![\"\u5982\u4f55\u4f7f\u7528python\u5236\u4f5c\u5c0f\u5de5\u5177\u5305\"](\"https:\/\/cdn-kb.worktile.com\/kb\/wp-content\/uploads\/2024\/04\/25072950\/951a778b-f6d5-4d16-b31f-1e2c2dd825c1.webp\")
<\/p>\n<\/p>\n
\u5982\u4f55\u4f7f\u7528Python\u5236\u4f5c\u5c0f\u5de5\u5177\u5305<\/strong><\/p>\n<\/p>\n \u4f7f\u7528Python\u5236\u4f5c\u5c0f\u5de5\u5177\u5305\u7684\u6838\u5fc3\u6b65\u9aa4\u662f\uff1a\u660e\u786e\u9700\u6c42\u3001\u8bbe\u8ba1\u6a21\u5757\u7ed3\u6784\u3001\u7f16\u5199\u4ee3\u7801\u3001\u6d4b\u8bd5\u4e0e\u8c03\u8bd5\u3001\u6253\u5305\u4e0e\u53d1\u5e03<\/strong>\u3002\u660e\u786e\u9700\u6c42\u662f\u7b2c\u4e00\u6b65\uff0c\u9700\u8981\u4e86\u89e3\u5de5\u5177\u5305\u9700\u8981\u89e3\u51b3\u7684\u95ee\u9898\u3002\u8bbe\u8ba1\u6a21\u5757\u7ed3\u6784\u662f\u4e3a\u4e86\u786e\u4fdd\u4ee3\u7801\u7684\u53ef\u7ef4\u62a4\u6027\u548c\u53ef\u6269\u5c55\u6027\u3002\u7f16\u5199\u4ee3\u7801\u65f6\u8981\u9075\u5faaPython\u7684\u7f16\u7801\u89c4\u8303\u3002\u6d4b\u8bd5\u4e0e\u8c03\u8bd5\u53ef\u4ee5\u786e\u4fdd\u4ee3\u7801\u7684\u6b63\u786e\u6027\u548c\u53ef\u9760\u6027\u3002\u6700\u540e\uff0c\u6253\u5305\u4e0e\u53d1\u5e03\u53ef\u4ee5\u8ba9\u5176\u4ed6\u4eba\u65b9\u4fbf\u5730\u4f7f\u7528\u4f60\u7684\u5de5\u5177\u5305\u3002\u4ee5\u4e0b\u8be6\u7ec6\u4ecb\u7ecd\u5982\u4f55\u8fdb\u884c\u8fd9\u51e0\u4e2a\u6b65\u9aa4\u3002<\/p>\n<\/p>\n \u5728\u5f00\u59cb\u7f16\u5199\u4ee3\u7801\u4e4b\u524d\uff0c\u9996\u5148\u9700\u8981\u660e\u786e\u4f60\u7684\u5de5\u5177\u5305\u8981\u89e3\u51b3\u7684\u95ee\u9898\u3002\u4e00\u4e2a\u6e05\u6670\u7684\u9700\u6c42\u5b9a\u4e49\u53ef\u4ee5\u5e2e\u52a9\u4f60\u66f4\u597d\u5730\u8bbe\u8ba1\u548c\u5b9e\u73b0\u529f\u80fd\u3002\u4f8b\u5982\uff0c\u5982\u679c\u4f60\u8981\u5236\u4f5c\u4e00\u4e2a\u6570\u636e\u5904\u7406\u7684\u5de5\u5177\u5305\uff0c\u90a3\u4e48\u4f60\u9700\u8981\u660e\u786e\u662f\u5904\u7406\u4ec0\u4e48\u7c7b\u578b\u7684\u6570\u636e\uff0c\u5177\u4f53\u9700\u8981\u54ea\u4e9b\u529f\u80fd\uff0c\u5982\u6570\u636e\u6e05\u6d17\u3001\u6570\u636e\u8f6c\u6362\u3001\u6570\u636e\u5206\u6790\u7b49\u3002<\/p>\n<\/p>\n \u9700\u6c42\u660e\u786e\u540e\uff0c\u53ef\u4ee5\u5217\u51fa\u5de5\u5177\u5305\u9700\u8981\u5b9e\u73b0\u7684\u529f\u80fd\u3002\u8fd9\u6837\u5728\u540e\u7eed\u7684\u8bbe\u8ba1\u548c\u5f00\u53d1\u8fc7\u7a0b\u4e2d\uff0c\u53ef\u4ee5\u6709\u4e00\u4e2a\u6e05\u6670\u7684\u53c2\u8003\u6807\u51c6\uff0c\u786e\u4fdd\u4e0d\u9057\u6f0f\u4efb\u4f55\u91cd\u8981\u529f\u80fd\u3002<\/p>\n<\/p>\n \u8bbe\u8ba1\u6a21\u5757\u7ed3\u6784\u662f\u5236\u4f5c\u5c0f\u5de5\u5177\u5305\u7684\u91cd\u8981\u6b65\u9aa4\u3002\u4e00\u4e2a\u826f\u597d\u7684\u6a21\u5757\u7ed3\u6784\u53ef\u4ee5\u8ba9\u4ee3\u7801\u66f4\u6613\u4e8e\u7ef4\u62a4\u548c\u6269\u5c55\u3002\u901a\u5e38\uff0c\u53ef\u4ee5\u6309\u7167\u529f\u80fd\u5212\u5206\u6a21\u5757\uff0c\u6bcf\u4e2a\u6a21\u5757\u5b9e\u73b0\u4e00\u4e2a\u6216\u591a\u4e2a\u76f8\u5173\u7684\u529f\u80fd\u3002<\/p>\n<\/p>\n \u4f8b\u5982\uff0c\u5982\u679c\u4f60\u8981\u5236\u4f5c\u4e00\u4e2a\u6570\u636e\u5904\u7406\u7684\u5de5\u5177\u5305\uff0c\u53ef\u4ee5\u5c06\u5176\u5206\u4e3a\u4ee5\u4e0b\u51e0\u4e2a\u6a21\u5757\uff1a<\/p>\n<\/p>\n \u9664\u4e86\u529f\u80fd\u6a21\u5757\uff0c\u8fd8\u53ef\u4ee5\u6dfb\u52a0\u4e00\u4e2a\u4e3b\u6a21\u5757\uff0c\u7528\u4e8e\u8c03\u7528\u5404\u4e2a\u529f\u80fd\u6a21\u5757\u3002<\/p>\n<\/p>\n \u5728\u8bbe\u8ba1\u597d\u6a21\u5757\u7ed3\u6784\u540e\uff0c\u5c31\u53ef\u4ee5\u5f00\u59cb\u7f16\u5199\u4ee3\u7801\u4e86\u3002\u5728\u7f16\u5199\u4ee3\u7801\u65f6\uff0c\u8981\u9075\u5faaPython\u7684\u7f16\u7801\u89c4\u8303\uff0c\u5982PEP 8\u3002\u8fd9\u6837\u53ef\u4ee5\u63d0\u9ad8\u4ee3\u7801\u7684\u53ef\u8bfb\u6027\u548c\u53ef\u7ef4\u62a4\u6027\u3002<\/p>\n<\/p>\n import pandas as pd<\/p>\n def remove_missing_values(df):<\/p>\n """\u53bb\u9664\u7f3a\u5931\u503c"""<\/p>\n return df.dropna()<\/p>\n def remove_duplicates(df):<\/p>\n """\u53bb\u9664\u91cd\u590d\u503c"""<\/p>\n return df.drop_duplicates()<\/p>\n <\/code><\/pre>\n<\/p>\n import pandas as pd<\/p>\n def convert_data_type(df, column, data_type):<\/p>\n """\u8f6c\u6362\u6570\u636e\u7c7b\u578b"""<\/p>\n df[column] = df[column].astype(data_type)<\/p>\n return df<\/p>\n def normalize_data(df, column):<\/p>\n """\u6807\u51c6\u5316\u6570\u636e"""<\/p>\n df[column] = (df[column] - df[column].mean()) \/ df[column].std()<\/p>\n return df<\/p>\n <\/code><\/pre>\n<\/p>\n import pandas as pd<\/p>\n import matplotlib.pyplot as plt<\/p>\n def calculate_statistics(df):<\/p>\n """\u8ba1\u7b97\u7edf\u8ba1\u6570\u636e"""<\/p>\n return df.describe()<\/p>\n def plot_data(df, column):<\/p>\n """\u7ed8\u5236\u6570\u636e\u56fe\u8868"""<\/p>\n df[column].plot()<\/p>\n plt.show()<\/p>\n <\/code><\/pre>\n<\/p>\n \u5728\u7f16\u5199\u5b8c\u4ee3\u7801\u540e\uff0c\u9700\u8981\u8fdb\u884c\u6d4b\u8bd5\u4e0e\u8c03\u8bd5\u3002\u53ef\u4ee5\u7f16\u5199\u6d4b\u8bd5\u7528\u4f8b\uff0c\u786e\u4fdd\u6bcf\u4e2a\u529f\u80fd\u6a21\u5757\u90fd\u80fd\u6b63\u5e38\u5de5\u4f5c\u3002\u53ef\u4ee5\u4f7f\u7528Python\u7684 import pandas as pd<\/p>\n from data_cleaning import remove_missing_values, remove_duplicates<\/p>\n from data_transformation import convert_data_type, normalize_data<\/p>\n from data_analysis import calculate_statistics, plot_data<\/p>\n class TestDataCleaning(unittest.TestCase):<\/p>\n def setUp(self):<\/p>\n self.df = pd.DataFrame({<\/p>\n 'A': [1, 2, None, 4, 5],<\/p>\n 'B': [1, 2, 2, 4, 5]<\/p>\n })<\/p>\n def test_remove_missing_values(self):<\/p>\n df_cleaned = remove_missing_values(self.df)<\/p>\n self.assertEqual(df_cleaned.isnull().sum().sum(), 0)<\/p>\n def test_remove_duplicates(self):<\/p>\n df_cleaned = remove_duplicates(self.df)<\/p>\n self.assertEqual(len(df_cleaned), 4)<\/p>\n class TestDataTransformation(unittest.TestCase):<\/p>\n def setUp(self):<\/p>\n self.df = pd.DataFrame({<\/p>\n 'A': [1, 2, 3, 4, 5]<\/p>\n })<\/p>\n def test_convert_data_type(self):<\/p>\n df_transformed = convert_data_type(self.df, 'A', 'float')<\/p>\n self.assertEqual(df_transformed['A'].dtype, 'float64')<\/p>\n def test_normalize_data(self):<\/p>\n df_normalized = normalize_data(self.df, 'A')<\/p>\n self.assertAlmostEqual(df_normalized['A'].mean(), 0, places=5)<\/p>\n self.assertAlmostEqual(df_normalized['A'].std(), 1, places=5)<\/p>\n class TestDataAnalysis(unittest.TestCase):<\/p>\n def setUp(self):<\/p>\n self.df = pd.DataFrame({<\/p>\n 'A': [1, 2, 3, 4, 5]<\/p>\n })<\/p>\n def test_calculate_statistics(self):<\/p>\n stats = calculate_statistics(self.df)<\/p>\n self.assertEqual(stats.loc['mean', 'A'], 3)<\/p>\n\u4e00\u3001\u660e\u786e\u9700\u6c42<\/h3>\n<\/p>\n
\u4e8c\u3001\u8bbe\u8ba1\u6a21\u5757\u7ed3\u6784<\/h3>\n<\/p>\n
\n
data_cleaning.py<\/code>\uff1a\u6570\u636e\u6e05\u6d17\u6a21\u5757\uff0c\u5305\u542b\u53bb\u9664\u7a7a\u503c\u3001\u5904\u7406\u91cd\u590d\u503c\u7b49\u529f\u80fd\u3002<\/li>\ndata_transformation.py<\/code>\uff1a\u6570\u636e\u8f6c\u6362\u6a21\u5757\uff0c\u5305\u542b\u6570\u636e\u7c7b\u578b\u8f6c\u6362\u3001\u6570\u636e\u6807\u51c6\u5316\u7b49\u529f\u80fd\u3002<\/li>\ndata_analysis.py<\/code>\uff1a\u6570\u636e\u5206\u6790\u6a21\u5757\uff0c\u5305\u542b\u6570\u636e\u7edf\u8ba1\u3001\u6570\u636e\u53ef\u89c6\u5316\u7b49\u529f\u80fd\u3002<\/li>\n<\/ul>\n\u4e09\u3001\u7f16\u5199\u4ee3\u7801<\/h3>\n<\/p>\n
1. \u6570\u636e\u6e05\u6d17\u6a21\u5757<\/h4>\n<\/p>\n
# data_cleaning.py<\/p>\n2. \u6570\u636e\u8f6c\u6362\u6a21\u5757<\/h4>\n<\/p>\n
# data_transformation.py<\/p>\n3. \u6570\u636e\u5206\u6790\u6a21\u5757<\/h4>\n<\/p>\n
# data_analysis.py<\/p>\n\u56db\u3001\u6d4b\u8bd5\u4e0e\u8c03\u8bd5<\/h3>\n<\/p>\n
unittest<\/code>\u6a21\u5757\u8fdb\u884c\u5355\u5143\u6d4b\u8bd5\u3002<\/p>\n<\/p>\n\u6d4b\u8bd5\u7528\u4f8b\u793a\u4f8b<\/h4>\n<\/p>\n
import unittest<\/p>\n