{"id":1118502,"date":"2025-01-08T18:35:59","date_gmt":"2025-01-08T10:35:59","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1118502.html"},"modified":"2025-01-08T18:36:01","modified_gmt":"2025-01-08T10:36:01","slug":"python%e5%a6%82%e4%bd%95%e5%81%9a%e5%90%91%e4%b8%8b%e7%9a%84%e6%9f%b1%e7%8a%b6%e5%9b%be","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1118502.html","title":{"rendered":"python\u5982\u4f55\u505a\u5411\u4e0b\u7684\u67f1\u72b6\u56fe"},"content":{"rendered":"

\"python\u5982\u4f55\u505a\u5411\u4e0b\u7684\u67f1\u72b6\u56fe\"<\/p>\n

\u5728Python\u4e2d\u5236\u4f5c\u5411\u4e0b\u7684\u67f1\u72b6\u56fe<\/strong><\/p>\n<\/p>\n

\u5728Python\u4e2d\u5236\u4f5c\u5411\u4e0b\u7684\u67f1\u72b6\u56fe\uff0c\u53ef\u4ee5\u901a\u8fc7\u4f7f\u7528Matplotlib\u5e93\u3002\u9996\u5148\uff0c\u5b89\u88c5Matplotlib\u5e93\u3001\u521b\u5efa\u6570\u636e\u3001\u8bbe\u7f6e\u6761\u5f62\u56fe\u53c2\u6570\u3001\u53cd\u8f6cY\u8f74<\/strong>\u3002\u4e0b\u9762\u6211\u5c06\u8be6\u7ec6\u63cf\u8ff0\u5176\u4e2d\u4e00\u4e2a\u6b65\u9aa4\uff0c\u5373\u5982\u4f55\u53cd\u8f6cY\u8f74\u3002<\/p>\n<\/p>\n

\u53cd\u8f6cY\u8f74\u662f\u5236\u4f5c\u5411\u4e0b\u67f1\u72b6\u56fe\u7684\u5173\u952e\u6b65\u9aa4\u3002\u901a\u8fc7\u8bbe\u7f6eMatplotlib\u7684invert_yaxis()<\/code>\u65b9\u6cd5\uff0c\u53ef\u4ee5\u8f7b\u677e\u5730\u5c06Y\u8f74\u65b9\u5411\u53cd\u8f6c\uff0c\u4f7f\u67f1\u72b6\u56fe\u5411\u4e0b\u5c55\u793a\u3002\u8fd9\u6837\uff0c\u53ef\u4ee5\u6e05\u6670\u5730\u5c55\u793a\u6570\u636e\u7684\u51cf\u5c11\u8d8b\u52bf\u6216\u8d1f\u503c\u6570\u636e\u7684\u5c55\u793a\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u5b89\u88c5\u548c\u5bfc\u5165\u5e93<\/p>\n<\/p>\n

\u5728\u5f00\u59cb\u7ed8\u5236\u5411\u4e0b\u7684\u67f1\u72b6\u56fe\u4e4b\u524d\uff0c\u9996\u5148\u9700\u8981\u786e\u4fdd\u5df2\u7ecf\u5b89\u88c5\u4e86Matplotlib\u5e93\u3002\u5982\u679c\u6ca1\u6709\u5b89\u88c5\uff0c\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u547d\u4ee4\u5b89\u88c5\uff1a<\/p>\n<\/p>\n

pip install matplotlib<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5b89\u88c5\u5b8c\u6210\u540e\uff0c\u5728Python\u811a\u672c\u4e2d\u5bfc\u5165Matplotlib\u5e93\uff1a<\/p>\n<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
import numpy as np<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e8c\u3001\u521b\u5efa\u6570\u636e<\/p>\n<\/p>\n
\u63a5\u4e0b\u6765\uff0c\u521b\u5efa\u7528\u4e8e\u7ed8\u5236\u67f1\u72b6\u56fe\u7684\u6570\u636e\u3002\u5047\u8bbe\u6211\u4eec\u6709\u4e00\u7ec4\u6570\u636e\uff0c\u8868\u793a\u67d0\u4e9b\u503c\u5728\u4e0d\u540c\u7c7b\u522b\u4e2d\u7684\u5206\u5e03\uff1a<\/p>\n<\/p>\n
categories = ['A', 'B', 'C', 'D', 'E']<\/p>\n
values = [10, 15, 7, 12, 9]<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e09\u3001\u8bbe\u7f6e\u6761\u5f62\u56fe\u53c2\u6570<\/p>\n<\/p>\n
\u4f7f\u7528Matplotlib\u7684barh<\/code>\u65b9\u6cd5\u521b\u5efa\u6a2a\u5411\u7684\u6761\u5f62\u56fe\uff0c\u5e76\u8bbe\u7f6e\u4e00\u4e9b\u53c2\u6570\u4ee5\u786e\u4fdd\u56fe\u8868\u7684\u5916\u89c2\u7b26\u5408\u9884\u671f\u3002\u53ef\u4ee5\u81ea\u5b9a\u4e49\u6761\u5f62\u56fe\u7684\u989c\u8272\u3001\u5bbd\u5ea6\u7b49\u53c2\u6570\uff1a<\/p>\n<\/p>\n
plt.figure(figsize=(10, 6))  # \u8bbe\u7f6e\u56fe\u8868\u7684\u5927\u5c0f<\/p>\n
plt.barh(categories, values, color='skyblue')  # \u521b\u5efa\u6a2a\u5411\u6761\u5f62\u56fe<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u56db\u3001\u53cd\u8f6cY\u8f74<\/p>\n<\/p>\n
\u4e3a\u4e86\u5c06\u6761\u5f62\u56fe\u5411\u4e0b\u5c55\u793a\uff0c\u9700\u8981\u53cd\u8f6cY\u8f74\u3002\u53ef\u4ee5\u901a\u8fc7\u8c03\u7528invert_yaxis<\/code>\u65b9\u6cd5\u6765\u5b9e\u73b0\u8fd9\u4e00\u70b9\uff1a<\/p>\n<\/p>\n
plt.gca().invert_yaxis()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e94\u3001\u6dfb\u52a0\u6807\u7b7e\u548c\u6807\u9898<\/p>\n<\/p>\n
\u4e3a\u4e86\u4f7f\u56fe\u8868\u66f4\u5177\u53ef\u8bfb\u6027\uff0c\u53ef\u4ee5\u6dfb\u52a0\u8f74\u6807\u7b7e\u548c\u6807\u9898\uff1a<\/p>\n<\/p>\n
plt.xlabel('Values')<\/p>\n
plt.ylabel('Categories')<\/p>\n
plt.title('Downward Bar Chart Example')<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u516d\u3001\u5c55\u793a\u56fe\u8868<\/p>\n<\/p>\n
\u6700\u540e\uff0c\u4f7f\u7528plt.show()<\/code>\u65b9\u6cd5\u6765\u5c55\u793a\u56fe\u8868\uff1a<\/p>\n<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5b8c\u6574\u7684Python\u4ee3\u7801\u5982\u4e0b\uff1a<\/p>\n<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
import numpy as np<\/p>\n
\u6570\u636e<\/strong><\/h2>\n
categories = ['A', 'B', 'C', 'D', 'E']<\/p>\n
values = [10, 15, 7, 12, 9]<\/p>\n
\u521b\u5efa\u56fe\u8868<\/strong><\/h2>\n
plt.figure(figsize=(10, 6))  # \u8bbe\u7f6e\u56fe\u8868\u7684\u5927\u5c0f<\/p>\n
plt.barh(categories, values, color='skyblue')  # \u521b\u5efa\u6a2a\u5411\u6761\u5f62\u56fe<\/p>\n
\u53cd\u8f6cY\u8f74<\/strong><\/h2>\n
plt.gca().invert_yaxis()<\/p>\n
\u6dfb\u52a0\u6807\u7b7e\u548c\u6807\u9898<\/strong><\/h2>\n
plt.xlabel('Values')<\/p>\n
plt.ylabel('Categories')<\/p>\n
plt.title('Downward Bar Chart Example')<\/p>\n
\u5c55\u793a\u56fe\u8868<\/strong><\/h2>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e8c\u3001\u4f7f\u7528Seaborn\u5236\u4f5c\u5411\u4e0b\u7684\u67f1\u72b6\u56fe<\/h3>\n<\/p>\n
Seaborn\u662f\u4e00\u4e2a\u57fa\u4e8eMatplotlib\u7684\u9ad8\u7ea7\u53ef\u89c6\u5316\u5e93\uff0c\u63d0\u4f9b\u4e86\u66f4\u52a0\u7f8e\u89c2\u548c\u7b80\u6d01\u7684API\u3002\u4f7f\u7528Seaborn\u4e5f\u53ef\u4ee5\u8f7b\u677e\u5730\u5236\u4f5c\u5411\u4e0b\u7684\u67f1\u72b6\u56fe\u3002<\/p>\n<\/p>\n
\u4e00\u3001\u5b89\u88c5\u548c\u5bfc\u5165\u5e93<\/p>\n<\/p>\n
\u9996\u5148\uff0c\u786e\u4fdd\u5df2\u7ecf\u5b89\u88c5\u4e86Seaborn\u5e93\u3002\u5982\u679c\u6ca1\u6709\u5b89\u88c5\uff0c\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u547d\u4ee4\u5b89\u88c5\uff1a<\/p>\n<\/p>\n
pip install seaborn<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5b89\u88c5\u5b8c\u6210\u540e\uff0c\u5728Python\u811a\u672c\u4e2d\u5bfc\u5165Seaborn\u5e93\uff1a<\/p>\n<\/p>\n
import seaborn as sns<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
import pandas as pd<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e8c\u3001\u521b\u5efa\u6570\u636e<\/p>\n<\/p>\n
\u521b\u5efa\u7528\u4e8e\u7ed8\u5236\u67f1\u72b6\u56fe\u7684\u6570\u636e\uff0c\u53ef\u4ee5\u4f7f\u7528Pandas DataFrame\u6765\u7ec4\u7ec7\u6570\u636e\uff1a<\/p>\n<\/p>\n
data = pd.DataFrame({<\/p>\n
    'categories': ['A', 'B', 'C', 'D', 'E'],<\/p>\n
    'values': [10, 15, 7, 12, 9]<\/p>\n
})<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e09\u3001\u4f7f\u7528Seaborn\u7ed8\u5236\u6761\u5f62\u56fe<\/p>\n<\/p>\n
\u4f7f\u7528Seaborn\u7684barplot<\/code>\u65b9\u6cd5\u521b\u5efa\u6761\u5f62\u56fe\uff0c\u5e76\u8bbe\u7f6e\u4e00\u4e9b\u53c2\u6570\u4ee5\u786e\u4fdd\u56fe\u8868\u7684\u5916\u89c2\u7b26\u5408\u9884\u671f\uff1a<\/p>\n<\/p>\n
plt.figure(figsize=(10, 6))  # \u8bbe\u7f6e\u56fe\u8868\u7684\u5927\u5c0f<\/p>\n
sns.barplot(x='values', y='categories', data=data, color='skyblue', orient='h')  # \u521b\u5efa\u6a2a\u5411\u6761\u5f62\u56fe<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u56db\u3001\u53cd\u8f6cY\u8f74<\/p>\n<\/p>\n
\u4e3a\u4e86\u5c06\u6761\u5f62\u56fe\u5411\u4e0b\u5c55\u793a\uff0c\u9700\u8981\u53cd\u8f6cY\u8f74\u3002\u53ef\u4ee5\u901a\u8fc7\u8c03\u7528invert_yaxis<\/code>\u65b9\u6cd5\u6765\u5b9e\u73b0\u8fd9\u4e00\u70b9\uff1a<\/p>\n<\/p>\n
plt.gca().invert_yaxis()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e94\u3001\u6dfb\u52a0\u6807\u7b7e\u548c\u6807\u9898<\/p>\n<\/p>\n
\u4e3a\u4e86\u4f7f\u56fe\u8868\u66f4\u5177\u53ef\u8bfb\u6027\uff0c\u53ef\u4ee5\u6dfb\u52a0\u8f74\u6807\u7b7e\u548c\u6807\u9898\uff1a<\/p>\n<\/p>\n
plt.xlabel('Values')<\/p>\n
plt.ylabel('Categories')<\/p>\n
plt.title('Downward Bar Chart Example with Seaborn')<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u516d\u3001\u5c55\u793a\u56fe\u8868<\/p>\n<\/p>\n
\u6700\u540e\uff0c\u4f7f\u7528plt.show()<\/code>\u65b9\u6cd5\u6765\u5c55\u793a\u56fe\u8868\uff1a<\/p>\n<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5b8c\u6574\u7684Python\u4ee3\u7801\u5982\u4e0b\uff1a<\/p>\n<\/p>\n
import seaborn as sns<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
import pandas as pd<\/p>\n
\u6570\u636e<\/strong><\/h2>\n
data = pd.DataFrame({<\/p>\n
    'categories': ['A', 'B', 'C', 'D', 'E'],<\/p>\n
    'values': [10, 15, 7, 12, 9]<\/p>\n
})<\/p>\n
\u521b\u5efa\u56fe\u8868<\/strong><\/h2>\n
plt.figure(figsize=(10, 6))  # \u8bbe\u7f6e\u56fe\u8868\u7684\u5927\u5c0f<\/p>\n
sns.barplot(x='values', y='categories', data=data, color='skyblue', orient='h')  # \u521b\u5efa\u6a2a\u5411\u6761\u5f62\u56fe<\/p>\n
\u53cd\u8f6cY\u8f74<\/strong><\/h2>\n
plt.gca().invert_yaxis()<\/p>\n
\u6dfb\u52a0\u6807\u7b7e\u548c\u6807\u9898<\/strong><\/h2>\n
plt.xlabel('Values')<\/p>\n
plt.ylabel('Categories')<\/p>\n
plt.title('Downward Bar Chart Example with Seaborn')<\/p>\n
\u5c55\u793a\u56fe\u8868<\/strong><\/h2>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e09\u3001\u5728Jupyter Notebook\u4e2d\u5c55\u793a\u5411\u4e0b\u7684\u67f1\u72b6\u56fe<\/h3>\n<\/p>\n
\u5728Jupyter Notebook\u4e2d\u5c55\u793a\u5411\u4e0b\u7684\u67f1\u72b6\u56fe\u4e0e\u5728Python\u811a\u672c\u4e2d\u5c55\u793a\u57fa\u672c\u76f8\u540c\uff0c\u4f46\u6709\u4e00\u4e9b\u7ec6\u5fae\u7684\u5dee\u5f02\u3002\u4ee5\u4e0b\u662f\u5982\u4f55\u5728Jupyter Notebook\u4e2d\u5c55\u793a\u5411\u4e0b\u7684\u67f1\u72b6\u56fe\u7684\u6b65\u9aa4\uff1a<\/p>\n<\/p>\n
\u4e00\u3001\u5bfc\u5165\u5e93\u5e76\u8bbe\u7f6e\u663e\u793a\u9009\u9879<\/p>\n<\/p>\n
\u5728Jupyter Notebook\u4e2d\uff0c\u9996\u5148\u5bfc\u5165\u5fc5\u8981\u7684\u5e93\uff0c\u5e76\u8bbe\u7f6eMatplotlib\u7684\u663e\u793a\u9009\u9879\uff1a<\/p>\n<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
import seaborn as sns<\/p>\n
import pandas as pd<\/p>\n
%matplotlib inline  # \u4f7f\u56fe\u8868\u5185\u5d4c\u5728Notebook\u4e2d<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e8c\u3001\u521b\u5efa\u6570\u636e<\/p>\n<\/p>\n
\u521b\u5efa\u7528\u4e8e\u7ed8\u5236\u67f1\u72b6\u56fe\u7684\u6570\u636e\uff0c\u4f7f\u7528Pandas DataFrame\u6765\u7ec4\u7ec7\u6570\u636e\uff1a<\/p>\n<\/p>\n
data = pd.DataFrame({<\/p>\n
    'categories': ['A', 'B', 'C', 'D', 'E'],<\/p>\n
    'values': [10, 15, 7, 12, 9]<\/p>\n
})<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e09\u3001\u4f7f\u7528Seaborn\u7ed8\u5236\u6761\u5f62\u56fe<\/p>\n<\/p>\n
\u4f7f\u7528Seaborn\u7684barplot<\/code>\u65b9\u6cd5\u521b\u5efa\u6761\u5f62\u56fe\uff0c\u5e76\u8bbe\u7f6e\u4e00\u4e9b\u53c2\u6570\u4ee5\u786e\u4fdd\u56fe\u8868\u7684\u5916\u89c2\u7b26\u5408\u9884\u671f\uff1a<\/p>\n<\/p>\n
plt.figure(figsize=(10, 6))  # \u8bbe\u7f6e\u56fe\u8868\u7684\u5927\u5c0f<\/p>\n
sns.barplot(x='values', y='categories', data=data, color='skyblue', orient='h')  # \u521b\u5efa\u6a2a\u5411\u6761\u5f62\u56fe<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u56db\u3001\u53cd\u8f6cY\u8f74<\/p>\n<\/p>\n
\u4e3a\u4e86\u5c06\u6761\u5f62\u56fe\u5411\u4e0b\u5c55\u793a\uff0c\u9700\u8981\u53cd\u8f6cY\u8f74\u3002\u53ef\u4ee5\u901a\u8fc7\u8c03\u7528invert_yaxis<\/code>\u65b9\u6cd5\u6765\u5b9e\u73b0\u8fd9\u4e00\u70b9\uff1a<\/p>\n<\/p>\n
plt.gca().invert_yaxis()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e94\u3001\u6dfb\u52a0\u6807\u7b7e\u548c\u6807\u9898<\/p>\n<\/p>\n
\u4e3a\u4e86\u4f7f\u56fe\u8868\u66f4\u5177\u53ef\u8bfb\u6027\uff0c\u53ef\u4ee5\u6dfb\u52a0\u8f74\u6807\u7b7e\u548c\u6807\u9898\uff1a<\/p>\n<\/p>\n
plt.xlabel('Values')<\/p>\n
plt.ylabel('Categories')<\/p>\n
plt.title('Downward Bar Chart Example in Jupyter Notebook')<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u516d\u3001\u5c55\u793a\u56fe\u8868<\/p>\n<\/p>\n
\u5728Jupyter Notebook\u4e2d\uff0c\u56fe\u8868\u4f1a\u81ea\u52a8\u663e\u793a\uff0c\u56e0\u6b64\u4e0d\u9700\u8981\u663e\u5f0f\u5730\u8c03\u7528plt.show()<\/code>\u65b9\u6cd5\uff1a<\/p>\n<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5b8c\u6574\u7684\u4ee3\u7801\u5982\u4e0b\uff1a<\/p>\n<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
import seaborn as sns<\/p>\n
import pandas as pd<\/p>\n
%matplotlib inline  # \u4f7f\u56fe\u8868\u5185\u5d4c\u5728Notebook\u4e2d<\/p>\n
\u6570\u636e<\/strong><\/h2>\n
data = pd.DataFrame({<\/p>\n
    'categories': ['A', 'B', 'C', 'D', 'E'],<\/p>\n
    'values': [10, 15, 7, 12, 9]<\/p>\n
})<\/p>\n
\u521b\u5efa\u56fe\u8868<\/strong><\/h2>\n
plt.figure(figsize=(10, 6))  # \u8bbe\u7f6e\u56fe\u8868\u7684\u5927\u5c0f<\/p>\n
sns.barplot(x='values', y='categories', data=data, color='skyblue', orient='h')  # \u521b\u5efa\u6a2a\u5411\u6761\u5f62\u56fe<\/p>\n
\u53cd\u8f6cY\u8f74<\/strong><\/h2>\n
plt.gca().invert_yaxis()<\/p>\n
\u6dfb\u52a0\u6807\u7b7e\u548c\u6807\u9898<\/strong><\/h2>\n
plt.xlabel('Values')<\/p>\n
plt.ylabel('Categories')<\/p>\n
plt.title('Downward Bar Chart Example in Jupyter Notebook')<\/p>\n
\u5c55\u793a\u56fe\u8868<\/strong><\/h2>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u56db\u3001\u5c06\u5411\u4e0b\u7684\u67f1\u72b6\u56fe\u4fdd\u5b58\u4e3a\u56fe\u50cf\u6587\u4ef6<\/h3>\n<\/p>\n
\u5728\u5b8c\u6210\u5411\u4e0b\u7684\u67f1\u72b6\u56fe\u7ed8\u5236\u540e\uff0c\u53ef\u80fd\u9700\u8981\u5c06\u56fe\u8868\u4fdd\u5b58\u4e3a\u56fe\u50cf\u6587\u4ef6\uff0c\u4ee5\u4fbf\u5728\u62a5\u544a\u6216\u5176\u4ed6\u6587\u6863\u4e2d\u4f7f\u7528\u3002\u4f7f\u7528Matplotlib\u53ef\u4ee5\u8f7b\u677e\u5730\u5c06\u56fe\u8868\u4fdd\u5b58\u4e3a\u591a\u79cd\u683c\u5f0f\u7684\u56fe\u50cf\u6587\u4ef6\uff0c\u5982PNG\u3001JPEG\u3001SVG\u7b49\u3002<\/p>\n<\/p>\n
\u4e00\u3001\u521b\u5efa\u5e76\u7ed8\u5236\u56fe\u8868<\/p>\n<\/p>\n
\u9996\u5148\uff0c\u6309\u7167\u524d\u9762\u7684\u6b65\u9aa4\u521b\u5efa\u5e76\u7ed8\u5236\u5411\u4e0b\u7684\u67f1\u72b6\u56fe\uff1a<\/p>\n<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
import seaborn as sns<\/p>\n
import pandas as pd<\/p>\n
\u6570\u636e<\/strong><\/h2>\n
data = pd.DataFrame({<\/p>\n
    'categories': ['A', 'B', 'C', 'D', 'E'],<\/p>\n
    'values': [10, 15, 7, 12, 9]<\/p>\n
})<\/p>\n
\u521b\u5efa\u56fe\u8868<\/strong><\/h2>\n
plt.figure(figsize=(10, 6))  # \u8bbe\u7f6e\u56fe\u8868\u7684\u5927\u5c0f<\/p>\n
sns.barplot(x='values', y='categories', data=data, color='skyblue', orient='h')  # \u521b\u5efa\u6a2a\u5411\u6761\u5f62\u56fe<\/p>\n
\u53cd\u8f6cY\u8f74<\/strong><\/h2>\n
plt.gca().invert_yaxis()<\/p>\n
\u6dfb\u52a0\u6807\u7b7e\u548c\u6807\u9898<\/strong><\/h2>\n
plt.xlabel('Values')<\/p>\n
plt.ylabel('Categories')<\/p>\n
plt.title('Downward Bar Chart Example')<\/p>\n
\u5c55\u793a\u56fe\u8868<\/strong><\/h2>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e8c\u3001\u4fdd\u5b58\u56fe\u8868\u4e3a\u56fe\u50cf\u6587\u4ef6<\/p>\n<\/p>\n
\u4f7f\u7528Matplotlib\u7684savefig<\/code>\u65b9\u6cd5\uff0c\u53ef\u4ee5\u5c06\u56fe\u8868\u4fdd\u5b58\u4e3a\u56fe\u50cf\u6587\u4ef6\u3002\u53ef\u4ee5\u6307\u5b9a\u6587\u4ef6\u540d\u548c\u6587\u4ef6\u683c\u5f0f\uff1a<\/p>\n<\/p>\n
plt.savefig('downward_bar_chart.png', format='png', dpi=300)  # \u4fdd\u5b58\u4e3aPNG\u683c\u5f0f\uff0c\u5206\u8fa8\u7387\u4e3a300 DPI<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e09\u3001\u5b8c\u6574\u4ee3\u7801\u793a\u4f8b<\/p>\n<\/p>\n
\u4ee5\u4e0b\u662f\u5b8c\u6574\u7684\u4ee3\u7801\u793a\u4f8b\uff0c\u5305\u62ec\u56fe\u8868\u7ed8\u5236\u548c\u4fdd\u5b58\u56fe\u50cf\u6587\u4ef6\u7684\u6b65\u9aa4\uff1a<\/p>\n<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
import seaborn as sns<\/p>\n
import pandas as pd<\/p>\n
\u6570\u636e<\/strong><\/h2>\n
data = pd.DataFrame({<\/p>\n
    'categories': ['A', 'B', 'C', 'D', 'E'],<\/p>\n
    'values': [10, 15, 7, 12, 9]<\/p>\n
})<\/p>\n
\u521b\u5efa\u56fe\u8868<\/strong><\/h2>\n
plt.figure(figsize=(10, 6))  # \u8bbe\u7f6e\u56fe\u8868\u7684\u5927\u5c0f<\/p>\n
sns.barplot(x='values', y='categories', data=data, color='skyblue', orient='h')  # \u521b\u5efa\u6a2a\u5411\u6761\u5f62\u56fe<\/p>\n
\u53cd\u8f6cY\u8f74<\/strong><\/h2>\n
plt.gca().invert_yaxis()<\/p>\n
\u6dfb\u52a0\u6807\u7b7e\u548c\u6807\u9898<\/strong><\/h2>\n
plt.xlabel('Values')<\/p>\n
plt.ylabel('Categories')<\/p>\n
plt.title('Downward Bar Chart Example')<\/p>\n
\u5c55\u793a\u56fe\u8868<\/strong><\/h2>\n
plt.show()<\/p>\n
\u4fdd\u5b58\u56fe\u8868\u4e3a\u56fe\u50cf\u6587\u4ef6<\/strong><\/h2>\n
plt.savefig('downward_bar_chart.png', format='png', dpi=300)  # \u4fdd\u5b58\u4e3aPNG\u683c\u5f0f\uff0c\u5206\u8fa8\u7387\u4e3a300 DPI<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u901a\u8fc7\u4e0a\u8ff0\u6b65\u9aa4\uff0c\u60a8\u53ef\u4ee5\u4f7f\u7528Python\u548cMatplotlib\u5e93\u8f7b\u677e\u5730\u521b\u5efa\u5411\u4e0b\u7684\u67f1\u72b6\u56fe\uff0c\u5e76\u5c06\u5176\u4fdd\u5b58\u4e3a\u56fe\u50cf\u6587\u4ef6\u3002\u65e0\u8bba\u662f\u5728\u6570\u636e\u5206\u6790\u3001\u62a5\u544a\u5236\u4f5c\u8fd8\u662f\u6570\u636e\u53ef\u89c6\u5316\u9879\u76ee\u4e2d\uff0c\u8fd9\u4e9b\u6280\u80fd\u90fd\u975e\u5e38\u5b9e\u7528\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
 \u5982\u4f55\u4f7f\u7528Python\u7ed8\u5236\u5411\u4e0b\u7684\u67f1\u72b6\u56fe\uff1f<\/strong>
\u5728Python\u4e2d\uff0c\u4f7f\u7528Matplotlib\u5e93\u53ef\u4ee5\u8f7b\u677e\u7ed8\u5236\u5411\u4e0b\u7684\u67f1\u72b6\u56fe\u3002\u60a8\u53ef\u4ee5\u901a\u8fc7\u8bbe\u7f6e\u67f1\u72b6\u56fe\u7684\u8d77\u59cb\u4f4d\u7f6e\u4e3a\u8d1f\u503c\u6765\u5b9e\u73b0\u8fd9\u4e00\u6548\u679c\u3002\u5177\u4f53\u6b65\u9aa4\u5305\u62ec\u5bfc\u5165Matplotlib\u5e93\uff0c\u521b\u5efa\u6570\u636e\uff0c\u8bbe\u7f6e\u67f1\u5b50\u7684\u9ad8\u5ea6\u4e3a\u8d1f\u503c\uff0c\u5e76\u7528bar<\/code>\u51fd\u6570\u7ed8\u5236\u56fe\u5f62\u3002<\/p>\n
\u5411\u4e0b\u67f1\u72b6\u56fe\u4e0e\u666e\u901a\u67f1\u72b6\u56fe\u6709\u4ec0\u4e48\u4e0d\u540c\u4e4b\u5904\uff1f<\/strong>
\u5411\u4e0b\u67f1\u72b6\u56fe\u7684\u67f1\u5b50\u662f\u4ece\u4e00\u4e2a\u57fa\u51c6\u7ebf\u5411\u4e0b\u5ef6\u4f38\uff0c\u800c\u666e\u901a\u7684\u67f1\u72b6\u56fe\u5219\u662f\u5411\u4e0a\u5ef6\u4f38\u3002\u901a\u8fc7\u5411\u4e0b\u7684\u67f1\u72b6\u56fe\uff0c\u60a8\u53ef\u4ee5\u66f4\u76f4\u89c2\u5730\u663e\u793a\u8d1f\u503c\u6570\u636e\u6216\u8d8b\u52bf\uff0c\u5c24\u5176\u5728\u6bd4\u8f83\u4e0d\u540c\u7c7b\u522b\u7684\u8d1f\u503c\u65f6\u975e\u5e38\u6709\u6548\u3002<\/p>\n
\u53ef\u4ee5\u4f7f\u7528\u54ea\u4e9bPython\u5e93\u6765\u5b9e\u73b0\u5411\u4e0b\u7684\u67f1\u72b6\u56fe\uff1f<\/strong>
\u9664\u4e86Matplotlib\uff0c\u60a8\u8fd8\u53ef\u4ee5\u4f7f\u7528Seaborn\u548cPlotly\u7b49\u5e93\u6765\u7ed8\u5236\u5411\u4e0b\u7684\u67f1\u72b6\u56fe\u3002Seaborn\u5728\u7f8e\u89c2\u6027\u4e0a\u6709\u5f88\u597d\u7684\u8868\u73b0\uff0c\u800cPlotly\u5219\u63d0\u4f9b\u4e86\u4ea4\u4e92\u5f0f\u7684\u56fe\u8868\u5c55\u793a\u3002\u9009\u62e9\u5408\u9002\u7684\u5e93\u53ef\u4ee5\u4f7f\u6570\u636e\u53ef\u89c6\u5316\u66f4\u5177\u5438\u5f15\u529b\u548c\u6613\u8bfb\u6027\u3002<\/p>\n
\u5982\u4f55\u81ea\u5b9a\u4e49\u5411\u4e0b\u67f1\u72b6\u56fe\u7684\u5916\u89c2\u548c\u6837\u5f0f\uff1f<\/strong>
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