# filter country which contains United df[df['Country'].str.contains('United')]
Rank
CCA3
Country
Capital
Continent
2022 Population
2020 Population
2015 Population
2010 Population
2000 Population
1990 Population
1980 Population
1970 Population
Area (km²)
Density (per km²)
Growth Rate
World Population Percentage
219
97
ARE
United Arab Emirates
Abu Dhabi
Asia
9441129.0
9287289.0
8916899.0
8481771.0
3275333.0
1900151.0
1014048.0
298084.0
83600.0
112.9322
1.0081
0.12
220
21
GBR
United Kingdom
London
Europe
67508936.0
67059474.0
65224364.0
62760039.0
58850043.0
57210442.0
56326328.0
55650166.0
242900.0
277.9289
1.0034
0.85
221
3
USA
United States
Washington, D.C.
North America
338289857.0
335942003.0
324607776.0
311182845.0
282398554.0
248083732.0
223140018.0
200328340.0
9372610.0
36.0935
1.0038
4.24
222
200
VIR
United States Virgin Islands
Charlotte Amalie
North America
99465.0
100442.0
102803.0
106142.0
108185.0
100685.0
96640.0
63446.0
347.0
286.6427
0.9937
0.00
1 2 3 4
# set index to country df2 = df.set_index('Country') # show only the columns Contient and CCA3 df2.filter(items = ['Continent', 'CCA3'])
Continent
CCA3
Country
Afghanistan
Asia
AFG
Albania
Europe
ALB
Algeria
Africa
DZA
American Samoa
Oceania
ASM
Andorra
Europe
AND
...
...
...
Wallis and Futuna
Oceania
WLF
Western Sahara
Africa
ESH
Yemen
Asia
YEM
Zambia
Africa
ZMB
Zimbabwe
Africa
ZWE
234 rows × 2 columns
1 2 3
# axis= is the vertical axis and axis=1 is the horizontal axis # The following returns 0 result as 'Continent', 'CCA3' do not exist in the vertical axis which is Country df2.filter(items=['Continent', 'CCA3'], axis=0)
Rank
CCA3
Capital
Continent
2022 Population
2020 Population
2015 Population
2010 Population
2000 Population
1990 Population
1980 Population
1970 Population
Area (km²)
Density (per km²)
Growth Rate
World Population Percentage
1 2
# but axis=1 exists as 'Continent', 'CCA3' exist in the horizontal axis df2.filter(items=['Continent', 'CCA3'], axis=1)
Continent
CCA3
Country
Afghanistan
Asia
AFG
Albania
Europe
ALB
Algeria
Africa
DZA
American Samoa
Oceania
ASM
Andorra
Europe
AND
...
...
...
Wallis and Futuna
Oceania
WLF
Western Sahara
Africa
ESH
Yemen
Asia
YEM
Zambia
Africa
ZMB
Zimbabwe
Africa
ZWE
234 rows × 2 columns
1 2
# This will return all the countries which contains "United" df2.filter(like='United', axis=0)
Rank
CCA3
Capital
Continent
2022 Population
2020 Population
2015 Population
2010 Population
2000 Population
1990 Population
1980 Population
1970 Population
Area (km²)
Density (per km²)
Growth Rate
World Population Percentage
Country
United Arab Emirates
97
ARE
Abu Dhabi
Asia
9441129.0
9287289.0
8916899.0
8481771.0
3275333.0
1900151.0
1014048.0
298084.0
83600.0
112.9322
1.0081
0.12
United Kingdom
21
GBR
London
Europe
67508936.0
67059474.0
65224364.0
62760039.0
58850043.0
57210442.0
56326328.0
55650166.0
242900.0
277.9289
1.0034
0.85
United States
3
USA
Washington, D.C.
North America
338289857.0
335942003.0
324607776.0
311182845.0
282398554.0
248083732.0
223140018.0
200328340.0
9372610.0
36.0935
1.0038
4.24
United States Virgin Islands
200
VIR
Charlotte Amalie
North America
99465.0
100442.0
102803.0
106142.0
108185.0
100685.0
96640.0
63446.0
347.0
286.6427
0.9937
0.00
1
df2.loc['United States']
Rank 3
CCA3 USA
Capital Washington, D.C.
Continent North America
2022 Population 338289857.0
2020 Population 335942003.0
2015 Population 324607776.0
2010 Population 311182845.0
2000 Population 282398554.0
1990 Population 248083732.0
1980 Population 223140018.0
1970 Population 200328340.0
Area (km²) 9372610.0
Density (per km²) 36.0935
Growth Rate 1.0038
World Population Percentage 4.24
Name: United States, dtype: object
1
df2.iloc[3]
Rank 213
CCA3 ASM
Capital Pago Pago
Continent Oceania
2022 Population 44273.0
2020 Population 46189.0
2015 Population 51368.0
2010 Population 54849.0
2000 Population 58230.0
1990 Population 47818.0
1980 Population 32886.0
1970 Population 27075.0
Area (km²) 199.0
Density (per km²) 222.4774
Growth Rate 0.9831
World Population Percentage 0.0
Name: American Samoa, dtype: object
1 2
# sort by Rank and Country in ascending df[df['Rank'] < 10].sort_values(by=['Rank', 'Country'], ascending=True)
Rank
CCA3
Country
Capital
Continent
2022 Population
2020 Population
2015 Population
2010 Population
2000 Population
1990 Population
1980 Population
1970 Population
Area (km²)
Density (per km²)
Growth Rate
World Population Percentage
41
1
CHN
China
Beijing
Asia
1.425887e+09
1.424930e+09
1.393715e+09
1.348191e+09
1.264099e+09
1.153704e+09
982372466.0
822534450.0
9706961.0
146.8933
1.0000
17.88
92
2
IND
India
New Delhi
Asia
1.417173e+09
1.396387e+09
1.322867e+09
1.240614e+09
1.059634e+09
NaN
NaN
557501301.0
3287590.0
431.0675
1.0068
17.77
221
3
USA
United States
Washington, D.C.
North America
3.382899e+08
3.359420e+08
3.246078e+08
3.111828e+08
2.823986e+08
2.480837e+08
223140018.0
200328340.0
9372610.0
36.0935
1.0038
4.24
93
4
IDN
Indonesia
Jakarta
Asia
2.755013e+08
2.718580e+08
2.590920e+08
2.440162e+08
2.140724e+08
1.821599e+08
148177096.0
115228394.0
1904569.0
144.6529
1.0064
3.45
156
5
PAK
Pakistan
Islamabad
Asia
2.358249e+08
2.271967e+08
2.109693e+08
1.944545e+08
1.543699e+08
1.154141e+08
80624057.0
59290872.0
881912.0
267.4018
1.0191
2.96
149
6
NGA
Nigeria
Abuja
Africa
2.185412e+08
2.083274e+08
1.839958e+08
1.609529e+08
1.228520e+08
9.521426e+07
72951439.0
55569264.0
923768.0
236.5759
1.0241
2.74
27
7
BRA
Brazil
Brasilia
South America
2.153135e+08
2.131963e+08
2.051882e+08
1.963535e+08
1.758737e+08
1.507064e+08
122288383.0
96369875.0
8515767.0
25.2841
1.0046
2.70
16
8
BGD
Bangladesh
Dhaka
Asia
1.711864e+08
1.674210e+08
1.578300e+08
1.483911e+08
1.291933e+08
1.071477e+08
83929765.0
67541860.0
147570.0
1160.0350
1.0108
2.15
171
9
RUS
Russia
Moscow
Europe
1.447133e+08
1.456173e+08
1.446684e+08
1.432426e+08
1.468448e+08
1.480057e+08
138257420.0
130093010.0
17098242.0
8.4636
0.9973
1.81
1 2
# sort by Rank in ascending and Country in descending df[df['Rank'] < 10].sort_values(by=['Rank', 'Country'], ascending=[True, False])