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 "<ipython-input-5-6eb71b2a5e94>", line 1, in <module> 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 <module> 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...