Pandas 입문 1

판다스

라이브러리 불러오기

import pandas as pd
print(pd.__version__)

1.3.5

테스트

데이터 프레임

temp_dic = {"col1" : [1, 2, 3], 
            "col2" : [3, 4, 5]} # 먼저 딕셔너리를 만든다.
          
df = pd.DataFrame(temp_dic) # 판다스는 객체가 시리즈와 데이터프레임으로 나뉜다.
print(type(df))
print(df)

<class 'pandas.core.frame.DataFrame'>
   col1  col2
0     1     3
1     2     4
2     3     5

시리즈

temp_dic = {'a':1, 'b':2, 'c':3} # 인덱스는 숫자나 문자나 모두 가능하다.
ser = pd.Series(temp_dic)
print(ser)
print(type(ser))

a    1
b    2
c    3
dtype: int64
<class 'pandas.core.series.Series'>

• 언뜻 보기에는 같아보인다.
• 그러나, 다른 클래스고, 메서드도 다르다. 조심해야 한다.

데이터 불러오기

구글 드라이브 연동

Lemonade2016.csv 파일

from google.colab import drive
drive.mount('/content/drive')

Mounted at /content/drive

DATA_PATH = '/content/drive/MyDrive/Colab Notebooks/data/Lemonade2016.csv'
juice = pd.read_csv(DATA_PATH) # 객체를 대문자로 썼는데, 개발자들이 좋아하는 방법이다. 눈에 띄는 걸 좋아한다. 
juice

	Date	Location	Lemon	Orange	Temperature	Leaflets	Price
0	7/1/2016	Park	97	67	70	90.0	0.25
1	7/2/2016	Park	98	67	72	90.0	0.25
2	7/3/2016	Park	110	77	71	104.0	0.25
3	7/4/2016	Beach	134	99	76	98.0	0.25
4	7/5/2016	Beach	159	118	78	135.0	0.25
5	7/6/2016	Beach	103	69	82	90.0	0.25
6	7/6/2016	Beach	103	69	82	90.0	0.25
7	7/7/2016	Beach	143	101	81	135.0	0.25
8	NaN	Beach	123	86	82	113.0	0.25
9	7/9/2016	Beach	134	95	80	126.0	0.25
10	7/10/2016	Beach	140	98	82	131.0	0.25
11	7/11/2016	Beach	162	120	83	135.0	0.25
12	7/12/2016	Beach	130	95	84	99.0	0.25
13	7/13/2016	Beach	109	75	77	99.0	0.25
14	7/14/2016	Beach	122	85	78	113.0	0.25
15	7/15/2016	Beach	98	62	75	108.0	0.50
16	7/16/2016	Beach	81	50	74	90.0	0.50
17	7/17/2016	Beach	115	76	77	126.0	0.50
18	7/18/2016	Park	131	92	81	122.0	0.50
19	7/19/2016	Park	122	85	78	113.0	0.50
20	7/20/2016	Park	71	42	70	NaN	0.50
21	7/21/2016	Park	83	50	77	90.0	0.50
22	7/22/2016	Park	112	75	80	108.0	0.50
23	7/23/2016	Park	120	82	81	117.0	0.50
24	7/24/2016	Park	121	82	82	117.0	0.50
25	7/25/2016	Park	156	113	84	135.0	0.50
26	7/26/2016	Park	176	129	83	158.0	0.35
27	7/27/2016	Park	104	68	80	99.0	0.35
28	7/28/2016	Park	96	63	82	90.0	0.35
29	7/29/2016	Park	100	66	81	95.0	0.35
30	7/30/2016	Beach	88	57	82	81.0	0.35
31	7/31/2016	Beach	76	47	82	68.0	0.35

<svg xmlns=”http://www.w3.org/2000/svg" height=”24px”viewBox=”0 0 24 24”
width=”24px”>

  <script>
    const buttonEl =
      document.querySelector('#df-377049de-a05e-4731-8fe4-a3adc97f89dd button.colab-df-convert');
    buttonEl.style.display =
      google.colab.kernel.accessAllowed ? 'block' : 'none';

    async function convertToInteractive(key) {
      const element = document.querySelector('#df-377049de-a05e-4731-8fe4-a3adc97f89dd');
      const dataTable =
        await google.colab.kernel.invokeFunction('convertToInteractive',
                                                 [key], {});
      if (!dataTable) return;

      const docLinkHtml = 'Like what you see? Visit the ' +
        '<a target="_blank" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'
        + ' to learn more about interactive tables.';
      element.innerHTML = '';
      dataTable['output_type'] = 'display_data';
      await google.colab.output.renderOutput(dataTable, element);
      const docLink = document.createElement('div');
      docLink.innerHTML = docLinkHtml;
      element.appendChild(docLink);
    }
  </script>
</div>

• 데이터를 불러왔다. 데이터프레임 메서드를 하나씩 해볼 것이다.
• 첫번째 파악해야 하는 것
  • 데이터 구조

juice.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 32 entries, 0 to 31
Data columns (total 7 columns):
 #   Column       Non-Null Count  Dtype  
---  ------       --------------  -----  
 0   Date         31 non-null     object 
 1   Location     32 non-null     object 
 2   Lemon        32 non-null     int64  
 3   Orange       32 non-null     int64  
 4   Temperature  32 non-null     int64  
 5   Leaflets     31 non-null     float64
 6   Price        32 non-null     float64
dtypes: float64(2), int64(3), object(2)
memory usage: 1.9+ KB

• 결측치(NaN)가 있다면 Non-Null Count의 수가 다르다. Date, Leaflets는 결측치가 하나씩 있기 때문에 다른 칼럼에 비해 수가 하나 모자르다. 31.
• .head() 함수는 상위값
• .tail() 함수는 하위값

juice.head()

	Date	Location	Lemon	Orange	Temperature	Leaflets	Price
0	7/1/2016	Park	97	67	70	90.0	0.25
1	7/2/2016	Park	98	67	72	90.0	0.25
2	7/3/2016	Park	110	77	71	104.0	0.25
3	7/4/2016	Beach	134	99	76	98.0	0.25
4	7/5/2016	Beach	159	118	78	135.0	0.25

<svg xmlns=”http://www.w3.org/2000/svg" height=”24px”viewBox=”0 0 24 24”
width=”24px”>

  <script>
    const buttonEl =
      document.querySelector('#df-85ef7723-5da0-4047-9470-852c38d80b3d button.colab-df-convert');
    buttonEl.style.display =
      google.colab.kernel.accessAllowed ? 'block' : 'none';

    async function convertToInteractive(key) {
      const element = document.querySelector('#df-85ef7723-5da0-4047-9470-852c38d80b3d');
      const dataTable =
        await google.colab.kernel.invokeFunction('convertToInteractive',
                                                 [key], {});
      if (!dataTable) return;

      const docLinkHtml = 'Like what you see? Visit the ' +
        '<a target="_blank" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'
        + ' to learn more about interactive tables.';
      element.innerHTML = '';
      dataTable['output_type'] = 'display_data';
      await google.colab.output.renderOutput(dataTable, element);
      const docLink = document.createElement('div');
      docLink.innerHTML = docLinkHtml;
      element.appendChild(docLink);
    }
  </script>
</div>

juice.tail()

	Date	Location	Lemon	Orange	Temperature	Leaflets	Price
27	7/27/2016	Park	104	68	80	99.0	0.35
28	7/28/2016	Park	96	63	82	90.0	0.35
29	7/29/2016	Park	100	66	81	95.0	0.35
30	7/30/2016	Beach	88	57	82	81.0	0.35
31	7/31/2016	Beach	76	47	82	68.0	0.35

<svg xmlns=”http://www.w3.org/2000/svg" height=”24px”viewBox=”0 0 24 24”
width=”24px”>

  <script>
    const buttonEl =
      document.querySelector('#df-2d82ba80-103f-4ffb-86a3-eb0b3ce806a0 button.colab-df-convert');
    buttonEl.style.display =
      google.colab.kernel.accessAllowed ? 'block' : 'none';

    async function convertToInteractive(key) {
      const element = document.querySelector('#df-2d82ba80-103f-4ffb-86a3-eb0b3ce806a0');
      const dataTable =
        await google.colab.kernel.invokeFunction('convertToInteractive',
                                                 [key], {});
      if (!dataTable) return;

      const docLinkHtml = 'Like what you see? Visit the ' +
        '<a target="_blank" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'
        + ' to learn more about interactive tables.';
      element.innerHTML = '';
      dataTable['output_type'] = 'display_data';
      await google.colab.output.renderOutput(dataTable, element);
      const docLink = document.createElement('div');
      docLink.innerHTML = docLinkHtml;
      element.appendChild(docLink);
    }
  </script>
</div>

• describe() 함수
• 기술통계량 확인해주는 함수

juice.describe()
# type(juice.describe()) -> dataframe

	Lemon	Orange	Temperature	Leaflets	Price
count	32.000000	32.000000	32.000000	31.000000	32.000000
mean	116.156250	80.000000	78.968750	108.548387	0.354687
std	25.823357	21.863211	4.067847	20.117718	0.113137
min	71.000000	42.000000	70.000000	68.000000	0.250000
25%	98.000000	66.750000	77.000000	90.000000	0.250000
50%	113.500000	76.500000	80.500000	108.000000	0.350000
75%	131.750000	95.000000	82.000000	124.000000	0.500000
max	176.000000	129.000000	84.000000	158.000000	0.500000

<svg xmlns=”http://www.w3.org/2000/svg" height=”24px”viewBox=”0 0 24 24”
width=”24px”>

  <script>
    const buttonEl =
      document.querySelector('#df-617326f6-852f-40cd-8bdb-9e8c2ac9098d button.colab-df-convert');
    buttonEl.style.display =
      google.colab.kernel.accessAllowed ? 'block' : 'none';

    async function convertToInteractive(key) {
      const element = document.querySelector('#df-617326f6-852f-40cd-8bdb-9e8c2ac9098d');
      const dataTable =
        await google.colab.kernel.invokeFunction('convertToInteractive',
                                                 [key], {});
      if (!dataTable) return;

      const docLinkHtml = 'Like what you see? Visit the ' +
        '<a target="_blank" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'
        + ' to learn more about interactive tables.';
      element.innerHTML = '';
      dataTable['output_type'] = 'display_data';
      await google.colab.output.renderOutput(dataTable, element);
      const docLink = document.createElement('div');
      docLink.innerHTML = docLinkHtml;
      element.appendChild(docLink);
    }
  </script>
</div>

• Location 칼럼은 문자라서 describe함수가 적용되지 않는다.
• value_counts()

print(juice['Location'].value_counts())
print(type(juice['Location'].value_counts())) # 얘는 시리즈네?

Beach    17
Park     15
Name: Location, dtype: int64
<class 'pandas.core.series.Series'>

데이터 다뤄보기

• 행과 열을 만져보자.
• 열 추가(칼럼 추가)

juice['sold'] = 0 # 새로운 컬럼 추가
print(juice.head(3))

       Date Location  Lemon  Orange  Temperature  Leaflets  Price  sold
0  7/1/2016     Park     97      67           70      90.0   0.25     0
1  7/2/2016     Park     98      67           72      90.0   0.25     0
2  7/3/2016     Park    110      77           71     104.0   0.25     0

juice['sold'] = juice['Lemon'] + juice['Orange']
print(juice.head(3))

       Date Location  Lemon  Orange  Temperature  Leaflets  Price  sold
0  7/1/2016     Park     97      67           70      90.0   0.25   164
1  7/2/2016     Park     98      67           72      90.0   0.25   165
2  7/3/2016     Park    110      77           71     104.0   0.25   187

• 퀴즈
  • 매출액 = 가격 * 판매량
  • Revenue

juice['Revenue'] = juice['Price'] * juice['sold']
print(juice.head(3))

       Date Location  Lemon  Orange  Temperature  Leaflets  Price  sold  \
0  7/1/2016     Park     97      67           70      90.0   0.25   164   
1  7/2/2016     Park     98      67           72      90.0   0.25   165   
2  7/3/2016     Park    110      77           71     104.0   0.25   187   

   Revenue  
0    41.00  
1    41.25  
2    46.75  

• 행과 열 제거
• drop(axis=0, 1)
  • axis를 0으로 설정 시, 행(=index) 방향으로 drop() 실행
  • axis를 1로 설정 시, 열 방향으로 drop 수행함.

juice_column_drop = juice.drop('sold', axis = 1) # 열 방향, 'sold'열 하나가 통째로 삭제
print(juice_column_drop.head(3))

       Date Location  Lemon  Orange  Temperature  Leaflets  Price  Revenue
0  7/1/2016     Park     97      67           70      90.0   0.25    41.00
1  7/2/2016     Park     98      67           72      90.0   0.25    41.25
2  7/3/2016     Park    110      77           71     104.0   0.25    46.75

juice_row_drop = juice.drop(0, axis = 0) # 행 방향, 인덱스 0이 통째로 삭제 
print(juice_row_drop.head(3))

       Date Location  Lemon  Orange  Temperature  Leaflets  Price  sold  \
1  7/2/2016     Park     98      67           72      90.0   0.25   165   
2  7/3/2016     Park    110      77           71     104.0   0.25   187   
3  7/4/2016    Beach    134      99           76      98.0   0.25   233   

   Revenue  
1    41.25  
2    46.75  
3    58.25  

데이터 인덱싱

juice[0:5]

	Date	Location	Lemon	Orange	Temperature	Leaflets	Price	sold	Revenue
0	7/1/2016	Park	97	67	70	90.0	0.25	164	41.00
1	7/2/2016	Park	98	67	72	90.0	0.25	165	41.25
2	7/3/2016	Park	110	77	71	104.0	0.25	187	46.75
3	7/4/2016	Beach	134	99	76	98.0	0.25	233	58.25
4	7/5/2016	Beach	159	118	78	135.0	0.25	277	69.25

<svg xmlns=”http://www.w3.org/2000/svg" height=”24px”viewBox=”0 0 24 24”
width=”24px”>

  <script>
    const buttonEl =
      document.querySelector('#df-3ff8756e-ccea-4443-937c-96dd4ef951a2 button.colab-df-convert');
    buttonEl.style.display =
      google.colab.kernel.accessAllowed ? 'block' : 'none';

    async function convertToInteractive(key) {
      const element = document.querySelector('#df-3ff8756e-ccea-4443-937c-96dd4ef951a2');
      const dataTable =
        await google.colab.kernel.invokeFunction('convertToInteractive',
                                                 [key], {});
      if (!dataTable) return;

      const docLinkHtml = 'Like what you see? Visit the ' +
        '<a target="_blank" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'
        + ' to learn more about interactive tables.';
      element.innerHTML = '';
      dataTable['output_type'] = 'display_data';
      await google.colab.output.renderOutput(dataTable, element);
      const docLink = document.createElement('div');
      docLink.innerHTML = docLinkHtml;
      element.appendChild(docLink);
    }
  </script>
</div>

boolean 값을 활용한 데이터추출

juice['Location'] == "Beach"

0     False
1     False
2     False
3      True
4      True
5      True
6      True
7      True
8      True
9      True
10     True
11     True
12     True
13     True
14     True
15     True
16     True
17     True
18    False
19    False
20    False
21    False
22    False
23    False
24    False
25    False
26    False
27    False
28    False
29    False
30     True
31     True
Name: Location, dtype: bool

# Location이 Beach인 경우
# juice['Location'].value_counts()
juice[juice['Location'] == "Beach"]

	Date	Location	Lemon	Orange	Temperature	Leaflets	Price	sold	Revenue
3	7/4/2016	Beach	134	99	76	98.0	0.25	233	58.25
4	7/5/2016	Beach	159	118	78	135.0	0.25	277	69.25
5	7/6/2016	Beach	103	69	82	90.0	0.25	172	43.00
6	7/6/2016	Beach	103	69	82	90.0	0.25	172	43.00
7	7/7/2016	Beach	143	101	81	135.0	0.25	244	61.00
8	NaN	Beach	123	86	82	113.0	0.25	209	52.25
9	7/9/2016	Beach	134	95	80	126.0	0.25	229	57.25
10	7/10/2016	Beach	140	98	82	131.0	0.25	238	59.50
11	7/11/2016	Beach	162	120	83	135.0	0.25	282	70.50
12	7/12/2016	Beach	130	95	84	99.0	0.25	225	56.25
13	7/13/2016	Beach	109	75	77	99.0	0.25	184	46.00
14	7/14/2016	Beach	122	85	78	113.0	0.25	207	51.75
15	7/15/2016	Beach	98	62	75	108.0	0.50	160	80.00
16	7/16/2016	Beach	81	50	74	90.0	0.50	131	65.50
17	7/17/2016	Beach	115	76	77	126.0	0.50	191	95.50
30	7/30/2016	Beach	88	57	82	81.0	0.35	145	50.75
31	7/31/2016	Beach	76	47	82	68.0	0.35	123	43.05

<svg xmlns=”http://www.w3.org/2000/svg" height=”24px”viewBox=”0 0 24 24”
width=”24px”>

  <script>
    const buttonEl =
      document.querySelector('#df-f434477b-a70d-4d15-a445-7a178f78849e button.colab-df-convert');
    buttonEl.style.display =
      google.colab.kernel.accessAllowed ? 'block' : 'none';

    async function convertToInteractive(key) {
      const element = document.querySelector('#df-f434477b-a70d-4d15-a445-7a178f78849e');
      const dataTable =
        await google.colab.kernel.invokeFunction('convertToInteractive',
                                                 [key], {});
      if (!dataTable) return;

      const docLinkHtml = 'Like what you see? Visit the ' +
        '<a target="_blank" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'
        + ' to learn more about interactive tables.';
      element.innerHTML = '';
      dataTable['output_type'] = 'display_data';
      await google.colab.output.renderOutput(dataTable, element);
      const docLink = document.createElement('div');
      docLink.innerHTML = docLinkHtml;
      element.appendChild(docLink);
    }
  </script>
</div>

iloc vs loc

• 차이를 확인한다.

juice.iloc[:, 0:2] # 전체 데이터를 가져와라 그리고 0번부터 1번(n-1) 칼럼을 가져와라

	Date	Location
0	7/1/2016	Park
1	7/2/2016	Park
2	7/3/2016	Park
3	7/4/2016	Beach
4	7/5/2016	Beach
5	7/6/2016	Beach
6	7/6/2016	Beach
7	7/7/2016	Beach
8	NaN	Beach
9	7/9/2016	Beach
10	7/10/2016	Beach
11	7/11/2016	Beach
12	7/12/2016	Beach
13	7/13/2016	Beach
14	7/14/2016	Beach
15	7/15/2016	Beach
16	7/16/2016	Beach
17	7/17/2016	Beach
18	7/18/2016	Park
19	7/19/2016	Park
20	7/20/2016	Park
21	7/21/2016	Park
22	7/22/2016	Park
23	7/23/2016	Park
24	7/24/2016	Park
25	7/25/2016	Park
26	7/26/2016	Park
27	7/27/2016	Park
28	7/28/2016	Park
29	7/29/2016	Park
30	7/30/2016	Beach
31	7/31/2016	Beach

<svg xmlns=”http://www.w3.org/2000/svg" height=”24px”viewBox=”0 0 24 24”
width=”24px”>

  <script>
    const buttonEl =
      document.querySelector('#df-d28556da-097b-4c4b-90f3-3548273e5785 button.colab-df-convert');
    buttonEl.style.display =
      google.colab.kernel.accessAllowed ? 'block' : 'none';

    async function convertToInteractive(key) {
      const element = document.querySelector('#df-d28556da-097b-4c4b-90f3-3548273e5785');
      const dataTable =
        await google.colab.kernel.invokeFunction('convertToInteractive',
                                                 [key], {});
      if (!dataTable) return;

      const docLinkHtml = 'Like what you see? Visit the ' +
        '<a target="_blank" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'
        + ' to learn more about interactive tables.';
      element.innerHTML = '';
      dataTable['output_type'] = 'display_data';
      await google.colab.output.renderOutput(dataTable, element);
      const docLink = document.createElement('div');
      docLink.innerHTML = docLinkHtml;
      element.appendChild(docLink);
    }
  </script>
</div>

%%time

juice.iloc[0:3, 0:2] #인덱스 0~2번, 0~1번 칼럼

CPU times: user 735 µs, sys: 0 ns, total: 735 µs
Wall time: 843 µs

	Date	Location
0	7/1/2016	Park
1	7/2/2016	Park
2	7/3/2016	Park

<svg xmlns=”http://www.w3.org/2000/svg" height=”24px”viewBox=”0 0 24 24”
width=”24px”>

  <script>
    const buttonEl =
      document.querySelector('#df-3313e9ec-a014-4d12-9209-3fc4ffa27eab button.colab-df-convert');
    buttonEl.style.display =
      google.colab.kernel.accessAllowed ? 'block' : 'none';

    async function convertToInteractive(key) {
      const element = document.querySelector('#df-3313e9ec-a014-4d12-9209-3fc4ffa27eab');
      const dataTable =
        await google.colab.kernel.invokeFunction('convertToInteractive',
                                                 [key], {});
      if (!dataTable) return;

      const docLinkHtml = 'Like what you see? Visit the ' +
        '<a target="_blank" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'
        + ' to learn more about interactive tables.';
      element.innerHTML = '';
      dataTable['output_type'] = 'display_data';
      await google.colab.output.renderOutput(dataTable, element);
      const docLink = document.createElement('div');
      docLink.innerHTML = docLinkHtml;
      element.appendChild(docLink);
    }
  </script>
</div>

loc

• 라벨 기반

%%time

juice.loc[0:2, ['Date', 'Location']] # 인덱스 라벨과 칼럼 라벨 # 인덱싱에서 n-1개념과 다르다.
# juice.loc[인덱스라벨, [칼럼 라벨]]

CPU times: user 2.58 ms, sys: 0 ns, total: 2.58 ms
Wall time: 6.81 ms

	Date	Location
0	7/1/2016	Park
1	7/2/2016	Park
2	7/3/2016	Park

<svg xmlns=”http://www.w3.org/2000/svg" height=”24px”viewBox=”0 0 24 24”
width=”24px”>

  <script>
    const buttonEl =
      document.querySelector('#df-69c0c626-2e18-4cf9-b0b9-4afab44368fd button.colab-df-convert');
    buttonEl.style.display =
      google.colab.kernel.accessAllowed ? 'block' : 'none';

    async function convertToInteractive(key) {
      const element = document.querySelector('#df-69c0c626-2e18-4cf9-b0b9-4afab44368fd');
      const dataTable =
        await google.colab.kernel.invokeFunction('convertToInteractive',
                                                 [key], {});
      if (!dataTable) return;

      const docLinkHtml = 'Like what you see? Visit the ' +
        '<a target="_blank" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'
        + ' to learn more about interactive tables.';
      element.innerHTML = '';
      dataTable['output_type'] = 'display_data';
      await google.colab.output.renderOutput(dataTable, element);
      const docLink = document.createElement('div');
      docLink.innerHTML = docLinkHtml;
      element.appendChild(docLink);
    }
  </script>
</div>

• iloc가 속도가 빠른 것을 %%time으로 확인 할 수 있다.

컬럼명 별도 추출

• loc만 할 수 있는 기능

juice.loc[juice['Leaflets'] >= 100, ['Date', 'Location']]

	Date	Location
2	7/3/2016	Park
4	7/5/2016	Beach
7	7/7/2016	Beach
8	NaN	Beach
9	7/9/2016	Beach
10	7/10/2016	Beach
11	7/11/2016	Beach
14	7/14/2016	Beach
15	7/15/2016	Beach
17	7/17/2016	Beach
18	7/18/2016	Park
19	7/19/2016	Park
22	7/22/2016	Park
23	7/23/2016	Park
24	7/24/2016	Park
25	7/25/2016	Park
26	7/26/2016	Park

<svg xmlns=”http://www.w3.org/2000/svg" height=”24px”viewBox=”0 0 24 24”
width=”24px”>

  <script>
    const buttonEl =
      document.querySelector('#df-cce733ce-3a3e-407b-a638-9ea77dfa236f button.colab-df-convert');
    buttonEl.style.display =
      google.colab.kernel.accessAllowed ? 'block' : 'none';

    async function convertToInteractive(key) {
      const element = document.querySelector('#df-cce733ce-3a3e-407b-a638-9ea77dfa236f');
      const dataTable =
        await google.colab.kernel.invokeFunction('convertToInteractive',
                                                 [key], {});
      if (!dataTable) return;

      const docLinkHtml = 'Like what you see? Visit the ' +
        '<a target="_blank" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'
        + ' to learn more about interactive tables.';
      element.innerHTML = '';
      dataTable['output_type'] = 'display_data';
      await google.colab.output.renderOutput(dataTable, element);
      const docLink = document.createElement('div');
      docLink.innerHTML = docLinkHtml;
      element.appendChild(docLink);
    }
  </script>
</div>

• iloc는 위가 안 된다.

정렬

• sort_values()

juice.sort_values(by=['Revenue'], ascending = False).head() # 내림차순으로

	Date	Location	Lemon	Orange	Temperature	Leaflets	Price	sold	Revenue
25	7/25/2016	Park	156	113	84	135.0	0.50	269	134.50
18	7/18/2016	Park	131	92	81	122.0	0.50	223	111.50
26	7/26/2016	Park	176	129	83	158.0	0.35	305	106.75
19	7/19/2016	Park	122	85	78	113.0	0.50	207	103.50
24	7/24/2016	Park	121	82	82	117.0	0.50	203	101.50

<svg xmlns=”http://www.w3.org/2000/svg" height=”24px”viewBox=”0 0 24 24”
width=”24px”>

  <script>
    const buttonEl =
      document.querySelector('#df-fdaed60a-bf98-46fe-a701-24e216acb756 button.colab-df-convert');
    buttonEl.style.display =
      google.colab.kernel.accessAllowed ? 'block' : 'none';

    async function convertToInteractive(key) {
      const element = document.querySelector('#df-fdaed60a-bf98-46fe-a701-24e216acb756');
      const dataTable =
        await google.colab.kernel.invokeFunction('convertToInteractive',
                                                 [key], {});
      if (!dataTable) return;

      const docLinkHtml = 'Like what you see? Visit the ' +
        '<a target="_blank" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'
        + ' to learn more about interactive tables.';
      element.innerHTML = '';
      dataTable['output_type'] = 'display_data';
      await google.colab.output.renderOutput(dataTable, element);
      const docLink = document.createElement('div');
      docLink.innerHTML = docLinkHtml;
      element.appendChild(docLink);
    }
  </script>
</div>

juice2 = juice.sort_values(by=['Price', 'Temperature'], ascending = [False, True]).reset_index(drop=True)
juice2
# 가격과 온도에 따라서 정렬을 해주다보니 인덱스 번호가 뒤죽박죽이 되어버렸다. 인덱스번호를 리셋 해주고 새로운 데이터셋으로 만들어준 것이다.
# 그리고 juice2라는 새로운 객체에 저장해준 것.

	Date	Location	Lemon	Orange	Temperature	Leaflets	Price	sold	Revenue
0	7/20/2016	Park	71	42	70	NaN	0.50	113	56.50
1	7/16/2016	Beach	81	50	74	90.0	0.50	131	65.50
2	7/15/2016	Beach	98	62	75	108.0	0.50	160	80.00
3	7/17/2016	Beach	115	76	77	126.0	0.50	191	95.50
4	7/21/2016	Park	83	50	77	90.0	0.50	133	66.50
5	7/19/2016	Park	122	85	78	113.0	0.50	207	103.50
6	7/22/2016	Park	112	75	80	108.0	0.50	187	93.50
7	7/18/2016	Park	131	92	81	122.0	0.50	223	111.50
8	7/23/2016	Park	120	82	81	117.0	0.50	202	101.00
9	7/24/2016	Park	121	82	82	117.0	0.50	203	101.50
10	7/25/2016	Park	156	113	84	135.0	0.50	269	134.50
11	7/27/2016	Park	104	68	80	99.0	0.35	172	60.20
12	7/29/2016	Park	100	66	81	95.0	0.35	166	58.10
13	7/28/2016	Park	96	63	82	90.0	0.35	159	55.65
14	7/30/2016	Beach	88	57	82	81.0	0.35	145	50.75
15	7/31/2016	Beach	76	47	82	68.0	0.35	123	43.05
16	7/26/2016	Park	176	129	83	158.0	0.35	305	106.75
17	7/1/2016	Park	97	67	70	90.0	0.25	164	41.00
18	7/3/2016	Park	110	77	71	104.0	0.25	187	46.75
19	7/2/2016	Park	98	67	72	90.0	0.25	165	41.25
20	7/4/2016	Beach	134	99	76	98.0	0.25	233	58.25
21	7/13/2016	Beach	109	75	77	99.0	0.25	184	46.00
22	7/5/2016	Beach	159	118	78	135.0	0.25	277	69.25
23	7/14/2016	Beach	122	85	78	113.0	0.25	207	51.75
24	7/9/2016	Beach	134	95	80	126.0	0.25	229	57.25
25	7/7/2016	Beach	143	101	81	135.0	0.25	244	61.00
26	7/6/2016	Beach	103	69	82	90.0	0.25	172	43.00
27	7/6/2016	Beach	103	69	82	90.0	0.25	172	43.00
28	NaN	Beach	123	86	82	113.0	0.25	209	52.25
29	7/10/2016	Beach	140	98	82	131.0	0.25	238	59.50
30	7/11/2016	Beach	162	120	83	135.0	0.25	282	70.50
31	7/12/2016	Beach	130	95	84	99.0	0.25	225	56.25

<svg xmlns=”http://www.w3.org/2000/svg" height=”24px”viewBox=”0 0 24 24”
width=”24px”>

  <script>
    const buttonEl =
      document.querySelector('#df-374e41cd-177f-4906-a3ce-e839f106f7a7 button.colab-df-convert');
    buttonEl.style.display =
      google.colab.kernel.accessAllowed ? 'block' : 'none';

    async function convertToInteractive(key) {
      const element = document.querySelector('#df-374e41cd-177f-4906-a3ce-e839f106f7a7');
      const dataTable =
        await google.colab.kernel.invokeFunction('convertToInteractive',
                                                 [key], {});
      if (!dataTable) return;

      const docLinkHtml = 'Like what you see? Visit the ' +
        '<a target="_blank" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'
        + ' to learn more about interactive tables.';
      element.innerHTML = '';
      dataTable['output_type'] = 'display_data';
      await google.colab.output.renderOutput(dataTable, element);
      const docLink = document.createElement('div');
      docLink.innerHTML = docLinkHtml;
      element.appendChild(docLink);
    }
  </script>
</div>

Groupby()

• 피벗 테이블을 만드는 것과 똑같음
• 요약하려고

juice.groupby(by = 'Location').count()

	Date	Lemon	Orange	Temperature	Leaflets	Price	sold	Revenue
Location
Beach	16	17	17	17	17	17	17	17
Park	15	15	15	15	14	15	15	15

<svg xmlns=”http://www.w3.org/2000/svg" height=”24px”viewBox=”0 0 24 24”
width=”24px”>

  <script>
    const buttonEl =
      document.querySelector('#df-9d46cfb2-4ae7-44c5-a9de-d88d60478500 button.colab-df-convert');
    buttonEl.style.display =
      google.colab.kernel.accessAllowed ? 'block' : 'none';

    async function convertToInteractive(key) {
      const element = document.querySelector('#df-9d46cfb2-4ae7-44c5-a9de-d88d60478500');
      const dataTable =
        await google.colab.kernel.invokeFunction('convertToInteractive',
                                                 [key], {});
      if (!dataTable) return;

      const docLinkHtml = 'Like what you see? Visit the ' +
        '<a target="_blank" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'
        + ' to learn more about interactive tables.';
      element.innerHTML = '';
      dataTable['output_type'] = 'display_data';
      await google.colab.output.renderOutput(dataTable, element);
      const docLink = document.createElement('div');
      docLink.innerHTML = docLinkHtml;
      element.appendChild(docLink);
    }
  </script>
</div>

import numpy as np
juice.groupby(['Location'])[['Revenue', 'Lemon']].agg([max, min, sum, np.mean])

	Revenue				Lemon
	max	min	sum	mean	max	min	sum	mean
Location
Beach	95.5	43.0	1002.8	58.988235	162	76	2020	118.823529
Park	134.5	41.0	1178.2	78.546667	176	71	1697	113.133333

<svg xmlns=”http://www.w3.org/2000/svg" height=”24px”viewBox=”0 0 24 24”
width=”24px”>

  <script>
    const buttonEl =
      document.querySelector('#df-d175d024-02cf-46a7-a0ef-d792b2802f03 button.colab-df-convert');
    buttonEl.style.display =
      google.colab.kernel.accessAllowed ? 'block' : 'none';

    async function convertToInteractive(key) {
      const element = document.querySelector('#df-d175d024-02cf-46a7-a0ef-d792b2802f03');
      const dataTable =
        await google.colab.kernel.invokeFunction('convertToInteractive',
                                                 [key], {});
      if (!dataTable) return;

      const docLinkHtml = 'Like what you see? Visit the ' +
        '<a target="_blank" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'
        + ' to learn more about interactive tables.';
      element.innerHTML = '';
      dataTable['output_type'] = 'display_data';
      await google.colab.output.renderOutput(dataTable, element);
      const docLink = document.createElement('div');
      docLink.innerHTML = docLinkHtml;
      element.appendChild(docLink);
    }
  </script>
</div>

Pandas 입문 1

http://griotold.github.io/2022/03/26/Pandas_tutorial_01/

Author

HS

Posted on

2022-03-26

Updated on

2022-03-26

Licensed under

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