Pada artikel ini akan dibahas mengenai cara mencari data pada Pandas DataFrame. Teknik pencarian yang dibahas yaitu mencari data yang sesuai dan pencarian menggunakan regular expression.

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Memuat data ke Pandas

In [1]:

import pandas as pd

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

Melihat data awal

In [2]:

df.head()

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

Mencari data menggunakan perintah loc

In [3]:

# Mencari Type 1 = Grass
df.loc[df['Type 1'] == 'Grass'].head()

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
48	43	Oddish	Grass	Poison	45	50	55	75	65	30	1	False

In [4]:

# Mencari Type 1 = Grass dan Type 2 = Poison 
df.loc[(df['Type 1'] == 'Grass') & (df['Type 2'] == 'Poison')].head()

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
48	43	Oddish	Grass	Poison	45	50	55	75	65	30	1	False

In [5]:

# Mencari Type 1 = Grass dan Type 2 = Poison dan HP > 70 
df.loc[(df['Type 1'] == 'Grass') & (df['Type 2'] == 'Poison') & (df['HP'] > 70)].head()

Out[5]:

	#	Name	Type 1	Type 2	HP	Attack	Defense	Sp. Atk	Sp. Def	Speed	Generation	Legendary
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
50	45	Vileplume	Grass	Poison	75	80	85	110	90	50	1	False
77	71	Victreebel	Grass	Poison	80	105	65	100	70	70	1	False
652	591	Amoonguss	Grass	Poison	114	85	70	85	80	30	5	False

In [6]:

# Mencari Type 1 = Grass atau Type 2 = Water
df.loc[(df['Type 1'] == 'Grass') | (df['Type 2'] == 'Water')]

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
48	43	Oddish	Grass	Poison	45	50	55	75	65	30	1	False
...	...	...	...	...	...	...	...	...	...	...	...	...
741	673	Gogoat	Grass	NaN	123	100	62	97	81	68	6	False
758	688	Binacle	Rock	Water	42	52	67	39	56	50	6	False
759	689	Barbaracle	Rock	Water	72	105	115	54	86	68	6	False
760	690	Skrelp	Poison	Water	50	60	60	60	60	30	6	False
799	721	Volcanion	Fire	Water	80	110	120	130	90	70	6	True

84 rows × 12 columns

In [7]:

# mencari name yang terdapat kata "Mega"
df.loc[df['Name'].str.contains('Mega')].head(20)

Out[7]:

	#	Name	Type 1	Type 2	HP	Attack	Defense	Sp. Atk	Sp. Def	Speed	Generation	Legendary
3	3	VenusaurMega Venusaur	Grass	Poison	80	100	123	122	120	80	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
12	9	BlastoiseMega Blastoise	Water	NaN	79	103	120	135	115	78	1	False
19	15	BeedrillMega Beedrill	Bug	Poison	65	150	40	15	80	145	1	False
23	18	PidgeotMega Pidgeot	Normal	Flying	83	80	80	135	80	121	1	False
71	65	AlakazamMega Alakazam	Psychic	NaN	55	50	65	175	95	150	1	False
87	80	SlowbroMega Slowbro	Water	Psychic	95	75	180	130	80	30	1	False
102	94	GengarMega Gengar	Ghost	Poison	60	65	80	170	95	130	1	False
124	115	KangaskhanMega Kangaskhan	Normal	NaN	105	125	100	60	100	100	1	False
137	127	PinsirMega Pinsir	Bug	Flying	65	155	120	65	90	105	1	False
141	130	GyaradosMega Gyarados	Water	Dark	95	155	109	70	130	81	1	False
154	142	AerodactylMega Aerodactyl	Rock	Flying	80	135	85	70	95	150	1	False
163	150	MewtwoMega Mewtwo X	Psychic	Fighting	106	190	100	154	100	130	1	True
164	150	MewtwoMega Mewtwo Y	Psychic	NaN	106	150	70	194	120	140	1	True
168	154	Meganium	Grass	NaN	80	82	100	83	100	80	2	False
196	181	AmpharosMega Ampharos	Electric	Dragon	90	95	105	165	110	45	2	False
224	208	SteelixMega Steelix	Steel	Ground	75	125	230	55	95	30	2	False
229	212	ScizorMega Scizor	Bug	Steel	70	150	140	65	100	75	2	False
232	214	HeracrossMega Heracross	Bug	Fighting	80	185	115	40	105	75	2	False

In [8]:

# mencari name yang tidak terdapat kata "Mega"
df.loc[~df['Name'].str.contains('Mega')]

Out[8]:

	#	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
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
...	...	...	...	...	...	...	...	...	...	...	...	...
794	718	Zygarde50% Forme	Dragon	Ground	108	100	121	81	95	95	6	True
795	719	Diancie	Rock	Fairy	50	100	150	100	150	50	6	True
797	720	HoopaHoopa Confined	Psychic	Ghost	80	110	60	150	130	70	6	True
798	720	HoopaHoopa Unbound	Psychic	Dark	80	160	60	170	130	80	6	True
799	721	Volcanion	Fire	Water	80	110	120	130	90	70	6	True

751 rows × 12 columns

Pemanfaatan regular expression untuk pencarian

In [9]:

import re

df.loc[df['Type 1'].str.contains('Fire|Grass', regex=True)]

Out[9]:

	#	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
...	...	...	...	...	...	...	...	...	...	...	...	...
735	667	Litleo	Fire	Normal	62	50	58	73	54	72	6	False
736	668	Pyroar	Fire	Normal	86	68	72	109	66	106	6	False
740	672	Skiddo	Grass	NaN	66	65	48	62	57	52	6	False
741	673	Gogoat	Grass	NaN	123	100	62	97	81	68	6	False
799	721	Volcanion	Fire	Water	80	110	120	130	90	70	6	True

122 rows × 12 columns

In [10]:

df.loc[df['Type 1'].str.contains('fire|grass', flags=re.I, regex=True)]

Out[10]:

	#	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
...	...	...	...	...	...	...	...	...	...	...	...	...
735	667	Litleo	Fire	Normal	62	50	58	73	54	72	6	False
736	668	Pyroar	Fire	Normal	86	68	72	109	66	106	6	False
740	672	Skiddo	Grass	NaN	66	65	48	62	57	52	6	False
741	673	Gogoat	Grass	NaN	123	100	62	97	81	68	6	False
799	721	Volcanion	Fire	Water	80	110	120	130	90	70	6	True

122 rows × 12 columns

Menyalin data hasil filter

In [11]:

df_new = df.loc[(df['Type 1'] == 'Grass') & (df['Type 2'] == 'Poison') & (df['HP'] > 70)].head(20)
df_new

Out[11]:

	#	Name	Type 1	Type 2	HP	Attack	Defense	Sp. Atk	Sp. Def	Speed	Generation	Legendary
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
50	45	Vileplume	Grass	Poison	75	80	85	110	90	50	1	False
77	71	Victreebel	Grass	Poison	80	105	65	100	70	70	1	False
652	591	Amoonguss	Grass	Poison	114	85	70	85	80	30	5	False

Mengatur ulang angka index

In [12]:

df_new.reset_index()

Out[12]:

	index	#	Name	Type 1	Type 2	HP	Attack	Defense	Sp. Atk	Sp. Def	Speed	Generation	Legendary
0	2	3	Venusaur	Grass	Poison	80	82	83	100	100	80	1	False
1	3	3	VenusaurMega Venusaur	Grass	Poison	80	100	123	122	120	80	1	False
2	50	45	Vileplume	Grass	Poison	75	80	85	110	90	50	1	False
3	77	71	Victreebel	Grass	Poison	80	105	65	100	70	70	1	False
4	652	591	Amoonguss	Grass	Poison	114	85	70	85	80	30	5	False

In [13]:

# menghilangkan kolom index
df_new.reset_index(drop=True)

Out[13]:

	#	Name	Type 1	Type 2	HP	Attack	Defense	Sp. Atk	Sp. Def	Speed	Generation	Legendary
0	3	Venusaur	Grass	Poison	80	82	83	100	100	80	1	False
1	3	VenusaurMega Venusaur	Grass	Poison	80	100	123	122	120	80	1	False
2	45	Vileplume	Grass	Poison	75	80	85	110	90	50	1	False
3	71	Victreebel	Grass	Poison	80	105	65	100	70	70	1	False
4	591	Amoonguss	Grass	Poison	114	85	70	85	80	30	5	False

Kesimpulan

Pencarian data pada Pandas DataFrame dapat menggunakan pencarian berdasarkan data yang dicari atau dapat juga menggunakan regular expression. Untuk artikel lain terkait dengan data science silahkan lihat kumpulan artikelnya disini.

Belajar Data Science – Mencari Data Pada DataFrame

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Kesimpulan

Rajo Intan

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Jupyter Notebook

Kesimpulan

Rajo Intan

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