[Python] Pandas - DataFrame

작성일자 : 2023-08-23

Ver 0.1.1

DataFrame

• Pandas의 Series가 1차원 형태의 자료구조라면 DataFrame은 여러 개의 열로 구성된 2차원 형태의 자료구조임
• numpy array를 받아 만들 수 있으며, Series 처럼 변환 가능한 오브젝트들을 갖고 있는 dict 형태를 인자로 넣어주어 DataFrame을 만들 수 있음

ex = pd.DataFrame({'A': 1.,
                   'B': pd.Timestamp('20130102'),
                   'C': pd.Series(1, index=list(range(5)), dtype='float32'),
                   'D': np.array(np.arange(3,8,1), dtype='int32'),
                   'E': pd.Categorical(['test', 'train', 'test', 'train','test']),
                   'F': 'foo'})
ex

 

	A	B	C	D	E	F
0	1.0	2013-01-02	1.0	3	test	foo
1	1.0	2013-01-02	1.0	4	train	foo
2	1.0	2013-01-02	1.0	5	test	foo
3	1.0	2013-01-02	1.0	6	train	foo
4	1.0	2013-01-02	1.0	7	test	foo

• DataFrame의 컬럼들은 각기 특별한 자료형을 갖고 있음
• 이는 DataFrame 내에 있는 dtypes라는 속성을 통해 확인 가능함
• 파이썬의 기본적인 소수점은 float64로 잡히고, 기본적은 문자열은 str이 아니라 object라는 자료형으로 나타남

ex.dtypes

A           float64
B    datetime64[ns]
C           float32
D             int32
E          category
F            object
dtype: object

ex2 = pd.DataFrame(np.random.randn(5,2), columns = ['A','B'])
ex2

 

	A	B
0	0.406282	0.368642
1	1.140642	-0.077410
2	-0.812914	-0.273615
3	-0.448528	0.854089
4	-0.237988	0.196074

ex2.head() #기본값 = 5

 

	A	B
0	0.406282	0.368642
1	1.140642	-0.077410
2	-0.812914	-0.273615
3	-0.448528	0.854089
4	-0.237988	0.196074

ex2.tail(3)

 

	A	B
2	-0.812914	-0.273615
3	-0.448528	0.854089
4	-0.237988	0.196074

ex2.index #check index

RangeIndex(start=0, stop=5, step=1)

ex2.columns #check columns

Index(['A', 'B'], dtype='object')

ex2.values #check values

array([[ 0.40628183,  0.36864168],
       [ 1.14064213, -0.07741036],
       [-0.81291384, -0.27361517],
       [-0.44852821,  0.85408865],
       [-0.23798752,  0.1960742 ]])

ex2.describe() #simple statistic information of DataFrame

 

	A	B
count	5.000000	5.000000
mean	0.009499	0.213556
std	0.772063	0.434924
min	-0.812914	-0.273615
25%	-0.448528	-0.077410
50%	-0.237988	0.196074
75%	0.406282	0.368642
max	1.140642	0.854089

ex2.sort_index(axis =1 , ascending = False)

 

	B	A
0	0.368642	0.406282
1	-0.077410	1.140642
2	-0.273615	-0.812914
3	0.854089	-0.448528
4	0.196074	-0.237988

ex2.sort_index(axis = 0 , ascending = False)

 

	A	B
4	-0.237988	0.196074
3	-0.448528	0.854089
2	-0.812914	-0.273615
1	1.140642	-0.077410
0	0.406282	0.368642

ex2.sort_index()

 

	A	B
0	0.406282	0.368642
1	1.140642	-0.077410
2	-0.812914	-0.273615
3	-0.448528	0.854089
4	-0.237988	0.196074

ex2.sort_values(by='B')

 

	A	B
2	-0.812914	-0.273615
1	1.140642	-0.077410
4	-0.237988	0.196074
0	0.406282	0.368642
3	-0.448528	0.854089

Selection using pandas

ex2

 

	A	B
0	0.406282	0.368642
1	1.140642	-0.077410
2	-0.812914	-0.273615
3	-0.448528	0.854089
4	-0.237988	0.196074

ex2['A']

0    0.406282
1    1.140642
2   -0.812914
3   -0.448528
4   -0.237988
Name: A, dtype: float64

ex2.A

0    0.406282
1    1.140642
2   -0.812914
3   -0.448528
4   -0.237988
Name: A, dtype: float64

ex2[['A']]

 

	A
0	0.406282
1	1.140642
2	-0.812914
3	-0.448528
4	-0.237988

type(ex2['A'])

pandas.core.series.Series

type(ex2[['A']])

pandas.core.frame.DataFrame

ex2[0:3]

 

	A	B
0	0.406282	0.368642
1	1.140642	-0.077410
2	-0.812914	-0.273615

Merge DataFrame

df1 = pd.DataFrame({'key' : list('ABCDE'),
                    'value' : np.random.randn(5)})
df1

 

	key	value
0	A	-0.604176
1	B	-0.882808
2	C	-0.253994
3	D	0.461608
4	E	-0.507770

df2 = pd.DataFrame({'key' : list('ABCXZ'),
                    'value' : np.random.randn(5)})
df2

keyvalue

	key	value
0	A	0.071220
1	B	0.957223
2	C	0.622761
3	X	1.802048
4	Z	-0.531795

pd.concat([df1,df2]) # axis = 0 (Default), concat by rows 

 

	key	value
0	A	-0.604176
1	B	-0.882808
2	C	-0.253994
3	D	0.461608
4	E	-0.507770
0	A	0.071220
1	B	0.957223
2	C	0.622761
3	X	1.802048
4	Z	-0.531795

pd.concat([df1, df2], axis = 0, ignore_index = True)

 

	key	value
0	A	-0.604176
1	B	-0.882808
2	C	-0.253994
3	D	0.461608
4	E	-0.507770
5	A	0.071220
6	B	0.957223
7	C	0.622761
8	X	1.802048
9	Z	-0.531795

pd.concat([df1, df2]).reset_index()

 

	index	key	value
0	0	A	-0.604176
1	1	B	-0.882808
2	2	C	-0.253994
3	3	D	0.461608
4	4	E	-0.507770
5	0	A	0.071220
6	1	B	0.957223
7	2	C	0.622761
8	3	X	1.802048
9	4	Z	-0.531795

pd.concat([df1,df2], axis = 1)

 

	key	value	key	value
0	A	-0.604176	A	0.071220
1	B	-0.882808	B	0.957223
2	C	-0.253994	C	0.622761
3	D	0.461608	X	1.802048
4	E	-0.507770	Z	-0.531795

df2.columns = ['key','values2']
df2

 

	key	values2
0	A	0.071220
1	B	0.957223
2	C	0.622761
3	X	1.802048
4	Z	-0.531795

pd.concat([df1,df2])

keyvaluevalues2

	key	value	values2
0	A	-0.604176	NaN
1	B	-0.882808	NaN
2	C	-0.253994	NaN
3	D	0.461608	NaN
4	E	-0.507770	NaN
0	A	NaN	0.071220
1	B	NaN	0.957223
2	C	NaN	0.622761
3	X	NaN	1.802048
4	Z	NaN	-0.531795

pd.merge()

df1

 

	key	value
0	A	-0.604176
1	B	-0.882808
2	C	-0.253994
3	D	0.461608
4	E	-0.507770

df2

 

	key	value2
0	A	0.071220
1	B	0.957223
2	C	0.622761
3	X	1.802048
4	Z	-0.531795

pd.merge(df1, df2, on = 'key', how = 'inner')

 

	key	value	values2
0	A	-0.604176	0.071220
1	B	-0.882808	0.957223
2	C	-0.253994	0.622761

pd.merge(df1, df2, on = 'key', how = 'left')

 

	key	value	values2
0	A	-0.604176	0.071220
1	B	-0.882808	0.957223
2	C	-0.253994	0.622761
3	D	0.461608	NaN
4	E	-0.507770	NaN

pd.merge(df1, df2, on = 'key', how = 'right')

 

	key	value	values2
0	A	-0.604176	0.071220
1	B	-0.882808	0.957223
2	C	-0.253994	0.622761
3	X	NaN	1.802048
4	Z	NaN	-0.531795

pd.merge(df1, df2, on = 'key', how = 'outer')

 

	key	value	values2
0	A	-0.604176	0.071220
1	B	-0.882808	0.957223
2	C	-0.253994	0.622761
3	D	0.461608	NaN
4	E	-0.507770	NaN
5	X	NaN	1.802048
6	Z	NaN	-0.531795

Practice using data set - iris dataset

from sklearn.datasets import load_iris

print(iris) # 로드된 데이터가 속성-스타일 접근을 제공하는 딕셔너리와 번치 객체로 표현된 것을 확인
print(iris.DESCR) # Description 속성을 이용해서 데이터셋의 정보를 확인

# 각 key에 저장된 value 확인
# feature
print(iris.data)
print(iris.feature_names)

# label
print(iris.target)
print(iris.target_names)

# feature_names 와 target을 레코드로 갖는 데이터프레임 생성
df = pd.DataFrame(data=iris.data, columns=iris.feature_names)

{'data': array([[5.1, 3.5, 1.4, 0.2],
       [4.9, 3. , 1.4, 0.2],
       [4.7, 3.2, 1.3, 0.2],
       [4.6, 3.1, 1.5, 0.2],
       [5. , 3.6, 1.4, 0.2],
       [5.4, 3.9, 1.7, 0.4],
       [4.6, 3.4, 1.4, 0.3],
       [5. , 3.4, 1.5, 0.2],
       [4.4, 2.9, 1.4, 0.2],
       [4.9, 3.1, 1.5, 0.1],
       [5.4, 3.7, 1.5, 0.2],
       [4.8, 3.4, 1.6, 0.2],
       [4.8, 3. , 1.4, 0.1],
       [4.3, 3. , 1.1, 0.1],
       [5.8, 4. , 1.2, 0.2],
       [5.7, 4.4, 1.5, 0.4],
       [5.4, 3.9, 1.3, 0.4],
       [5.1, 3.5, 1.4, 0.3],
       [5.7, 3.8, 1.7, 0.3],
       [5.1, 3.8, 1.5, 0.3],
       [5.4, 3.4, 1.7, 0.2],
       [5.1, 3.7, 1.5, 0.4],
       [4.6, 3.6, 1. , 0.2],
       [5.1, 3.3, 1.7, 0.5],
       [4.8, 3.4, 1.9, 0.2],
       [5. , 3. , 1.6, 0.2],
       [5. , 3.4, 1.6, 0.4],
       [5.2, 3.5, 1.5, 0.2],
       [5.2, 3.4, 1.4, 0.2],
       [4.7, 3.2, 1.6, 0.2],
       [4.8, 3.1, 1.6, 0.2],
       [5.4, 3.4, 1.5, 0.4],
       [5.2, 4.1, 1.5, 0.1],
       [5.5, 4.2, 1.4, 0.2],
       [4.9, 3.1, 1.5, 0.2],
       [5. , 3.2, 1.2, 0.2],
       [5.5, 3.5, 1.3, 0.2],
       [4.9, 3.6, 1.4, 0.1],
       [4.4, 3. , 1.3, 0.2],
       [5.1, 3.4, 1.5, 0.2],
       [5. , 3.5, 1.3, 0.3],
       [4.5, 2.3, 1.3, 0.3],
       [4.4, 3.2, 1.3, 0.2],
       [5. , 3.5, 1.6, 0.6],
       [5.1, 3.8, 1.9, 0.4],
       [4.8, 3. , 1.4, 0.3],
       [5.1, 3.8, 1.6, 0.2],
       [4.6, 3.2, 1.4, 0.2],
       [5.3, 3.7, 1.5, 0.2],
       [5. , 3.3, 1.4, 0.2],
       [7. , 3.2, 4.7, 1.4],
       [6.4, 3.2, 4.5, 1.5],
       [6.9, 3.1, 4.9, 1.5],
       [5.5, 2.3, 4. , 1.3],
       [6.5, 2.8, 4.6, 1.5],
       [5.7, 2.8, 4.5, 1.3],
       [6.3, 3.3, 4.7, 1.6],
       [4.9, 2.4, 3.3, 1. ],
       [6.6, 2.9, 4.6, 1.3],
       [5.2, 2.7, 3.9, 1.4],
       [5. , 2. , 3.5, 1. ],
       [5.9, 3. , 4.2, 1.5],
       [6. , 2.2, 4. , 1. ],
       [6.1, 2.9, 4.7, 1.4],
       [5.6, 2.9, 3.6, 1.3],
       [6.7, 3.1, 4.4, 1.4],
       [5.6, 3. , 4.5, 1.5],
       [5.8, 2.7, 4.1, 1. ],
       [6.2, 2.2, 4.5, 1.5],
       [5.6, 2.5, 3.9, 1.1],
       [5.9, 3.2, 4.8, 1.8],
       [6.1, 2.8, 4. , 1.3],
       [6.3, 2.5, 4.9, 1.5],
       [6.1, 2.8, 4.7, 1.2],
       [6.4, 2.9, 4.3, 1.3],
       [6.6, 3. , 4.4, 1.4],
       [6.8, 2.8, 4.8, 1.4],
       [6.7, 3. , 5. , 1.7],
       [6. , 2.9, 4.5, 1.5],
       [5.7, 2.6, 3.5, 1. ],
       [5.5, 2.4, 3.8, 1.1],
       [5.5, 2.4, 3.7, 1. ],
       [5.8, 2.7, 3.9, 1.2],
       [6. , 2.7, 5.1, 1.6],
       [5.4, 3. , 4.5, 1.5],
       [6. , 3.4, 4.5, 1.6],
       [6.7, 3.1, 4.7, 1.5],
       [6.3, 2.3, 4.4, 1.3],
       [5.6, 3. , 4.1, 1.3],
       [5.5, 2.5, 4. , 1.3],
       [5.5, 2.6, 4.4, 1.2],
       [6.1, 3. , 4.6, 1.4],
       [5.8, 2.6, 4. , 1.2],
       [5. , 2.3, 3.3, 1. ],
       [5.6, 2.7, 4.2, 1.3],
       [5.7, 3. , 4.2, 1.2],
       [5.7, 2.9, 4.2, 1.3],
       [6.2, 2.9, 4.3, 1.3],
       [5.1, 2.5, 3. , 1.1],
       [5.7, 2.8, 4.1, 1.3],
       [6.3, 3.3, 6. , 2.5],
       [5.8, 2.7, 5.1, 1.9],
       [7.1, 3. , 5.9, 2.1],
       [6.3, 2.9, 5.6, 1.8],
       [6.5, 3. , 5.8, 2.2],
       [7.6, 3. , 6.6, 2.1],
       [4.9, 2.5, 4.5, 1.7],
       [7.3, 2.9, 6.3, 1.8],
       [6.7, 2.5, 5.8, 1.8],
       [7.2, 3.6, 6.1, 2.5],
       [6.5, 3.2, 5.1, 2. ],
       [6.4, 2.7, 5.3, 1.9],
       [6.8, 3. , 5.5, 2.1],
       [5.7, 2.5, 5. , 2. ],
       [5.8, 2.8, 5.1, 2.4],
       [6.4, 3.2, 5.3, 2.3],
       [6.5, 3. , 5.5, 1.8],
       [7.7, 3.8, 6.7, 2.2],
       [7.7, 2.6, 6.9, 2.3],
       [6. , 2.2, 5. , 1.5],
       [6.9, 3.2, 5.7, 2.3],
       [5.6, 2.8, 4.9, 2. ],
       [7.7, 2.8, 6.7, 2. ],
       [6.3, 2.7, 4.9, 1.8],
       [6.7, 3.3, 5.7, 2.1],
       [7.2, 3.2, 6. , 1.8],
       [6.2, 2.8, 4.8, 1.8],
       [6.1, 3. , 4.9, 1.8],
       [6.4, 2.8, 5.6, 2.1],
       [7.2, 3. , 5.8, 1.6],
       [7.4, 2.8, 6.1, 1.9],
       [7.9, 3.8, 6.4, 2. ],
       [6.4, 2.8, 5.6, 2.2],
       [6.3, 2.8, 5.1, 1.5],
       [6.1, 2.6, 5.6, 1.4],
       [7.7, 3. , 6.1, 2.3],
       [6.3, 3.4, 5.6, 2.4],
       [6.4, 3.1, 5.5, 1.8],
       [6. , 3. , 4.8, 1.8],
       [6.9, 3.1, 5.4, 2.1],
       [6.7, 3.1, 5.6, 2.4],
       [6.9, 3.1, 5.1, 2.3],
       [5.8, 2.7, 5.1, 1.9],
       [6.8, 3.2, 5.9, 2.3],
       [6.7, 3.3, 5.7, 2.5],
       [6.7, 3. , 5.2, 2.3],
       [6.3, 2.5, 5. , 1.9],
       [6.5, 3. , 5.2, 2. ],
       [6.2, 3.4, 5.4, 2.3],
       [5.9, 3. , 5.1, 1.8]]), 'target': array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]), 'frame': None, 'target_names': array(['setosa', 'versicolor', 'virginica'], dtype='<U10'), 'DESCR': '.. _iris_dataset:\n\nIris plants dataset\n--------------------\n\n**Data Set Characteristics:**\n\n    :Number of Instances: 150 (50 in each of three classes)\n    :Number of Attributes: 4 numeric, predictive attributes and the class\n    :Attribute Information:\n        - sepal length in cm\n        - sepal width in cm\n        - petal length in cm\n        - petal width in cm\n        - class:\n                - Iris-Setosa\n                - Iris-Versicolour\n                - Iris-Virginica\n                \n    :Summary Statistics:\n\n    ============== ==== ==== ======= ===== ====================\n                    Min  Max   Mean    SD   Class Correlation\n    ============== ==== ==== ======= ===== ====================\n    sepal length:   4.3  7.9   5.84   0.83    0.7826\n    sepal width:    2.0  4.4   3.05   0.43   -0.4194\n    petal length:   1.0  6.9   3.76   1.76    0.9490  (high!)\n    petal width:    0.1  2.5   1.20   0.76    0.9565  (high!)\n    ============== ==== ==== ======= ===== ====================\n\n    :Missing Attribute Values: None\n    :Class Distribution: 33.3% for each of 3 classes.\n    :Creator: R.A. Fisher\n    :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\n    :Date: July, 1988\n\nThe famous Iris database, first used by Sir R.A. Fisher. The dataset is taken\nfrom Fisher\'s paper. Note that it\'s the same as in R, but not as in the UCI\nMachine Learning Repository, which has two wrong data points.\n\nThis is perhaps the best known database to be found in the\npattern recognition literature.  Fisher\'s paper is a classic in the field and\nis referenced frequently to this day.  (See Duda & Hart, for example.)  The\ndata set contains 3 classes of 50 instances each, where each class refers to a\ntype of iris plant.  One class is linearly separable from the other 2; the\nlatter are NOT linearly separable from each other.\n\n.. topic:: References\n\n   - Fisher, R.A. "The use of multiple measurements in taxonomic problems"\n     Annual Eugenics, 7, Part II, 179-188 (1936); also in "Contributions to\n     Mathematical Statistics" (John Wiley, NY, 1950).\n   - Duda, R.O., & Hart, P.E. (1973) Pattern Classification and Scene Analysis.\n     (Q327.D83) John Wiley & Sons.  ISBN 0-471-22361-1.  See page 218.\n   - Dasarathy, B.V. (1980) "Nosing Around the Neighborhood: A New System\n     Structure and Classification Rule for Recognition in Partially Exposed\n     Environments".  IEEE Transactions on Pattern Analysis and Machine\n     Intelligence, Vol. PAMI-2, No. 1, 67-71.\n   - Gates, G.W. (1972) "The Reduced Nearest Neighbor Rule".  IEEE Transactions\n     on Information Theory, May 1972, 431-433.\n   - See also: 1988 MLC Proceedings, 54-64.  Cheeseman et al"s AUTOCLASS II\n     conceptual clustering system finds 3 classes in the data.\n   - Many, many more ...', 'feature_names': ['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)'], 'filename': 'iris.csv', 'data_module': 'sklearn.datasets.data'}
.. _iris_dataset:

Iris plants dataset
--------------------

**Data Set Characteristics:**

    :Number of Instances: 150 (50 in each of three classes)
    :Number of Attributes: 4 numeric, predictive attributes and the class
    :Attribute Information:
        - sepal length in cm
        - sepal width in cm
        - petal length in cm
        - petal width in cm
        - class:
                - Iris-Setosa
                - Iris-Versicolour
                - Iris-Virginica

    :Summary Statistics:

    ============== ==== ==== ======= ===== ====================
                    Min  Max   Mean    SD   Class Correlation
    ============== ==== ==== ======= ===== ====================
    sepal length:   4.3  7.9   5.84   0.83    0.7826
    sepal width:    2.0  4.4   3.05   0.43   -0.4194
    petal length:   1.0  6.9   3.76   1.76    0.9490  (high!)
    petal width:    0.1  2.5   1.20   0.76    0.9565  (high!)
    ============== ==== ==== ======= ===== ====================

    :Missing Attribute Values: None
    :Class Distribution: 33.3% for each of 3 classes.
    :Creator: R.A. Fisher
    :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)
    :Date: July, 1988

The famous Iris database, first used by Sir R.A. Fisher. The dataset is taken
from Fisher's paper. Note that it's the same as in R, but not as in the UCI
Machine Learning Repository, which has two wrong data points.

This is perhaps the best known database to be found in the
pattern recognition literature.  Fisher's paper is a classic in the field and
is referenced frequently to this day.  (See Duda & Hart, for example.)  The
data set contains 3 classes of 50 instances each, where each class refers to a
type of iris plant.  One class is linearly separable from the other 2; the
latter are NOT linearly separable from each other.

.. topic:: References

   - Fisher, R.A. "The use of multiple measurements in taxonomic problems"
     Annual Eugenics, 7, Part II, 179-188 (1936); also in "Contributions to
     Mathematical Statistics" (John Wiley, NY, 1950).
   - Duda, R.O., & Hart, P.E. (1973) Pattern Classification and Scene Analysis.
     (Q327.D83) John Wiley & Sons.  ISBN 0-471-22361-1.  See page 218.
   - Dasarathy, B.V. (1980) "Nosing Around the Neighborhood: A New System
     Structure and Classification Rule for Recognition in Partially Exposed
     Environments".  IEEE Transactions on Pattern Analysis and Machine
     Intelligence, Vol. PAMI-2, No. 1, 67-71.
   - Gates, G.W. (1972) "The Reduced Nearest Neighbor Rule".  IEEE Transactions
     on Information Theory, May 1972, 431-433.
   - See also: 1988 MLC Proceedings, 54-64.  Cheeseman et al"s AUTOCLASS II
     conceptual clustering system finds 3 classes in the data.
   - Many, many more ...
[[5.1 3.5 1.4 0.2]
 [4.9 3.  1.4 0.2]
 [4.7 3.2 1.3 0.2]
 [4.6 3.1 1.5 0.2]
 [5.  3.6 1.4 0.2]
 [5.4 3.9 1.7 0.4]
 [4.6 3.4 1.4 0.3]
 [5.  3.4 1.5 0.2]
 [4.4 2.9 1.4 0.2]
 [4.9 3.1 1.5 0.1]
 [5.4 3.7 1.5 0.2]
 [4.8 3.4 1.6 0.2]
 [4.8 3.  1.4 0.1]
 [4.3 3.  1.1 0.1]
 [5.8 4.  1.2 0.2]
 [5.7 4.4 1.5 0.4]
 [5.4 3.9 1.3 0.4]
 [5.1 3.5 1.4 0.3]
 [5.7 3.8 1.7 0.3]
 [5.1 3.8 1.5 0.3]
 [5.4 3.4 1.7 0.2]
 [5.1 3.7 1.5 0.4]
 [4.6 3.6 1.  0.2]
 [5.1 3.3 1.7 0.5]
 [4.8 3.4 1.9 0.2]
 [5.  3.  1.6 0.2]
 [5.  3.4 1.6 0.4]
 [5.2 3.5 1.5 0.2]
 [5.2 3.4 1.4 0.2]
 [4.7 3.2 1.6 0.2]
 [4.8 3.1 1.6 0.2]
 [5.4 3.4 1.5 0.4]
 [5.2 4.1 1.5 0.1]
 [5.5 4.2 1.4 0.2]
 [4.9 3.1 1.5 0.2]
 [5.  3.2 1.2 0.2]
 [5.5 3.5 1.3 0.2]
 [4.9 3.6 1.4 0.1]
 [4.4 3.  1.3 0.2]
 [5.1 3.4 1.5 0.2]
 [5.  3.5 1.3 0.3]
 [4.5 2.3 1.3 0.3]
 [4.4 3.2 1.3 0.2]
 [5.  3.5 1.6 0.6]
 [5.1 3.8 1.9 0.4]
 [4.8 3.  1.4 0.3]
 [5.1 3.8 1.6 0.2]
 [4.6 3.2 1.4 0.2]
 [5.3 3.7 1.5 0.2]
 [5.  3.3 1.4 0.2]
 [7.  3.2 4.7 1.4]
 [6.4 3.2 4.5 1.5]
 [6.9 3.1 4.9 1.5]
 [5.5 2.3 4.  1.3]
 [6.5 2.8 4.6 1.5]
 [5.7 2.8 4.5 1.3]
 [6.3 3.3 4.7 1.6]
 [4.9 2.4 3.3 1. ]
 [6.6 2.9 4.6 1.3]
 [5.2 2.7 3.9 1.4]
 [5.  2.  3.5 1. ]
 [5.9 3.  4.2 1.5]
 [6.  2.2 4.  1. ]
 [6.1 2.9 4.7 1.4]
 [5.6 2.9 3.6 1.3]
 [6.7 3.1 4.4 1.4]
 [5.6 3.  4.5 1.5]
 [5.8 2.7 4.1 1. ]
 [6.2 2.2 4.5 1.5]
 [5.6 2.5 3.9 1.1]
 [5.9 3.2 4.8 1.8]
 [6.1 2.8 4.  1.3]
 [6.3 2.5 4.9 1.5]
 [6.1 2.8 4.7 1.2]
 [6.4 2.9 4.3 1.3]
 [6.6 3.  4.4 1.4]
 [6.8 2.8 4.8 1.4]
 [6.7 3.  5.  1.7]
 [6.  2.9 4.5 1.5]
 [5.7 2.6 3.5 1. ]
 [5.5 2.4 3.8 1.1]
 [5.5 2.4 3.7 1. ]
 [5.8 2.7 3.9 1.2]
 [6.  2.7 5.1 1.6]
 [5.4 3.  4.5 1.5]
 [6.  3.4 4.5 1.6]
 [6.7 3.1 4.7 1.5]
 [6.3 2.3 4.4 1.3]
 [5.6 3.  4.1 1.3]
 [5.5 2.5 4.  1.3]
 [5.5 2.6 4.4 1.2]
 [6.1 3.  4.6 1.4]
 [5.8 2.6 4.  1.2]
 [5.  2.3 3.3 1. ]
 [5.6 2.7 4.2 1.3]
 [5.7 3.  4.2 1.2]
 [5.7 2.9 4.2 1.3]
 [6.2 2.9 4.3 1.3]
 [5.1 2.5 3.  1.1]
 [5.7 2.8 4.1 1.3]
 [6.3 3.3 6.  2.5]
 [5.8 2.7 5.1 1.9]
 [7.1 3.  5.9 2.1]
 [6.3 2.9 5.6 1.8]
 [6.5 3.  5.8 2.2]
 [7.6 3.  6.6 2.1]
 [4.9 2.5 4.5 1.7]
 [7.3 2.9 6.3 1.8]
 [6.7 2.5 5.8 1.8]
 [7.2 3.6 6.1 2.5]
 [6.5 3.2 5.1 2. ]
 [6.4 2.7 5.3 1.9]
 [6.8 3.  5.5 2.1]
 [5.7 2.5 5.  2. ]
 [5.8 2.8 5.1 2.4]
 [6.4 3.2 5.3 2.3]
 [6.5 3.  5.5 1.8]
 [7.7 3.8 6.7 2.2]
 [7.7 2.6 6.9 2.3]
 [6.  2.2 5.  1.5]
 [6.9 3.2 5.7 2.3]
 [5.6 2.8 4.9 2. ]
 [7.7 2.8 6.7 2. ]
 [6.3 2.7 4.9 1.8]
 [6.7 3.3 5.7 2.1]
 [7.2 3.2 6.  1.8]
 [6.2 2.8 4.8 1.8]
 [6.1 3.  4.9 1.8]
 [6.4 2.8 5.6 2.1]
 [7.2 3.  5.8 1.6]
 [7.4 2.8 6.1 1.9]
 [7.9 3.8 6.4 2. ]
 [6.4 2.8 5.6 2.2]
 [6.3 2.8 5.1 1.5]
 [6.1 2.6 5.6 1.4]
 [7.7 3.  6.1 2.3]
 [6.3 3.4 5.6 2.4]
 [6.4 3.1 5.5 1.8]
 [6.  3.  4.8 1.8]
 [6.9 3.1 5.4 2.1]
 [6.7 3.1 5.6 2.4]
 [6.9 3.1 5.1 2.3]
 [5.8 2.7 5.1 1.9]
 [6.8 3.2 5.9 2.3]
 [6.7 3.3 5.7 2.5]
 [6.7 3.  5.2 2.3]
 [6.3 2.5 5.  1.9]
 [6.5 3.  5.2 2. ]
 [6.2 3.4 5.4 2.3]
 [5.9 3.  5.1 1.8]]
['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']
[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2
 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
 2 2]
['setosa' 'versicolor' 'virginica']

df.head()

	sepal length(cm)	sepal width(cm)	petal length (cm)	petal width (cm)
0	5.1	3.5	1.4	0.2
1	4.9	3.0	1.4	0.2
2	4.7	3.2	1.3	0.2
3	4.6	3.1	1.5	0.2
4	5.0	3.6	1.4	0.2

df.tail()

	sepal length(cm)	sepal width(cm)	petal length (cm)	petal width (cm)
145	6.7	3.0	5.2	2.3
146	6.3	2.5	5.0	1.9
147	6.5	3.0	5.2	2.0
148	6.2	3.4	5.4	2.3
149	5.9	3.0	5.1	1.8

df.index #인덱스 확인

RangeIndex(start=0, stop=150, step=1)

df.columns #컬럼 확인

Index(['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)',
       'petal width (cm)'],
      dtype='object')

df.dtypes #형식 확인

sepal length (cm)    float64
sepal width (cm)     float64
petal length (cm)    float64
petal width (cm)     float64
dtype: object

df[['sepal length (cm)', 'sepal width (cm)']] = df[['sepal length (cm)', 'sepal width (cm)']].astype(object)
df.dtypes #형식 변경

sepal length (cm)     object
sepal width (cm)      object
petal length (cm)    float64
petal width (cm)     float64
dtype: object

df[['sepal length (cm)', 'sepal width (cm)']] = df[['sepal length (cm)', 'sepal width (cm)']].astype(float)
df.dtypes

sepal length (cm)    float64
sepal width (cm)     float64
petal length (cm)    float64
petal width (cm)     float64
dtype: object

df.info() #데이터 타입, 각 아이템 개수 확인

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 150 entries, 0 to 149
Data columns (total 4 columns):
 #   Column             Non-Null Count  Dtype  
---  ------             --------------  -----  
 0   sepal length (cm)  150 non-null    float64
 1   sepal width (cm)   150 non-null    float64
 2   petal length (cm)  150 non-null    float64
 3   petal width (cm)   150 non-null    float64
dtypes: float64(4)
memory usage: 4.8 KB

---

데이터 전처리

df = df.rename(columns={'sepal length (cm)': 'sepal length', 'sepal width (cm)': 'sepal width',
                        'petal length (cm)' : 'petal length', 'petal width (cm)': 'petal width',
                        'variety' : 'species'}) #변수 이름 변경
df.head()

	sepal length	sepal width	petal length	petal width
0	5.1	3.5	1.4	0.2
1	4.9	3.0	1.4	0.2
2	4.7	3.2	1.3	0.2
3	4.6	3.1	1.5	0.2
4	5.0	3.6	1.4	0.2

Dataframe column 선택

• dataframe[ ] 으로 컬럼 추출
• [] -> Series로 변환
• [[]] -> dataframe으로 반환

df.columns

Index(['sepal length', 'sepal width', 'petal length', 'petal width'], dtype='object')

df['sepal length']

0      5.1
1      4.9
2      4.7
3      4.6
4      5.0
      ... 
145    6.7
146    6.3
147    6.5
148    6.2
149    5.9
Name: sepal length, Length: 150, dtype: float64

df[['sepal length']]

	sepal length
0	5.1
1	4.9
2	4.7
3	4.6
4	5.0
...	...
145	6.7
146	6.3
147	6.5
148	6.2
149	5.9

150 rows × 1 columns

df[['sepal length', 'sepal width']]

 

	sepal length	sepal width
0	5.1	3.5
1	4.9	3.0
2	4.7	3.2
3	4.6	3.1
4	5.0	3.6
...	...	...
145	6.7	3.0
146	6.3	2.5
147	6.5	3.0
148	6.2	3.4
149	5.9	3.0

150 rows × 2 columns

dataframe row 선택

• dataframe의 경우 []연산자는 컬럼(column) 선택, 하지만 슬라이싱(slicing)은 행(row) 선택
• .loc(),iloc()로 행 선택 가능
  • .loc() : 인덱스 자체를 사용
  • .iloc() : 0 based 인덱스 사용

df.head(10)

  

	sepal length	sepal width	petal length	petal width
0	5.1	3.5	1.4	0.2
1	4.9	3.0	1.4	0.2
2	4.7	3.2	1.3	0.2
3	4.6	3.1	1.5	0.2
4	5.0	3.6	1.4	0.2
5	5.4	3.9	1.7	0.4
6	4.6	3.4	1.4	0.3
7	5.0	3.4	1.5	0.2
8	4.4	2.9	1.4	0.2
9	4.9	3.1	1.5	0.1

df[0:5]

	sepal length	sepal width	petal length	petal width
0	5.1	3.5	1.4	0.2
1	4.9	3.0	1.4	0.2
2	4.7	3.2	1.3	0.2
3	4.6	3.1	1.5	0.2
4	5.0	3.6	1.4	0.2

df.index = df.index + 100
df.head()

	sepal length	sepal width	petal length	petal width
100	5.1	3.5	1.4	0.2
101	4.9	3.0	1.4	0.2
102	4.7	3.2	1.3	0.2
103	4.6	3.1	1.5	0.2
104	5.0	3.6	1.4	0.2

df.loc[[100]]

	sepal length	sepal width	petal length	petal width
100	5.1	3.5	1.4	0.2

df.iloc[[30]]

	sepal length	sepal width	petal length	petal width
130	4.8	3.1	1.6	0.2

df.iloc[[0]]

	sepal length	sepal width	petal length	petal width
100	5.1	3.5	1.4	0.2

df.loc[[100,101,102],["sepal length", "sepal width"]]

 

	sepal length	sepal width
100	5.1	3.5
101	4.9	3.0
102	4.7	3.2

df.iloc[[0,1,2],[0,3]]

	sepal length	petal width
100	5.1	0.2
101	4.9	0.2
102	4.7	0.2