{"id":1117564,"date":"2025-01-08T18:27:09","date_gmt":"2025-01-08T10:27:09","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1117564.html"},"modified":"2025-01-08T18:27:11","modified_gmt":"2025-01-08T10:27:11","slug":"python3%e5%a6%82%e4%bd%95%e5%af%bc%e5%85%a5dat%e6%95%b0%e6%8d%ae%e6%96%87%e4%bb%b6-2","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1117564.html","title":{"rendered":"python3\u5982\u4f55\u5bfc\u5165dat\u6570\u636e\u6587\u4ef6"},"content":{"rendered":"

\"python3\u5982\u4f55\u5bfc\u5165dat\u6570\u636e\u6587\u4ef6\"<\/p>\n

Python 3 \u5bfc\u5165 DAT \u6570\u636e\u6587\u4ef6\u7684\u65b9\u6cd5\u6709\u591a\u79cd\uff0c\u4e3b\u8981\u5305\u62ec\u4f7f\u7528Pandas\u3001Numpy\u3001\u5185\u7f6e\u51fd\u6570\u548c\u81ea\u5b9a\u4e49\u89e3\u6790\u5668\u7b49\u3002<\/strong> \u5176\u4e2d\uff0cPandas<\/strong> \u662f\u4e00\u79cd\u9ad8\u6548\u4e14\u5e38\u7528\u7684\u65b9\u5f0f\uff0c\u56e0\u4e3a\u5b83\u63d0\u4f9b\u4e86\u5f3a\u5927\u7684\u6570\u636e\u5904\u7406\u529f\u80fd\u548c\u53cb\u597d\u7684\u63a5\u53e3\u3002\u4e0b\u9762\u8be6\u7ec6\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528 Pandas \u5bfc\u5165 DAT \u6570\u636e\u6587\u4ef6\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u4f7f\u7528 Pandas \u5bfc\u5165 DAT \u6587\u4ef6<\/h3>\n<\/p>\n

Pandas \u662f Python \u4e2d\u975e\u5e38\u6d41\u884c\u7684\u6570\u636e\u5206\u6790\u5e93\uff0c\u5b83\u53ef\u4ee5\u8f7b\u677e\u5730\u5904\u7406\u5404\u79cd\u683c\u5f0f\u7684\u6570\u636e\u6587\u4ef6\uff0c\u5305\u62ec DAT \u6587\u4ef6\u3002Pandas \u63d0\u4f9b\u4e86 read_csv<\/code> \u51fd\u6570\uff0c\u53ef\u4ee5\u8bfb\u53d6\u5927\u591a\u6570\u7684\u6587\u672c\u6587\u4ef6\u683c\u5f0f\u3002<\/p>\n<\/p>\n

1\u3001\u5b89\u88c5 Pandas<\/h4>\n<\/p>\n

\u5728\u4f7f\u7528 Pandas \u4e4b\u524d\uff0c\u9700\u8981\u5148\u5b89\u88c5 Pandas \u5e93\u3002\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u547d\u4ee4\u901a\u8fc7 pip \u5b89\u88c5\uff1a<\/p>\n<\/p>\n

pip install pandas<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u8bfb\u53d6 DAT \u6587\u4ef6<\/h4>\n<\/p>\n
\u5047\u8bbe\u6709\u4e00\u4e2a\u540d\u4e3a data.dat<\/code> \u7684\u6587\u4ef6\uff0c\u6587\u4ef6\u5185\u5bb9\u5982\u4e0b\uff1a<\/p>\n<\/p>\n
Name, Age, Salary<\/p>\n
John, 28, 50000<\/p>\n
Jane, 32, 60000<\/p>\n
Doe, 25, 45000<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u4ee3\u7801\u8bfb\u53d6\u8fd9\u4e2a\u6587\u4ef6\uff1a<\/p>\n<\/p>\n
import pandas as pd<\/p>\n
\u8bfb\u53d6 DAT \u6587\u4ef6<\/strong><\/h2>\n
df = pd.read_csv('data.dat')<\/p>\n
\u6253\u5370\u6570\u636e\u6846<\/strong><\/h2>\n
print(df)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e0a\u8ff0\u4ee3\u7801\u4e2d\uff0cpd.read_csv<\/code> \u51fd\u6570\u5c06\u8bfb\u53d6 data.dat<\/code> \u6587\u4ef6\u5e76\u8fd4\u56de\u4e00\u4e2a Pandas \u6570\u636e\u6846\u3002<\/p>\n<\/p>\n
3\u3001\u5904\u7406\u65e0\u6807\u9898\u884c\u7684 DAT \u6587\u4ef6<\/h4>\n<\/p>\n
\u5982\u679c\u6587\u4ef6\u6ca1\u6709\u6807\u9898\u884c\uff0c\u53ef\u4ee5\u4f7f\u7528 header=None<\/code> \u53c2\u6570\uff0c\u5e76\u624b\u52a8\u6307\u5b9a\u5217\u540d\uff1a<\/p>\n<\/p>\n
import pandas as pd<\/p>\n
\u8bfb\u53d6 DAT \u6587\u4ef6\uff0c\u65e0\u6807\u9898\u884c<\/strong><\/h2>\n
df = pd.read_csv('data.dat', header=None, names=['Name', 'Age', 'Salary'])<\/p>\n
\u6253\u5370\u6570\u636e\u6846<\/strong><\/h2>\n
print(df)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e8c\u3001\u4f7f\u7528 Numpy \u5bfc\u5165 DAT \u6587\u4ef6<\/h3>\n<\/p>\n
Numpy \u662f\u53e6\u4e00\u4e2a\u5f3a\u5927\u7684\u6570\u636e\u5904\u7406\u5e93\uff0c\u7279\u522b\u9002\u5408\u5904\u7406\u6570\u503c\u6570\u636e\u3002\u53ef\u4ee5\u4f7f\u7528 numpy.loadtxt<\/code> \u51fd\u6570\u8bfb\u53d6 DAT \u6587\u4ef6\u3002<\/p>\n<\/p>\n
1\u3001\u5b89\u88c5 Numpy<\/h4>\n<\/p>\n
\u5728\u4f7f\u7528 Numpy \u4e4b\u524d\uff0c\u9700\u8981\u5148\u5b89\u88c5 Numpy \u5e93\u3002\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u547d\u4ee4\u901a\u8fc7 pip \u5b89\u88c5\uff1a<\/p>\n<\/p>\n
pip install numpy<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u8bfb\u53d6 DAT \u6587\u4ef6<\/h4>\n<\/p>\n
\u5047\u8bbe\u6709\u4e00\u4e2a\u540d\u4e3a data.dat<\/code> \u7684\u6587\u4ef6\uff0c\u6587\u4ef6\u5185\u5bb9\u5982\u4e0b\uff1a<\/p>\n<\/p>\n
28 50000<\/p>\n
32 60000<\/p>\n
25 45000<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u4ee3\u7801\u8bfb\u53d6\u8fd9\u4e2a\u6587\u4ef6\uff1a<\/p>\n<\/p>\n
import numpy as np<\/p>\n
\u8bfb\u53d6 DAT \u6587\u4ef6<\/strong><\/h2>\n
data = np.loadtxt('data.dat')<\/p>\n
\u6253\u5370\u6570\u636e<\/strong><\/h2>\n
print(data)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e0a\u8ff0\u4ee3\u7801\u4e2d\uff0cnp.loadtxt<\/code> \u51fd\u6570\u5c06\u8bfb\u53d6 data.dat<\/code> \u6587\u4ef6\u5e76\u8fd4\u56de\u4e00\u4e2a Numpy \u6570\u7ec4\u3002<\/p>\n<\/p>\n
3\u3001\u5904\u7406\u5e26\u5206\u9694\u7b26\u7684 DAT \u6587\u4ef6<\/h4>\n<\/p>\n
\u5982\u679c\u6587\u4ef6\u4e2d\u7684\u6570\u636e\u4f7f\u7528\u7279\u5b9a\u7684\u5206\u9694\u7b26\uff0c\u53ef\u4ee5\u4f7f\u7528 delimiter<\/code> \u53c2\u6570\u6307\u5b9a\u5206\u9694\u7b26\uff1a<\/p>\n<\/p>\n
import numpy as np<\/p>\n
\u8bfb\u53d6 DAT \u6587\u4ef6\uff0c\u6570\u636e\u7528\u9017\u53f7\u5206\u9694<\/strong><\/h2>\n
data = np.loadtxt('data.dat', delimiter=',')<\/p>\n
\u6253\u5370\u6570\u636e<\/strong><\/h2>\n
print(data)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e09\u3001\u4f7f\u7528\u5185\u7f6e\u51fd\u6570\u5bfc\u5165 DAT \u6587\u4ef6<\/h3>\n<\/p>\n
Python \u63d0\u4f9b\u4e86\u5185\u7f6e\u7684\u6587\u4ef6\u64cd\u4f5c\u51fd\u6570\uff0c\u53ef\u4ee5\u7528\u6765\u8bfb\u53d6 DAT \u6587\u4ef6\u3002\u867d\u7136\u8fd9\u79cd\u65b9\u6cd5\u4e0d\u5982 Pandas \u548c Numpy \u65b9\u4fbf\uff0c\u4f46\u5bf9\u4e8e\u7b80\u5355\u7684\u6570\u636e\u6587\u4ef6\u4e5f\u662f\u4e00\u79cd\u53ef\u884c\u7684\u65b9\u5f0f\u3002<\/p>\n<\/p>\n
1\u3001\u8bfb\u53d6\u6574\u4e2a\u6587\u4ef6<\/h4>\n<\/p>\n
\u53ef\u4ee5\u4f7f\u7528 open<\/code> \u51fd\u6570\u8bfb\u53d6\u6574\u4e2a\u6587\u4ef6\uff0c\u5e76\u4f7f\u7528 split<\/code> \u51fd\u6570\u6309\u884c\u5206\u5272\uff1a<\/p>\n<\/p>\n
# \u8bfb\u53d6\u6587\u4ef6<\/p>\n
with open('data.dat', 'r') as file:<\/p>\n
    data = file.read().splitlines()<\/p>\n
\u6253\u5370\u6570\u636e<\/strong><\/h2>\n
for line in data:<\/p>\n
    print(line)<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u9010\u884c\u8bfb\u53d6\u6587\u4ef6<\/h4>\n<\/p>\n
\u4e5f\u53ef\u4ee5\u4f7f\u7528 open<\/code> \u51fd\u6570\u9010\u884c\u8bfb\u53d6\u6587\u4ef6\uff1a<\/p>\n<\/p>\n
# \u9010\u884c\u8bfb\u53d6\u6587\u4ef6<\/p>\n
with open('data.dat', 'r') as file:<\/p>\n
    for line in file:<\/p>\n
        print(line.strip())<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u56db\u3001\u81ea\u5b9a\u4e49\u89e3\u6790\u5668\u5bfc\u5165 DAT \u6587\u4ef6<\/h3>\n<\/p>\n
\u6709\u65f6\u5019\uff0cDAT \u6587\u4ef6\u7684\u683c\u5f0f\u53ef\u80fd\u6bd4\u8f83\u590d\u6742\uff0c\u4f7f\u7528 Pandas \u6216 Numpy \u53ef\u80fd\u4e0d\u592a\u65b9\u4fbf\u3002\u8fd9\u65f6\u53ef\u4ee5\u7f16\u5199\u81ea\u5b9a\u4e49\u89e3\u6790\u5668\u6765\u8bfb\u53d6\u6587\u4ef6\u3002<\/p>\n<\/p>\n
1\u3001\u5b9a\u4e49\u89e3\u6790\u51fd\u6570<\/h4>\n<\/p>\n
\u5047\u8bbe\u6709\u4e00\u4e2a\u540d\u4e3a data.dat<\/code> \u7684\u6587\u4ef6\uff0c\u6587\u4ef6\u5185\u5bb9\u5982\u4e0b\uff1a<\/p>\n<\/p>\n
Name: John, Age: 28, Salary: 50000<\/p>\n
Name: Jane, Age: 32, Salary: 60000<\/p>\n
Name: Doe, Age: 25, Salary: 45000<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u53ef\u4ee5\u7f16\u5199\u4e00\u4e2a\u89e3\u6790\u51fd\u6570\u6765\u8bfb\u53d6\u8fd9\u4e2a\u6587\u4ef6\uff1a<\/p>\n<\/p>\n
def parse_dat_file(filename):<\/p>\n
    data = []<\/p>\n
    with open(filename, 'r') as file:<\/p>\n
        for line in file:<\/p>\n
            parts = line.split(',')<\/p>\n
            record = {}<\/p>\n
            for part in parts:<\/p>\n
                key, value = part.split(':')<\/p>\n
                record[key.strip()] = value.strip()<\/p>\n
            data.append(record)<\/p>\n
    return data<\/p>\n
\u89e3\u6790\u6587\u4ef6<\/strong><\/h2>\n
data = parse_dat_file('data.dat')<\/p>\n
\u6253\u5370\u6570\u636e<\/strong><\/h2>\n
for record in data:<\/p>\n
    print(record)<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u5904\u7406\u590d\u6742\u683c\u5f0f\u7684 DAT \u6587\u4ef6<\/h4>\n<\/p>\n
\u5982\u679c\u6587\u4ef6\u683c\u5f0f\u66f4\u52a0\u590d\u6742\uff0c\u53ef\u4ee5\u6839\u636e\u5177\u4f53\u60c5\u51b5\u8c03\u6574\u89e3\u6790\u903b\u8f91\u3002\u4f8b\u5982\uff0c\u5904\u7406\u5305\u542b\u5d4c\u5957\u7ed3\u6784\u7684\u6587\u4ef6\uff1a<\/p>\n<\/p>\n
def parse_complex_dat_file(filename):<\/p>\n
    data = []<\/p>\n
    with open(filename, 'r') as file:<\/p>\n
        for line in file:<\/p>\n
            record = {}<\/p>\n
            parts = line.split('|')<\/p>\n
            for part in parts:<\/p>\n
                key, value = part.split(':')<\/p>\n
                if key.strip() == 'Address':<\/p>\n
                    address_parts = value.split(',')<\/p>\n
                    record['Address'] = {<\/p>\n
                        'Street': address_parts[0].strip(),<\/p>\n
                        'City': address_parts[1].strip(),<\/p>\n
                        'Zip': address_parts[2].strip()<\/p>\n
                    }<\/p>\n
                else:<\/p>\n
                    record[key.strip()] = value.strip()<\/p>\n
            data.append(record)<\/p>\n
    return data<\/p>\n
\u5047\u8bbe\u6587\u4ef6\u5185\u5bb9\u5982\u4e0b<\/strong><\/h2>\n
Name: John | Age: 28 | Salary: 50000 | Address: 123 MAI<\/a>n St, Springfield, 12345<\/strong><\/h2>\n
data = parse_complex_dat_file('complex_data.dat')<\/p>\n
\u6253\u5370\u6570\u636e<\/strong><\/h2>\n
for record in data:<\/p>\n
    print(record)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4ee5\u4e0a\u4ecb\u7ecd\u4e86\u56db\u79cd\u4e0d\u540c\u7684\u65b9\u6cd5\u5bfc\u5165 DAT \u6587\u4ef6\uff0c\u5177\u4f53\u9009\u62e9\u54ea\u79cd\u65b9\u6cd5\u53d6\u51b3\u4e8e\u6587\u4ef6\u7684\u683c\u5f0f\u548c\u6570\u636e\u5904\u7406\u7684\u9700\u6c42\u3002Pandas \u548c Numpy \u63d0\u4f9b\u4e86\u9ad8\u6548\u7684\u6570\u636e\u5904\u7406\u80fd\u529b\uff0c\u9002\u5408\u5904\u7406\u5927\u591a\u6570\u7684DAT \u6587\u4ef6\uff0c\u800c\u5185\u7f6e\u51fd\u6570\u548c\u81ea\u5b9a\u4e49\u89e3\u6790\u5668\u5219\u9002\u5408\u5904\u7406\u66f4\u52a0\u590d\u6742\u548c\u7279\u6b8a\u7684\u6587\u4ef6\u683c\u5f0f\u3002\u65e0\u8bba\u9009\u62e9\u54ea\u79cd\u65b9\u6cd5\uff0c\u90fd\u53ef\u4ee5\u6839\u636e\u5177\u4f53\u9700\u6c42\u8fdb\u884c\u7075\u6d3b\u8c03\u6574\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
 \u5982\u4f55\u5728Python3\u4e2d\u8bfb\u53d6.dat\u6587\u4ef6\u7684\u5185\u5bb9\uff1f<\/strong>
\u8981\u8bfb\u53d6.dat\u6587\u4ef6\uff0c\u60a8\u53ef\u4ee5\u4f7f\u7528Python\u5185\u7f6e\u7684\u6587\u4ef6\u64cd\u4f5c\u529f\u80fd\u3002\u4f7f\u7528open()<\/code>\u51fd\u6570\u6253\u5f00\u6587\u4ef6\uff0c\u7136\u540e\u4f7f\u7528read()<\/code>\u3001readlines()<\/code>\u6216readline()<\/code>\u65b9\u6cd5\u6765\u8bfb\u53d6\u6587\u4ef6\u5185\u5bb9\u3002\u901a\u5e38\uff0c\u60a8\u8fd8\u9700\u8981\u6839\u636e\u6570\u636e\u7684\u683c\u5f0f\u8fdb\u884c\u9002\u5f53\u7684\u89e3\u6790\uff0c\u6bd4\u5982\u4f7f\u7528split()<\/code>\u51fd\u6570\u5c06\u6570\u636e\u5206\u9694\u5f00\u3002<\/p>\n
\u4f7f\u7528\u54ea\u79cd\u5e93\u53ef\u4ee5\u66f4\u65b9\u4fbf\u5730\u5904\u7406.dat\u6587\u4ef6\uff1f<\/strong>
\u5982\u679c.dat\u6587\u4ef6\u5305\u542b\u7ed3\u6784\u5316\u6570\u636e\uff0c\u60a8\u53ef\u4ee5\u4f7f\u7528pandas<\/code>\u5e93\u6765\u5904\u7406\u3002\u901a\u8fc7pandas.read_csv()<\/code>\u51fd\u6570\uff0c\u60a8\u53ef\u4ee5\u6307\u5b9a\u5206\u9694\u7b26\uff0c\u8f7b\u677e\u8bfb\u53d6\u5e76\u8f6c\u6362\u6210DataFrame\u683c\u5f0f\uff0c\u8fd9\u6837\u53ef\u4ee5\u66f4\u65b9\u4fbf\u5730\u8fdb\u884c\u6570\u636e\u5206\u6790\u548c\u64cd\u4f5c\u3002<\/p>\n
\u5982\u4f55\u5904\u7406.dat\u6587\u4ef6\u4e2d\u7684\u7f3a\u5931\u503c\u6216\u5f02\u5e38\u6570\u636e\uff1f<\/strong>
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