Pada artikel ini akan dibahas mengenai cara mengubah data yang telah dimuat ke Pandas DataFrame.

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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')

Menampilkan 10 data teratas

In [2]:

df.head(10)

Out[2]:

	#	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
5	5	Charmeleon	Fire	NaN	58	64	58	80	65	80	1	False
6	6	Charizard	Fire	Flying	78	84	78	109	85	100	1	False
7	6	CharizardMega Charizard X	Fire	Dragon	78	130	111	130	85	100	1	False
8	6	CharizardMega Charizard Y	Fire	Flying	78	104	78	159	115	100	1	False
9	7	Squirtle	Water	NaN	44	48	65	50	64	43	1	False

Mengubah data pada Type 1 dari Fire menjadi Flamer

In [3]:

df.loc[df['Type 1'] == 'Fire', 'Type 1'] = 'Flamer'
df.head(10)

Out[3]:

	#	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	Flamer	NaN	39	52	43	60	50	65	1	False
5	5	Charmeleon	Flamer	NaN	58	64	58	80	65	80	1	False
6	6	Charizard	Flamer	Flying	78	84	78	109	85	100	1	False
7	6	CharizardMega Charizard X	Flamer	Dragon	78	130	111	130	85	100	1	False
8	6	CharizardMega Charizard Y	Flamer	Flying	78	104	78	159	115	100	1	False
9	7	Squirtle	Water	NaN	44	48	65	50	64	43	1	False

Kembalikan ke data awal

In [4]:

df.loc[df['Type 1'] == 'Flamer', 'Type 1'] = 'Fire'
df.head(10)

Out[4]:

	#	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
5	5	Charmeleon	Fire	NaN	58	64	58	80	65	80	1	False
6	6	Charizard	Fire	Flying	78	84	78	109	85	100	1	False
7	6	CharizardMega Charizard X	Fire	Dragon	78	130	111	130	85	100	1	False
8	6	CharizardMega Charizard Y	Fire	Flying	78	104	78	159	115	100	1	False
9	7	Squirtle	Water	NaN	44	48	65	50	64	43	1	False

Mengubah data berdasarkan data kolom yang lainnya

In [5]:

df.loc[df['Type 1'] == 'Fire', 'Legendary'] = 'True'
df.head(10)

Out[5]:

	#	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	True
5	5	Charmeleon	Fire	NaN	58	64	58	80	65	80	1	True
6	6	Charizard	Fire	Flying	78	84	78	109	85	100	1	True
7	6	CharizardMega Charizard X	Fire	Dragon	78	130	111	130	85	100	1	True
8	6	CharizardMega Charizard Y	Fire	Flying	78	104	78	159	115	100	1	True
9	7	Squirtle	Water	NaN	44	48	65	50	64	43	1	False

Mengubah data beberapa kolom berdasarkan 1 kondisi

In [6]:

df.loc[df['HP'] >= 80, ['Generation','Legendary']] = [2,'True']
df.head(10)

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	2	True
3	3	VenusaurMega Venusaur	Grass	Poison	80	100	123	122	120	80	2	True
4	4	Charmander	Fire	NaN	39	52	43	60	50	65	1	True
5	5	Charmeleon	Fire	NaN	58	64	58	80	65	80	1	True
6	6	Charizard	Fire	Flying	78	84	78	109	85	100	1	True
7	6	CharizardMega Charizard X	Fire	Dragon	78	130	111	130	85	100	1	True
8	6	CharizardMega Charizard Y	Fire	Flying	78	104	78	159	115	100	1	True
9	7	Squirtle	Water	NaN	44	48	65	50	64	43	1	False

Mengubah data berdasarkan 2 kondisi

In [7]:

df.loc[(df['HP'] >= 50) & (df['Attack'] >= 60), 'Generation'] = 2
df.head(10)

Out[7]:

	#	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	2	False
2	3	Venusaur	Grass	Poison	80	82	83	100	100	80	2	True
3	3	VenusaurMega Venusaur	Grass	Poison	80	100	123	122	120	80	2	True
4	4	Charmander	Fire	NaN	39	52	43	60	50	65	1	True
5	5	Charmeleon	Fire	NaN	58	64	58	80	65	80	2	True
6	6	Charizard	Fire	Flying	78	84	78	109	85	100	2	True
7	6	CharizardMega Charizard X	Fire	Dragon	78	130	111	130	85	100	2	True
8	6	CharizardMega Charizard Y	Fire	Flying	78	104	78	159	115	100	2	True
9	7	Squirtle	Water	NaN	44	48	65	50	64	43	1	False

Kesimpulan

Pengubahan data dapat dilakukan melalui perintah yang telah disediakan oleh Python pada Pandas DataFrame. Untuk artikel lain terkait dengan data science silahkan lihat kumpulan artikelnya disini.

Belajar Data Science – Mengubah Data Berdasarkan Kondisi

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Kesimpulan

Rajo Intan

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Rajo Intan

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