read_csv()读取文件

1.python读取文件的几种方式

read_csv 从文件，url，文件型对象中加载带分隔符的数据。默认分隔符为逗号

read_table 从文件，url，文件型对象中加载带分隔符的数据。默认分隔符为制表符(“\t”)

read_fwf 读取定宽列格式数据(也就是没有分隔符)

read_cliboard 读取剪切板中的数据，可以看做read_table的剪切板。在将网页转换为表格时很有用

2.读取文件的简单实现

程序代码：

df=pd.read_csv('D:/project/python_instruct/test_data1.csv')

print('用read_csv读取的csv文件：', df)

df=pd.read_table('D:/project/python_instruct/test_data1.csv', sep=',')

print('用read_table读取csv文件：', df)

df=pd.read_csv('D:/project/python_instruct/test_data2.csv', header=None)

print('用read_csv读取无标题行的csv文件：', df)

df=pd.read_csv('D:/project/python_instruct/test_data2.csv', names=['a', 'b', 'c', 'd', 'message'])

print('用read_csv读取自定义标题行的csv文件：', df)

names=['a', 'b', 'c', 'd', 'message']

df=pd.read_csv('D:/project/python_instruct/test_data2.csv', names=names, index_col='message')

print('read_csv读取时指定索引：', df)

parsed=pd.read_csv('D:/project/python_instruct/test_data3.csv', index_col=['key1', 'key2'])

print('read_csv将多个列做成一个层次化索引：')

print(parsed)

print(list(open('D:/project/python_instruct/test_data1.txt')))

result=pd.read_table('D:/project/python_instruct/test_data1.txt', sep='\s+')

print('read_table利用正则表达式处理文件读取：')

print(result)

输出结果：

用read_csv读取的csv文件：

a b c d message

0 1 2 3 4 hello

1 5 6 7 8 world

2 9 10 11 12 foo

用read_table读取csv文件：

a b c d message

0 1 2 3 4 hello

1 5 6 7 8 world

2 9 10 11 12 foo

用read_csv读取无标题行的csv文件：

0 1 2 3 4

0 1 2 3 4 hello

1 5 6 7 8 world

2 9 10 11 12 foo

用read_csv读取自定义标题行的csv文件：

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读取时指定索引：

a b c d

message

hello 1 2 3 4

world 5 6 7 8

foo 9 10 11 12

read_csv将多个列做成一个层次化索引：

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

[' A B C \n', 'aaa -0.26 -0.1 -0.4\n', 'bbb -0.92 -0.4 -0.7\n', 'ccc -0.34 -0.5 -0.8\n', 'ddd -0.78 -0.3 -0.2']

read_table利用正则表达式处理文件读取：

A B C

aaa -0.26 -0.1 -0.4

bbb -0.92 -0.4 -0.7

ccc -0.34 -0.5 -0.8

ddd -0.78 -0.3 -0.2

3.分块读取大型数据集

先看代码：

reslt=pd.read_csv('D:\project\python_instruct\weibo_network.txt')

print('原始文件：', result)

输出：

Traceback (most recent call last):

File "", line 1, in runfile('D:/project/python_instruct/Test.py', wdir='D:/project/python_instruct')

File "D:\Anaconda3\lib\site-packages\spyder\utils\site\sitecustomize.py", line 866, in runfile

execfile(filename, namespace)

File "D:\Anaconda3\lib\site-packages\spyder\utils\site\sitecustomize.py", line 102, in execfile

exec(compile(f.read(), filename, 'exec'), namespace)

File "D:/project/python_instruct/Test.py", line 75, in reslt=pd.read_csv('D:\project\python_instruct\weibo_network.txt')

File "D:\Anaconda3\lib\site-packages\pandas\io\parsers.py", line 562, in parser_f

return _read(filepath_or_buffer, kwds)

File "D:\Anaconda3\lib\site-packages\pandas\io\parsers.py", line 325, in _read

return parser.read()

File "D:\Anaconda3\lib\site-packages\pandas\io\parsers.py", line 815, in read

ret = self._engine.read(nrows)

File "D:\Anaconda3\lib\site-packages\pandas\io\parsers.py", line 1314, in read

data = self._reader.read(nrows)

File "pandas\parser.pyx", line 805, in pandas.parser.TextReader.read (pandas\parser.c:8748)

File "pandas\parser.pyx", line 827, in pandas.parser.TextReader._read_low_memory (pandas\parser.c:9003)

File "pandas\parser.pyx", line 881, in pandas.parser.TextReader._read_rows (pandas\parser.c:9731)

File "pandas\parser.pyx", line 868, in pandas.parser.TextReader._tokenize_rows (pandas\parser.c:9602)

File "pandas\parser.pyx", line 1865, in pandas.parser.raise_parser_error (pandas\parser.c:23325)

CParserError: Error tokenizing data. C error: out of memory

发现数据集大得已经超出内存。我们可以读取几行看看，如前10行：

result=pd.read_csv('D:\project\python_instruct\weibo_network.txt', nrows=10)

print('只读取几行：')

print(result)

输出结果：

0 0\t296\t3\t1\t10\t1\t12\t1\t13\t1\t14\t1\t16\t...

1 1\t271\t8\t1\t17\t1\t22\t1\t31\t0\t34\t1\t6742...

2 2\t158\t0\t0\t5\t1\t10\t1\t11\t1\t13\t1\t16\t0...

3 3\t413\t0\t1\t5\t1\t194\t1\t354\t1\t3462\t1\t8...

4 4\t142\t1\t0\t5\t1\t7\t1\t11\t1\t14\t1\t18\t1\...

5 5\t272\t2\t1\t3\t1\t4\t1\t12\t1\t13\t1\t14\t1\...

6 6\t59\t9\t1\t13\t1\t46991\t0\t66930\t0\t85672\...

7 7\t131\t4\t1\t11\t1\t20\t1\t24\t1\t26\t0\t30\t...

8 8\t326\t0\t0\t1\t1\t12\t1\t13\t1\t17\t1\t19\t1...

9 9\t12\t0\t0\t6\t1\t10\t1\t13\t1\t18\t0\t466527...