Datawhale_数据分析组队学习task3

读写文本格式的数据

# read_csv读入DataFrame
import pandas as pd
df = pd.read_csv(r'/Users/faye/Desktop/examples/ex1.csv')
df
>>>

a	b	c	d	message
0	1	2	3	4	hello
1	5	6	7	8	world
2	9	10	11	12	foo
In [4]:

# read_table读入，并指定分隔符
pd.read_table(r'/Users/faye/Desktop/examples/ex1.csv', sep = ',')
/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:2: FutureWarning: read_table is deprecated, use read_csv instead.
  
Out[4]:
a	b	c	d	message
0	1	2	3	4	hello
1	5	6	7	8	world
2	9	10	11	12	foo
In [7]:

# 原文件无标题行，分配默认的列名
pd.read_csv(r'/Users/faye/Desktop/examples/ex2.csv',header = None)
Out[7]:
0	1	2	3	4
0	1	2	3	4	hello
1	5	6	7	8	world
2	9	10	11	12	foo
In [9]:

# 自定义列名
pd.read_csv(r'/Users/faye/Desktop/examples/ex2.csv',names = ['a','b','c','d','message'])
Out[9]:
a	b	c	d	message
0	1	2	3	4	hello
1	5	6	7	8	world
2	9	10	11	12	foo
In [10]:

# index_col指定索引位置
pd.read_csv(r'/Users/faye/Desktop/examples/ex2.csv',names = ['a','b','c','d','message'],index_col = 'message')
Out[10]:
a	b	c	d
message				
hello	1	2	3	4
world	5	6	7	8
foo	9	10	11	12
In [12]:

# 将多个列转化成层次化索引
parsed = pd.read_csv(r'/Users/faye/Desktop/examples/csv_mindex.csv',index_col = ['key1','key2'])
parsed
Out[12]:
value1	value2
key1	key2		
one	a	1	2
b	3	4
c	5	6
d	7	8
two	a	9	10
b	11	12
c	13	14
d	15	16
In [13]:

# 正则表达式\s+作为read_table
result = pd.read_table(r'/Users/faye/Desktop/examples/ex3.txt',sep = '\s+')
result
/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:2: FutureWarning: read_table is deprecated, use read_csv instead.
  
Out[13]:
A	B	C
aaa	-0.264438	-1.026059	-0.619500
bbb	0.927272	0.302904	-0.032399
ccc	-0.264273	-0.386314	-0.217601
ddd	-0.871858	-0.348382	1.100491
In [14]:

# skiprows跳过文件的行
pd.read_csv(r'/Users/faye/Desktop/examples/ex4.csv',skiprows = [0,2,3]) # 跳过第一、三、四行
Out[14]:
a	b	c	d	message
0	1	2	3	4	hello
1	5	6	7	8	world
2	9	10	11	12	foo

read_csv/read_table函数的参数：