Practice #8 Slicing

import pandas as pd

df1 = pd.read_excel("E09EXAMPLE.xlsx", skiprows=1)
df1

	Unnamed: 0	Year	Team	Match	Champion	Runners-up	Third	Fourth	Goal	Attendance	HostCountry	Unnamed: 11	Unnamed: 12	Unnamed: 13	Unnamed: 14	Unnamed: 15	Unnamed: 16	Unnamed: 17
0	NaN	1930	13	18	Uruguay	Argentina	USA	Yugoslavia	70	590.549	Uruguay	NaN	NaN	NaN	NaN	NaN	NaN	NaN
1	NaN	1934	16	17	Italy	Czechoslovakia	Germany	Austria	70	363	Italy	NaN	좌측 표는 역대 월드컵대회의 자료이다	NaN	NaN	NaN	NaN	NaN
2	NaN	1938	15	18	Italy	Hungary	Brazil	Sweden	84	375.7	France	NaN	좌측 표를	NaN	NaN	NaN	NaN	NaN
3	NaN	1950	13	22	Uruguay	Brazil	Sweden	Spain	88	1.045.246	Brazil	NaN	NaN	NaN	NaN	NaN	NaN	NaN
4	NaN	1954	16	26	Germany	Hungary	Austria	Uruguay	140	768.607	Switzerland	NaN	NaN	NaN	NaN	NaN	NaN	NaN
5	NaN	1958	16	35	Brazil	Sweden	France	Germany	126	819.81	Sweden	NaN	년도	개최국	1위	2위	3위	4위
6	NaN	1962	16	32	Brazil	Czechoslovakia	Chile	Yugoslavia	89	893.172	Chile	NaN	1930	Uruguay	Uruguay	Argentina	USA	Yugoslavia
7	NaN	1966	16	32	England	Germany	Portugal	Soviet Union	89	1.563.135	England	NaN	...	...	...	...	...	...
8	NaN	1970	16	32	Brazil	Italy	Germany	Uruguay	95	1.603.975	Mexico	NaN	2014	Brazil	Germany	Argentina	Netherlands	Brazil
9	NaN	1974	16	38	Germany	Netherlands	Poland	Brazil	97	1.865.753	Germany	NaN	NaN	NaN	NaN	NaN	NaN	NaN
10	NaN	1978	16	38	Argentina	Netherlands	Brazil	Italy	102	1.545.791	Argentina	NaN	NaN	NaN	NaN	NaN	NaN	NaN
11	NaN	1982	24	52	Italy	Germany	Poland	France	146	2.109.723	Spain	NaN	NaN	NaN	NaN	NaN	NaN	NaN
12	NaN	1986	24	52	Argentina	Germany	France	Belgium	132	2.394.031	Mexico	NaN	NaN	NaN	NaN	NaN	NaN	NaN
13	NaN	1990	24	52	Germany	Argentina	Italy	England	115	2.516.215	Italy	NaN	NaN	NaN	NaN	NaN	NaN	NaN
14	NaN	1994	24	52	Brazil	Italy	Sweden	Bulgaria	141	3.587.538	USA	NaN	NaN	NaN	NaN	NaN	NaN	NaN
15	NaN	1998	32	64	France	Brazil	Croatia	Netherlands	171	2.785.100	France	NaN	NaN	NaN	NaN	NaN	NaN	NaN
16	NaN	2002	32	64	Brazil	Germany	Turkey	Korea Republic	161	2.705.197	Korea/Japan	NaN	NaN	NaN	NaN	NaN	NaN	NaN
17	NaN	2006	32	64	Italy	France	Germany	Portugal	147	3.359.439	Germany	NaN	NaN	NaN	NaN	NaN	NaN	NaN
18	NaN	2010	32	64	Spain	Netherlands	Germany	Uruguay	145	3.178.856	South Africa	NaN	NaN	NaN	NaN	NaN	NaN	NaN
19	NaN	2014	32	64	Germany	Argentina	Netherlands	Brazil	171	3.386.810	Brazil	NaN	NaN	NaN	NaN	NaN	NaN	NaN

df1 = df1.loc[:,"Year":"HostCountry" ]
df1 = df1.iloc[:, [0, 9, 3, 4, 5, 6]]
df1

	Year	HostCountry	Champion	Runners-up	Third	Fourth
0	1930	Uruguay	Uruguay	Argentina	USA	Yugoslavia
1	1934	Italy	Italy	Czechoslovakia	Germany	Austria
2	1938	France	Italy	Hungary	Brazil	Sweden
3	1950	Brazil	Uruguay	Brazil	Sweden	Spain
4	1954	Switzerland	Germany	Hungary	Austria	Uruguay
5	1958	Sweden	Brazil	Sweden	France	Germany
6	1962	Chile	Brazil	Czechoslovakia	Chile	Yugoslavia
7	1966	England	England	Germany	Portugal	Soviet Union
8	1970	Mexico	Brazil	Italy	Germany	Uruguay
9	1974	Germany	Germany	Netherlands	Poland	Brazil
10	1978	Argentina	Argentina	Netherlands	Brazil	Italy
11	1982	Spain	Italy	Germany	Poland	France
12	1986	Mexico	Argentina	Germany	France	Belgium
13	1990	Italy	Germany	Argentina	Italy	England
14	1994	USA	Brazil	Italy	Sweden	Bulgaria
15	1998	France	France	Brazil	Croatia	Netherlands
16	2002	Korea/Japan	Brazil	Germany	Turkey	Korea Republic
17	2006	Germany	Italy	France	Germany	Portugal
18	2010	South Africa	Spain	Netherlands	Germany	Uruguay
19	2014	Brazil	Germany	Argentina	Netherlands	Brazil

df1.to_clipboard(index=False)

import pandas as pd

df1 = pd.read_excel("E09EXAMPLE.xlsx", skiprows=1)
df1 = df1.loc[:,"Year":"HostCountry"]
df1 = df1.iloc[:,[0,9,3,4,5,6]]
df1.to_clipboard(index=False)