[머신러닝] Pandas 기초

0. 팬더스, 데이터프레임, 시리즈¶

In [ ]:

import pandas as pd

data_frame = pd.read_csv("test.csv") # csv파일 읽어서 데이터 프레임 생성

In [15]:

data_frame

Out[15]:

	name	age	job
0	John	20	student
1	Julia	30	teacher
2	Brian	45	manager
3	Chris	25	intern

In [17]:

data_frame.head(2) # 데이터 프레임의 맨 앞 2행

Out[17]:

	name	age	job
0	John	20	student
1	Julia	30	teacher

In [19]:

data_frame.tail(2) # 데이터 프레임의 맨 뒤 2행

Out[19]:

	name	age	job
2	Brian	45	manager
3	Chris	25	intern

In [209]:

type(data_frame.job) # data frame의 각 열은 Series이다.

Out[209]:

pandas.core.series.Series

In [28]:

s1 = pd.core.series.Series([1, 2, 3])
s2 = pd.core.series.Series(["one", "two", "three"])

pd.DataFrame(data=dict(num=s1, word=s2)) # series들로 data frame 생성

Out[28]:

	num	word
0	1	one
1	2	two
2	3	three

1. 파일에서 데이터 불러오기¶

In [54]:

# csv파일 읽어서 데이터 프레임 생성 (delimiter 명시, header 명시)
# delimiter 기본값: ','    |   header 기본값: 0
# header로 명시된 행은 header가 되고, header로 명시된 행 + 1 부터는 데이터가 된다.
# header가 None이면, header는 0, 1, 2, 3, ...으로 생성된다.
df = pd.read_csv("data/friend_list.csv", delimiter = ',', header = None)
df

Out[54]:

	0	1	2
0	name	age	job
1	John	20	student
2	Jenny	30	developer
3	Nate	30	teacher
4	Julia	40	dentist
5	Brian	45	manager
6	Chris	25	intern

In [69]:

# data frame의 column header 변경하기
df.columns = ['이름', '나이', '직업']
df

Out[69]:

	이름	나이	직업
0	Jenny	30	developer
1	Nate	30	teacher
2	Julia	40	dentist
3	Brian	45	manager
4	Chris	25	intern

In [4]:

# 파일을 불러올 때 names를 통해 따로 헤더명을 명시할 수 있다.
df = pd.read_csv("data/friend_list_no_head.csv", header = None, names = ['name', 'age', 'job'])
df

Out[4]:

	name	age	job
0	John	20	student
1	Jenny	30	developer
2	Nate	30	teacher
3	Julia	40	dentist
4	Brian	45	manager
5	Chris	25	intern

2. 데이터프레임 생성하기¶

In [54]:

# dict는 순서가 없기 때문에, column의 순서는 랜덤하게 결정된다.
friend_dict_list = [
    {"name": "John", "age": 25, "job": "student"},
    {"name": "Nate", "age": 30, "job": "teacher"}
]

In [65]:

df = pd.DataFrame(friend_dict_list)
df.head()

Out[65]:

	name	age	job
0	John	25	student
1	Nate	30	teacher

In [60]:

# dataframe의 column순서 변경하기
df = df[["name", "job", "age"]]
df.head()

Out[60]:

	name	job	age
0	John	student	25
1	Nate	teacher	30

In [79]:

from collections import OrderedDict # dataframe 생성시 column순서를 보장하기 위함

In [71]:

# tuple을 (key(column name),[value(column element)]) 형식으로 list를 만들어서 OrderedDict를 생성한다.
friend_ordered_dict = OrderedDict(
    [
        ("name", ["John", "Nate"]),
        ("age", [25, 30]),
        ("job", ["student", "teacher"])
    ]
)

df = pd.DataFrame.from_dict(friend_ordered_dict)
df.head()

Out[71]:

	name	age	job
0	John	25	student
1	Nate	30	teacher

In [75]:

# list를 이용해서 dataframe을 생성하기 (data, header 따로 정의)
friend_list = [
    ["John", 20, "student"],
    ["Nate", 30, "teacher"]
]
column_name = ["name", "age", "job"]

df = pd.DataFrame.from_records(friend_list, columns = column_name)
df.head()

Out[75]:

	name	age	job
0	John	20	student
1	Nate	30	teacher

In [82]:

# list를 이용해서 dataframe을 생성하기 (data, header 한번에 정의) (OrderedDict를 이용한 간접적인 방식)
friend_list = [
    ["name", ["John", "Nate"]],
    ["age", [20, 30]],
    ["job", ["student", "teacher"]]
]

df = pd.DataFrame.from_dict(OrderedDict(friend_list))
df.head()

Out[82]:

	name	age	job
0	John	20	student
1	Nate	30	teacher

3. 데이터프레임 파일로 저장하기¶

In [88]:

friends = [
    {"name": "John", "age": 25, "job": "student"},
    {"name": "Nate", "age": 30, "job": "teacher"},
    {"name": "Jenny", "age": 30, "job": None}
]

df = pd.DataFrame(friends)
df = df[["name", "age", "job"]]
df.head()

Out[88]:

	name	age	job
0	John	25	student
1	Nate	30	teacher
2	Jenny	30	None

In [90]:

df.to_csv('friends.csv', index = True, header = True, na_rep = '-') # index와 header의 기본값은 True이다. na_rep은 None value를 해당 값으로 저장한다.

4. 데이터프레임 행, 열 선택 및 필터 하기¶

In [125]:

friend_list = OrderedDict(
    [
        ("name", ["John", "Nate", "Jenny"]),
        ("age", [25, 30, 30]),
        ("job", ["student", "teacher", "developer"])
    ]
)

df = pd.DataFrame.from_dict(friend_list)
df.head()

Out[125]:

	name	age	job
0	John	25	student
1	Nate	30	teacher
2	Jenny	30	developer

In [95]:

df[1:3] # slicing (row 기준) 된 복사본을 return.

Out[95]:

	name	age	job
1	Nate	30	teacher
2	Jenny	30	developer

In [97]:

df.loc[ [0, 2] ] # 불연속적인 특정 row만 선택해서 return

Out[97]:

	name	age	job
0	John	25	student
2	Jenny	30	developer

column condition 사용하기¶

In [106]:

df[df.age > 25]

Out[106]:

	name	age	job
1	Nate	30	teacher
2	Jenny	30	developer

In [102]:

df.query('age > 25')

Out[102]:

	name	age	job
1	Nate	30	teacher
2	Jenny	30	developer

In [126]:

df[(df.age > 25) & (df.name == "Nate")]

Out[126]:

	name	age	job
1	Nate	30	teacher

Filter column by index¶

In [119]:

friend_list = [
    ["John", 20, "student"],
    ["Nate", 30, "teacher"],
    ["Jenny", 30, "developer"]
]

df = pd.DataFrame.from_records(friend_list)
df

Out[119]:

	0	1	2
0	John	20	student
1	Nate	30	teacher
2	Jenny	30	developer

In [121]:

df.iloc[:, 0:2]  # [row, column]

Out[121]:

	0	1
0	John	20
1	Nate	30
2	Jenny	30

In [123]:

df.iloc[0:2, 0:2]

Out[123]:

	0	1
0	John	20
1	Nate	30

Filter column by index¶

In [128]:

df = pd.read_csv('data/friend_list_no_head.csv', header = None, names=["name", "age", "job"])
df

Out[128]:

	name	age	job
0	John	20	student
1	Jenny	30	developer
2	Nate	30	teacher
3	Julia	40	dentist
4	Brian	45	manager
5	Chris	25	intern

In [132]:

df[["name", "age"]] # name, age column만 return

Out[132]:

	name	age
0	John	20
1	Jenny	30
2	Nate	30
3	Julia	40
4	Brian	45
5	Chris	25

In [134]:

df.filter(items = ["age", "job"]) # age, job column만 return

Out[134]:

	age	job
0	20	student
1	30	developer
2	30	teacher
3	40	dentist
4	45	manager
5	25	intern

In [142]:

# column name에 'a'가 포함된 column을 return.
# axis = 0: 행
# axis = 1: 열
df.filter(like = "a", axis = 1)

Out[142]:

	name	age
0	John	20
1	Jenny	30
2	Nate	30
3	Julia	40
4	Brian	45
5	Chris	25

In [146]:

# regex 사용
df.filter(regex="b$", axis=1) # b로 끝나는 column을 return.

Out[146]:

	job
0	student
1	developer
2	teacher
3	dentist
4	manager
5	intern

5. 데이터프레임 행, 열 삭제하기¶

In [161]:

friends = [
    {"age": 15, "job": "student"},
    {"age": 25, "job": "developer"},
    {"age": 30, "job": "teacher"}
]

df = pd.DataFrame(friends, index = ["John", "Jenny", "Nate"], columns = ["age", "job"])
df

Out[161]:

	age	job
John	15	student
Jenny	25	developer
Nate	30	teacher

In [153]:

df.drop(["John", "Nate"]) # 행 삭제해서 return

Out[153]:

	age	job
Jenny	25	developer

In [160]:

df.drop(["John", "Nate"], inplace = True) # inplcae를 True로 하면 원본이 수정된다.
df

Out[160]:

	age	job
Jenny	25	developer

In [163]:

friends = [
    {"name": "John", "age": 15, "job": "student"},
    {"name": "Jenny", "age": 25, "job": "developer"},
    {"name": "Nate", "age": 30, "job": "teacher"}
]

df = pd.DataFrame(friends, columns = ["name", "age", "job"])
df

Out[163]:

	name	age	job
0	John	15	student
1	Jenny	25	developer
2	Nate	30	teacher

In [164]:

df.drop(df.index[[0, 2]]) # index이용 행 삭제

Out[164]:

	name	age	job
1	Jenny	25	developer

In [166]:

df.drop(["age"], axis = 1) # 열 삭제해서 return

Out[166]:

	name	job
0	John	student
1	Jenny	developer
2	Nate	teacher

6. 행, 열 생성 및 수정하기¶

In [167]:

friends = [
    {"name": "John", "age": 15, "job": "student"},
    {"name": "Jenny", "age": 25, "job": "developer"},
    {"name": "Nate", "age": 30, "job": "teacher"}
]

df = pd.DataFrame(friends, columns = ["name", "age", "job"])
df

Out[167]:

	name	age	job
0	John	15	student
1	Jenny	25	developer
2	Nate	30	teacher

In [169]:

df['salary'] = 0 # 'salary' 열 추가
df

Out[169]:

	name	age	job	salary
0	John	15	student	0
1	Jenny	25	developer	0
2	Nate	30	teacher	0

In [172]:

import numpy as np

df['salary'] = np.where(df['job'] != "student", "yes", "no") # job이 student이면 salary를 no, 그 외는 yes로 한다.
df

Out[172]:

	name	age	job	salary
0	John	15	student	no
1	Jenny	25	developer	yes
2	Nate	30	teacher	yes

In [174]:

friends = [
    {"name": "John", "midterm": 95, "final": 85},
    {"name": "Jenny", "midterm": 85, "final": 80},
    {"name": "Nate", "midterm": 30, "final": 10}
]

df = pd.DataFrame(friends, columns = ["name", "midterm", "final"])
df

Out[174]:

	name	midterm	final
0	John	95	85
1	Jenny	85	80
2	Nate	30	10

In [177]:

df['total'] = df['midterm'] + df['final']
df

Out[177]:

	name	midterm	final	total
0	John	95	85	180
1	Jenny	85	80	165
2	Nate	30	10	40

In [179]:

df['average'] = df['total'] / 2
df

Out[179]:

	name	midterm	final	total	average
0	John	95	85	180	90.0
1	Jenny	85	80	165	82.5
2	Nate	30	10	40	20.0

In [193]:

grades = []

for row in df['average']:
    if row >= 90:
        grades.append('A')
    elif row >= 80:
        grades.append('B')
    else:
        grades.append('F')
        
df['grade'] = grades # list로 새로운 열 생성 가능.
df

Out[193]:

	name	midterm	final	total	average	grade
0	John	95	85	180	90.0	A
1	Jenny	85	80	165	82.5	B
2	Nate	30	10	40	20.0	F

In [194]:

def pass_or_fail(row):
    if row != "F":
        return "Pass"
    else:
        return "Fail"

df.grade = df.grade.apply(pass_or_fail) # 인자로 받은 함수(pass_or_fail)의 인자로 각 element가 들어가고, return값들로 column을 생성한다.
df

Out[194]:

	name	midterm	final	total	average	grade
0	John	95	85	180	90.0	Pass
1	Jenny	85	80	165	82.5	Pass
2	Nate	30	10	40	20.0	Fail

In [197]:

date_list = [
    {"yyyy-mm-dd": "2000-06-27"},
    {"yyyy-mm-dd": "2007-10-27"}
]

df = pd.DataFrame(date_list, columns = ["yyyy-mm-dd"])
df

Out[197]:

	yyyy-mm-dd
0	2000-06-27
1	2007-10-27

In [198]:

# 년도만 추출해서 새로운 column 추가
def extract_year(row):
    return row.split('-')[0]

df['year'] = df['yyyy-mm-dd'].apply(extract_year)
df

Out[198]:

	yyyy-mm-dd	year
0	2000-06-27	2000
1	2007-10-27	2007

In [206]:

friends = [
    {"name": "John", "midterm": 95, "final": 85},
    {"name": "Jenny", "midterm": 85, "final": 80},
    {"name": "Nate", "midterm": 30, "final": 10}
]

df = pd.DataFrame(friends, columns = ["name", "midterm", "final"])
df

Out[206]:

	name	midterm	final
0	John	95	85
1	Jenny	85	80
2	Nate	30	10

In [201]:

df2 = pd.DataFrame([["Ben", 50, 50]], columns = ["name", "midterm", "final"])
df2

Out[201]:

	name	midterm	final
0	Ben	50	50

In [208]:

#row 추가하기 (다른 datafrma을 이용해서 (df와 df2의 column구성은 같음))
df.append(df2, ignore_index = True)

Out[208]:

	name	midterm	final
0	John	95	85
1	Jenny	85	80
2	Nate	30	10
3	Ben	50	50

In [210]:

# list로 새로운 row 추가하기
df.append(pd.Series(["Ben", 50, 50], index=df.columns), ignore_index = True)

Out[210]:

	name	midterm	final
0	John	95	85
1	Jenny	85	80
2	Nate	30	10
3	Ben	50	50

7. 데이터 그룹 만들기¶

In [213]:

student_list = [
    {"name": "John", "major": "Computer Science", "sex": "male"},
    {"name": "Nate", "major": "Computer Science", "sex": "male"},
    {"name": "Abraham", "major": "Physics", "sex": "male"},
    {"name": "Brian", "major": "Psychology", "sex": "male"},
    {"name": "Janny", "major": "Economics", "sex": "female"},
    {"name": "Yuna", "major": "Economics", "sex": "female"},
    {"name": "Jeniffer", "major": "Computer Science", "sex": "female"},
    {"name": "Edward", "major": "Computer Science", "sex": "male"},
    {"name": "Zara", "major": "Psycholog", "sex": "female"},
    {"name": "Wendy", "major": "Economics", "sex": "female"},
    {"name": "Sara", "major": "Psychology", "sex": "female"}
]

df = pd.DataFrame(student_list, columns=["name", "major", "sex"])
df

Out[213]:

	name	major	sex
0	John	Computer Science	male
1	Nate	Computer Science	male
2	Abraham	Physics	male
3	Brian	Psychology	male
4	Janny	Economics	female
5	Yuna	Economics	female
6	Jeniffer	Computer Science	female
7	Edward	Computer Science	male
8	Zara	Psycholog	female
9	Wendy	Economics	female
10	Sara	Psychology	female

In [215]:

groupby_major = df.groupby('major')
groupby_major.groups

Out[215]:

{'Computer Science': [0, 1, 6, 7], 'Economics': [4, 5, 9], 'Physics': [2], 'Psycholog': [8], 'Psychology': [3, 10]}

In [217]:

for name, group in groupby_major:
    print(name + " : " + str(len(group)))
    print(group)
    print()

Computer Science : 4
       name             major     sex
0      John  Computer Science    male
1      Nate  Computer Science    male
6  Jeniffer  Computer Science  female
7    Edward  Computer Science    male

Economics : 3
    name      major     sex
4  Janny  Economics  female
5   Yuna  Economics  female
9  Wendy  Economics  female

Physics : 1
      name    major   sex
2  Abraham  Physics  male

Psycholog : 1
   name      major     sex
8  Zara  Psycholog  female

Psychology : 2
     name       major     sex
3   Brian  Psychology    male
10   Sara  Psychology  female

In [220]:

df_major_cnt = pd.DataFrame({"count": groupby_major.size()}).reset_index()
df_major_cnt

Out[220]:

	major	count
0	Computer Science	4
1	Economics	3
2	Physics	1
3	Psycholog	1
4	Psychology	2

In [221]:

groupby_sex = df.groupby('sex')
groupby_sex.groups

Out[221]:

{'female': [4, 5, 6, 8, 9, 10], 'male': [0, 1, 2, 3, 7]}

In [223]:

for name, group in groupby_sex:
    print(name + " : " + str(len(group)))
    print(group)
    print()

female : 6
        name             major     sex
4      Janny         Economics  female
5       Yuna         Economics  female
6   Jeniffer  Computer Science  female
8       Zara         Psycholog  female
9      Wendy         Economics  female
10      Sara        Psychology  female

male : 5
      name             major   sex
0     John  Computer Science  male
1     Nate  Computer Science  male
2  Abraham           Physics  male
3    Brian        Psychology  male
7   Edward  Computer Science  male

8. 중복 데이터 삭제하기¶

In [225]:

student_list = [
    {"name": "John", "major": "Computer Science", "sex": "male"},
    {"name": "Nate", "major": "Computer Science", "sex": "male"},
    {"name": "Abraham", "major": "Physics", "sex": "male"},
    {"name": "Brian", "major": "Psychology", "sex": "male"},
    {"name": "John", "major": "Computer Science", "sex": "male"},
]

df = pd.DataFrame(student_list, columns=["name", "major", "sex"])
df

Out[225]:

	name	major	sex
0	John	Computer Science	male
1	Nate	Computer Science	male
2	Abraham	Physics	male
3	Brian	Psychology	male
4	John	Computer Science	male

In [227]:

# 위 df의 4번째 row는 0번째 row와 완전히 같은 값을 갖고 있다.
df.duplicated()

Out[227]:

0    False
1    False
2    False
3    False
4     True
dtype: bool

In [229]:

# 중복된 row 제거하기
df.drop_duplicates()

Out[229]:

	name	major	sex
0	John	Computer Science	male
1	Nate	Computer Science	male
2	Abraham	Physics	male
3	Brian	Psychology	male

In [230]:

student_list = [
    {"name": "John", "major": "Computer Science", "sex": "male"},
    {"name": "Nate", "major": "Computer Science", "sex": "male"},
    {"name": "Abraham", "major": "Physics", "sex": "male"},
    {"name": "Brian", "major": "Psychology", "sex": "male"},
    {"name": "John", "major": "Economics", "sex": "male"},
    {"name": "Nate", "major": "Physics", "sex": "male"},
]

df = pd.DataFrame(student_list, columns=["name", "major", "sex"])
df

Out[230]:

	name	major	sex
0	John	Computer Science	male
1	Nate	Computer Science	male
2	Abraham	Physics	male
3	Brian	Psychology	male
4	John	Economics	male
5	Nate	Physics	male

In [232]:

# 'name' column에 대해서만 중복 검사
df.duplicated(["name"])

Out[232]:

0    False
1    False
2    False
3    False
4     True
5     True
dtype: bool

In [236]:

# 'name' column에 대해서만 중복된 row 제거
# keep은 'first'이면 중복된것 중에서 맨 첫번째 값만 남기고, 'last'이면 맨 마지막 값만 남긴다. 그리고 False이면 모든 중복된 것을 지운다.
df.drop_duplicates(["name"], keep = 'first')

Out[236]:

	name	major	sex
0	John	Computer Science	male
1	Nate	Computer Science	male
2	Abraham	Physics	male
3	Brian	Psychology	male

9. NaN 찾아서 다른 값으로 변경하기¶

In [258]:

school_id_list = [
    {"name": "John", "job": "teacher", "age": 40},
    {"name": "Nate", "job": "teacher", "age": 35},
    {"name": "Yuna", "job": "teacher", "age": 37},
    {"name": "Abraham", "job": "student", "age": 10},
    {"name": "Brian", "job": "student", "age": 12},
    {"name": "Janny", "job": "student", "age": 11},
    {"name": "Nate", "job": "teacher", "age": None},
    {"name": "John", "job": "student", "age": None}
]

df = pd.DataFrame(school_id_list, columns=["name", "job", "age"])
df

Out[258]:

	name	job	age
0	John	teacher	40.0
1	Nate	teacher	35.0
2	Yuna	teacher	37.0
3	Abraham	student	10.0
4	Brian	student	12.0
5	Janny	student	11.0
6	Nate	teacher	NaN
7	John	student	NaN

In [241]:

df.shape

Out[241]:

(8, 3)

In [243]:

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 8 entries, 0 to 7
Data columns (total 3 columns):
 #   Column  Non-Null Count  Dtype  
---  ------  --------------  -----  
 0   name    8 non-null      object 
 1   job     8 non-null      object 
 2   age     6 non-null      float64
dtypes: float64(1), object(2)
memory usage: 320.0+ bytes

In [249]:

df.isna()

Out[249]:

	name	job	age
0	False	False	False
1	False	False	False
2	False	False	False
3	False	False	False
4	False	False	False
5	False	False	False
6	False	False	True
7	False	False	True

In [247]:

df.isnull()

Out[247]:

	name	job	age
0	False	False	False
1	False	False	False
2	False	False	False
3	False	False	False
4	False	False	False
5	False	False	False
6	False	False	True
7	False	False	True

In [259]:

df.age = df.age.fillna(0)
df

Out[259]:

	name	job	age
0	John	teacher	40.0
1	Nate	teacher	35.0
2	Yuna	teacher	37.0
3	Abraham	student	10.0
4	Brian	student	12.0
5	Janny	student	11.0
6	Nate	teacher	0.0
7	John	student	0.0

In [266]:

df = pd.DataFrame(school_id_list, columns=["name", "job", "age"])

In [262]:

# age column의 Nan 값들을 job별로(teacher, student)의 age 중앙값으로 채운다.
df['age'].fillna(df.groupby('job')['age'].transform('median'), inplace = True)
df

Out[262]:

	name	job	age
0	John	teacher	40.0
1	Nate	teacher	35.0
2	Yuna	teacher	37.0
3	Abraham	student	10.0
4	Brian	student	12.0
5	Janny	student	11.0
6	Nate	teacher	37.0
7	John	student	11.0

10. apply 함수 활용¶

In [268]:

date_list = [
    {"yyyy-mm-dd": "2000-06-27"},
    {"yyyy-mm-dd": "2005-09-24"},
    {"yyyy-mm-dd": "2007-12-20"}
]

df = pd.DataFrame(date_list, columns = ["yyyy-mm-dd"])
df

Out[268]:

	yyyy-mm-dd
0	2000-06-27
1	2005-09-24
2	2007-12-20

In [270]:

def extract_year(column):
    return column.split("-")[0]

df['year'] = df['yyyy-mm-dd'].apply(extract_year)
df

Out[270]:

	yyyy-mm-dd	year
0	2000-06-27	2000
1	2005-09-24	2005
2	2007-12-20	2007

In [276]:

def get_age(year, current_year):
    return current_year - int(year)

df['age'] = df['year'].apply(get_age, current_year = 2018) # year의 파라미터는 명시하지 않았는데, year column의 element가 자동으로 들어간다.
df

Out[276]:

	yyyy-mm-dd	year	age
0	2000-06-27	2000	18
1	2005-09-24	2005	13
2	2007-12-20	2007	11

In [281]:

def get_introduce(age, prefix, suffix):
    return prefix + str(age) + suffix

df['introduce'] = df['age'].apply(get_introduce, prefix = "I am ", suffix = " years old.")
df

Out[281]:

	yyyy-mm-dd	year	age	introduce
0	2000-06-27	2000	18	I am 18 years old.
1	2005-09-24	2005	13	I am 13 years old.
2	2007-12-20	2007	11	I am 11 years old.

In [283]:

# 여러개의 column에 apply function 사용하기
def get_introduce_2(row):
    return "I was born in " + str(row.year) + " my age is " + str(row.age)

df.introduce = df.apply(get_introduce_2, axis = 1)
df

Out[283]:

	yyyy-mm-dd	year	age	introduce
0	2000-06-27	2000	18	I was born in 2000 my age is 18
1	2005-09-24	2005	13	I was born in 2005 my age is 13
2	2007-12-20	2007	11	I was born in 2007 my age is 11

11. map, applymap 함수 활용¶

In [288]:

friends = [
    {"age": 15, "job": "student"},
    {"age": 25, "job": "developer"},
    {"age": 30, "job": "teacher"}
]

df = pd.DataFrame(friends, columns = ["age", "job"])
df

Out[288]:

	age	job
0	15	student
1	25	developer
2	30	teacher

In [290]:

# map: column별로 적용할 때 사용
df.job = df.job.map({"student": 1, "developer": 2, "teacher": 3})
df

Out[290]:

	age	job
0	15	1
1	25	2
2	30	3

In [291]:

x_y_z = [
    {"x": 5.5, "y": -5.6, "z": -1.1},
    {"x": -5.2, "y": 5.5, "z": -2.2},
    {"x": -1.6, "y": -4.5, "z": -3.3}
]

df = pd.DataFrame(x_y_z)
df

Out[291]:

	x	y	z
0	5.5	-5.6	-1.1
1	-5.2	5.5	-2.2
2	-1.6	-4.5	-3.3

In [296]:

# applymap: dataframe 전체에 적용할 때 사용
df = df.applymap(np.around) # np.around는 함수이다.
df

Out[296]:

	x	y	z
0	6.0	-6.0	-1.0
1	-5.0	6.0	-2.0
2	-2.0	-4.0	-3.0

12. Column 내에서 유니크한 값 list 뽑아내고 갯수 확인하기¶

In [298]:

job_list = [{'name': 'John', 'job': "teacher"},
                {'name': 'Nate', 'job': "teacher"},
                {'name': 'Fred', 'job': "teacher"},
                {'name': 'Abraham', 'job': "student"},
                {'name': 'Brian', 'job': "student"},
                {'name': 'Janny', 'job': "developer"},
                {'name': 'Nate', 'job': "teacher"},
                {'name': 'Obrian', 'job': "dentist"},
                {'name': 'Yuna', 'job': "teacher"},
                {'name': 'Rob', 'job': "lawyer"},
                {'name': 'Brian', 'job': "student"},
                {'name': 'Matt', 'job': "student"},
                {'name': 'Wendy', 'job': "banker"},
                {'name': 'Edward', 'job': "teacher"},
                {'name': 'Ian', 'job': "teacher"},
                {'name': 'Chris', 'job': "banker"},
                {'name': 'Philip', 'job': "lawyer"},
                {'name': 'Janny', 'job': "basketball player"},
                {'name': 'Gwen', 'job': "teacher"},
                {'name': 'Jessy', 'job': "student"}
         ]
df = pd.DataFrame(job_list, columns = ['name', 'job'])
df

Out[298]:

	name	job
0	John	teacher
1	Nate	teacher
2	Fred	teacher
3	Abraham	student
4	Brian	student
5	Janny	developer
6	Nate	teacher
7	Obrian	dentist
8	Yuna	teacher
9	Rob	lawyer
10	Brian	student
11	Matt	student
12	Wendy	banker
13	Edward	teacher
14	Ian	teacher
15	Chris	banker
16	Philip	lawyer
17	Janny	basketball player
18	Gwen	teacher
19	Jessy	student

In [300]:

df.job.unique()

Out[300]:

array(['teacher', 'student', 'developer', 'dentist', 'lawyer', 'banker',
       'basketball player'], dtype=object)

In [302]:

df.job.value_counts()

Out[302]:

teacher              8
student              5
lawyer               2
banker               2
dentist              1
basketball player    1
developer            1
Name: job, dtype: int64

13. 두개의 데이터프레임 합치기¶

In [303]:

l1 = [{'name': 'John', 'job': "teacher"},
      {'name': 'Nate', 'job': "student"},
      {'name': 'Fred', 'job': "developer"}]

l2 = [{'name': 'Ed', 'job': "dentist"},
      {'name': 'Jack', 'job': "farmer"},
      {'name': 'Ted', 'job': "designer"}]
         
df1 = pd.DataFrame(l1, columns = ['name', 'job'])
df2 = pd.DataFrame(l2, columns = ['name', 'job'])

In [305]:

df1

Out[305]:

	name	job
0	John	teacher
1	Nate	student
2	Fred	developer

In [306]:

df2

Out[306]:

	name	job
0	Ed	dentist
1	Jack	farmer
2	Ted	designer

In [308]:

# df1과 df2를 row로 합치기 (방법 1)
result = pd.concat([df1, df2], ignore_index = True)
result

Out[308]:

	name	job
0	John	teacher
1	Nate	student
2	Fred	developer
3	Ed	dentist
4	Jack	farmer
5	Ted	designer

In [310]:

# df1과 df2를 row로 합치기 (방법 2)
result = df1.append(df2, ignore_index = True)
result

Out[310]:

	name	job
0	John	teacher
1	Nate	student
2	Fred	developer
3	Ed	dentist
4	Jack	farmer
5	Ted	designer

In [315]:

l3 = [{'name': 'John', 'job': "teacher"},
      {'name': 'Nate', 'job': "student"},
      {'name': 'Jack', 'job': "developer"}]

l4 = [{'age': 25, 'country': "U.S."},
      {'age': 30, 'country': "U.K."},
      {'age': 45, 'country': "Korea"}]

df3 = pd.DataFrame(l3, columns = ['name', 'job'])
df4 = pd.DataFrame(l4, columns = ['age', 'country'])

In [313]:

df3

Out[313]:

	name	job
0	John	teacher
1	Nate	student
2	Jack	developer

In [314]:

df4

Out[314]:

	age	country
0	25	U.S.
1	30	U.K.
2	45	Korea

In [320]:

# df3과 df4를 column으로 합치기
result = pd.concat([df3, df4], axis = 1, ignore_index = False)
result

Out[320]:

	name	job	age	country
0	John	teacher	25	U.S.
1	Nate	student	30	U.K.
2	Jack	developer	45	Korea

[출처] www.inflearn.com/course/pandas-%ED%8C%AC%EB%8D%94%EC%8A%A4-%EB%8D%B0%EC%9D%B4%ED%84%B0%EB%B6%84%EC%84%9D-%EA%B8%B0%EC%B4%88

Pandas 팬더스 데이터분석 기초 실습 - 인프런