{"id":70386,"date":"2026-02-01T22:33:15","date_gmt":"2026-02-01T14:33:15","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/70386.html"},"modified":"2026-02-01T22:33:15","modified_gmt":"2026-02-01T14:33:15","slug":"%e6%95%b0%e6%8d%ae%e6%95%b4%e5%ae%b9%e6%9c%af%ef%bc%9apandasmatplotlib%e8%ae%a9%e4%bd%a0%e7%9a%84%e6%95%b0%e6%8d%ae%e4%bc%9a%e8%af%b4%e8%af%9d","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/70386.html","title":{"rendered":"\u6570\u636e\u201c\u6574\u5bb9\u201d\u672f\uff1aPandas+Matplotlib\u8ba9\u4f60\u7684\u6570\u636e\u4f1a\u8bf4\u8bdd"},"content":{"rendered":"

\u6700\u8fd1\u5728\u5b66\u4e60 Python \u6570\u636e\u5206\u6790，\u6211\u7528 PyCharm \u5199\u4e86\u51e0\u6bb5\u4ee3\u7801，\u628a\u4ece\u6570\u636e\u6e05\u6d17\u5230\u53ef\u89c6\u5316\u7684\u5b8c\u6574\u6d41\u7a0b\u8dd1\u4e86\u4e00\u904d\u3002\u4eca\u5929\u5c31\u6765\u5206\u4eab\u4e00\u4e0b\u6211\u7684\u5b9e\u6218\u5fc3\u5f97，\u5e0c\u671b\u80fd\u5e2e\u5230\u521a\u5165\u95e8\u7684\u670b\u53cb\u3002
\n\u00a0<\/p>\n

📊 \u4e00\u3001Pandas：\u6570\u636e\u5904\u7406\u7684\u57fa\u77f3<\/p>\n

\u6570\u636e\u5206\u6790\u7684\u7b2c\u4e00\u6b65，\u6c38\u8fdc\u662f\u5904\u7406\u539f\u59cb\u6570\u636e\u3002Pandas \u4f5c\u4e3a Python \u6570\u636e\u5206\u6790\u7684\u6838\u5fc3\u5e93，\u8ba9\u8fd9\u4e2a\u8fc7\u7a0b\u53d8\u5f97\u975e\u5e38\u9ad8\u6548\u3002<\/p>\n

1. \u521b\u5efa\u4e0e\u67e5\u770b DataFrame\u6211\u4eec\u53ef\u4ee5\u7528\u5b57\u5178\u5feb\u901f\u6784\u9020\u4e00\u4e2a\u6570\u636e\u96c6，\u7136\u540e\u8f6c\u6362\u6210 DataFrame \u8868\u683c，\u8fd9\u6837\u5c31\u80fd\u76f4\u89c2\u5730\u770b\u5230\u6570\u636e\u7ed3\u6784\u3002<\/p>\n

import pandas as pd<\/p>\n

dic = {'name': ['kiti', 'beta', 'peter', 'tom'],
\n 'age':[20,18, 35,21],
\n 'gender': ['f', 'f', 'm', 'm']}
\ndf = pd.DataFrame(dic)
\nprint(df)<\/p>\n

df = df.sort_values(by=['age'])
\ndf = df.sort_values(by=['age'], ascending=False)
\na= df['gender']
\nb = a.replace(['m', 'f'],['male', 'female'])
\ndf['gender'] = b<\/p>\n

\"\"
\n\u00a0<\/p>\n

2. \u6570\u636e\u6392\u5e8f<\/p>\n

sort_values() \u662f\u6211\u6700\u5e38\u7528\u7684\u51fd\u6570\u4e4b\u4e00，\u5b83\u53ef\u4ee5\u8ba9\u6211\u5feb\u901f\u6309\u67d0\u4e00\u5217\u6392\u5e8f，\u627e\u5230\u6781\u503c\u3002<\/p>\n

# \u6309\u5e74\u9f84\u5347\u5e8f
\ndf = df.sort_values(by=['age'])
\n# \u6309\u5e74\u9f84\u964d\u5e8f
\ndf = df.sort_values(by=['age'], ascending=False)<\/p>\n

\u8fd9\u4e00\u6b65\u80fd\u5e2e\u6211\u5feb\u901f\u5b9a\u4f4d\u5230\u5e74\u9f84\u6700\u5927\u6216\u6700\u5c0f\u7684\u8bb0\u5f55，\u4e3a\u540e\u7eed\u5206\u6790\u63d0\u4f9b\u7ebf\u7d22\u3002<\/p>\n

3. \u6570\u636e\u6620\u5c04\u4e0e\u66ff\u6362<\/p>\n

\u539f\u59cb\u6570\u636e\u91cc\u7684\u7b80\u5199（\u6bd4\u5982 'm' \u548c 'f'）\u53ef\u8bfb\u6027\u5f88\u5dee，\u7528 replace() \u53ef\u4ee5\u8f7b\u677e\u6620\u5c04\u6210\u66f4\u76f4\u89c2\u7684\u6587\u5b57\u3002<\/p>\n

a = df['gender']
\nb = a.replace(['m', 'f'], ['male', 'female'])
\ndf['gender'] = b<\/p>\n

\u5904\u7406\u540e，gender \u5217\u5c31\u4f1a\u663e\u793a male \u548c female，\u6574\u4e2a\u6570\u636e\u96c6\u7684\u53ef\u8bfb\u6027\u7acb\u523b\u63d0\u5347\u3002<\/p>\n

4. \u6587\u4ef6\u8bfb\u5199<\/p>\n

\u6570\u636e\u5206\u6790\u79bb\u4e0d\u5f00\u548c\u5916\u90e8\u6587\u4ef6\u6253\u4ea4\u9053，Pandas \u652f\u6301\u591a\u79cd\u683c\u5f0f\u7684\u8bfb\u5199，\u975e\u5e38\u65b9\u4fbf\u3002<\/p>\n

import pandas as pd
\ndf_1 = pd.read_csv("..\/numpy\/data1.csv")<\/p>\n

df_2 = pd.read_csv("..\/numpy\/data2.csv", encoding='utf8', header=None)<\/p>\n

df_3 = pd.read_excel("data3.xlsx") #<\/p>\n

df_4 = pd.read_table("data4.txt",sep=',',header=None)<\/p>\n

df_1.to_csv("\u5bfc\u51fa.csv",index=True, header=True)<\/p>\n

df_1.to_excel("\u5bfc\u51fa.xlsx",index=True, header=True)<\/p>\n

\"\"
\n📌 \u5c0f\u6280\u5de7：\u9047\u5230\u4e71\u7801\u95ee\u9898\u65f6，\u53ef\u4ee5\u8bd5\u8bd5 encoding='utf8' \u6216 'gbk'；\u5982\u679c\u6587\u4ef6\u6ca1\u6709\u8868\u5934，\u8bb0\u5f97\u8bbe\u7f6e header=None\u3002<\/p>\n

5. \u7f3a\u5931\u503c\u5904\u7406<\/p>\n

\u771f\u5b9e\u6570\u636e\u603b\u4f1a\u6709\u7f3a\u5931\u503c，Pandas \u63d0\u4f9b\u4e86\u4e24\u79cd\u6838\u5fc3\u5904\u7406\u65b9\u5f0f：<\/p>\n

import pandas as pd<\/p>\n

df = pd.read_csv(r"..\/numpy\/data1.csv", encoding='gbk')
\nna = df.isnull()<\/p>\n

df1 = df.fillna('1')
\ndf2 = df.dropna()
\nprint(df2)<\/p>\n

\"\"
\n\u2022 fillna()：\u9002\u5408\u7f3a\u5931\u503c\u8f83\u5c11，\u4e14\u53ef\u4ee5\u7528\u9ed8\u8ba4\u503c\u6216\u7edf\u8ba1\u503c\u586b\u5145\u7684\u573a\u666f\u3002<\/p>\n

\u2022 dropna()：\u9002\u5408\u7f3a\u5931\u503c\u6bd4\u4f8b\u8f83\u9ad8，\u5220\u9664\u540e\u4e0d\u5f71\u54cd\u6574\u4f53\u5206\u6790\u7684\u573a\u666f\u3002
\n\u00a0<\/p>\n

🎨 \u4e8c\u3001Matplotlib：\u8ba9\u6570\u636e\u201c\u6d3b\u201d\u8d77\u6765<\/p>\n

\u5904\u7406\u5b8c\u6570\u636e，\u4e0b\u4e00\u6b65\u5c31\u662f\u628a\u5b83\u53d8\u6210\u76f4\u89c2\u7684\u56fe\u8868\u3002Matplotlib \u662f Python \u6700\u7ecf\u5178\u7684\u53ef\u89c6\u5316\u5e93，\u4e0a\u624b\u4e5f\u5f88\u5feb\u3002<\/p>\n

1. \u57fa\u7840\u6563\u70b9\u56fe\u6563\u70b9\u56fe\u53ef\u4ee5\u6e05\u6670\u5730\u5c55\u793a\u4e24\u4e2a\u53d8\u91cf\u4e4b\u95f4\u7684\u5173\u7cfb，\u7528 plot() \u51fd\u6570\u5c31\u80fd\u5feb\u901f\u5b9e\u73b0\u3002<\/p>\n

import matplotlib.pyplot as plt
\nimport numpy as np
\nx = np.array([1, 2, 3, 4, 5])
\ny = np.array([10, 20, 25, 30, 40])
\nplt.plot(x, y, color='red', marker='*')
\nplt.title('Scatter Plot Example')
\nplt.xlabel('X Axis')
\nplt.ylabel('Y Axis')
\nplt.show()<\/p>\n

\"\"
\n\u8fd0\u884c\u8fd9\u6bb5\u4ee3\u7801，\u4f60\u5c31\u80fd\u770b\u5230\u4e00\u4e2a\u5e26\u661f\u578b\u6807\u8bb0\u7684\u7ea2\u8272\u6563\u70b9\u56fe，\u975e\u5e38\u76f4\u89c2\u3002<\/p>\n

2. \u591a\u56fe\u5e03\u5c40<\/p>\n

\u5982\u679c\u60f3\u5728\u4e00\u5f20\u56fe\u91cc\u5c55\u793a\u591a\u4e2a\u56fe\u8868，subplots() \u662f\u4e2a\u597d\u5e2e\u624b\u3002\u6211\u7528\u5b83\u753b\u4e86\u56db\u4e2a\u4e09\u89d2\u51fd\u6570\u7684\u5bf9\u6bd4\u56fe：<\/p>\n

import numpy as np
\nx = np.linspace(0, 10, 10000)#
\ny1 = np.sin(x)
\ny2 = np.cos(x)
\ny3 = np.tan(x)
\ny4 = np.sin(x ** 2)
\nfig, axs = plt.subplots(2, 2, figsize=(10, 8))#
\naxs[0, 0].plot(x, y1, 'r')
\naxs[0, 0].set_title('sin(x)')
\naxs[0, 1].plot(x, y2, 'g')<\/p>\n

axs[0, 1].set_title('cos(x)')
\naxs[1, 0].plot(x, y3, 'b')
\naxs[1, 0].set_title('tan(x)')
\naxs[1, 1].plot(x, y4, 'm')
\naxs[1, 1].set_title('sin(x^2)')
\nplt.show()<\/p>\n

\"\"
\n\u8fd9\u6837，\u6211\u5c31\u80fd\u5728\u4e00\u4e2a\u753b\u5e03\u4e0a\u540c\u65f6\u5bf9\u6bd4\u6b63\u5f26\u3001\u4f59\u5f26\u3001\u6b63\u5207\u548c\u6b63\u5f26\u5e73\u65b9\u51fd\u6570\u7684\u66f2\u7ebf\u53d8\u5316，\u4e00\u76ee\u4e86\u7136\u3002<\/p>\n<\/p>\n

\u2728 \u5199\u5728\u6700\u540e<\/p>\n

\u4ece\u6570\u636e\u5904\u7406\u5230\u53ef\u89c6\u5316，\u8fd9\u4e00\u5957\u6d41\u7a0b\u8d70\u4e0b\u6765，\u6211\u6df1\u523b\u4f53\u4f1a\u5230 Python \u6570\u636e\u5206\u6790\u7684\u9b45\u529b\u3002Pandas \u8ba9\u6211\u4eec\u80fd\u9ad8\u6548\u5730\u6e05\u6d17\u548c\u6574\u7406\u6570\u636e，\u800c Matplotlib \u5219\u8ba9\u8fd9\u4e9b\u6570\u636e\u53d8\u6210\u4e86\u4f1a\u8bf4\u8bdd\u7684\u56fe\u8868\u3002<\/p>\n

\u8fd9\u4e9b\u53ea\u662f\u5165\u95e8\u7ea7\u7684\u64cd\u4f5c，\u4f46\u5b83\u4eec\u662f\u6570\u636e\u5206\u6790\u7684\u57fa\u77f3\u3002\u968f\u7740\u5b66\u4e60\u7684\u6df1\u5165，\u6211\u8fd8\u4f1a\u63a2\u7d22\u66f4\u591a\u8fdb\u9636\u6280\u5de7，\u6bd4\u5982\u7528 Seaborn \u753b\u66f4\u6f02\u4eae\u7684\u56fe，\u6216\u8005\u7528 Scikit-learn \u505a\u673a\u5668\u5b66\u4e60\u9884\u6d4b\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"

\u6700\u8fd1\u5728\u5b66\u4e60 Python \u6570\u636e\u5206\u6790，\u6211\u7528 PyCharm \u5199\u4e86\u51e0\u6bb5\u4ee3\u7801，\u628a\u4ece\u6570\u636e\u6e05\u6d17\u5230\u53ef\u89c6\u5316\u7684\u5b8c\u6574\u6d41\u7a0b\u8dd1\u4e86\u4e00\u904d\u3002\u4eca\u5929\u5c31\u6765\u5206\u4eab\u4e00\u4e0b\u6211\u7684\u5b9e\u6218\u5fc3\u5f97，\u5e0c\u671b\u80fd\u5e2e\u5230\u521a\u5165\u95e8\u7684\u670b\u53cb\u3002📊 \u4e00\u3001Pandas：\u6570\u636e\u5904\u7406\u7684\u57fa\u77f3\u6570\u636e\u5206\u6790\u7684\u7b2c\u4e00\u6b65，\u6c38\u8fdc\u662f\u5904\u7406\u539f\u59cb\u6570\u636e\u3002Pandas \u4f5c\u4e3a Python \u6570\u636e\u5206\u6790\u7684\u6838\u5fc3\u5e93，\u8ba9\u8fd9\u4e2a\u8fc7\u7a0b\u53d8\u5f97\u975e\u5e38\u9ad8\u6548\u30021. \u521b\u5efa\u4e0e\u67e5\u770b DataFrame\u6211\u4eec\u53ef\u4ee5\u7528\u5b57\u5178\u5feb\u901f\u6784\u9020\u4e00\u4e2a\u6570<\/p>\n","protected":false},"author":2,"featured_media":70381,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[2468,4278],"topic":[],"class_list":["post-70386","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-server","tag-matplotlib","tag-pandas"],"yoast_head":"\n\u6570\u636e\u201c\u6574\u5bb9\u201d\u672f\uff1aPandas+Matplotlib\u8ba9\u4f60\u7684\u6570\u636e\u4f1a\u8bf4\u8bdd - 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