Pandas 데이터분석 기초 실습 -4

데이터 프레임 행, 열 삭제하기¶

In [11]:

import pandas as pd
friends =[
    {'age':15,'job':'student'},
    {'age':20,'job':'developer'},
    {'age':30,'job':'teacher'}
]
index = ['John','Jenny','Nate']
column = ['age','job']
df = pd.DataFrame(friends,
index= index,
columns=column
)
df

Out[11]:

	age	job
John	15	student
Jenny	20	developer
Nate	30	teacher

In [12]:

df.drop(['John','Nate'])    #John, Nate 없애기

Out[12]:

	age	job
Jenny	20	developer

In [13]:

df = df.drop(['John','Nate'])
df

Out[13]:

	age	job
Jenny	20	developer

In [20]:

friends =[
    {'age':15,'job':'student'},
    {'age':20,'job':'developer'},
    {'age':30,'job':'teacher'}
]
index = ['John','Jenny','Nate']
column = ['age','job']
df = pd.DataFrame(friends,
index= index,
columns=column
)
df

Out[20]:

	age	job
John	15	student
Jenny	20	developer
Nate	30	teacher

In [21]:

#inplace df에 바로적용
df.drop(['John','Nate'],inplace = True)
df

Out[21]:

	age	job
Jenny	20	developer

In [32]:

friends =[
    {'name':'John','age':15,'job':'student'},
    {'name':'Ben','age':20,'job':'developer'},
    {'name':'Jenny','age':30,'job':'teacher'}
]
column = ['name','age','job']
df = pd.DataFrame(friends,
columns=column
)
df

Out[32]:

	name	age	job
0	John	15	student
1	Ben	20	developer
2	Jenny	30	teacher

In [33]:

df = df.drop(df.index[[0,2]])  #John과 Jenny 삭제
#원하는 row 삭제
df

Out[33]:

	name	age	job
1	Ben	20	developer

In [37]:

friends =[
    {'name':'John','age':15,'job':'student'},
    {'name':'Ben','age':25,'job':'developer'},
    {'name':'Jenny','age':30,'job':'teacher'}
]
column = ['name','age','job']
df = pd.DataFrame(friends,
columns=column
)
df

Out[37]:

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

조건에 의한 삭제¶

In [38]:

df = df[df.age > 20]
df

Out[38]:

	name	age	job
1	Ben	25	developer
2	Jenny	30	teacher

In [46]:

friends =[
    {'name':'John','age':15,'job':'student'},
    {'name':'Ben','age':25,'job':'developer'},
    {'name':'Jenny','age':30,'job':'teacher'}
]
column = ['name','age','job']
df = pd.DataFrame(friends,
columns=column
)
df

Out[46]:

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

In [47]:

df.drop('age', axis= 1,inplace =True)
#column 중에서 age를 삭제
df

Out[47]:

	name	job
0	John	student
1	Ben	developer
2	Jenny	teacher

In [ ]: