea KOR 1929000.0 POLYGON ((126.17476 37.74969, 126.23734 37.840... South Korea South Korea 170.7 cm (5 ft 7 in) 157.4 cm (5 ft 2 in) 1.08 20+ (N= m:2,750 f:2,445, Median= m:170.7 cm (5... 96.5% Measured 2010 [71] 170.70 South Korea
114 51181299 Asia South Korea KOR 1929000.0 POLYGON ((126.17476 37.74969, 126.23734 37.840... South Korea South Korea 173.5 cm (5 ft 8 1⁄2 in) NaN NaN Conscripts, 18–19 (N= m:323,800) 3.8% Measured 2017 [72] 173.50 South Korea
116 3068243 Asia Mongolia MNG 37000.0 POLYGON ((87.75126 49.29720, 88.80557 49.47052... MNG Mongolia 168.4 cm (5 ft 6 1⁄2 in) 157.7 cm (5 ft 2 in) 1.07 25–34 (N= m:158 f:181) 27.6% Measured 2006 [84] 168.40 Mongolia
117 1281935911 Asia India IND 8721000.0 POLYGON ((97.32711 28.26158, 97.40256 27.88254... IND India – Urban 174.3 cm (5 ft 8 1⁄2 in) 158.5 cm (5 ft 2 1⁄2 in) 1.10 Private school students, 18 (N= m:34,411 f:30,... NaN Measured 2011 [55] 174.30 India
118 1281935911 Asia India IND 8721000.0 POLYGON ((97.32711 28.26158, 97.40256 27.88254... IND India – Rural 161.5 cm (5 ft 3 1⁄2 in) 152.5 cm (5 ft 0 in) 1.06 17 (SD= m:7.0 cm (3 in) f:6.3 cm (2 1⁄2 in)) NaN Measured 2002 [56] 161.50 India
119 1281935911 Asia India IND 8721000.0 POLYGON ((97.32711 28.26158, 97.40256 27.88254... IND India 164.7 cm (5 ft 5 in) 152.6 cm (5 ft 0 in) 1.08 20–49 (N= m:69,245 f:118,796) 44.3% Measured 2005-2006 [57] 164.70 India
120 1281935911 Asia India IND 8721000.0 POLYGON ((97.32711 28.26158, 97.40256 27.88254... IND India – Patiala, Punjab 177.3 cm (5 ft 10 in) NaN NaN Students, Punjabi, 18-25 (N: 149, SD = 7.88 cm... 22.4% Measured 2013 [58] 177.30 India
123 29384297 Asia Nepal NPL 71520.0 POLYGON ((88.12044 27.87654, 88.04313 27.44582... NPL Nepal 163.0 cm (5 ft 4 in) 150.8 cm (4 ft 11 1⁄2 in) NaN 25–49 (N= f:6,280, SD= f:5.5 cm (2 in)) 52.9% Self-reported 2006 [15] 163.00 Nepal
129 82021564 Asia Iran IRN 1459000.0 POLYGON ((48.56797 29.92678, 48.01457 30.45246... Iran Iran 170.3 cm (5 ft 7 in) 157.2 cm (5 ft 2 in) 1.08 21+ (N= m/f:89,532, SD= m:8.05 cm (3 in) f:7.2... 88.1% Measured 2005 [60] 170.30 Iran
132 9960487 Europe Sweden SWE 498100.0 POLYGON ((11.02737 58.85615, 11.46827 59.43239... SWE Sweden 181.5 cm (5 ft 11 1⁄2 in) 166.8 cm (5 ft 5 1⁄2 in) 1.09 20–29 15.6% Measured 2008 [116] 181.50 Sweden
133 9960487 Europe Sweden SWE 498100.0 POLYGON ((11.02737 58.85615, 11.46827 59.43239... SWE Sweden 177.9 cm (5 ft 10 in) 164.6 cm (5 ft 5 in) 1.08 20–74 86.3% Self-reported 1987–1994 [117] 177.90 Sweden
136 38476269 Europe Poland POL 1052000.0 POLYGON ((23.48413 53.91250, 23.52754 53.47012... POL Poland 172.2 cm (5 ft 8 in) 159.4 cm (5 ft 3 in) 1.07 44-69 (N= m:4336 f: 4559) 39.4% Measured 2007 [93] 172.20 Poland
137 38476269 Europe Poland POL 1052000.0 POLYGON ((23.48413 53.91250, 23.52754 53.47012... POL Poland 178.7 cm (5 ft 10 1⁄2 in) 165.1 cm (5 ft 5 in) 1.08 18 (N= m:846 f:1,126) 1.6% Measured 2010 [94] 178.70 Poland
138 8754413 Europe Austria AUT 416600.0 POLYGON ((16.97967 48.12350, 16.90375 47.71487... AUT Austria 179 cm (5 ft 10 1⁄2 in) 166 cm (5 ft 5 1⁄2 in) 1.08 20–49 54.3% Measured 2006 [17] 179.00 Austria
139 9850845 Europe Hungary HUN 267600.0 POLYGON ((22.08561 48.42226, 22.64082 48.15024... HUN Hungary 176 cm (5 ft 9 1⁄2 in) 164 cm (5 ft 4 1⁄2 in) 1.07 Adults NaN Measured 2000s [53] 176.00 Hungary
140 9850845 Europe Hungary HUN 267600.0 POLYGON ((22.08561 48.42226, 22.64082 48.15024... HUN Hungary 177.3 cm (5 ft 10 in) NaN NaN 18 (N= m:1,080, SD= m:5.99 cm (2 1⁄2 in)) 1.7% Measured 2005 [54] 177.30 Hungary
142 21529967 Europe Romania ROU 441000.0 POLYGON ((28.23355 45.48828, 28.67978 45.30403... ROU Romania 172 cm (5 ft 7 1⁄2 in) 157 cm (5 ft 2 in) 1.10 NaN NaN Measured 2007 [97] 172.00 Romania
143 2823859 Europe Lithuania LTU 85620.0 POLYGON ((26.49433 55.61511, 26.58828 55.16718... LTU Lithuania – Urban 178.4 cm (5 ft 10 in) NaN NaN Conscripts, 19–25 (N= m:91 SD= m:6.7 cm (2 1⁄2... 9.9% Measured 2005[75] [76] 178.40 Lithuania
144 2823859 Europe Lithuania LTU 85620.0 POLYGON ((26.49433 55.61511, 26.58828 55.16718... LTU Lithuania – Rural 176.2 cm (5 ft 9 1⁄2 in) NaN NaN Conscripts, 19–25 (N= m:106 SD= m:5.9 cm (2 1⁄... 4.9% Measured 2005[75] [76] 176.20 Lithuania
145 2823859 Europe Lithuania LTU 85620.0 POLYGON ((26.49433 55.61511, 26.58828 55.16718... LTU Lithuania 181.3 cm (5 ft 11 1⁄2 in) 167.5 cm (5 ft 6 in) 1.08 18 2.1% Measured 2001 [77] 181.30 Lithuania
147 1251581 Europe Estonia EST 38700.0 POLYGON ((27.98113 59.47537, 27.98112 59.47537... EST Estonia 179.1 cm (5 ft 10 1⁄2 in) NaN NaN 17 2.3% Measured 2003 [42] 179.10 Estonia
148 80594017 Europe Germany DEU 3979000.0 POLYGON ((14.11969 53.75703, 14.35332 53.24817... DEU Germany 175.4 cm (5 ft 9 in) 162.8 cm (5 ft 4 in) 1.08 18–79 (N= m/f:19,768) 94.3% Measured 2007 [6] 175.40 Germany
149 80594017 Europe Germany DEU 3979000.0 POLYGON ((14.11969 53.75703, 14.35332 53.24817... DEU Germany 178 cm (5 ft 10 in) 165 cm (5 ft 5 in) 1.08 18+ (N= m:25,112 f:25,560) 100.0% Self-reported 2009 [48] 178.00 Germany
150 7101510 Europe Bulgaria BGR 143100.0 POLYGON ((22.65715 44.23492, 22.94483 43.82379... BGR Bulgaria 175.2 cm (5 ft 9 in) 163.2 cm (5 ft 4 1⁄2 in) 1.07 NaN NaN NaN 2010 [27] 175.20 Bulgaria
151 10768477 Europe Greece GRC 290500.0 MULTIPOLYGON (((26.29000 35.29999, 26.16500 35... GRC Greece 177 cm (5 ft 9 1⁄2 in) 165 cm (5 ft 5 in) 1.07 18–49 56.3% Measured 2003 [17] 177.00 Greece
152 80845215 Asia Turkey TUR 1670000.0 MULTIPOLYGON (((44.77268 37.17044, 44.29345 37... TUR Turkey 173.6 cm (5 ft 8 1⁄2 in) 161.9 cm (5 ft 3 1⁄2 in) 1.07 20-22 (N= m:322 f:247) 8.3% Measured 2007 [11][21][125] 173.60 Turkey
153 80845215 Asia Turkey TUR 1670000.0 MULTIPOLYGON (((44.77268 37.17044, 44.29345 37... TUR Turkey – Ankara 174.1 cm (5 ft 8 1⁄2 in) 158.9 cm (5 ft 2 1⁄2 in) 1.10 18–59 (N= m:703 f:512, Median= m:169.7 cm (5 f... 5.1%[126] Measured 2004–2006 [127] 174.10 Turkey
155 3047987 Europe Albania ALB 33900.0 POLYGON ((21.02004 40.84273, 20.99999 40.58000... ALB Albania 174.0 cm (5 ft 8 1⁄2 in) 161.8 cm (5 ft 3 1⁄2 in) 1.08 20–29 (N= m:649 f:1,806) 23.5% Measured 2008–2009 [11][12] 174.00 Albania
156 4292095 Europe Croatia HRV 94240.0 POLYGON ((16.56481 46.50375, 16.88252 46.38063... HRV Croatia 180.4 cm (5 ft 11 in) 166.49 cm (5 ft 5 1⁄2 in) 1.09 18 (N= m:358 f:360, SD= m:6.8 cm (2 1⁄2 in) f:... 1.6% Measured 2006–2008 [34] 180.40 Croatia
157 8236303 Europe Switzerland CHE 496300.0 POLYGON ((9.59423 47.52506, 9.63293 47.34760, ... CHE Switzerland 178.2 cm (5 ft 10 in) NaN NaN Conscripts, 19 (N= m:12,447, Median= m:178.0 c... 1.5% Measured 2009 [118] 178.20 Switzerland
158 8236303 Europe Switzerland CHE 496300.0 POLYGON ((9.59423 47.52506, 9.63293 47.34760, ... CHE Switzerland 175.4 cm (5 ft 9 in) 164 cm (5 ft 4 1⁄2 in) 1.07 20–74 88.8% Self-reported 1987–1994 [117] 175.40 Switzerland
160 11491346 Europe Belgium BEL 508600.0 POLYGON ((6.15666 50.80372, 6.04307 50.12805, ... BEL Belgium 178.6 cm (5 ft 10 1⁄2 in) 168.1 cm (5 ft 6 in) 1.06 21 (N= m:20–49 f:20–49, SD= m:6.6 cm (2 1⁄2 in... 1.7% Self-reported 2001 [22] 178.60 Belgium
161 17084719 Europe Netherlands NLD 870800.0 POLYGON ((6.90514 53.48216, 7.09205 53.14404, ... NLD Netherlands 180.8 cm (5 ft 11 in) 167.5 cm (5 ft 6 in) 1.08 20+ 96.8% Self-reported 2013 [9][26][86] 180.80 Netherlands
162 10839514 Europe Portugal PRT 297100.0 POLYGON ((-9.03482 41.88057, -8.67195 42.13469... PRT Portugal 173.9 cm (5 ft 8 1⁄2 in) NaN NaN 18 (N= m:696) 1.5% Measured 2008 [11][95] 173.90 Portugal
163 10839514 Europe Portugal PRT 297100.0 POLYGON ((-9.03482 41.88057, -8.67195 42.13469... PRT Portugal 171 cm (5 ft 7 1⁄2 in) 161 cm (5 ft 3 1⁄2 in) 1.06 20–50 56.7% Self-reported 2001 [17] 171.00 Portugal
164 10839514 Europe Portugal PRT 297100.0 POLYGON ((-9.03482 41.88057, -8.67195 42.13469... PRT Portugal 173.7 cm (5 ft 8 1⁄2 in) 163.7 cm (5 ft 4 1⁄2 in) 1.06 21 (N= m:87 f:106, SD= m:8.2 cm (3 in) f:5.3 c... 1.9% Self-reported 2001 [22] 173.70 Portugal
165 48958159 Europe Spain ESP 1690000.0 POLYGON ((-7.45373 37.09779, -7.53711 37.42890... ESP Spain 173.1 cm (5 ft 8 in) NaN NaN 18–70 (N= m:1,298 [s][112] ) 88.2% Measured 2013–2014 [113][114] 173.10 Spain
167 48958159 Europe Spain ESP 1690000.0 POLYGON ((-7.45373 37.09779, -7.53711 37.42890... ESP Spain 174 cm (5 ft 8 1⁄2 in) 163 cm (5 ft 4 in) 1.07 20–49 57.0% Self-reported 2007 [17] 174.00 Spain
168 5011102 Europe Ireland IRL 322000.0 POLYGON ((-6.19788 53.86757, -6.03299 53.15316... IRL Ireland 177 cm (5 ft 9 1⁄2 in) 163 cm (5 ft 4 in) 1.09 20–49 61.8% Measured 2007 [17] 177.00 Ireland
169 5011102 Europe Ireland IRL 322000.0 POLYGON ((-6.19788 53.86757, -6.03299 53.15316... IRL Ireland 179 cm (5 ft 10 1⁄2 in) 165 cm (5 ft 5 in) 1.08 18 - Measured 2014 [62][63] 179.00 Ireland
172 4510327 Oceania New Zealand NZL 174800.0 MULTIPOLYGON (((176.88582 -40.06598, 176.50802... NZL New Zealand 177 cm (5 ft 9 1⁄2 in) 164 cm (5 ft 4 1⁄2 in) 1.08 20–49 56.9% Measured 2007 [17] 177.00 New Zealand
173 23232413 Oceania Australia AUS 1189000.0 MULTIPOLYGON (((147.68926 -40.80826, 148.28907... AUS Australia 175.6 cm (5 ft 9 in) 161.8 cm (5 ft 3 1⁄2 in) 1.09 18+ 100.0% Measured 2011–2012 [16] 175.60 Australia
174 22409381 Asia Sri Lanka LKA 236700.0 POLYGON ((81.78796 7.52306, 81.63732 6.48178, ... LKA Sri Lanka 163.6 cm (5 ft 4 1⁄2 in) 151.4 cm (4 ft 11 1⁄2 in) 1.08 18+ (N= m:1,768 f:2,709, SD= m:6.9 cm (2 1⁄2 i... 100.0% Measured 2005–2006 [111] 163.60 Sri Lanka
175 1379302771 Asia China CHN 21140000.0 MULTIPOLYGON (((109.47521 18.19770, 108.65521 ... CHN China 169.5 cm (5 ft 6 1⁄2 in) 158.0 cm (5 ft 2 in) 1.07 18-69 (N=172,422) 76.8% Measured 2014 [31] 169.50 China
176 1379302771 Asia China CHN 21140000.0 MULTIPOLYGON (((109.47521 18.19770, 108.65521 ... CHN China – Beijing – Urban 175.2 cm (5 ft 9 in) 162.6 cm (5 ft 4 in) 1.08 Urban, 18 (N= m:448 f:405) 0.5% Measured 2011 [32] 175.20 China
177 23508428 Asia Taiwan TWN 1127000.0 POLYGON ((121.77782 24.39427, 121.17563 22.790... TWN Taiwan 171.4 cm (5 ft 7 1⁄2 in) 159.9 cm (5 ft 3 in) 1.07 17 (N= m:200 f:200) 1.7% Measured 2011 [119][120][121] 171.40 Taiwan
178 62137802 Europe Italy ITA 2221000.0 MULTIPOLYGON (((10.44270 46.89355, 11.04856 46... ITA Italy 176.5 cm (5 ft 9 1⁄2 in) 162.5 cm (5 ft 4 in) 1.09 18 1.4% Measured 1999–2004 [11][21][65] 176.50 Italy
179 62137802 Europe Italy ITA 2221000.0 MULTIPOLYGON (((10.44270 46.89355, 11.04856 46... ITA Italy 177.2 cm (5 ft 10 in) 167.8 cm (5 ft 6 in) 1.06 21 (N= m:106 f:92, SD= m:6.0 cm (2 1⁄2 in) f:6... 1.4% Self-reported 2001 [22] 177.20 Italy
180 5605948 Europe Denmark DNK 264800.0 MULTIPOLYGON (((9.92191 54.98310, 9.28205 54.8... DNK Denmark 180.4 cm (5 ft 11 in) 167.2 cm (5 ft 6 in) NaN Conscripts, 18–20 (N= m:38,025) 5.3% Measured 2012 [37] 180.40 Denmark
181 64769452 Europe United Kingdom GBR 2788000.0 MULTIPOLYGON (((-6.19788 53.86757, -6.95373 54... GBR United Kingdom – England 175.3 cm (5 ft 9 in) 161.9 cm (5 ft 3 1⁄2 in) 1.08 16+ (N= m:3,154 f:3,956) 103.2%[129] Measured 2012 [5] 175.30 United Kingdom
182 64769452 Europe United Kingdom GBR 2788000.0 MULTIPOLYGON (((-6.19788 53.86757, -6.95373 54... GBR United Kingdom – Scotland 175.0 cm (5 ft 9 in) 161.3 cm (5 ft 3 1⁄2 in) 1.08 16+ (N= m:2,512 f:3,180, Median= m:174.8 cm (5... 103.0%[129] Measured 2008 [130] 175.00 United Kingdom
183 64769452 Europe United Kingdom GBR 2788000.0 MULTIPOLYGON (((-6.19788 53.86757, -6.95373 54... GBR United Kingdom – Wales 177.0 cm (5 ft 9 1⁄2 in) 162.0 cm (5 ft 4 in) 1.09 16+ 103.2%[129] Self-reported 2009 [131] 177.00 United Kingdom
184 339747 Europe Iceland ISL 16150.0 POLYGON ((-14.50870 66.45589, -14.73964 65.808... ISL Iceland 181 cm (5 ft 11 1⁄2 in) 168 cm (5 ft 6 in) 1.08 20–49 43.6% Self-reported 2007 [17] 181.00 Iceland
185 9961396 Asia Azerbaijan AZE 167900.0 MULTIPOLYGON (((46.40495 41.86068, 46.68607 41... AZE Azerbaijan 171.8 cm (5 ft 7 1⁄2 in) 165.4 cm (5 ft 5 in) 1.04 16+ 106.5% Measured 2005 [18] 171.80 Azerbaijan
187 104256076 Asia Philippines PHL 801900.0 MULTIPOLYGON (((120.83390 12.70450, 120.32344 ... PHL Philippines 163.5 cm (5 ft 4 1⁄2 in) 151.8 cm (5 ft 0 in) 1.08 20–39 31.5%[91] Measured 2003 [92] 163.50 Philippines
188 31381992 Asia Malaysia MYS 863000.0 MULTIPOLYGON (((100.08576 6.46449, 100.25960 6... MYS Malaysia 166.3 cm (5 ft 5 1⁄2 in) 154.7 cm (5 ft 1 in) 1.07 Malay, 20–24 (N= m:749 f:893, Median= m:166 cm... 9.7%[79] Measured 1996 [80] 166.30 Malaysia
189 31381992 Asia Malaysia MYS 863000.0 MULTIPOLYGON (((100.08576 6.46449, 100.25960 6... MYS Malaysia 168.5 cm (5 ft 6 1⁄2 in) 158.1 cm (5 ft 2 in) 1.07 Chinese, 20–24 (N= m:407 f:453, Median= m:169 ... 4.1%[79] Measured 1996 [80] 168.50 Malaysia
190 31381992 Asia Malaysia MYS 863000.0 MULTIPOLYGON (((100.08576 6.46449, 100.25960 6... MYS Malaysia 169.1 cm (5 ft 6 1⁄2 in) 155.4 cm (5 ft 1 in) 1.09 Indian, 20–24 (N= m:113 f:140, Median= m:168 c... 1.2%[79] Measured 1996 [80] 169.10 Malaysia
191 31381992 Asia Malaysia MYS 863000.0 MULTIPOLYGON (((100.08576 6.46449, 100.25960 6... MYS Malaysia 163.3 cm (5 ft 4 1⁄2 in) 151.9 cm (5 ft 0 in) 1.08 Other indigenous, 20–24 (N= m:257 f:380, Media... 0.4%[79] Measured 1996 [80] 163.30 Malaysia
193 1972126 Europe Slovenia SVN 68350.0 POLYGON ((13.80648 46.50931, 14.63247 46.43182... SVN Slovenia – Ljubljana 180.3 cm (5 ft 11 in) 167.4 cm (5 ft 6 in) 1.08 19 0.2%[108] Measured 2011 [109] 180.30 Slovenia
194 5491218 Europe Finland FIN 224137.0 POLYGON ((28.59193 69.06478, 28.44594 68.36461... FIN Finland 178.9 cm (5 ft 10 1⁄2 in) 165.3 cm (5 ft 5 in) 1.08 25–34 (N= m/f:2,305) 19.0% Measured 1994 [43] 178.90 Finland
195 5491218 Europe Finland FIN 224137.0 POLYGON ((28.59193 69.06478, 28.44594 68.36461... FIN Finland 180.7 cm (5 ft 11 in) 167.2 cm (5 ft 6 in) 1.08 −25 (N= m/f:26,636) 9.2% Measured 2010–2011 [43][44] 180.70 Finland
196 5445829 Europe Slovakia SVK 168800.0 POLYGON ((22.55814 49.08574, 22.28084 48.82539... SVK Slovakia 179.4 cm (5 ft 10 1⁄2 in) 165.6 cm (5 ft 5 in) 1.08 18 2.0% Measured 2004 [107] 179.40 Slovakia
197 10674723 Europe Czechia CZE 350900.0 POLYGON ((15.01700 51.10667, 15.49097 50.78473... CZE Czech Republic 180.3 cm (5 ft 11 in) 167.22 cm (5 ft 6 in) 1.08 17 1.6% Measured 2001 [36] 180.30 Czech Republic
199 126451398 Asia Japan JPN 4932000.0 MULTIPOLYGON (((141.88460 39.18086, 140.95949 ... JPN Japan 172 cm (5 ft 7 1⁄2 in) 158 cm (5 ft 2 in) 1.08 20–49 47.2% Measured 2005 [17] 172.00 Japan
200 126451398 Asia Japan JPN 4932000.0 MULTIPOLYGON (((141.88460 39.18086, 140.95949 ... JPN Japan 172.0 cm (5 ft 7 1⁄2 in) 158.70 cm (5 ft 2 1⁄2 in) 1.08 20–24 (N= m:1,708 f:1,559, SD= m:5.42 cm (2 in... 7.2% Measured 2004 [67] 172.00 Japan
201 126451398 Asia Japan JPN 4932000.0 MULTIPOLYGON (((141.88460 39.18086, 140.95949 ... JPN Japan 170.7 cm (5 ft 7 in) 158.0 cm (5 ft 2 in) 1.08 17 1.2% Measured 2013 [68] 170.70 Japan
204 28571770 Asia Saudi Arabia SAU 1731000.0 POLYGON ((34.95604 29.35655, 36.06894 29.19749... SAU Saudi Arabia 168.9 cm (5 ft 6 1⁄2 in) 156.3 cm (5 ft 1 1⁄2 in) 1.08 18 3.0% Measured 2010 [21][100] 168.90 Saudi Arabia
205 28571770 Asia Saudi Arabia SAU 1731000.0 POLYGON ((34.95604 29.35655, 36.06894 29.19749... SAU Saudi Arabia 174 cm (5 ft 8 1⁄2 in) NaN NaN NaN NaN NaN 2017 [101] 174.00 Saudi Arabia
210 97041072 Africa Egypt EGY 1105000.0 POLYGON ((36.86623 22.00000, 32.90000 22.00000... EGY Egypt 170.3 cm (5 ft 7 in) 158.9 cm (5 ft 2 1⁄2 in) 1.07 20–24 (N= m:845 f:1,059) 16.6% Measured 2008 [41] 170.30 Egypt
220 7111024 Europe Serbia SRB 101800.0 POLYGON ((18.82982 45.90887, 18.82984 45.90888... SRB Serbia 182.0 cm (5 ft 11 1⁄2 in) 166.8 cm (5 ft 5 1⁄2 in) 1.09 Students at UNS,18–30 (N= m:318 f:76, SD= m:6.... 0.7%[102] Measured 2012 [103] 182.00 Serbia
221 642550 Europe Montenegro MNE 10610.0 POLYGON ((20.07070 42.58863, 19.80161 42.50009... MNE Montenegro 183.4 cm (6 ft 0 in) 169.4 cm (5 ft 6 1⁄2 in) 1.09 17-20 (N= m:981 f:1107, SD= m:6.89 cm (2 1⁄2 i... 5.2% Measured 2017 [85] 183.40 Montenegro
222 1895250 Europe Kosovo -99 18490.0 POLYGON ((20.59025 41.85541, 20.52295 42.21787... Kosovo Kosovo – Prishtina 179.52 cm (5 ft 10 1⁄2 in) 165.72 cm (5 ft 5 in) NaN Conscripts, 18-20 (N= m:830 f:793, SD= m:7.02 ... 63.0% Measured 2017 [74] 179.52 Kosovo
Also, notice that we also removed the rows, which has no Average male height.
Step 5: Create the map with our data
Now we have done all the hard work.
It is time to use folium to do the last piece of work.
Let’s put it all together.
import pandas as pd
import numpy as np
import folium
import geopandas
import pycountry
# Helper function to map country names to alpha_3 representation - though some are not known by library
def lookup_country_code(country):
try:
return pycountry.countries.lookup(country).alpha_3
except LookupError:
return country
# The URL we will read our data from
url = 'https://en.wikipedia.org/wiki/Average_human_height_by_country'
# read_html returns a list of tables from the URL
tables = pd.read_html(url)
# The data is in the first table
table = tables[0]
# To avoid writing it all the time
AVG_MH = 'Average male height'
CR = 'Country/Region'
COUNTRY = 'Country'
AMH_F = 'Aveage male height (float)'
A3 = 'alpha3'
# Remove duplicate rows with 'Average male height'
table = table.loc[table[AVG_MH] != AVG_MH].copy()
# Clean up data to have height in cm
table[AMH_F] = table.apply(lambda row: float(row[AVG_MH].split(' ')[0]) if row[AVG_MH] is not np.nan else np.nan,
axis=1)
# Clean up the names if used a dash before
table[COUNTRY] = table.apply(
lambda row: row[CR].split(' – ')[0] if ' – ' in row[CR] else row[CR],
axis=1)
# Map the country name to the alpha3 representation
table[A3] = table.apply(lambda row: lookup_country_code(row[COUNTRY]), axis=1)
# Read the geopandas dataset
world = geopandas.read_file(geopandas.datasets.get_path('naturalearth_lowres'))
# Do the same mapping to alpha3
world[A3] = world.apply(lambda row: lookup_country_code(row['name']), axis=1)
# Merge the data
table = world.merge(table, how="left", left_on=[A3], right_on=[A3])
# Remove countries with no data
table = table.dropna(subset=[AMH_F])
# Creating a map
my_map = folium.Map()
# Adding the data from our table
folium.Choropleth(
geo_data=table,
name='choropleth',
data=table,
columns=[A3, AMH_F],
key_on='feature.properties.alpha3',
fill_color='YlGn',
fill_opacity=0.7,
line_opacity=0.2,
legend_name='Male height'
).add_to(my_map)
# Save the map to an html file
my_map.save('height_map.html')
Which should result in a map like this you can use in your browser. Zoom in and out.
This is nice. Good job.
Learn Python
Learn Python A BEGINNERS GUIDE TO PYTHON
- 70 pages to get you started on your journey to master Python.
- How to install your setup with Anaconda.
- Written description and introduction to all concepts.
- Jupyter Notebooks prepared for 17 projects.
Python 101: A CRASH COURSE
- How to get started with this 8 hours Python 101: A CRASH COURSE.
- Best practices for learning Python.
- How to download the material to follow along and create projects.
- A chapter for each lesson with a description, code snippets for easy reference, and links to a lesson video.
Expert Data Science Blueprint
Expert Data Science Blueprint
- Master the Data Science Workflow for actionable data insights.
- How to download the material to follow along and create projects.
- A chapter to each lesson with a Description, Learning Objective, and link to the lesson video.
Machine Learning
Machine Learning – The Simple Path to Mastery
- How to get started with Machine Learning.
- How to download the material to follow along and make the projects.
- One chapter for each lesson with a Description, Learning Objectives, and link to the lesson video.
