Pada artikel ini akan dibahas cara mengurutkan data pada data yang telah dimuat ke dalam Pandas DataFrame . Selain itu, akan dibahas juga mengenai cara menambahkan kolom pada data tersebut.

Jupyter Notebook

Memuat data ke Dataframe Pandas

In [1]:

import pandas as pd

df = pd.read_csv('https://raw.githubusercontent.com/Onestringlab/notebook/main/pokemon_data.csv')

Deskripsi data

In [2]:

df.describe()

Out[2]:

	#	HP	Attack	Defense	Sp. Atk	Sp. Def	Speed	Generation
count	800.000000	800.000000	800.000000	800.000000	800.000000	800.000000	800.000000	800.00000
mean	362.813750	69.258750	79.001250	73.842500	72.820000	71.902500	68.277500	3.32375
std	208.343798	25.534669	32.457366	31.183501	32.722294	27.828916	29.060474	1.66129
min	1.000000	1.000000	5.000000	5.000000	10.000000	20.000000	5.000000	1.00000
25%	184.750000	50.000000	55.000000	50.000000	49.750000	50.000000	45.000000	2.00000
50%	364.500000	65.000000	75.000000	70.000000	65.000000	70.000000	65.000000	3.00000
75%	539.250000	80.000000	100.000000	90.000000	95.000000	90.000000	90.000000	5.00000
max	721.000000	255.000000	190.000000	230.000000	194.000000	230.000000	180.000000	6.00000

Mengurutkan data

In [3]:

df.sort_values('Name',ascending=True)

Out[3]:

	#	Name	Type 1	Type 2	HP	Attack	Defense	Sp. Atk	Sp. Def	Speed	Generation	Legendary
510	460	Abomasnow	Grass	Ice	90	92	75	92	85	60	4	False
511	460	AbomasnowMega Abomasnow	Grass	Ice	90	132	105	132	105	30	4	False
68	63	Abra	Psychic	NaN	25	20	15	105	55	90	1	False
392	359	Absol	Dark	NaN	65	130	60	75	60	75	3	False
393	359	AbsolMega Absol	Dark	NaN	65	150	60	115	60	115	3	False
...	...	...	...	...	...	...	...	...	...	...	...	...
632	571	Zoroark	Dark	NaN	60	105	60	120	60	105	5	False
631	570	Zorua	Dark	NaN	40	65	40	80	40	65	5	False
46	41	Zubat	Poison	Flying	40	45	35	30	40	55	1	False
695	634	Zweilous	Dark	Dragon	72	85	70	65	70	58	5	False
794	718	Zygarde50% Forme	Dragon	Ground	108	100	121	81	95	95	6	True

800 rows × 12 columns

In [4]:

df.sort_values(['Type 1','HP'],ascending=[True,False])

Out[4]:

	#	Name	Type 1	Type 2	HP	Attack	Defense	Sp. Atk	Sp. Def	Speed	Generation	Legendary
520	469	Yanmega	Bug	Flying	86	76	86	116	56	95	4	False
698	637	Volcarona	Bug	Fire	85	60	65	135	105	100	5	False
231	214	Heracross	Bug	Fighting	80	125	75	40	95	85	2	False
232	214	HeracrossMega Heracross	Bug	Fighting	80	185	115	40	105	75	2	False
678	617	Accelgor	Bug	NaN	80	70	40	100	60	145	5	False
...	...	...	...	...	...	...	...	...	...	...	...	...
106	98	Krabby	Water	NaN	30	105	90	25	25	50	1	False
125	116	Horsea	Water	NaN	30	40	70	70	25	60	1	False
129	120	Staryu	Water	NaN	30	45	55	70	55	85	1	False
139	129	Magikarp	Water	NaN	20	10	55	15	20	80	1	False
381	349	Feebas	Water	NaN	20	15	20	10	55	80	3	False

800 rows × 12 columns

Menambahkan kolom Total

In [5]:

df['Total'] = df['HP'] + df['Attack'] + df['Defense'] + df['Sp. Atk'] + df['Sp. Def'] + df['Speed']
df.head(5)

Out[5]:

	#	Name	Type 1	Type 2	HP	Attack	Defense	Sp. Atk	Sp. Def	Speed	Generation	Legendary	Total
0	1	Bulbasaur	Grass	Poison	45	49	49	65	65	45	1	False	318
1	2	Ivysaur	Grass	Poison	60	62	63	80	80	60	1	False	405
2	3	Venusaur	Grass	Poison	80	82	83	100	100	80	1	False	525
3	3	VenusaurMega Venusaur	Grass	Poison	80	100	123	122	120	80	1	False	625
4	4	Charmander	Fire	NaN	39	52	43	60	50	65	1	False	309

Menghapus kolom Total

In [6]:

df = df.drop(columns = ['Total'])
df.head()

Out[6]:

	#	Name	Type 1	Type 2	HP	Attack	Defense	Sp. Atk	Sp. Def	Speed	Generation	Legendary
0	1	Bulbasaur	Grass	Poison	45	49	49	65	65	45	1	False
1	2	Ivysaur	Grass	Poison	60	62	63	80	80	60	1	False
2	3	Venusaur	Grass	Poison	80	82	83	100	100	80	1	False
3	3	VenusaurMega Venusaur	Grass	Poison	80	100	123	122	120	80	1	False
4	4	Charmander	Fire	NaN	39	52	43	60	50	65	1	False

Menambahkan kolom Total

In [7]:

df['Total'] = df.iloc[:,4:10].sum(axis=1)
df.head()

Out[7]:

	#	Name	Type 1	Type 2	HP	Attack	Defense	Sp. Atk	Sp. Def	Speed	Generation	Legendary	Total
0	1	Bulbasaur	Grass	Poison	45	49	49	65	65	45	1	False	318
1	2	Ivysaur	Grass	Poison	60	62	63	80	80	60	1	False	405
2	3	Venusaur	Grass	Poison	80	82	83	100	100	80	1	False	525
3	3	VenusaurMega Venusaur	Grass	Poison	80	100	123	122	120	80	1	False	625
4	4	Charmander	Fire	NaN	39	52	43	60	50	65	1	False	309

Mengubah urutan kolom

In [8]:

cols = list(df.columns)

df = df[cols[0:10] + [cols[-1]] + cols[10:12]]
df.head(5)

Out[8]:

	#	Name	Type 1	Type 2	HP	Attack	Defense	Sp. Atk	Sp. Def	Speed	Total	Generation	Legendary
0	1	Bulbasaur	Grass	Poison	45	49	49	65	65	45	318	1	False
1	2	Ivysaur	Grass	Poison	60	62	63	80	80	60	405	1	False
2	3	Venusaur	Grass	Poison	80	82	83	100	100	80	525	1	False
3	3	VenusaurMega Venusaur	Grass	Poison	80	100	123	122	120	80	625	1	False
4	4	Charmander	Fire	NaN	39	52	43	60	50	65	309	1	False

Kesimpulan

Cara mengurutkan data dan menambahkan kolom pada Pandas DataFrame mudah untuk dilakukan. Untuk artikel lain terkait dengan data science silahkan lihat kumpulan artikelnya disini.

Belajar Data Science – Mengurutkan dan Menambahkan Kolom Pada Dataframe

Jupyter Notebook

Kesimpulan

Rajo Intan

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Jupyter Notebook

Kesimpulan

Rajo Intan

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