{"id":1186917,"date":"2025-01-15T19:58:43","date_gmt":"2025-01-15T11:58:43","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1186917.html"},"modified":"2025-01-15T19:58:45","modified_gmt":"2025-01-15T11:58:45","slug":"%e5%a6%82%e4%bd%95%e7%94%a8python%e7%bb%9f%e8%ae%a1%e8%a1%a8%e6%a0%bc","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1186917.html","title":{"rendered":"\u5982\u4f55\u7528python\u7edf\u8ba1\u8868\u683c"},"content":{"rendered":"

\"\u5982\u4f55\u7528python\u7edf\u8ba1\u8868\u683c\"<\/p>\n

\u4f7f\u7528Python\u7edf\u8ba1\u8868\u683c\u7684\u65b9\u6cd5\u6709\uff1a\u4f7f\u7528Pandas\u5e93\u3001\u4f7f\u7528Numpy\u5e93\u3001\u4f7f\u7528\u7edf\u8ba1\u51fd\u6570\u3002<\/strong>\u5176\u4e2d\uff0c\u4f7f\u7528Pandas\u5e93<\/strong>\u662f\u6700\u5e38\u89c1\u4e14\u529f\u80fd\u5f3a\u5927\u7684\u65b9\u6cd5\u3002Pandas\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u6570\u636e\u5904\u7406\u548c\u5206\u6790\u5de5\u5177\uff0c\u80fd\u591f\u8f7b\u677e\u8bfb\u53d6\u3001\u5904\u7406\u548c\u7edf\u8ba1\u8868\u683c\u6570\u636e\u3002\u4e0b\u9762\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528Pandas\u5e93\u8fdb\u884c\u8868\u683c\u7edf\u8ba1\u3002<\/p>\n<\/p>\n

\u4e00\u3001PANDAS\u5e93\u7684\u4ecb\u7ecd\u4e0e\u5b89\u88c5<\/h3>\n<\/p>\n

Pandas\u662f\u4e00\u4e2a\u5f00\u6e90\u6570\u636e\u5206\u6790\u548c\u64cd\u4f5c\u5e93\uff0c\u63d0\u4f9b\u4e86\u9ad8\u6548\u7684\u3001\u4fbf\u6377\u7684\u6570\u636e\u7ed3\u6784\u548c\u6570\u636e\u5206\u6790\u5de5\u5177\u3002Pandas\u7684\u6838\u5fc3\u6570\u636e\u7ed3\u6784\u5305\u62ecSeries\uff08\u5e8f\u5217\uff09\u548cDataFrame\uff08\u6570\u636e\u6846\uff09\uff0c\u5b83\u4eec\u53ef\u4ee5\u8f7b\u677e\u5904\u7406\u4e00\u7ef4\u548c\u4e8c\u7ef4\u7684\u6570\u636e\u3002Pandas\u8fd8\u652f\u6301\u8bfb\u53d6\u548c\u5199\u5165\u591a\u79cd\u6587\u4ef6\u683c\u5f0f\uff0c\u5982CSV\u3001Excel\u3001SQL\u6570\u636e\u5e93\u7b49\u3002<\/p>\n<\/p>\n

\u5b89\u88c5Pandas<\/h4>\n<\/p>\n

\u5728\u4f7f\u7528Pandas\u4e4b\u524d\uff0c\u9700\u8981\u786e\u4fdd\u5df2\u5b89\u88c5\u8be5\u5e93\u3002\u53ef\u4ee5\u901a\u8fc7\u4ee5\u4e0b\u547d\u4ee4\u5b89\u88c5Pandas\uff1a<\/p>\n<\/p>\n

pip install pandas<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e8c\u3001\u8bfb\u53d6\u8868\u683c\u6570\u636e<\/h3>\n<\/p>\n
Pandas\u53ef\u4ee5\u8bfb\u53d6\u591a\u79cd\u7c7b\u578b\u7684\u8868\u683c\u6587\u4ef6\uff0c\u5982CSV\u3001Excel\u3001SQL\u6570\u636e\u5e93\u7b49\u3002\u4ee5\u4e0b\u662f\u4e00\u4e9b\u5e38\u89c1\u7684\u8bfb\u53d6\u65b9\u6cd5\uff1a<\/p>\n<\/p>\n
\u8bfb\u53d6CSV\u6587\u4ef6<\/h4>\n<\/p>\n
import pandas as pd<\/p>\n
\u8bfb\u53d6CSV\u6587\u4ef6<\/strong><\/h2>\n
df = pd.read_csv('data.csv')<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8bfb\u53d6Excel\u6587\u4ef6<\/h4>\n<\/p>\n
import pandas as pd<\/p>\n
\u8bfb\u53d6Excel\u6587\u4ef6<\/strong><\/h2>\n
df = pd.read_excel('data.xlsx', sheet_name='Sheet1')<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8bfb\u53d6SQL\u6570\u636e\u5e93<\/h4>\n<\/p>\n
import pandas as pd<\/p>\n
import sqlite3<\/p>\n
\u8fde\u63a5\u5230SQL\u6570\u636e\u5e93<\/strong><\/h2>\n
conn = sqlite3.connect('database.db')<\/p>\n
\u8bfb\u53d6SQL\u6570\u636e\u8868<\/strong><\/h2>\n
df = pd.read_sql_query('SELECT * FROM table_name', conn)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e09\u3001\u6570\u636e\u9884\u5904\u7406<\/h3>\n<\/p>\n
\u5728\u8fdb\u884c\u7edf\u8ba1\u5206\u6790\u4e4b\u524d\uff0c\u901a\u5e38\u9700\u8981\u5bf9\u6570\u636e\u8fdb\u884c\u9884\u5904\u7406\uff0c\u5305\u62ec\u5904\u7406\u7f3a\u5931\u503c\u3001\u6570\u636e\u7c7b\u578b\u8f6c\u6362\u3001\u6570\u636e\u6e05\u6d17\u7b49\u3002<\/p>\n<\/p>\n
\u67e5\u770b\u6570\u636e\u57fa\u672c\u4fe1\u606f<\/h4>\n<\/p>\n
# \u67e5\u770b\u6570\u636e\u6846\u7684\u524d5\u884c<\/p>\n
print(df.head())<\/p>\n
\u67e5\u770b\u6570\u636e\u6846\u7684\u57fa\u672c\u4fe1\u606f<\/strong><\/h2>\n
print(df.info())<\/p>\n
\u67e5\u770b\u6570\u636e\u6846\u7684\u63cf\u8ff0\u6027\u7edf\u8ba1\u4fe1\u606f<\/strong><\/h2>\n
print(df.describe())<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5904\u7406\u7f3a\u5931\u503c<\/h4>\n<\/p>\n
# \u67e5\u770b\u6bcf\u5217\u7684\u7f3a\u5931\u503c\u6570\u91cf<\/p>\n
print(df.isnull().sum())<\/p>\n
\u5220\u9664\u5305\u542b\u7f3a\u5931\u503c\u7684\u884c<\/strong><\/h2>\n
df.dropna(inplace=True)<\/p>\n
\u4f7f\u7528\u7279\u5b9a\u503c\u586b\u5145\u7f3a\u5931\u503c<\/strong><\/h2>\n
df.fillna(value={'column_name': 0}, inplace=True)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u6570\u636e\u7c7b\u578b\u8f6c\u6362<\/h4>\n<\/p>\n
# \u5c06\u67d0\u5217\u7684\u6570\u636e\u7c7b\u578b\u8f6c\u6362\u4e3a\u6574\u6570<\/p>\n
df['column_name'] = df['column_name'].astype(int)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u56db\u3001\u6570\u636e\u7edf\u8ba1\u4e0e\u5206\u6790<\/h3>\n<\/p>\n
Pandas\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u7edf\u8ba1\u51fd\u6570\u548c\u65b9\u6cd5\uff0c\u53ef\u4ee5\u5bf9\u8868\u683c\u6570\u636e\u8fdb\u884c\u7edf\u8ba1\u5206\u6790\uff0c\u5982\u8ba1\u7b97\u5747\u503c\u3001\u6807\u51c6\u5dee\u3001\u9891\u7387\u5206\u5e03\u7b49\u3002<\/p>\n<\/p>\n
\u8ba1\u7b97\u57fa\u672c\u7edf\u8ba1\u91cf<\/h4>\n<\/p>\n
# \u8ba1\u7b97\u67d0\u5217\u7684\u5747\u503c<\/p>\n
mean_value = df['column_name'].mean()<\/p>\n
\u8ba1\u7b97\u67d0\u5217\u7684\u6807\u51c6\u5dee<\/strong><\/h2>\n
std_value = df['column_name'].std()<\/p>\n
\u8ba1\u7b97\u67d0\u5217\u7684\u4e2d\u4f4d\u6570<\/strong><\/h2>\n
median_value = df['column_name'].median()<\/p>\n
\u8ba1\u7b97\u67d0\u5217\u7684\u6700\u5927\u503c\u548c\u6700\u5c0f\u503c<\/strong><\/h2>\n
max_value = df['column_name'].max()<\/p>\n
min_value = df['column_name'].min()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u9891\u7387\u5206\u5e03\u7edf\u8ba1<\/h4>\n<\/p>\n
# \u8ba1\u7b97\u67d0\u5217\u4e2d\u6bcf\u4e2a\u503c\u7684\u9891\u7387\u5206\u5e03<\/p>\n
value_counts = df['column_name'].value_counts()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5206\u7ec4\u7edf\u8ba1<\/h4>\n<\/p>\n
# \u6309\u67d0\u5217\u5206\u7ec4\u5e76\u8ba1\u7b97\u5747\u503c<\/p>\n
grouped_mean = df.groupby('group_column')['value_column'].mean()<\/p>\n
\u6309\u67d0\u5217\u5206\u7ec4\u5e76\u8ba1\u7b97\u591a\u4e2a\u7edf\u8ba1\u91cf<\/strong><\/h2>\n
grouped_stats = df.groupby('group_column')['value_column'].agg(['mean', 'std', 'min', 'max'])<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e94\u3001\u6570\u636e\u53ef\u89c6\u5316<\/h3>\n<\/p>\n
\u6570\u636e\u53ef\u89c6\u5316\u662f\u6570\u636e\u5206\u6790\u7684\u91cd\u8981\u73af\u8282\uff0c\u53ef\u4ee5\u5e2e\u52a9\u6211\u4eec\u66f4\u76f4\u89c2\u5730\u7406\u89e3\u6570\u636e\u3002Pandas\u96c6\u6210\u4e86Matplotlib\u5e93\uff0c\u53ef\u4ee5\u65b9\u4fbf\u5730\u7ed8\u5236\u5404\u79cd\u56fe\u8868\u3002<\/p>\n<\/p>\n
\u5b89\u88c5Matplotlib<\/h4>\n<\/p>\n
\u5728\u4f7f\u7528Matplotlib\u4e4b\u524d\uff0c\u9700\u8981\u786e\u4fdd\u5df2\u5b89\u88c5\u8be5\u5e93\u3002\u53ef\u4ee5\u901a\u8fc7\u4ee5\u4e0b\u547d\u4ee4\u5b89\u88c5Matplotlib\uff1a<\/p>\n<\/p>\n
pip install matplotlib<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u7ed8\u5236\u57fa\u672c\u56fe\u8868<\/h4>\n<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
\u7ed8\u5236\u6298\u7ebf\u56fe<\/strong><\/h2>\n
df['column_name'].plot(kind='line')<\/p>\n
plt.show()<\/p>\n
\u7ed8\u5236\u67f1\u72b6\u56fe<\/strong><\/h2>\n
df['column_name'].plot(kind='bar')<\/p>\n
plt.show()<\/p>\n
\u7ed8\u5236\u76f4\u65b9\u56fe<\/strong><\/h2>\n
df['column_name'].plot(kind='hist')<\/p>\n
plt.show()<\/p>\n
\u7ed8\u5236\u6563\u70b9\u56fe<\/strong><\/h2>\n
df.plot(kind='scatter', x='column_x', y='column_y')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u81ea\u5b9a\u4e49\u56fe\u8868\u6837\u5f0f<\/h4>\n<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
\u8bbe\u7f6e\u56fe\u8868\u5927\u5c0f<\/strong><\/h2>\n
plt.figure(figsize=(10, 6))<\/p>\n
\u7ed8\u5236\u6298\u7ebf\u56fe\u5e76\u8bbe\u7f6e\u6807\u9898\u548c\u6807\u7b7e<\/strong><\/h2>\n
plt.plot(df['column_name'])<\/p>\n
plt.title('Line Chart')<\/p>\n
plt.xlabel('X-axis Label')<\/p>\n
plt.ylabel('Y-axis Label')<\/p>\n
\u663e\u793a\u56fe\u8868<\/strong><\/h2>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u516d\u3001\u9ad8\u7ea7\u6570\u636e\u5206\u6790<\/h3>\n<\/p>\n
Pandas\u8fd8\u652f\u6301\u4e00\u4e9b\u9ad8\u7ea7\u6570\u636e\u5206\u6790\u64cd\u4f5c\uff0c\u5982\u900f\u89c6\u8868\u3001\u591a\u7d22\u5f15\u3001\u591a\u91cd\u5408\u5e76\u7b49\u3002<\/p>\n<\/p>\n
\u521b\u5efa\u900f\u89c6\u8868<\/h4>\n<\/p>\n
\u900f\u89c6\u8868\u662f\u4e00\u79cd\u6570\u636e\u6c47\u603b\u5de5\u5177\uff0c\u53ef\u4ee5\u6309\u6307\u5b9a\u7ef4\u5ea6\u8fdb\u884c\u6570\u636e\u805a\u5408\u548c\u8ba1\u7b97\u3002<\/p>\n<\/p>\n
# \u521b\u5efa\u900f\u89c6\u8868<\/p>\n
pivot_table = pd.pivot_table(df, values='value_column', index='index_column', columns='columns_column', aggfunc='mean')<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u591a\u7d22\u5f15\u64cd\u4f5c<\/h4>\n<\/p>\n
Pandas\u652f\u6301\u591a\u7d22\u5f15\uff08MultiIndex\uff09\uff0c\u53ef\u4ee5\u5bf9\u6570\u636e\u8fdb\u884c\u591a\u7ef4\u5ea6\u7d22\u5f15\u548c\u64cd\u4f5c\u3002<\/p>\n<\/p>\n
# \u8bbe\u7f6e\u591a\u7d22\u5f15<\/p>\n
df.set_index(['index_column1', 'index_column2'], inplace=True)<\/p>\n
\u901a\u8fc7\u591a\u7d22\u5f15\u8bbf\u95ee\u6570\u636e<\/strong><\/h2>\n
data = df.loc[('index_value1', 'index_value2')]<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u591a\u91cd\u5408\u5e76<\/h4>\n<\/p>\n
Pandas\u652f\u6301\u5bf9\u591a\u4e2a\u6570\u636e\u6846\u8fdb\u884c\u5408\u5e76\u64cd\u4f5c\uff0c\u53ef\u4ee5\u6309\u6307\u5b9a\u5217\u8fdb\u884c\u8fde\u63a5\uff08join\uff09\u548c\u5408\u5e76\uff08merge\uff09\u3002<\/p>\n<\/p>\n
# \u8bfb\u53d6\u4e24\u4e2a\u6570\u636e\u6846<\/p>\n
df1 = pd.read_csv('data1.csv')<\/p>\n
df2 = pd.read_csv('data2.csv')<\/p>\n
\u6309\u6307\u5b9a\u5217\u8fdb\u884c\u5408\u5e76<\/strong><\/h2>\n
merged_df = pd.merge(df1, df2, on='common_column', how='inner')<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e03\u3001\u5b9e\u9645\u6848\u4f8b\u5206\u6790<\/h3>\n<\/p>\n
\u4e0b\u9762\u901a\u8fc7\u4e00\u4e2a\u5b9e\u9645\u6848\u4f8b\uff0c\u6f14\u793a\u5982\u4f55\u7528Python\u548cPandas\u8fdb\u884c\u8868\u683c\u6570\u636e\u7edf\u8ba1\u4e0e\u5206\u6790\u3002<\/p>\n<\/p>\n
\u5047\u8bbe\u6211\u4eec\u6709\u4e00\u4e2a\u5305\u542b\u9500\u552e\u6570\u636e\u7684CSV\u6587\u4ef6\uff0c\u6587\u4ef6\u5185\u5bb9\u5982\u4e0b\uff1a<\/p>\n<\/p>\n
Date,Product,Sales,Quantity<\/p>\n
2023-01-01,Product A,100,5<\/p>\n
2023-01-01,Product B,150,3<\/p>\n
2023-01-02,Product A,200,8<\/p>\n
2023-01-02,Product C,300,10<\/p>\n
2023-01-03,Product B,250,7<\/p>\n
2023-01-03,Product C,350,12<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8bfb\u53d6\u6570\u636e<\/h4>\n<\/p>\n
import pandas as pd<\/p>\n
\u8bfb\u53d6CSV\u6587\u4ef6<\/strong><\/h2>\n
df = pd.read_csv('sales_data.csv')<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u67e5\u770b\u6570\u636e\u57fa\u672c\u4fe1\u606f<\/h4>\n<\/p>\n
# \u67e5\u770b\u6570\u636e\u6846\u7684\u524d5\u884c<\/p>\n
print(df.head())<\/p>\n
\u67e5\u770b\u6570\u636e\u6846\u7684\u57fa\u672c\u4fe1\u606f<\/strong><\/h2>\n
print(df.info())<\/p>\n
\u67e5\u770b\u6570\u636e\u6846\u7684\u63cf\u8ff0\u6027\u7edf\u8ba1\u4fe1\u606f<\/strong><\/h2>\n
print(df.describe())<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5904\u7406\u7f3a\u5931\u503c<\/h4>\n<\/p>\n
\u5047\u8bbe\u6211\u4eec\u7684\u6570\u636e\u6ca1\u6709\u7f3a\u5931\u503c\uff0c\u56e0\u6b64\u53ef\u4ee5\u8df3\u8fc7\u8fd9\u4e00\u6b65\u3002\u5982\u679c\u6709\u7f3a\u5931\u503c\uff0c\u53ef\u4ee5\u4f7f\u7528\u524d\u9762\u4ecb\u7ecd\u7684\u65b9\u6cd5\u8fdb\u884c\u5904\u7406\u3002<\/p>\n<\/p>\n
\u8ba1\u7b97\u57fa\u672c\u7edf\u8ba1\u91cf<\/h4>\n<\/p>\n
# \u8ba1\u7b97\u603b\u9500\u552e\u989d<\/p>\n
total_sales = df['Sales'].sum()<\/p>\n
print(f'Total Sales: {total_sales}')<\/p>\n
\u8ba1\u7b97\u5e73\u5747\u9500\u552e\u989d<\/strong><\/h2>\n
average_sales = df['Sales'].mean()<\/p>\n
print(f'Average Sales: {average_sales}')<\/p>\n
\u8ba1\u7b97\u9500\u91cf\u6700\u5927\u503c\u548c\u6700\u5c0f\u503c<\/strong><\/h2>\n
max_sales = df['Sales'].max()<\/p>\n
min_sales = df['Sales'].min()<\/p>\n
print(f'Max Sales: {max_sales}, Min Sales: {min_sales}')<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u9891\u7387\u5206\u5e03\u7edf\u8ba1<\/h4>\n<\/p>\n
# \u8ba1\u7b97\u6bcf\u79cd\u4ea7\u54c1\u7684\u9500\u552e\u9891\u7387<\/p>\n
product_sales_counts = df['Product'].value_counts()<\/p>\n
print(product_sales_counts)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5206\u7ec4\u7edf\u8ba1<\/h4>\n<\/p>\n
# \u6309\u4ea7\u54c1\u5206\u7ec4\u5e76\u8ba1\u7b97\u603b\u9500\u552e\u989d<\/p>\n
grouped_sales = df.groupby('Product')['Sales'].sum()<\/p>\n
print(grouped_sales)<\/p>\n
\u6309\u65e5\u671f\u5206\u7ec4\u5e76\u8ba1\u7b97\u603b\u9500\u552e\u989d<\/strong><\/h2>\n
grouped_sales_by_date = df.groupby('Date')['Sales'].sum()<\/p>\n
print(grouped_sales_by_date)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u6570\u636e\u53ef\u89c6\u5316<\/h4>\n<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
\u7ed8\u5236\u6309\u4ea7\u54c1\u5206\u7ec4\u7684\u603b\u9500\u552e\u989d\u67f1\u72b6\u56fe<\/strong><\/h2>\n
grouped_sales.plot(kind='bar')<\/p>\n
plt.title('Total Sales by Product')<\/p>\n
plt.xlabel('Product')<\/p>\n
plt.ylabel('Total Sales')<\/p>\n
plt.show()<\/p>\n
\u7ed8\u5236\u6309\u65e5\u671f\u5206\u7ec4\u7684\u603b\u9500\u552e\u989d\u6298\u7ebf\u56fe<\/strong><\/h2>\n
grouped_sales_by_date.plot(kind='line')<\/p>\n
plt.title('Total Sales by Date')<\/p>\n
plt.xlabel('Date')<\/p>\n
plt.ylabel('Total Sales')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u521b\u5efa\u900f\u89c6\u8868<\/h4>\n<\/p>\n
# \u521b\u5efa\u6309\u65e5\u671f\u548c\u4ea7\u54c1\u7684\u9500\u552e\u900f\u89c6\u8868<\/p>\n
sales_pivot_table = pd.pivot_table(df, values='Sales', index='Date', columns='Product', aggfunc='sum')<\/p>\n
print(sales_pivot_table)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u901a\u8fc7\u4e0a\u8ff0\u6b65\u9aa4\uff0c\u6211\u4eec\u5b8c\u6210\u4e86\u5bf9\u9500\u552e\u6570\u636e\u7684\u8bfb\u53d6\u3001\u9884\u5904\u7406\u3001\u7edf\u8ba1\u5206\u6790\u548c\u53ef\u89c6\u5316\u3002Pandas\u5e93\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u6570\u636e\u64cd\u4f5c\u548c\u5206\u6790\u5de5\u5177\uff0c\u4f7f\u5f97\u6570\u636e\u5206\u6790\u8fc7\u7a0b\u66f4\u52a0\u9ad8\u6548\u548c\u4fbf\u6377\u3002<\/p>\n<\/p>\n
\u516b\u3001\u603b\u7ed3<\/h3>\n<\/p>\n
\u4f7f\u7528Python\u7edf\u8ba1\u8868\u683c\u6570\u636e\u7684\u65b9\u6cd5\u975e\u5e38\u591a\u6837\u5316\u3002Pandas\u5e93\u662f\u5176\u4e2d\u6700\u5e38\u7528\u7684\u5de5\u5177\uff0c\u63d0\u4f9b\u4e86\u5f3a\u5927\u7684\u6570\u636e\u5904\u7406\u548c\u5206\u6790\u529f\u80fd\u3002\u901a\u8fc7Pandas\uff0c\u6211\u4eec\u53ef\u4ee5\u8f7b\u677e\u5730\u8bfb\u53d6\u5404\u79cd\u8868\u683c\u6587\u4ef6\uff0c\u5bf9\u6570\u636e\u8fdb\u884c\u9884\u5904\u7406\uff0c\u8ba1\u7b97\u5404\u79cd\u7edf\u8ba1\u91cf\uff0c\u8fdb\u884c\u5206\u7ec4\u7edf\u8ba1\u548c\u6570\u636e\u53ef\u89c6\u5316\u3002\u6b64\u5916\uff0cPandas\u8fd8\u652f\u6301\u9ad8\u7ea7\u6570\u636e\u5206\u6790\u64cd\u4f5c\uff0c\u5982\u900f\u89c6\u8868\u3001\u591a\u7d22\u5f15\u548c\u591a\u91cd\u5408\u5e76\uff0c\u4f7f\u5f97\u6570\u636e\u5206\u6790\u8fc7\u7a0b\u66f4\u52a0\u7075\u6d3b\u548c\u5f3a\u5927\u3002<\/p>\n<\/p>\n
\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0c\u6839\u636e\u5177\u4f53\u7684\u9700\u6c42\u548c\u6570\u636e\u7279\u70b9\uff0c\u9009\u62e9\u5408\u9002\u7684\u5de5\u5177\u548c\u65b9\u6cd5\u8fdb\u884c\u6570\u636e\u7edf\u8ba1\u548c\u5206\u6790\uff0c\u80fd\u591f\u5927\u5927\u63d0\u9ad8\u5de5\u4f5c\u6548\u7387\u548c\u5206\u6790\u51c6\u786e\u6027\u3002\u5e0c\u671b\u672c\u7bc7\u6587\u7ae0\u5bf9\u5982\u4f55\u7528Python\u7edf\u8ba1\u8868\u683c\u6570\u636e\u63d0\u4f9b\u4e86\u4e00\u4e9b\u6709\u7528\u7684\u6307\u5bfc\u548c\u53c2\u8003\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
 \u5982\u4f55\u4f7f\u7528Python\u8bfb\u53d6\u8868\u683c\u6570\u636e\uff1f<\/strong>
\u5728Python\u4e2d\uff0c\u8bfb\u53d6\u8868\u683c\u6570\u636e\u901a\u5e38\u53ef\u4ee5\u4f7f\u7528Pandas\u5e93\u3002\u901a\u8fc7pandas.read_csv()<\/code>\u53ef\u4ee5\u8f7b\u677e\u8bfb\u53d6CSV\u683c\u5f0f\u7684\u8868\u683c\uff0c\u800c\u5bf9\u4e8eExcel\u6587\u4ef6\uff0c\u53ef\u4ee5\u4f7f\u7528pandas.read_excel()<\/code>\u3002\u8fd9\u4e9b\u65b9\u6cd5\u53ef\u4ee5\u5c06\u8868\u683c\u6570\u636e\u52a0\u8f7d\u5230DataFrame\u4e2d\uff0c\u65b9\u4fbf\u540e\u7eed\u7684\u5206\u6790\u548c\u7edf\u8ba1\u3002<\/p>\n
Python\u4e2d\u6709\u54ea\u4e9b\u65b9\u6cd5\u53ef\u4ee5\u7edf\u8ba1\u8868\u683c\u4e2d\u7684\u6570\u636e\uff1f<\/strong>
\u5728Python\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528Pandas\u5e93\u8fdb\u884c\u6570\u636e\u7edf\u8ba1\u3002\u5e38\u7528\u7684\u65b9\u6cd5\u5305\u62ecDataFrame.describe()<\/code>\uff0c\u53ef\u4ee5\u83b7\u53d6\u6570\u636e\u7684\u57fa\u672c\u7edf\u8ba1\u4fe1\u606f\uff0c\u5982\u5747\u503c\u3001\u6807\u51c6\u5dee\u3001\u6700\u5c0f\u503c\u548c\u6700\u5927\u503c\u7b49\uff1b\u4f7f\u7528DataFrame.groupby()<\/code>\u53ef\u4ee5\u5bf9\u6570\u636e\u8fdb\u884c\u5206\u7ec4\u7edf\u8ba1\uff1b\u8fd8\u6709DataFrame.value_counts()<\/code>\u53ef\u4ee5\u7edf\u8ba1\u67d0\u5217\u4e2d\u5404\u4e2a\u503c\u7684\u51fa\u73b0\u9891\u7387\u3002\u8fd9\u4e9b\u529f\u80fd\u4f7f\u5f97\u6570\u636e\u5206\u6790\u53d8\u5f97\u76f4\u89c2\u800c\u9ad8\u6548\u3002<\/p>\n
\u5982\u4f55\u7528Python\u53ef\u89c6\u5316\u7edf\u8ba1\u7ed3\u679c\uff1f<\/strong>
\u53ef\u89c6\u5316\u662f\u6570\u636e\u5206\u6790\u7684\u91cd\u8981\u73af\u8282\u3002\u5728Python\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528Matplotlib\u548cSeaborn\u7b49\u5e93\u8fdb\u884c\u6570\u636e\u53ef\u89c6\u5316\u3002\u901a\u8fc7\u7ed8\u5236\u67f1\u72b6\u56fe\u3001\u6298\u7ebf\u56fe\u548c\u997c\u56fe\u7b49\uff0c\u53ef\u4ee5\u66f4\u6e05\u6670\u5730\u5c55\u793a\u7edf\u8ba1\u7ed3\u679c\u3002\u4f7f\u7528matplotlib.pyplot<\/code>\u4e2d\u7684plt.plot()<\/code>\u548cseaborn.barplot()<\/code>\u7b49\u51fd\u6570\uff0c\u53ef\u4ee5\u65b9\u4fbf\u5730\u5c06\u7edf\u8ba1\u6570\u636e\u8f6c\u6362\u4e3a\u56fe\u5f62\uff0c\u5e2e\u52a9\u7406\u89e3\u6570\u636e\u7684\u8d8b\u52bf\u548c\u5206\u5e03\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u4f7f\u7528Python\u7edf\u8ba1\u8868\u683c\u7684\u65b9\u6cd5\u6709\uff1a\u4f7f\u7528Pandas\u5e93\u3001\u4f7f\u7528Numpy\u5e93\u3001\u4f7f\u7528\u7edf\u8ba1\u51fd\u6570\u3002\u5176\u4e2d\uff0c\u4f7f\u7528Pandas\u5e93 […]","protected":false},"author":3,"featured_media":1186924,"comment_status":"closed","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[37],"tags":[],"acf":[],"_links":{"self":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1186917"}],"collection":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/comments?post=1186917"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1186917\/revisions"}],"predecessor-version":[{"id":1186925,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1186917\/revisions\/1186925"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1186924"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1186917"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1186917"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1186917"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}