In [1]:
!conda list | grep pandas


pandas                    0.18.1              np111py35_0  

In [2]:
import pandas as pd

In [8]:
df = pd.read_csv('./friend_list.csv')

In [9]:
df


Out[9]:
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 [10]:
print(df)


    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 [11]:
df


Out[11]:
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 [12]:
#처음 부터 5개의 데이터를 보여준다.
df.head()


Out[12]:
name age job
0 John 20 student
1 Jenny 30 developer
2 Nate 30 teacher
3 Julia 40 dentist
4 Brian 45 manager

In [13]:
#처음 부터 2개의 데이터를 보여준다.
df.head(2)


Out[13]:
name age job
0 John 20 student
1 Jenny 30 developer

In [14]:
#뒤에 부터 보고 싶다.
df.tail()


Out[14]:
name age job
1 Jenny 30 developer
2 Nate 30 teacher
3 Julia 40 dentist
4 Brian 45 manager
5 Chris 25 intern

In [15]:
df.tail(3)


Out[15]:
name age job
3 Julia 40 dentist
4 Brian 45 manager
5 Chris 25 intern

In [16]:
df.head(1)


Out[16]:
name age job
0 John 20 student

In [19]:
help(df.head)


Help on method head in module pandas.core.generic:

head(n=5) method of pandas.core.frame.DataFrame instance
    Returns first n rows


In [20]:
help(df.tail)


Help on method tail in module pandas.core.generic:

tail(n=5) method of pandas.core.frame.DataFrame instance
    Returns last n rows


In [21]:
df.head(n=4)


Out[21]:
name age job
0 John 20 student
1 Jenny 30 developer
2 Nate 30 teacher
3 Julia 40 dentist

In [22]:
df2 = pd.read_csv('friend_list.txt')

In [23]:
df2


Out[23]:
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 [24]:
df = pd.read_csv('friend_list_tab.txt')

In [25]:
df


Out[25]:
name age job
0 John\t20\tstudent
1 Jenny\t30\tdeveloper
2 Nate\t30\tteacher
3 Julia\t40\tdentist
4 Brian\t45\tmanager
5 Chris\t25\tintern

In [27]:
df = pd.read_csv('friend_list_tab.txt', delimiter='\t')

In [28]:
df


Out[28]:
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 [31]:
df = pd.read_csv('friend_list_no_head.csv', header=None)

In [32]:
df


Out[32]:
0 1 2
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 [33]:
df.columns = ['name','age','job']

In [34]:
df


Out[34]:
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 [36]:
df = pd.read_csv('friend_list_no_head.csv', header=None, names=['name','age','job'])

In [37]:
df


Out[37]:
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 [ ]: