Jupyter Notebook Archives - Onestring Lab

Belajar Data Science – Menyimpan Datafame ke File (.csv, .xlsx, .txt)

Rajo Intan — Thu, 21 Oct 2021 03:32:58 +0000

Setelah proses pengolahan data selesai dilakukan maka yang selanjutnya dilakukan adalah menyimpan data tersebut ke file lain.

Jupiter Notebook

Memuat data ke Dataframe Pandas

In [1]:

import pandas as pd
from google.colab import files

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

Deskripsi data

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

Mengurutkan data berdasarkan Top 10 Attack

In [3]:

top10attack = df.sort_values('Attack',ascending=False).head(10)

Menyimpan file ke .csv

In [4]:

top10attack.to_csv('top10attack.csv', index=False)

# Jika menggunakan Google Colabs maka lakukan ini
files.download('top10attack.csv')

Menyimpan file ke .xlsx

In [5]:

top10attack.to_excel('top10attack.xlsx', index=False)

# Jika menggunakan Google Colabs maka lakukan ini
files.download('top10attack.xlsx')

Menyimpan file ke .txt

In [6]:

top10attack.to_csv('top10attack.txt', index=False,sep='\t')

# Jika menggunakan Google Colabs maka lakukan ini
files.download('top10attack.txt')

Kesimpulan

Ternyata mudah sekali menyimpan data yang telah dikelola ke bentuk file yang diinginkan. Untuk artikel lain terkait dengan data science silahkan lihat kumpulan artikelnya disini.

The post Belajar Data Science – Menyimpan Datafame ke File (.csv, .xlsx, .txt) appeared first on Onestring Lab.

Belajar Data Science – Mengurutkan dan Menambahkan Kolom Pada Dataframe

Rajo Intan — Wed, 20 Oct 2021 07:26:50 +0000

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.

The post Belajar Data Science – Mengurutkan dan Menambahkan Kolom Pada Dataframe appeared first on Onestring Lab.

Belajar Data Science – Memuat Data CSV ke DataFrame

Rajo Intan — Tue, 19 Oct 2021 15:54:06 +0000

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

Jupyter Notebook

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.

The post Belajar Data Science – Memuat Data CSV ke DataFrame appeared first on Onestring Lab.