Pandas tutorial 1

데이터 전처리 프로세스

• 중복값 제거 및 결측치 처리
  • 완전 무작위(MCAR) / 무작위(MAR) / 비무작위(NMAR)
  • 제거, 치환, 모델 기반 처리 등
• 이상치 탐지 및 처리
  • 삭제, 대체, 변환(스케일링) 등
• Feature Engineering
  • 정규화, 표준화, 로그변환, 벡터화 등
  • PCA, EFA 등을 통한 차원 축소

Pandas 라이브러리

Pandas의 기본 자료형

• Series 객체, DataFrame 객체
  • Index: 숫자 또는 문자, 중복X
  • Series: Index & Column 1개
  • DataFrame: Index & Column 2개 이상
• 각 객체에 따라 사용 가능한 method가 상이함

Pandas 라이브러리 불러오기

import pandas as pd
print(pd.__version__)

1.3.5

테스트

# DataFrame 객체
temp_dic = {'col1' : [1, 2, 3], 'col2' : [4, 5, 6]}
df = pd.DataFrame(temp_dic)
print(df)
print(type(df))

   col1  col2
0     1     4
1     2     5
2     3     6
<class 'pandas.core.frame.DataFrame'>

# Series 객체
temp_dic = {'a':1, 'b':2, 'c':3}
ser = pd.Series(temp_dic)
print(ser)
print(type(ser))

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

구글 드라이브 연동

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

Mounted at /content/drive

데이터 불러오기

DATA_PATH = '/content/drive/MyDrive/Colab Notebooks/Data/Lemonade2016.csv'
juice = pd.read_csv(DATA_PATH)
print(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

# 전체적인 구조, 결측치 개수, 데이터 타입 파악
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

print(juice.head())
print("-------------------------------------------------------------------")
print(juice.tail())

       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
-------------------------------------------------------------------
         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

• describe() : 기술통계량 확인 (int형, float형 변수)
• value_counts() : 범주형 변수 빈도 수 확인

print(juice.describe())
print("** type(juice.describe()) :", type(juice.describe())) # DataFrame 객체로 반환

            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
** type(juice.describe()) : <class 'pandas.core.frame.DataFrame'>

print(juice['Location'].value_counts())
print("** type(juice.describe()) :", type(juice['Location'].value_counts())) # Series 객체로 반환

Beach    17
Park     15
Name: Location, dtype: int64
** type(juice.describe()) : <class 'pandas.core.series.Series'>

데이터 다루기

juice['Sold'] = 0
juice.head()

	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
3	7/4/2016	Beach	134	99	76	98.0	0.25	0
4	7/5/2016	Beach	159	118	78	135.0	0.25	0

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

  <script>
    const buttonEl =
      document.querySelector('#df-d7644e91-b826-4359-8387-1580b5266658 button.colab-df-convert');
    buttonEl.style.display =
      google.colab.kernel.accessAllowed ? 'block' : 'none';

    async function convertToInteractive(key) {
      const element = document.querySelector('#df-d7644e91-b826-4359-8387-1580b5266658');
      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['Sold'] = juice['Lemon'] + juice['Orange']
juice.head()

	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
3	7/4/2016	Beach	134	99	76	98.0	0.25	233
4	7/5/2016	Beach	159	118	78	135.0	0.25	277

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

  <script>
    const buttonEl =
      document.querySelector('#df-2d21cec2-5539-4100-b9f5-ac71828a5d25 button.colab-df-convert');
    buttonEl.style.display =
      google.colab.kernel.accessAllowed ? 'block' : 'none';

    async function convertToInteractive(key) {
      const element = document.querySelector('#df-2d21cec2-5539-4100-b9f5-ac71828a5d25');
      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>

• 매출액(Revenue) = 가격(Price) * 판매량(Sold)

juice['Revenue'] = juice['Price'] * juice['Sold']
juice.head()

	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-506a14b7-9d00-4793-9a3f-5c54252c47b5 button.colab-df-convert');
    buttonEl.style.display =
      google.colab.kernel.accessAllowed ? 'block' : 'none';

    async function convertToInteractive(key) {
      const element = document.querySelector('#df-506a14b7-9d00-4793-9a3f-5c54252c47b5');
      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>

• 행 또는 열 제거 : drop(axis=0|1)
  • axis=0 : 행 방향(index) 실행
  • axis=1 : 열 방향(column) 실행

juice_col_drop = juice.drop('Sold', axis=1)
juice_col_drop.head()

	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
3	7/4/2016	Beach	134	99	76	98.0	0.25	58.25
4	7/5/2016	Beach	159	118	78	135.0	0.25	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-4ee85edb-efbb-47d3-84c6-8b0cab09eb0f button.colab-df-convert');
    buttonEl.style.display =
      google.colab.kernel.accessAllowed ? 'block' : 'none';

    async function convertToInteractive(key) {
      const element = document.querySelector('#df-4ee85edb-efbb-47d3-84c6-8b0cab09eb0f');
      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_ind_drop = juice.drop(2, axis=0)
juice_ind_drop.head()

	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
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

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

  <script>
    const buttonEl =
      document.querySelector('#df-8329c7e5-dc41-41e9-aa8f-d31cdc115b69 button.colab-df-convert');
    buttonEl.style.display =
      google.colab.kernel.accessAllowed ? 'block' : 'none';

    async function convertToInteractive(key) {
      const element = document.querySelector('#df-8329c7e5-dc41-41e9-aa8f-d31cdc115b69');
      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>

데이터 인덱싱

juice[5:10]

	Date	Location	Lemon	Orange	Temperature	Leaflets	Price	Sold	Revenue
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

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

  <script>
    const buttonEl =
      document.querySelector('#df-e5a46c70-33e1-4ae6-868e-0fbcf1b0c121 button.colab-df-convert');
    buttonEl.style.display =
      google.colab.kernel.accessAllowed ? 'block' : 'none';

    async function convertToInteractive(key) {
      const element = document.querySelector('#df-e5a46c70-33e1-4ae6-868e-0fbcf1b0c121');
      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 값 활용 (조건식)

# Location 값이 Park인 경우
juice[juice['Location'] == 'Park']

	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
18	7/18/2016	Park	131	92	81	122.0	0.50	223	111.50
19	7/19/2016	Park	122	85	78	113.0	0.50	207	103.50
20	7/20/2016	Park	71	42	70	NaN	0.50	113	56.50
21	7/21/2016	Park	83	50	77	90.0	0.50	133	66.50
22	7/22/2016	Park	112	75	80	108.0	0.50	187	93.50
23	7/23/2016	Park	120	82	81	117.0	0.50	202	101.00
24	7/24/2016	Park	121	82	82	117.0	0.50	203	101.50
25	7/25/2016	Park	156	113	84	135.0	0.50	269	134.50
26	7/26/2016	Park	176	129	83	158.0	0.35	305	106.75
27	7/27/2016	Park	104	68	80	99.0	0.35	172	60.20
28	7/28/2016	Park	96	63	82	90.0	0.35	159	55.65
29	7/29/2016	Park	100	66	81	95.0	0.35	166	58.10

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

  <script>
    const buttonEl =
      document.querySelector('#df-6440b49e-9b42-4a3b-9edd-37f9b7d5b0a6 button.colab-df-convert');
    buttonEl.style.display =
      google.colab.kernel.accessAllowed ? 'block' : 'none';

    async function convertToInteractive(key) {
      const element = document.querySelector('#df-6440b49e-9b42-4a3b-9edd-37f9b7d5b0a6');
      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>

# Leaflets 값이 120 이상인 경우
juice[juice['Leaflets'] >= 120]

	Date	Location	Lemon	Orange	Temperature	Leaflets	Price	Sold	Revenue
4	7/5/2016	Beach	159	118	78	135.0	0.25	277	69.25
7	7/7/2016	Beach	143	101	81	135.0	0.25	244	61.00
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
17	7/17/2016	Beach	115	76	77	126.0	0.50	191	95.50
18	7/18/2016	Park	131	92	81	122.0	0.50	223	111.50
25	7/25/2016	Park	156	113	84	135.0	0.50	269	134.50
26	7/26/2016	Park	176	129	83	158.0	0.35	305	106.75

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

  <script>
    const buttonEl =
      document.querySelector('#df-92086f96-d263-4c70-85ec-af04ffb445c1 button.colab-df-convert');
    buttonEl.style.display =
      google.colab.kernel.accessAllowed ? 'block' : 'none';

    async function convertToInteractive(key) {
      const element = document.querySelector('#df-92086f96-d263-4c70-85ec-af04ffb445c1');
      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

• iloc : index 기반, 속도↑, 대용량 데이터에 적합
  • syntax : df.iloc[row_index, column_index]
• loc : label or boolean(조건식) 기반, 가독성↑
  • syntax : df.loc[row_label, column_label]

%%time
juice.iloc[0:3, 0:2] # 해당 인덱스 미포함

CPU times: user 514 µs, sys: 40 µs, total: 554 µs
Wall time: 523 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-219659e3-19a4-418f-84d6-7d01a8076e00 button.colab-df-convert');
    buttonEl.style.display =
      google.colab.kernel.accessAllowed ? 'block' : 'none';

    async function convertToInteractive(key) {
      const element = document.querySelector('#df-219659e3-19a4-418f-84d6-7d01a8076e00');
      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.loc[0:2, ["Date",'Location']] # 해당 라벨명 포함

CPU times: user 1.67 ms, sys: 0 ns, total: 1.67 ms
Wall time: 5.19 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-5e3530fd-7c31-4a6f-8a4f-dea8d6d42e78 button.colab-df-convert');
    buttonEl.style.display =
      google.colab.kernel.accessAllowed ? 'block' : 'none';

    async function convertToInteractive(key) {
      const element = document.querySelector('#df-5e3530fd-7c31-4a6f-8a4f-dea8d6d42e78');
      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.iloc[juice['Leaflets'] >= 130, ['Date', 'Location', 'Leaflets']]
# Error: iLocation based boolean indexing on an integer type is not available

juice.loc[juice['Leaflets'] >= 130, ['Date', 'Location', 'Leaflets']]

	Date	Location	Leaflets
4	7/5/2016	Beach	135.0
7	7/7/2016	Beach	135.0
10	7/10/2016	Beach	131.0
11	7/11/2016	Beach	135.0
25	7/25/2016	Park	135.0
26	7/26/2016	Park	158.0

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

  <script>
    const buttonEl =
      document.querySelector('#df-80998d16-2836-479c-9ea3-75efcac52299 button.colab-df-convert');
    buttonEl.style.display =
      google.colab.kernel.accessAllowed ? 'block' : 'none';

    async function convertToInteractive(key) {
      const element = document.querySelector('#df-80998d16-2836-479c-9ea3-75efcac52299');
      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>

데이터 정렬

• sort_values() 함수

# Revenue 기준 오름차순
juice.sort_values(by=['Revenue']).head()

	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
6	7/6/2016	Beach	103	69	82	90.0	0.25	172	43.00
5	7/6/2016	Beach	103	69	82	90.0	0.25	172	43.00
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-4d6ed323-d5df-47ed-893d-db57c2b39110 button.colab-df-convert');
    buttonEl.style.display =
      google.colab.kernel.accessAllowed ? 'block' : 'none';

    async function convertToInteractive(key) {
      const element = document.querySelector('#df-4d6ed323-d5df-47ed-893d-db57c2b39110');
      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>

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

	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-ecf0c228-c2c7-4d62-aa08-e194d9fd1e4e button.colab-df-convert');
    buttonEl.style.display =
      google.colab.kernel.accessAllowed ? 'block' : 'none';

    async function convertToInteractive(key) {
      const element = document.querySelector('#df-ecf0c228-c2c7-4d62-aa08-e194d9fd1e4e');
      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>

# Price 기준 내림차순, Temperature 기준 오름차순
juice.sort_values(by=['Price', 'Temperature'], ascending = [False, True])

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

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

  <script>
    const buttonEl =
      document.querySelector('#df-56042f49-547e-4908-99b7-0e79a9445a3d button.colab-df-convert');
    buttonEl.style.display =
      google.colab.kernel.accessAllowed ? 'block' : 'none';

    async function convertToInteractive(key) {
      const element = document.querySelector('#df-56042f49-547e-4908-99b7-0e79a9445a3d');
      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>

# index를 새로 지정해서 새로운 객체로 저장
juice2 = juice.sort_values(by=['Price', 'Temperature'], ascending = [False, True]).reset_index(drop=True)
juice2

	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-95b0ec19-306b-4312-a2fb-6c6bf238bb16 button.colab-df-convert');
    buttonEl.style.display =
      google.colab.kernel.accessAllowed ? 'block' : 'none';

    async function convertToInteractive(key) {
      const element = document.querySelector('#df-95b0ec19-306b-4312-a2fb-6c6bf238bb16');
      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>

데이터 그룹화

• groupby() 함수
• 그룹별 집계함수를 통해 피벗테이블 생성

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-464f987e-371e-4805-8b50-ee52bdc321e8 button.colab-df-convert');
    buttonEl.style.display =
      google.colab.kernel.accessAllowed ? 'block' : 'none';

    async function convertToInteractive(key) {
      const element = document.querySelector('#df-464f987e-371e-4805-8b50-ee52bdc321e8');
      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

# Location 그룹별 Lemon, Orange 변수에 대한 집계함수
juice.groupby(by='Location')[['Lemon','Orange']].agg([max, min, sum, np.mean])

	Lemon				Orange
	max	min	sum	mean	max	min	sum	mean
Location
Beach	162	76	2020	118.823529	120	47	1402	82.470588
Park	176	71	1697	113.133333	129	42	1158	77.200000

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

  <script>
    const buttonEl =
      document.querySelector('#df-1d771570-530b-4471-945e-e1db8f8b3457 button.colab-df-convert');
    buttonEl.style.display =
      google.colab.kernel.accessAllowed ? 'block' : 'none';

    async function convertToInteractive(key) {
      const element = document.querySelector('#df-1d771570-530b-4471-945e-e1db8f8b3457');
      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>

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