Pada artikel ini akan di bahas mengenai menampilkan data dari sebuah file .csv dan juga beberapa cara untuk pencarian terhadap data yang ada.

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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')

Menampilkan 5 data pertama

In [2]:

print(df.head(5))

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

   Sp. Def  Speed  Generation  Legendary  
0       65     45           1      False  
1       80     60           1      False  
2      100     80           1      False  
3      120     80           1      False  
4       50     65           1      False

Menampilkan 10 data terakhir

In [3]:

print(df.tail(5))

       #                 Name   Type 1 Type 2  HP  Attack  Defense  Sp. Atk  \
795  719              Diancie     Rock  Fairy  50     100      150      100   
796  719  DiancieMega Diancie     Rock  Fairy  50     160      110      160   
797  720  HoopaHoopa Confined  Psychic  Ghost  80     110       60      150   
798  720   HoopaHoopa Unbound  Psychic   Dark  80     160       60      170   
799  721            Volcanion     Fire  Water  80     110      120      130   

     Sp. Def  Speed  Generation  Legendary  
795      150     50           6       True  
796      110    110           6       True  
797      130     70           6       True  
798      130     80           6       True  
799       90     70           6       True

Menampilkan nama judul kolom

In [4]:

print(df.columns)

Index(['#', 'Name', 'Type 1', 'Type 2', 'HP', 'Attack', 'Defense', 'Sp. Atk',
       'Sp. Def', 'Speed', 'Generation', 'Legendary'],
      dtype='object')

Menampilkan data berdasarkan kolom

In [5]:

print(df[['Name','Type 1', 'HP']])

                      Name   Type 1  HP
0                Bulbasaur    Grass  45
1                  Ivysaur    Grass  60
2                 Venusaur    Grass  80
3    VenusaurMega Venusaur    Grass  80
4               Charmander     Fire  39
..                     ...      ...  ..
795                Diancie     Rock  50
796    DiancieMega Diancie     Rock  50
797    HoopaHoopa Confined  Psychic  80
798     HoopaHoopa Unbound  Psychic  80
799              Volcanion     Fire  80

[800 rows x 3 columns]

Menampilkan data berdasarkan baris

In [6]:

print(df.iloc[0:4])

   #                   Name Type 1  Type 2  HP  Attack  Defense  Sp. Atk  \
0  1              Bulbasaur  Grass  Poison  45      49       49       65   
1  2                Ivysaur  Grass  Poison  60      62       63       80   
2  3               Venusaur  Grass  Poison  80      82       83      100   
3  3  VenusaurMega Venusaur  Grass  Poison  80     100      123      122   

   Sp. Def  Speed  Generation  Legendary  
0       65     45           1      False  
1       80     60           1      False  
2      100     80           1      False  
3      120     80           1      False

In [7]:

for index, row in df.iterrows():
  print(index,row['Name'])
  if index == 50:
    break

0 Bulbasaur
1 Ivysaur
2 Venusaur
3 VenusaurMega Venusaur
4 Charmander
5 Charmeleon
6 Charizard
7 CharizardMega Charizard X
8 CharizardMega Charizard Y
9 Squirtle
10 Wartortle
11 Blastoise
12 BlastoiseMega Blastoise
13 Caterpie
14 Metapod
15 Butterfree
16 Weedle
17 Kakuna
18 Beedrill
19 BeedrillMega Beedrill
20 Pidgey
21 Pidgeotto
22 Pidgeot
23 PidgeotMega Pidgeot
24 Rattata
25 Raticate
26 Spearow
27 Fearow
28 Ekans
29 Arbok
30 Pikachu
31 Raichu
32 Sandshrew
33 Sandslash
34 Nidoran (Female)
35 Nidorina
36 Nidoqueen
37 Nidoran (Male)
38 Nidorino
39 Nidoking
40 Clefairy
41 Clefable
42 Vulpix
43 Ninetales
44 Jigglypuff
45 Wigglytuff
46 Zubat
47 Golbat
48 Oddish
49 Gloom
50 Vileplume

In [8]:

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

     #                       Name Type 1  Type 2  HP  Attack  Defense  \
4    4                 Charmander   Fire     NaN  39      52       43   
5    5                 Charmeleon   Fire     NaN  58      64       58   
6    6                  Charizard   Fire  Flying  78      84       78   
7    6  CharizardMega Charizard X   Fire  Dragon  78     130      111   
8    6  CharizardMega Charizard Y   Fire  Flying  78     104       78   
42  37                     Vulpix   Fire     NaN  38      41       40   
43  38                  Ninetales   Fire     NaN  73      76       75   
63  58                  Growlithe   Fire     NaN  55      70       45   
64  59                   Arcanine   Fire     NaN  90     110       80   
83  77                     Ponyta   Fire     NaN  50      85       55   

    Sp. Atk  Sp. Def  Speed  Generation  Legendary  
4        60       50     65           1      False  
5        80       65     80           1      False  
6       109       85    100           1      False  
7       130       85    100           1      False  
8       159      115    100           1      False  
42       50       65     65           1      False  
43       81      100    100           1      False  
63       70       50     60           1      False  
64      100       80     95           1      False  
83       65       65     90           1      False

Kesimpulan

Dataframe merupakan sebuah format yang dimiliki oleh library Pandas. Ini merupakan format yang biasa digunakan para data scientist untuk mengolah data yang dimiliki. Untuk artikel lain terkait dengan data science silahkan lihat kumpulan artikelnya disini.

Belajar Data Science – Memuat Data CSV ke DataFrame

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Kesimpulan

Rajo Intan

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Jupyter Notebook

Kesimpulan

Rajo Intan

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