{"id":81568,"date":"2026-07-25T02:59:00","date_gmt":"2026-07-24T18:59:00","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/81568.html"},"modified":"2026-07-25T02:59:00","modified_gmt":"2026-07-24T18:59:00","slug":"%e3%80%90seaborn-%e5%ad%a6%e4%b9%a0%e7%ac%94%e8%ae%b0%e3%80%91p1-%e6%95%a3%e7%82%b9%e5%9b%be-scatterplot%e3%80%81%e6%8a%98%e7%ba%bf%e5%9b%be-lineplot%e3%80%81%e5%85%b3%e7%b3%bb%e5%9b%be-relplot","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/81568.html","title":{"rendered":"\u3010Seaborn \u5b66\u4e60\u7b14\u8bb0\u3011P1. \u6563\u70b9\u56fe scatterplot\u3001\u6298\u7ebf\u56fe lineplot\u3001\u5173\u7cfb\u56fe relplot"},"content":{"rendered":"

matplotlib \u753b\u56fe，seaborn \u753b\u53d8\u91cf\u5173\u7cfb<\/span><\/p>\n

seaborn \u672c\u8d28\u4e0a\u662f\u5728 matplotlib \u7684\u753b\u5e03\u4e0a\u753b\u56fe，\u5747\u4f7f\u7528 plt.show() \u6765\u663e\u793a\u7ed8\u5236\u7684\u56fe\u50cf<\/span><\/span><\/p>\n

\n

\u5b89\u88c5 Seaborn<\/span><\/h2>\n

\u4f7f\u7528 pip \u5b89\u88c5：<\/span><\/p>\n

pip install seaborn<\/p>\n

\u6ce8\u610f： Seaborn \u4f9d\u8d56 pandas + matplotlib + numpy，\u5b89\u88c5 seaborn \u65f6\u4f1a\u4e00\u5e76\u5b89\u88c5\u8fd9\u4e9b\u4f9d\u8d56<\/span><\/p>\n

\n

\u5728 Python \u4e2d\u5bfc\u5165 Seaborn \u5e93<\/span><\/h2>\n

\u5bfc\u5165\u5e76\u53d6\u522b\u540d\u4e3a sns：<\/span><\/p>\n

import seaborn as sns<\/p>\n

\n

\u52a0\u8f7d Seaborn \u5185\u7f6e\u6570\u636e\u96c6<\/span><\/h2>\n

Seaborn \u62e5\u6709\u5185\u7f6e\u6570\u636e\u96c6，\u53ef\u4ee5\u76f4\u63a5\u52a0\u8f7d，\u8fd4\u56de DataFrame \u7c7b\u578b\u7684\u6570\u636e\u96c6<\/span><\/p>\n

1. \u67e5\u770b\u5185\u7f6e\u6570\u636e\u96c6<\/span><\/h3>\n

import seaborn as sns
\ndataset_names = sns.get_dataset_names()
\nprint(dataset_names)<\/p>\n

\u8fd4\u56de\u5982\u4e0b\u7ed3\u679c：<\/span><\/p>\n

['anagrams', 'anscombe', 'attention', 'brain_networks', 'car_crashes', 'diamonds', 'dots', 'dowjones', 'exercise', 'flights', 'fmri', 'geyser', 'glue', 'healthexp', 'iris', 'mpg', 'penguins', 'planets', 'seaice', 'taxis', 'tips', 'titanic']<\/span><\/p>\n

2. \u52a0\u8f7d\u6570\u636e\u96c6<\/span><\/h3>\n

\u8fd4\u56de\u7684\u662f pandas \u7684 DataFrame \u6570\u636e\u7c7b\u578b<\/span><\/p>\n

import seaborn as sns<\/p>\n

# \u4f8b\u5982\u52a0\u8f7d iris \u6570\u636e\u96c6
\ndf = sns.load_dataset('iris') # \u8fd4\u56de DataFrame \u6570\u636e\u7c7b\u578b<\/p>\n

# \u67e5\u770b\u524d\u4e94\u6761\u8bb0\u5f55
\nprint(df.head())<\/p>\n

\u7ed3\u679c\u5982\u4e0b：<\/span><\/p>\n

\u00a0 \u00a0sepal_length \u00a0sepal_width \u00a0petal_length \u00a0petal_width species
\n0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 5.1 \u00a0 \u00a0 \u00a0 \u00a0 \u00a03.5 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 1.4 \u00a0 \u00a0 \u00a0 \u00a0 \u00a00.2 \u00a0setosa
\n1 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 4.9 \u00a0 \u00a0 \u00a0 \u00a0 \u00a03.0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 1.4 \u00a0 \u00a0 \u00a0 \u00a0 \u00a00.2 \u00a0setosa
\n2 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 4.7 \u00a0 \u00a0 \u00a0 \u00a0 \u00a03.2 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 1.3 \u00a0 \u00a0 \u00a0 \u00a0 \u00a00.2 \u00a0setosa
\n3 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 4.6 \u00a0 \u00a0 \u00a0 \u00a0 \u00a03.1 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 1.5 \u00a0 \u00a0 \u00a0 \u00a0 \u00a00.2 \u00a0setosa
\n4 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 5.0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a03.6 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 1.4 \u00a0 \u00a0 \u00a0 \u00a0 \u00a00.2 \u00a0setosa<\/span><\/p>\n

\n

\u6563\u70b9\u56fe\u7ed8\u5236 sns.scatterplot<\/span><\/h2>\n

sns.scatterplot(
\n data=df, # \u6570\u636e
\n x='\u6a2a\u8f74\u53d8\u91cf\u7279\u5f81', # \u6a2a\u8f74\u53d8\u91cf
\n y='\u7eb5\u8f74\u53d8\u91cf\u7279\u5f81', # \u7eb5\u8f74\u53d8\u91cf
\n hue='\u989c\u8272\u5206\u7ec4\u7279\u5f81', # \u6309\u8be5\u5217\u6620\u5c04\u989c\u8272
\n style='\u5f62\u72b6\u5206\u7ec4\u7279\u5f81', # \u6309\u8be5\u5217\u6620\u5c04\u6807\u8bb0\u5f62\u72b6
\n size='\u70b9\u5927\u5c0f\u6620\u5c04\u7279\u5f81', # \u6309\u8be5\u5217\u6620\u5c04\u70b9\u7684\u5927\u5c0f
\n s=60, # \u56fa\u5b9a\u70b9\u5927\u5c0f（\u82e5\u540c\u65f6\u4f7f\u7528 size \u53c2\u6570，s \u5c06\u88ab\u5ffd\u7565，\u63a8\u8350\u4e8c\u9009\u4e00）
\n alpha=0.7, # \u900f\u660e\u5ea6
\n ax=ax # \u6307\u5b9a\u5b50\u56fe
\n)<\/p>\n

\u6bcf\u6761\u8bb0\u5f55\u5bf9\u5e94\u4e00\u4e2a\u70b9<\/span><\/p>\n

seaborn \u7684 sns.scatterplot \u76f8\u6bd4 matplotlib \u7684 plt.scatter \u591a\u4e86\u4e09\u4e2a\u5173\u952e\u7edf\u8ba1\u8bed\u4e49：<\/span><\/p>\n

  • hue：\u5206\u7ec4，\u6307\u5b9a\u4e00\u4e2a\u7279\u5f81\u7ed9 hue，\u5bf9\u4e8e\u4e0d\u540c\u7684\u8be5\u7279\u5f81，\u7ed8\u5236\u7684\u70b9\u6620\u5c04\u4e0d\u540c\u989c\u8272<\/span><\/li>\n
  • style：\u70b9\u7684\u6837\u5f0f，\u6307\u5b9a\u4e00\u4e2a\u7279\u5f81\u7ed9 style，\u5bf9\u4e8e\u4e0d\u540c\u7684\u8be5\u7279\u5f81，\u7ed8\u5236\u7684\u70b9\u4f1a\u4f7f\u7528\u4e0d\u540c\u7684\u6837\u5f0f\u3002\u7528\u4e8e\u533a\u5206\u7b2c\u4e8c\u5206\u7c7b\u7ef4\u5ea6<\/span><\/li>\n
  • size：\u70b9\u7684\u5927\u5c0f，\u6307\u5b9a\u4e00\u4e2a\u7279\u5f81\u7ed9 size，\u82e5\u4e00\u6761\u8bb0\u5f55\u7684\u8be5\u7279\u5f81\u8d8a\u5927，\u7ed8\u5236\u7684\u70b9\u4e5f\u5c31\u8d8a\u5927<\/span><\/li>\n

    \u793a\u4f8b<\/span><\/h3>\n

    \u7ed8\u5236 penguins \u6570\u636e\u96c6\u4e2d\u4f01\u9e45\u7684\u9ccd\u957f（flipper_length_mm）\u5173\u4e8e\u4f53\u91cd（body_mass_g）\u7684\u6563\u70b9\u56fe\u3002\u9996\u5148\u52a0\u8f7d\u6570\u636e\u96c6：<\/span><\/p>\n

    import seaborn as sns<\/p>\n

    # \u52a0\u8f7d\u6570\u636e\u96c6
    \ndf = sns.load_dataset('penguins')
    \ndf.head()<\/p>\n

    peguins \u6570\u636e\u96c6\u5f62\u5982：<\/span><\/p>\n

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

    1. \u7ed8\u5236\u4e00\u4e2a\u666e\u901a\u7684\u6563\u70b9\u56fe<\/span><\/p>\n

    # \u7ed8\u5236\u6563\u70b9\u56fe
    \nsns.scatterplot(data=df, x='flipper_length_mm', y='body_mass_g')
    \n# seaborn \u5728\u7ed8\u56fe\u524d\u4f1a\u8c03\u7528 dropna()，\u81ea\u52a8\u5220\u9664 x \/ y \/ hue \/ style \/ size \u4e2d\u542b\u6709 NaN \u7684\u884c<\/p>\n

    plt.show() # \u4f7f\u7528 mpl \u6765\u663e\u793a\u56fe\u7247 -> sns \u5728 mpl \u7684\u753b\u5e03\u4e0a\u753b\u56fe，\u7531 mpl \u6765\u663e\u793a\u56fe\u7247<\/p>\n

    \u7ed8\u56fe\u7ed3\u679c\u5982\u4e0b：<\/span><\/p>\n

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

    2. \u52a0\u5165\u53c2\u6570\u00a0hue，\u5bf9\u6307\u5b9a\u7279\u5f81\u4e0d\u540c\u7684\u8bb0\u5f55\u5728\u753b\u56fe\u65f6\u6620\u5c04\u4e0d\u540c\u7684\u989c\u8272<\/span><\/p>\n

    # \u52a0\u5165\u5206\u7c7b\u53d8\u91cf hue
    \nsns.scatterplot(
    \n data=df,
    \n x='flipper_length_mm',
    \n y='body_mass_g',
    \n hue='species' # \u5c06\u5bf9\u6bcf\u4e2a\u70b9\u6839\u636e\u5176 species \u6807\u6ce8\u4e0d\u540c\u989c\u8272
    \n)
    \n# \u6839\u636e hue \u6620\u5c04\u989c\u8272
    \n# seaborn \u4f1a\u81ea\u52a8\u751f\u6210 legend<\/p>\n

    plt.show()<\/p>\n

    \u7ed8\u56fe\u7ed3\u679c\u5982\u4e0b：<\/span><\/p>\n

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

    3. \u52a0\u5165\u53c2\u6570 style，\u5bf9\u6307\u5b9a\u7279\u5f81\u4e0d\u540c\u7684\u8bb0\u5f55\u5728\u753b\u56fe\u65f6\u4f7f\u7528\u4e0d\u540c\u7684\u70b9\u7684\u6837\u5f0f<\/span><\/p>\n

    # \u518d\u52a0\u5165\u70b9\u7684\u6837\u5f0f style
    \nsns.scatterplot(
    \n data=df,
    \n x='flipper_length_mm',
    \n y='body_mass_g',
    \n hue='species', # \u5c06\u5bf9\u6bcf\u4e2a\u70b9\u6839\u636e\u5176 species \u6807\u6ce8\u4e0d\u540c\u989c\u8272
    \n style='sex' # \u5c06\u5bf9\u6bcf\u4e2a\u70b9\u6839\u636e\u5176 sex \u9009\u62e9\u4e0d\u540c\u7684\u70b9\u7684\u6837\u5f0f
    \n)
    \n# \u6839\u636e style \u9009\u62e9\u4e0d\u540c\u6807\u8bb0
    \n# \u9002\u5408\u5728 hue \u7684\u57fa\u7840\u4e0a\u8fdb\u884c\u4e8c\u6b21\u5206\u7c7b\u7684\u60c5\u5f62<\/p>\n

    plt.show()<\/p>\n

    \u7ed8\u56fe\u7ed3\u679c\u5982\u4e0b：<\/span><\/p>\n

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

    \n

    \u4f7f\u7528 Axes \u5bf9\u8c61\u7cbe\u7ec6\u5316\u63a7\u5236<\/span><\/h2>\n

    Seaborn \u7684\u5927\u90e8\u5206\u7ed8\u56fe\u51fd\u6570\u90fd\u4f1a\u8fd4\u56de\u4e00\u4e2a matplotlib.axes.Axes \u5bf9\u8c61，\u56e0\u6b64\u4e0d\u9700\u8981\u663e\u793a\u521b\u5efa\u753b\u5e03\u5c31\u53ef\u4ee5\u62ff\u5230\u7ed8\u56fe\u540e\u7684 Axes，\u5e76\u901a\u8fc7\u8fd9\u4e2a Axes \u5bf9\u7ed8\u5236\u7684\u56fe\u5f62\u8fdb\u884c\u7cbe\u7ec6\u5316\u63a7\u5236\u3002<\/span><\/p>\n

    Axes \u5bf9\u8c61\u6709\u5173\u65b9\u6cd5<\/span><\/h3>\n\n\u65b9\u6cd5<\/span>\u529f\u80fd<\/span><\/tr>\n\n\n\n\n\n\n\n\n\n\n
    ax.set_title('\u6807\u9898')<\/span><\/td>\n\u6807\u9898<\/span><\/td>\n<\/tr>\n
    \n

    ax.set_xlabel('x\u8f74\u6807\u7b7e')<\/span><\/p>\n

    ax.set_ylabel('y\u8f74\u6807\u7b7e')<\/span><\/p>\n<\/td>\n

    		\u5750\u6807\u8f74\u6807\u7b7e<\/span><\/td>\n<\/tr>\n
    \n

    ax.set_xlim(left=…, right=…)<\/span><\/p>\n

    ax.set_ylim(bottom=…, top=…)<\/span><\/p>\n<\/td>\n

    		\u5750\u6807\u8f74\u8303\u56f4<\/span><\/td>\n<\/tr>\n
    \n

    ax.set_xticks(x\u503c\u7684\u5e8f\u5217)<\/span><\/p>\n

    ax.set_yticks(y\u503c\u7684\u5e8f\u5217)<\/span><\/p>\n<\/td>\n

    		\u523b\u5ea6<\/span><\/td>\n<\/tr>\n
    \n

    ax.legend(title='\u56fe\u4f8b\u6807\u9898', loc='best')<\/span><\/p>\n

    loc\u00a0\u8868\u793a\u56fe\u4f8b\u7684\u4f4d\u7f6e，\u8fd8\u53ef\u4e3a ：<\/span><\/p>\n

    1.\u00a0\u6700\u4f73\u4f4d\u7f6e：'best'，\u81ea\u52a8\u9009\u6700\u4e0d\u6321\u6570\u636e\u7684\u4f4d\u7f6e（\u5df2\u9010\u6b65\u88ab\u5f03\u7528，\u5efa\u8bae\u663e\u5f0f\u6307\u5b9a\u4f4d\u7f6e）<\/span><\/p>\n

    2.\u00a0\u56db\u4e2a\u89d2\u4e0e\u56db\u6761\u8fb9\u4e2d\u95f4：'upper', 'lower', 'center' \u4e0e 'left', 'right', 'center' \u7684\u7ec4\u5408（\u4e2d\u95f4\u7528\u7a7a\u683c\u9694\u5f00），\u4f8b\u5982 'upper center' \u8868\u793a\u4e0a\u65b9\u5c45\u4e2d<\/span><\/p>\n

    3. \u5750\u6807\u7cfb\u6b63\u4e2d\u95f4：'center'，\u5bb9\u6613\u6321\u4f4f\u6570\u636e<\/span><\/p>\n<\/td>\n

    		\u56fe\u4f8b<\/span><\/td>\n<\/tr>\n
    \n

    ax.grid(True)\u00a0 \u00a0 \u00a0 \u00a0 # x\u3001y\u8f74\u7f51\u683c\u7ebf<\/span><\/p>\n

    ax.grid(True, axis='x')\u00a0 \u00a0 \u00a0 \u00a0 # \u4ec5 x \u8f74\u7f51\u683c\u7ebf<\/span><\/p>\n

    ax.grid(True, axis='y')\u00a0 \u00a0 \u00a0 \u00a0 # \u4ec5 y \u8f74\u7f51\u683c\u7ebf<\/span><\/p>\n<\/td>\n

    		\u7f51\u683c<\/span><\/td>\n<\/tr>\n
    \n

    ax.axhline()<\/span><\/p>\n

    ax.axvline()<\/span><\/p>\n<\/td>\n

    		\u53c2\u8003\u7ebf<\/span><\/td>\n<\/tr>\n
    \n

    ax.text()<\/span><\/p>\n

    ax.annotate()<\/span><\/p>\n<\/td>\n

    		\u6587\u672c<\/span><\/td>\n<\/tr>\n
    \n

    ax.spines[]\u00a0 \u00a0 \u00a0 \u00a0 # \u7528\u5b57\u5178\u5f62\u5f0f\u5bf9\u67d0\u8fb9\u6846\u8fdb\u884c\u64cd\u4f5c<\/span><\/p>\n

    \u5305\u62ec\u8fb9\u6846\u663e\u793a\u4e0e\u9690\u85cf\u3001\u7c97\u7ec6\u3001\u989c\u8272\u7b49<\/span><\/p>\n

    # \u9690\u85cf\u4e0a\u3001\u53f3\u8fb9\u6846<\/span><\/p>\n

    for spine in ['top', 'right']:<\/span><\/p>\n

    \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0ax.spines[spine].set_visible(False)<\/span><\/p>\n<\/td>\n

    		\u8fb9\u6846<\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

    \u00a01. \u793a\u4f8b<\/span><\/h4>\n

    import seaborn as sns
    \nimport matplotlib as mpl
    \nimport matplotlib.pyplot as plt<\/p>\n

    df = sns.load_dataset('penguins')
    \ndf.head()<\/p>\n

    mpl.rcParams['font.family'] = 'SimHei'
    \nmpl.rcParams['font.size'] = 15<\/p>\n

    # \u8fd9\u91cc\u5c06\u8fd4\u56de\u7684 Axes \u5bf9\u8c61\u5b58\u53d8\u91cf ax
    \nax = sns.scatterplot(
    \n data=df,
    \n x='flipper_length_mm',
    \n y='body_mass_g',
    \n hue='species'
    \n)<\/p>\n

    # \u6dfb\u52a0 x\u3001y \u8f74\u6807\u7b7e
    \nax.set_xlabel('\u9ccd\u957f (mm)')
    \nax.set_ylabel('\u4f53\u91cd (g)')<\/p>\n

    ax.legend(title='\u7269\u79cd') # \u4fee\u6539\u56fe\u4f8b\u6807\u9898
    \nax.grid(True) # \u7f51\u683c\u7ebf<\/p>\n

    plt.show()<\/p>\n

    \u7ed8\u56fe\u7ed3\u679c\u5982\u4e0b：<\/span><\/p>\n

    \"\"<\/p>\n

    2. \u56fe\u4f8b\u5916\u7f6e<\/span><\/h4>\n

    \u82e5\u56fe\u4f8b\u5728\u56fe\u4e2d\u4f1a\u906e\u6321\u6570\u636e，\u53ef\u4ee5\u4f7f\u7528 ax.legend() \u65b9\u6cd5\u5c06\u5176\u653e\u5728\u56fe\u7684\u5916\u9762<\/span><\/p>\n

    \u8981\u914d\u5408 plt.tight_layout() \u4f7f\u7528，\u5426\u5219\u56fe\u4f8b\u53ef\u80fd\u4f1a\u8dd1\u51fa\u753b\u5e03<\/span><\/span><\/p>\n

    ax.legend(
    \n title='\u56fe\u4f8b\u6807\u9898',
    \n bbox_to_anchor=(x\u5750\u6807, y\u5750\u6807), # \u5bf9\u9f50\u5750\u6807，(0, 0) \u8868\u793a ax \u7684\u5de6\u4e0b\u89d2，(1, 1) \u8868\u793a ax \u7684\u53f3\u4e0a\u89d2
    \n loc='\u5bf9\u9f50\u65b9\u5f0f' # \u6307\u5b9a\u56fe\u4f8b\u7684\u54ea\u4e2a\u89d2\u6216\u8fb9\u4e2d\u70b9\u4e0e\u8be5\u5750\u6807\u5bf9\u9f50
    \n)<\/p>\n

    \u4f8b\u5982：<\/span><\/p>\n

    \u5c06\u56fe\u4f8b\u653e\u5728\u56fe\u7684\u53f3\u4fa7\u5e38\u7528\u5199\u6cd5：<\/span><\/p>\n

    ax.legend(
    \n title='\u56fe\u4f8b\u6807\u9898',
    \n bbox_to_anchor=(1.02, 1), # \u5bf9\u9f50\u5750\u6807，(1.02, 1)，\u8868\u793a\u56fe\u7684\u53f3\u4e0a\u89d2\u5f80\u53f30.02\u8ddd\u79bb\u7684\u70b9
    \n loc='upper left' # \u6307\u5b9a\u56fe\u4f8b\u7684\u5de6\u4e0a\u89d2\u4e0e (1.02, 1) \u70b9\u5bf9\u9f50
    \n)<\/p>\n

    \u7ed3\u679c\u5f62\u5982：<\/span><\/p>\n

    \"\"<\/p>\n

    \u4f7f\u7528 tight_layout(rect=…) \u4e3a\u56fe\u4f8b\u9884\u7559\u7a7a\u95f4：<\/span><\/p>\n

    \u4f8b\u5982：<\/span><\/p>\n

    \u5728\u53f3\u4fa7\u9884\u7559\u56fe\u4f8b\u7a7a\u95f4<\/span><\/p>\n

    # rect=[\u5de6\u8fb9\u8ddd, \u5e95\u8fb9\u8ddd, \u53f3\u8fb9\u8ddd, \u4e0a\u8fb9\u8ddd]，\u4f7f\u7528 figure \u5750\u6807\u7cfb（0\u20131），\u6307\u5b9a\u4e00\u4e2a\u77e9\u5f62\u533a\u57df
    \n# tight_layout \u4ec5\u5728 rect \u6307\u5b9a\u7684\u77e9\u5f62\u533a\u57df\u5185\u6392\u5e03\u5b50\u56fe
    \nplt.tight_layout(rect=(0, 0, 0.82, 1)) # \u53f3\u4fa7\u7559 18% \u7a7a\u95f4\u7ed9\u56fe\u4f8b，\u9632\u6b62 tight_layout \u5c06\u5176\u88c1\u6389<\/p>\n

    \n

    \u6563\u70b9\u56fe\u7ec3\u4e60<\/span><\/h2>\n

    1. \u4f7f\u7528 penguins \u6570\u636e\u96c6，x \u8f74\u4e3a\u5599\u957f（bill_length_mm），y \u8f74\u4e3a\u5599\u6df1（bill_depth_mm）\u5e76\u4e14\u6309\u00a0species\u00a0\u6620\u5c04\u989c\u8272\u753b\u51fa\u6563\u70b9\u56fe，\u52a0\u4e0a\u5408\u9002\u7684\u6807\u9898\u548c\u8f74\u6807\u7b7e，\u753b\u5728\u5b50\u56fe1<\/span><\/p>\n

    2. \u5728 1 \u7684\u57fa\u7840\u4e0a\u7528 sex \u533a\u5206\u6027\u522b，\u753b\u5728\u5b50\u56fe2<\/span><\/p>\n

    （\u56fe\u4f8b\u653e\u5728\u56fe\u7684\u53f3\u4fa7\u4e2d\u90e8）<\/span><\/p>\n

    # \u672c\u4eba\u601d\u8def，\u4ec5\u4f9b\u53c2\u8003
    \nimport seaborn as sns
    \nimport matplotlib as mpl
    \nimport matplotlib.pyplot as plt<\/p>\n

    df = sns.load_dataset('penguins')
    \ndf.head()<\/p>\n

    mpl.rcParams['font.family'] = 'SimHei'
    \nmpl.rcParams['font.size'] = 15<\/p>\n

    # \u4f7f\u7528 2\u00d71 \u7684\u5782\u76f4\u5e03\u5c40，\u4fbf\u4e8e\u5bf9\u6bd4\u201c\u4ec5\u6309\u79cd\u7c7b\u7740\u8272\u201d\u4e0e\u201c\u6309\u79cd\u7c7b+\u6027\u522b\u533a\u5206\u201d\u7684\u5dee\u5f02
    \nfig, (ax1, ax2) = plt.subplots(2, 1, figsize=(12, 14), dpi=100) # \u540c\u65f6\u83b7\u5f97\u753b\u5e03 fig \u4e0e\u4e24\u5e45\u5b50\u56fe ax1, ax2<\/p>\n

    # \u5b50\u56fe1
    \nsns.scatterplot(
    \n data=df,
    \n x='bill_length_mm',
    \n y='bill_depth_mm',
    \n hue='species',
    \n ax=ax1
    \n)
    \nax1.set_title('\u5599\u957f-\u5599\u6df1 \u5bf9\u6bd4\u56fe')
    \nax1.set_xlabel('\u5599\u957f (mm)')
    \nax1.set_ylabel('\u5599\u6df1 (mm)')
    \n# \u5c06\u56fe\u4f8b\u653e\u5230\u56fe\u7684\u53f3\u4fa7
    \nax1.legend(
    \n title='\u7269\u79cd',
    \n loc='upper left', # \u56fe\u4f8b\u7684\u5de6\u4e0a\u89d2
    \n bbox_to_anchor=(1.02, 1) # \u5bf9\u9f50\u5230 ax1 \u7684\u53f3\u4fa7
    \n)<\/p>\n

    # \u5b50\u56fe2
    \nsns.scatterplot(
    \n data=df,
    \n x='bill_length_mm',
    \n y='bill_depth_mm',
    \n hue='species',
    \n style='sex',
    \n ax=ax2
    \n)
    \nax2.set_title('\u5599\u957f-\u5599\u6df1 \u5bf9\u6bd4\u56fe')
    \nax2.set_xlabel('\u5599\u957f (mm)')
    \nax2.set_ylabel('\u5599\u6df1 (mm)')
    \n# \u5c06\u56fe\u4f8b\u653e\u5230\u56fe\u7684\u53f3\u4fa7\u4e2d\u90e8
    \nax2.legend(
    \n title='\u7269\u79cd \/ \u6027\u522b',
    \n loc='upper left', # \u56fe\u4f8b\u7684\u5de6\u4e0a\u89d2
    \n bbox_to_anchor=(1.02, 1) # \u5bf9\u9f50\u5230 ax2 \u7684\u53f3\u4fa7
    \n)<\/p>\n

    plt.tight_layout(rect=(0, 0, 0.82, 1))
    \nplt.show()<\/p>\n

    \u7ed8\u56fe\u7ed3\u679c\u5982\u4e0b：<\/span><\/p>\n

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

    \n

    \u6298\u7ebf\u56fe\u7ed8\u5236 sns.lineplot<\/span><\/h2>\n

    sns.lineplot(
    \n data=df, # \u6570\u636e
    \n x='\u6a2a\u8f74\u53d8\u91cf\u7279\u5f81', # \u6a2a\u8f74\u53d8\u91cf
    \n y='\u7eb5\u8f74\u53d8\u91cf\u7279\u5f81', # \u7eb5\u8f74\u53d8\u91cf
    \n hue='\u989c\u8272\u5206\u7ec4\u7279\u5f81', # \u6309\u8be5\u5217\u6620\u5c04\u989c\u8272
    \n style='\u7ebf\u578b\u5206\u7ec4\u7279\u5f81', # \u6309\u8be5\u5217\u6620\u5c04\u7ebf\u578b
    \n markers=True, # \u662f\u5426\u663e\u793a\u70b9
    \n dashes=False, # False：\u6240\u6709\u5206\u7ec4\u5747\u4e3a\u5b9e\u7ebf，True：\u4e0d\u540c\u5206\u7ec4\u4f7f\u7528\u4e0d\u540c\u865a\u7ebf\u6837\u5f0f（Seaborn \u9ed8\u8ba4\u884c\u4e3a），\u4e5f\u53ef\u4f20\u5165\u5217\u8868\u6216\u5b57\u5178\u81ea\u5b9a\u4e49\u865a\u7ebf
    \n estimator='mean', # \u5bf9\u540c\u4e00 x \u4e0b\u7684\u591a\u4e2a y \u6c42\u5747\u503c（\u9ed8\u8ba4\u503c，\u53ef\u7701\u7565），lineplot \u9ed8\u8ba4\u4f1a\u5bf9\u76f8\u540c x \u503c\u5bf9\u5e94\u7684\u591a\u4e2a y \u503c\u8fdb\u884c\u805a\u5408
    \n errorbar=None, # None \/ 'sd' \/ ('pi', 95)
    \n ax=ax # \u6307\u5b9a\u5b50\u56fe
    \n)<\/p>\n

    \u7ed8\u5236\u6298\u7ebf\u56fe\u65f6，\u82e5\u4e0d\u6307\u5b9a\u00a0errorbar=None，\u5219\u9ed8\u8ba4\u81ea\u52a8\u751f\u6210\u7f6e\u4fe1\u533a\u95f4（CI）<\/span><\/p>\n

    \u793a\u4f8b<\/span><\/h3>\n

    \u7ed8\u5236 flights \u6570\u636e\u96c6\u4e2d\u4e58\u5ba2\u6570\u91cf\u968f\u5e74\u4efd\u7684\u53d8\u5316\u8d8b\u52bf：<\/span><\/p>\n

    （1）\u5b50\u56fe1：\u624b\u52a8\u805a\u5408，\u8ba1\u7b97\u5404\u5e74\u4efd\u6240\u6709\u6708\u4efd\u7684\u4e58\u5ba2\u6570\u5747\u503c，\u7ed8\u5236\u6298\u7ebf\u56fe<\/span><\/p>\n

    （2）\u5b50\u56fe2：\u76f4\u63a5\u4f7f\u7528\u539f\u59cb\u6570\u636e\u7ed8\u5236 lineplot，\u7531 Seaborn \u81ea\u52a8\u5bf9\u540c\u4e00 year\u4e0b\u7684\u591a\u4e2a\u89c2\u6d4b\u503c\u8fdb\u884c\u805a\u5408，\u5e76\u663e\u793a\u9ed8\u8ba4\u7684 95% \u7f6e\u4fe1\u533a\u95f4<\/span><\/p>\n

    （3）\u5b50\u56fe3：\u540c\u4e0a，\u4f46\u4f7f\u7528\u00a0errorbar=None \u5173\u95ed\u7f6e\u4fe1\u533a\u95f4<\/span><\/p>\n

    （4）\u5b50\u56fe4：\u4ee5 month \u4e3a\u5206\u7ec4\u53d8\u91cf（hue='month'），\u5728\u540c\u4e00\u5750\u6807\u7cfb\u4e2d\u7ed8\u5236\u591a\u6761\u6298\u7ebf，\u5c55\u793a\u4e0d\u540c\u6708\u4efd\u7684\u5e74\u5ea6\u53d8\u5316\u8d8b\u52bf<\/span><\/p>\n<\/p>\n

    flights \u6570\u636e\u96c6\u5f62\u5982：<\/span><\/p>\n

    \"\"<\/p>\n

    \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0<\/span><\/p>\n

    \u7ed8\u56fe\u7ed3\u679c\u5982\u4e0b：<\/span><\/p>\n

    \"\"<\/p>\n

    \n

    \u6298\u7ebf\u56fe\u7ec3\u4e60<\/span><\/h2>\n

    tips \u6570\u636e\u96c6\u5f62\u5982：<\/span><\/p>\n

    \"\"<\/p>\n

    \u4f7f\u7528 tips \u6570\u636e\u96c6，\u7ed8\u5236\u6298\u7ebf\u56fe，\u4f53\u73b0\u5348\u9910（Lunch）\u548c\u665a\u9910（Dinner）\u7684\u5e73\u5747\u8d26\u5355\u91d1\u989d\u5728\u4e00\u5468\u5185\u7684\u53d8\u5316\u8d8b\u52bf<\/span><\/p>\n

    \u8981\u6c42：<\/span><\/p>\n

    （1）x \u8f74：day（\u661f\u671f\u51e0）\u00a0\u3001y \u8f74：total_bill \u7684\u5e73\u5747\u503c<\/span><\/p>\n

    （2）\u4e2d\u6587\u6807\u9898 + \u4e2d\u6587\u8f74\u6807\u7b7e<\/span><\/p>\n

    （3）\u56fe\u4f8b\u653e\u5728\u56fe\u5916\u53f3\u4fa7<\/span><\/p>\n

    （4）\u5173\u95ed\u7f6e\u4fe1\u533a\u95f4（\u56e0\u4e3a\u5929\u6570\u5f88\u5c11，CI \u4e0d\u7a33\u5b9a）<\/span><\/p>\n

    （5）\u7528\u6563\u70b9\u56fe\u6807\u51fa\u6298\u7ebf\u8282\u70b9（mean \u805a\u5408\u70b9）<\/span><\/p>\n

    # \u672c\u4eba\u601d\u8def，\u4ec5\u4f9b\u53c2\u8003
    \nimport seaborn as sns
    \nimport matplotlib.pyplot as plt
    \nimport matplotlib as mpl<\/p>\n

    df = sns.load_dataset('tips')
    \nprint(df.head())<\/p>\n

    mpl.rcParams['font.family'] = 'SimHei'
    \nmpl.rcParams['font.size'] = 15<\/p>\n

    fig, ax = plt.subplots(figsize=(8, 6), dpi=100)<\/p>\n

    sns.lineplot(
    \n data=df,
    \n x='day',
    \n y='total_bill',
    \n hue='time',
    \n errorbar=None,
    \n ax=ax
    \n)<\/p>\n

    agg1 = (
    \n df.groupby(['day', 'time'], observed=True)['total_bill'] # observed=True \u4ec5\u4fdd\u7559\u6570\u636e\u4e2d\u5b9e\u9645\u51fa\u73b0\u7684\u7ec4\u5408；observed=False：\u5206\u7ec4\u4e2d\u82e5\u67d0\u9879\u4e0d\u5b58\u5728，\u5219\u663e\u793a NaN
    \n .mean()
    \n .reset_index()
    \n)
    \nprint(agg1)<\/p>\n

    sns.scatterplot(
    \n data=agg1,
    \n x='day',
    \n y='total_bill',
    \n hue='time',
    \n legend=False # \u4e0d\u663e\u793a\u56fe\u4f8b
    \n)<\/p>\n

    ax.set_xlabel('\u661f\u671f')
    \nax.set_ylabel('\u8ba2\u5355\u603b\u989d\u5e73\u5747\u503c')
    \nax.set_title('\u8ba2\u5355\u603b\u989d\u5e73\u5747\u503c\u968f\u661f\u671f\u53d8\u5316\u56fe')
    \nax.legend(
    \n title='\u7528\u9910\u65f6\u6bb5',
    \n bbox_to_anchor=(1.02, 1),
    \n loc='upper left'
    \n)<\/p>\n

    plt.tight_layout(rect=(0, 0, 0.82, 1))
    \nplt.show()<\/p>\n

    \u7ed8\u56fe\u7ed3\u679c\u5982\u4e0b：<\/span><\/p>\n

    \"\"<\/p>\n

    \n

    \u7070\u5ea6\u663e\u793a<\/span><\/h2>\n

    \u7ed8\u56fe\u65f6\u52a0\u5165\u5982\u4e0b\u53c2\u6570\u8fdb\u884c\u7070\u5ea6\u663e\u793a：<\/span><\/p>\n

    palette='gray', # \u53bb\u8272<\/p>\n

    \u914d\u5408 style \u53ef\u4ee5\u5728\u4e0d\u4f7f\u7528\u989c\u8272\u7684\u60c5\u51b5\u4e0b，\u901a\u8fc7\u6807\u8bb0\u5f62\u72b6（\u6563\u70b9\u56fe）\u6216\u7ebf\u578b（\u6298\u7ebf\u56fe）\u533a\u5206\u4e0d\u540c\u7c7b\u522b<\/span><\/p>\n

    \u5e38\u7528\u4e8e\u9ed1\u767d\u6253\u5370\u3001\u5b66\u672f\u671f\u520a\u53ca\u7070\u5ea6\u663e\u793a\u573a\u666f<\/span><\/p>\n

    \n

    \u5173\u7cfb\u56fe sns.relplot<\/span><\/h2>\n

    relplot（\u5173\u7cfb\u56fe，Relational Plot）\u7528\u4e8e\u5728\u56fe\u4e2d\u5c55\u793a\u53d8\u91cf\u4e4b\u95f4\u7684\u5173\u7cfb，\u53ef\u901a\u8fc7 kind \u53c2\u6570\u5207\u6362\u4e3a\u6563\u70b9\u56fe\u6216\u6298\u7ebf\u56fe<\/span><\/p>\n

  • kind='scatter' -> scatterplot \u6563\u70b9\u56fe（\u9ed8\u8ba4）<\/span><\/li>\n
  • kind='line' -> lineplot \u6298\u7ebf\u56fe<\/span><\/li>\n

    \u57fa\u7840\u53c2\u6570：<\/span><\/p>\n

    sns.relplot(
    \n data=df, # \u6570\u636e
    \n x='\u6a2a\u8f74\u53d8\u91cf', # \u6a2a\u8f74
    \n y='\u7eb5\u8f74\u53d8\u91cf', # \u7eb5\u8f74
    \n hue='\u989c\u8272\u5206\u7ec4\u7279\u5f81', # \u989c\u8272\u533a\u5206
    \n style='\u6837\u5f0f\u5206\u7ec4\u7279\u5f81', # \u6807\u8bb0 \/ \u7ebf\u578b\u533a\u5206
    \n size='\u5927\u5c0f\u6620\u5c04\u7279\u5f81', # \u70b9\u5927\u5c0f\u6620\u5c04（\u6563\u70b9\u56fe）
    \n markers=True, # \u662f\u5426\u663e\u793a\u70b9（\u6298\u7ebf\u56fe）
    \n dashes=False, # \u662f\u5426\u4f7f\u7528\u4e0d\u540c\u865a\u7ebf
    \n kind='scatter', # 'scatter' \u6216 'line'
    \n)<\/p>\n

    \u5206\u9762<\/span><\/h3>\n

    sns.relplot(
    \n '''
    \n \u5176\u4f59\u53c2\u6570
    \n '''
    \n col='\u6309\u5217\u5206\u9762\u7279\u5f81', # \u6309\u5217\u5206\u9762
    \n col_wrap=… # \u4e00\u884c\u6700\u591a\u7684\u5b50\u56fe\u6570\u91cf
    \n row='\u6309\u884c\u5206\u9762\u7279\u5f81', # \u6309\u884c\u5206\u9762
    \n)<\/p>\n

    1. col, row \u5206\u5217\/\u5206\u884c<\/span><\/h4>\n

    \u6307\u5b9a col \u6216\u00a0row \u4e3a\u67d0\u4e00\u7279\u5f81，\u5219\u4f1a\u6309\u8be5\u7279\u5f81\u7684\u6bcf\u4e2a\u53d6\u503c\u751f\u6210\u4e00\u4e2a\u5b50\u56fe：<\/span><\/p>\n

  • col \u63a7\u5236\u6a2a\u5411\u6392\u5217（\u5206\u5217）<\/span><\/li>\n
  • row \u63a7\u5236\u7eb5\u5411\u6392\u5217（\u5206\u884c）<\/span><\/li>\n

    \u9002\u5408\u5728\u591a\u5b50\u56fe\u4e2d\u5bf9\u6bd4\u4e0d\u540c\u7ec4\u522b\u7684\u6570\u636e\u5206\u5e03\u4e0e\u5173\u7cfb<\/span><\/p>\n

    2. col_wrap \u6307\u5b9a\u4e00\u884c\u6700\u591a\u7684\u5b50\u56fe\u6570\u91cf<\/span><\/h4>\n

    col_wrap \u6307\u5b9a\u4e00\u884c\u6700\u591a\u7684\u5b50\u56fe\u6570\u91cf（\u6700\u5927\u5206\u5217\u6570），\u914d\u5408 col \u4f7f\u7528<\/span><\/p>\n

    \u5728\u4ec5\u6307\u5b9a col\u3001\u4e0d\u6307\u5b9a row \u7684\u60c5\u51b5\u4e0b\u624d\u53ef\u4f7f\u7528 col_wrap<\/p>\n

    \u793a\u4f8b<\/span><\/h3>\n

    tips \u6570\u636e\u96c6\u5f62\u5982：<\/span><\/p>\n

    \"\"<\/p>\n

    \u4f7f\u7528 tips \u6570\u636e\u96c6，\u7528 relplot \u7ed8\u56fe：<\/span><\/p>\n

    （1）\u5206\u9762：x \u8f74\u4e3a\u661f\u671f（day）\u3001y \u8f74\u4e3a\u8d26\u5355\u91d1\u989d（total_bill） \u7684\u5e73\u5747\u503c，\u6309\u6027\u522b（sex）\u5206\u5217，\u6309smoker（\u662f\u5426\u5438\u70df）\u5206\u884c，\u7ed8\u5236\u6298\u7ebf\u56fe，\u5173\u95ed\u7f6e\u4fe1\u533a\u95f4<\/span><\/p>\n

    import seaborn as sns
    \nimport matplotlib.pyplot as plt
    \nimport matplotlib as mpl<\/p>\n

    df = sns.load_dataset('tips')
    \nprint(df.head())<\/p>\n

    mpl.rcParams['font.family'] = 'SimHei'
    \nmpl.rcParams['font.size'] = 15<\/p>\n

    sns.relplot(
    \n data=df,
    \n x='day',
    \n y='total_bill',
    \n kind='line',
    \n hue='time',
    \n col='sex', # \u6309 'sex' \u5206\u5217
    \n row='smoker', # \u6309 'smoker' \u5206\u884c
    \n errorbar=None
    \n)<\/p>\n

    plt.show()<\/p>\n

    \u7ed8\u56fe\u7ed3\u679c\u5982\u4e0b：<\/p>\n

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

    （2）\u5206\u9762：x \u8f74\u4e3a\u8d26\u5355\u91d1\u989d（total_bill）\u3001y \u8f74\u4e3a\u5c0f\u8d39（tips），\u7ed8\u5236\u6563\u70b9\u56fe，\u6839\u636e\u6027\u522b（sex）\u6620\u5c04\u4e0d\u540c\u989c\u8272，\u6839\u636e\u5c0f\u8d39（tips）\u7684\u591a\u5c11\u6620\u5c04\u70b9\u7684\u5927\u5c0f，\u6309\u661f\u671f（day）\u5206\u5217，\u8bbe\u7f6e\u6bcf\u884c\u5b50\u56fe\u6570\u6700\u591a\u4e3a 2<\/span><\/p>\n<\/p>\n

    import seaborn as sns
    \nimport matplotlib.pyplot as plt
    \nimport matplotlib as mpl<\/p>\n

    df = sns.load_dataset('tips')
    \nprint(df.head())<\/p>\n

    mpl.rcParams['font.family'] = 'SimHei'
    \nmpl.rcParams['font.size'] = 15<\/p>\n

    sns.relplot(
    \n data=df,
    \n x='total_bill',
    \n y='tip',
    \n kind='scatter',
    \n hue='sex',
    \n size='tip',
    \n col='day',
    \n col_wrap=2 # \u6bcf\u884c\u6700\u591a\u4e24\u5e45\u5b50\u56fe
    \n)<\/p>\n

    plt.show()<\/p>\n

    \u7ed8\u56fe\u7ed3\u679c\u5982\u4e0b：<\/p>\n

    \"\"<\/p>\n

    \n

    relplot \u4e0e scatterplot, lineplot \u7684\u5173\u7cfb<\/span><\/h2>\n

    \u6b64\u524d\u7684 scatterplot \u4e0e lineplot \u4e3a Axes-level \u7684，\u753b\u5728\u4e00\u4e2a Axes \u4e0a，\u800c relplot \u4e3a Figure-level \u7684，\u753b\u5728\u4e00\u6574\u4e2a Figure \u4e0a，\u8fd4\u56de\u4e00\u4e2a FacetGrid（\u5206\u9762\u56fe）\u5bf9\u8c61，\u81ea\u52a8 legend\u3001\u81ea\u52a8\u5206\u9762\u3001\u81ea\u52a8\u7ba1\u7406\u5e03\u5c40<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"

    matplotlib \u753b\u56fe，seaborn \u753b\u53d8\u91cf\u5173\u7cfbseaborn \u672c\u8d28\u4e0a\u662f\u5728 matplotlib \u7684\u753b\u5e03\u4e0a\u753b\u56fe，\u5747\u4f7f\u7528 plt.show() \u6765\u663e\u793a\u7ed8\u5236\u7684\u56fe\u50cf\u5b89\u88c5 Seaborn\u4f7f\u7528 pip \u5b89\u88c5：pip install seaborn\u6ce8\u610f： Seaborn \u4f9d\u8d56 pandas matplotlib numpy，\u5b89\u88c5 seaborn \u65f6\u4f1a\u4e00\u5e76\u5b89\u88c5\u8fd9\u4e9b\u4f9d\u8d56\u5728 Python \u4e2d\u5bfc\u5165 Seaborn \u5e93<\/p>\n","protected":false},"author":2,"featured_media":81555,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[2468,81,371,305],"topic":[],"class_list":["post-81568","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-server","tag-matplotlib","tag-python","tag-371","tag-305"],"yoast_head":"\n\u3010Seaborn \u5b66\u4e60\u7b14\u8bb0\u3011P1. \u6563\u70b9\u56fe scatterplot\u3001\u6298\u7ebf\u56fe lineplot\u3001\u5173\u7cfb\u56fe relplot - 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