pip install pandas<\/p>\n<\/code><\/pre>\n<\/p>\n
\u5b89\u88c5\u5b8c\u6210\u540e\uff0c\u53ef\u4ee5\u901a\u8fc7\u4ee5\u4e0b\u4ee3\u7801\u5bfc\u5165pandas\u5e93\uff1a<\/p>\n<\/p>\n
import pandas as pd<\/p>\n<\/code><\/pre>\n<\/p>\n
\u4e09\u3001PANDAS\u5e93\u7684\u57fa\u672c\u64cd\u4f5c<\/strong><\/p>\n<\/p>\n\u4ee5\u4e0b\u662f\u4e00\u4e9bpandas\u5e93\u7684\u57fa\u672c\u64cd\u4f5c\u793a\u4f8b\uff1a<\/p>\n<\/p>\n
\n- \u521b\u5efaSeries\u548cDataFrame<\/strong><\/li>\n<\/ol>\n
import pandas as pd<\/p>\n\u521b\u5efaSeries<\/strong><\/h2>\ns = pd.Series([1, 2, 3, 4, 5])<\/p>\n
print(s)<\/p>\n
\u521b\u5efaDataFrame<\/strong><\/h2>\ndata = {'Name': ['Tom', 'Jerry', 'Mike'], 'Age': [20, 21, 19]}<\/p>\n
df = pd.DataFrame(data)<\/p>\n
print(df)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\n- \u6570\u636e\u8bfb\u53d6\u4e0e\u5b58\u50a8<\/strong><\/li>\n<\/ol>\n
# \u8bfb\u53d6CSV\u6587\u4ef6<\/p>\ndf = pd.read_csv('data.csv')<\/p>\n
\u8bfb\u53d6Excel\u6587\u4ef6<\/strong><\/h2>\ndf = pd.read_excel('data.xlsx')<\/p>\n
\u5b58\u50a8\u4e3aCSV\u6587\u4ef6<\/strong><\/h2>\ndf.to_csv('output.csv', index=False)<\/p>\n
\u5b58\u50a8\u4e3aExcel\u6587\u4ef6<\/strong><\/h2>\ndf.to_excel('output.xlsx', index=False)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\n- \u6570\u636e\u6e05\u6d17<\/strong><\/li>\n<\/ol>\n
# \u5904\u7406\u7f3a\u5931\u503c<\/p>\ndf.fillna(0, inplace=True) # \u75280\u586b\u5145\u7f3a\u5931\u503c<\/p>\n
df.dropna(inplace=True) # \u5220\u9664\u5305\u542b\u7f3a\u5931\u503c\u7684\u884c<\/p>\n
\u5904\u7406\u91cd\u590d\u503c<\/strong><\/h2>\ndf.drop_duplicates(inplace=True) # \u5220\u9664\u91cd\u590d\u503c<\/p>\n
\u6570\u636e\u7c7b\u578b\u8f6c\u6362<\/strong><\/h2>\ndf['Age'] = df['Age'].astype(int) # \u5c06Age\u5217\u8f6c\u6362\u4e3a\u6574\u6570\u7c7b\u578b<\/p>\n
<\/code><\/pre>\n<\/p>\n
\n- \u6570\u636e\u5904\u7406<\/strong><\/li>\n<\/ol>\n
# \u6570\u636e\u7b5b\u9009<\/p>\nfiltered_df = df[df['Age'] > 20] # \u7b5b\u9009Age\u5927\u4e8e20\u7684\u884c<\/p>\n
\u6570\u636e\u6392\u5e8f<\/strong><\/h2>\nsorted_df = df.sort_values(by='Age', ascending=False) # \u6309Age\u964d\u5e8f\u6392\u5e8f<\/p>\n
\u6570\u636e\u5206\u7ec4\u4e0e\u805a\u5408<\/strong><\/h2>\ngrouped_df = df.groupby('Name').mean() # \u6309Name\u5206\u7ec4\u5e76\u8ba1\u7b97\u5747\u503c<\/p>\n
<\/code><\/pre>\n<\/p>\n
\n- \u6570\u636e\u5206\u6790<\/strong><\/li>\n<\/ol>\n
# \u7edf\u8ba1\u5206\u6790<\/p>\nmean_age = df['Age'].mean() # \u8ba1\u7b97Age\u5217\u7684\u5747\u503c<\/p>\n
max_age = df['Age'].max() # \u8ba1\u7b97Age\u5217\u7684\u6700\u5927\u503c<\/p>\n
\u65f6\u95f4\u5e8f\u5217\u5206\u6790<\/strong><\/h2>\ndf['Date'] = pd.to_datetime(df['Date'])<\/p>\n
df.set_index('Date', inplace=True)<\/p>\n
monthly_df = df.resample('M').mean() # \u6309\u6708\u91cd\u65b0\u91c7\u6837\u5e76\u8ba1\u7b97\u5747\u503c<\/p>\n
<\/code><\/pre>\n<\/p>\n
\n- \u6570\u636e\u53ef\u89c6\u5316<\/strong><\/li>\n<\/ol>\n
import matplotlib.pyplot as plt<\/p>\n\u7ed8\u5236\u6298\u7ebf\u56fe<\/strong><\/h2>\ndf['Age'].plot()<\/p>\n
plt.show()<\/p>\n
\u7ed8\u5236\u67f1\u72b6\u56fe<\/strong><\/h2>\ndf.plot(kind='bar', x='Name', y='Age')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u56db\u3001\u4f7f\u7528PANDAS\u5e93\u5bfc\u5165Excel\u6587\u4ef6<\/strong><\/p>\n<\/p>\n\u5bfc\u5165Excel\u6587\u4ef6\u662fpandas\u5e93\u7684\u4e00\u4e2a\u5e38\u89c1\u64cd\u4f5c\u3002\u4ee5\u4e0b\u662f\u4e00\u4e9b\u5bfc\u5165Excel\u6587\u4ef6\u7684\u793a\u4f8b\uff1a<\/p>\n<\/p>\n
import pandas as pd<\/p>\n\u8bfb\u53d6Excel\u6587\u4ef6\uff0c\u6307\u5b9a\u6587\u4ef6\u8def\u5f84<\/strong><\/h2>\ndf = pd.read_excel('data.xlsx')<\/p>\n
\u8bfb\u53d6\u6307\u5b9a\u5de5\u4f5c\u8868<\/strong><\/h2>\ndf = pd.read_excel('data.xlsx', sheet_name='Sheet1')<\/p>\n
\u8bfb\u53d6\u6307\u5b9a\u5217<\/strong><\/h2>\ndf = pd.read_excel('data.xlsx', usecols=['Name', 'Age'])<\/p>\n
\u5904\u7406\u7f3a\u5931\u503c<\/strong><\/h2>\ndf = pd.read_excel('data.xlsx', na_values=['NA', 'NaN'])<\/p>\n
\u67e5\u770b\u6570\u636e<\/strong><\/h2>\nprint(df.head())<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e94\u3001\u4f7f\u7528PANDAS\u5e93\u5bfc\u51faExcel\u6587\u4ef6<\/strong><\/p>\n<\/p>\n\u5bfc\u51faExcel\u6587\u4ef6\u662fpandas\u5e93\u7684\u53e6\u4e00\u4e2a\u5e38\u89c1\u64cd\u4f5c\u3002\u4ee5\u4e0b\u662f\u4e00\u4e9b\u5bfc\u51faExcel\u6587\u4ef6\u7684\u793a\u4f8b\uff1a<\/p>\n<\/p>\n
import pandas as pd<\/p>\n\u521b\u5efaDataFrame<\/strong><\/h2>\ndata = {'Name': ['Tom', 'Jerry', 'Mike'], 'Age': [20, 21, 19]}<\/p>\n
df = pd.DataFrame(data)<\/p>\n
\u5bfc\u51faDataFrame\u5230Excel\u6587\u4ef6<\/strong><\/h2>\ndf.to_excel('output.xlsx', index=False)<\/p>\n
\u5bfc\u51fa\u5230\u6307\u5b9a\u5de5\u4f5c\u8868<\/strong><\/h2>\ndf.to_excel('output.xlsx', sheet_name='Sheet1', index=False)<\/p>\n
\u5bfc\u51fa\u6307\u5b9a\u5217<\/strong><\/h2>\ndf.to_excel('output.xlsx', columns=['Name'], index=False)<\/p>\n
\u5904\u7406\u7f3a\u5931\u503c<\/strong><\/h2>\ndf.to_excel('output.xlsx', na_rep='NA', index=False)<\/p>\n
\u67e5\u770b\u5bfc\u51fa\u7684\u6587\u4ef6<\/strong><\/h2>\nprint('Data exported successfully!')<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u516d\u3001PANDAS\u5e93\u7684\u9ad8\u7ea7\u64cd\u4f5c<\/strong><\/p>\n<\/p>\n\u9664\u4e86\u57fa\u672c\u7684\u5bfc\u5165\u5bfc\u51fa\u64cd\u4f5c\uff0cpandas\u5e93\u8fd8\u652f\u6301\u8bb8\u591a\u9ad8\u7ea7\u64cd\u4f5c\uff0c\u5982\u591a\u5de5\u4f5c\u8868\u5904\u7406\u3001\u683c\u5f0f\u8bbe\u7f6e\u3001\u516c\u5f0f\u5199\u5165\u7b49\u3002<\/p>\n<\/p>\n
\n- \u591a\u5de5\u4f5c\u8868\u5904\u7406<\/strong><\/li>\n<\/ol>\n
import pandas as pd<\/p>\n\u8bfb\u53d6\u591a\u4e2a\u5de5\u4f5c\u8868<\/strong><\/h2>\ndf1 = pd.read_excel('data.xlsx', sheet_name='Sheet1')<\/p>\n
df2 = pd.read_excel('data.xlsx', sheet_name='Sheet2')<\/p>\n
\u5bfc\u51fa\u5230\u591a\u4e2a\u5de5\u4f5c\u8868<\/strong><\/h2>\nwith pd.ExcelWriter('output.xlsx') as writer:<\/p>\n
df1.to_excel(writer, sheet_name='Sheet1', index=False)<\/p>\n
df2.to_excel(writer, sheet_name='Sheet2', index=False)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\n- \u683c\u5f0f\u8bbe\u7f6e<\/strong><\/li>\n<\/ol>\n
import pandas as pd<\/p>\nfrom openpyxl import load_workbook<\/p>\n
\u521b\u5efaDataFrame<\/strong><\/h2>\ndata = {'Name': ['Tom', 'Jerry', 'Mike'], 'Age': [20, 21, 19]}<\/p>\n
df = pd.DataFrame(data)<\/p>\n
\u5bfc\u51faDataFrame\u5230Excel\u6587\u4ef6<\/strong><\/h2>\ndf.to_excel('output.xlsx', index=False)<\/p>\n
\u52a0\u8f7dExcel\u6587\u4ef6<\/strong><\/h2>\nbook = load_workbook('output.xlsx')<\/p>\n
sheet = book.active<\/p>\n
\u8bbe\u7f6e\u5217\u5bbd<\/strong><\/h2>\nsheet.column_dimensions['A'].width = 20<\/p>\n
sheet.column_dimensions['B'].width = 10<\/p>\n
\u8bbe\u7f6e\u5b57\u4f53<\/strong><\/h2>\nfrom openpyxl.styles import Font<\/p>\n
font = Font(name='Arial', size=12, bold=True)<\/p>\n
for cell in sheet['A'] + sheet[1]:<\/p>\n
cell.font = font<\/p>\n
\u4fdd\u5b58\u6587\u4ef6<\/strong><\/h2>\nbook.save('output.xlsx')<\/p>\n
<\/code><\/pre>\n<\/p>\n
\n- \u516c\u5f0f\u5199\u5165<\/strong><\/li>\n<\/ol>\n
import pandas as pd<\/p>\n\u521b\u5efaDataFrame<\/strong><\/h2>\ndata = {'Name': ['Tom', 'Jerry', 'Mike'], 'Age': [20, 21, 19]}<\/p>\n
df = pd.DataFrame(data)<\/p>\n
\u5bfc\u51faDataFrame\u5230Excel\u6587\u4ef6<\/strong><\/h2>\ndf.to_excel('output.xlsx', index=False)<\/p>\n
\u52a0\u8f7dExcel\u6587\u4ef6<\/strong><\/h2>\nfrom openpyxl import load_workbook<\/p>\n
book = load_workbook('output.xlsx')<\/p>\n
sheet = book.active<\/p>\n
\u5199\u5165\u516c\u5f0f<\/strong><\/h2>\nsheet['C1'] = 'Sum of Ages'<\/p>\n
sheet['C2'] = '=SUM(B2:B4)'<\/p>\n
\u4fdd\u5b58\u6587\u4ef6<\/strong><\/h2>\nbook.save('output.xlsx')<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e03\u3001PANDAS\u5e93\u4e0e\u5176\u4ed6\u5e93\u7684\u7ed3\u5408\u4f7f\u7528<\/strong><\/p>\n<\/p>\npandas\u5e93\u53ef\u4ee5\u4e0e\u5176\u4ed6\u5e93\u7ed3\u5408\u4f7f\u7528\uff0c\u5982NumPy\u3001Matplotlib\u3001Seaborn\u7b49\uff0c\u8fdb\u884c\u66f4\u590d\u6742\u7684\u6570\u636e\u5206\u6790\u548c\u5904\u7406\u3002<\/p>\n<\/p>\n
\n- \u4e0eNumPy\u7ed3\u5408\u4f7f\u7528<\/strong><\/li>\n<\/ol>\n
import pandas as pd<\/p>\nimport numpy as np<\/p>\n
\u521b\u5efaDataFrame<\/strong><\/h2>\ndata = {'Name': ['Tom', 'Jerry', 'Mike'], 'Age': [20, 21, 19]}<\/p>\n
df = pd.DataFrame(data)<\/p>\n
\u4f7f\u7528NumPy\u8fdb\u884c\u6570\u636e\u5904\u7406<\/strong><\/h2>\ndf['Age_Square'] = np.square(df['Age'])<\/p>\n
print(df)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\n- \u4e0eMatplotlib\u7ed3\u5408\u4f7f\u7528<\/strong><\/li>\n<\/ol>\n
import pandas as pd<\/p>\nimport matplotlib.pyplot as plt<\/p>\n
\u521b\u5efaDataFrame<\/strong><\/h2>\ndata = {'Name': ['Tom', 'Jerry', 'Mike'], 'Age': [20, 21, 19]}<\/p>\n
df = pd.DataFrame(data)<\/p>\n
\u4f7f\u7528Matplotlib\u8fdb\u884c\u6570\u636e\u53ef\u89c6\u5316<\/strong><\/h2>\ndf['Age'].plot(kind='bar')<\/p>\n
plt.title('Age of Individuals')<\/p>\n
plt.xlabel('Name')<\/p>\n
plt.ylabel('Age')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\n- \u4e0eSeaborn\u7ed3\u5408\u4f7f\u7528<\/strong><\/li>\n<\/ol>\n
import pandas as pd<\/p>\nimport seaborn as sns<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
\u521b\u5efaDataFrame<\/strong><\/h2>\ndata = {'Name': ['Tom', 'Jerry', 'Mike'], 'Age': [20, 21, 19]}<\/p>\n
df = pd.DataFrame(data)<\/p>\n
\u4f7f\u7528Seaborn\u8fdb\u884c\u6570\u636e\u53ef\u89c6\u5316<\/strong><\/h2>\nsns.barplot(x='Name', y='Age', data=df)<\/p>\n
plt.title('Age of Individuals')<\/p>\n
plt.xlabel('Name')<\/p>\n
plt.ylabel('Age')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u901a\u8fc7\u4ee5\u4e0a\u6b65\u9aa4\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528pandas\u5e93\u8f7b\u677e\u5730\u5bfc\u5165\u548c\u5bfc\u51faExcel\u6587\u4ef6\uff0c\u4ee5\u53ca\u8fdb\u884c\u5404\u79cd\u6570\u636e\u5904\u7406\u548c\u5206\u6790\u64cd\u4f5c\u3002pandas\u5e93\u7684\u5f3a\u5927\u529f\u80fd\u548c\u7b80\u6d01\u6613\u7528\u7684\u63a5\u53e3\uff0c\u4f7f\u5f97\u5b83\u6210\u4e3a\u6570\u636e\u79d1\u5b66\u548c\u673a\u5668\u5b66\u4e60\u9886\u57df\u4e2d\u4e0d\u53ef\u6216\u7f3a\u7684\u5de5\u5177\u3002\u5e0c\u671b\u8fd9\u7bc7\u6587\u7ae0\u80fd\u591f\u5e2e\u52a9\u60a8\u66f4\u597d\u5730\u7406\u89e3\u548c\u4f7f\u7528pandas\u5e93\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n \u5982\u4f55\u4f7f\u7528Python\u8bfb\u53d6Excel\u6587\u4ef6\uff1f<\/strong>
Python\u53ef\u4ee5\u901a\u8fc7\u591a\u79cd\u5e93\u6765\u8bfb\u53d6Excel\u6587\u4ef6\uff0c\u5176\u4e2d\u6700\u5e38\u7528\u7684\u662fpandas<\/code>\u548copenpyxl<\/code>\u3002\u4f7f\u7528pandas<\/code>\u5e93\uff0c\u53ef\u4ee5\u901a\u8fc7pd.read_excel()<\/code>\u51fd\u6570\u65b9\u4fbf\u5730\u8bfb\u53d6Excel\u6587\u4ef6\uff0c\u5e76\u5c06\u5176\u8f6c\u6362\u4e3aDataFrame\uff0c\u4fbf\u4e8e\u6570\u636e\u5206\u6790\u548c\u5904\u7406\u3002\u786e\u4fdd\u5728\u4f7f\u7528\u4e4b\u524d\u5b89\u88c5\u4e86\u76f8\u5e94\u7684\u5e93\uff0c\u53ef\u4ee5\u4f7f\u7528\u547d\u4ee4pip install pandas openpyxl<\/code>\u8fdb\u884c\u5b89\u88c5\u3002<\/p>\nPython\u5bfc\u51fa\u6570\u636e\u5230Excel\u65f6\uff0c\u652f\u6301\u54ea\u4e9b\u6587\u4ef6\u683c\u5f0f\uff1f<\/strong>
\u5728Python\u4e2d\uff0c\u4f7f\u7528pandas<\/code>\u5e93\u5bfc\u51fa\u6570\u636e\u65f6\uff0c\u901a\u5e38\u652f\u6301.xlsx<\/code>\u548c.xls<\/code>\u4e24\u79cd\u683c\u5f0f\u3002\u901a\u8fc7DataFrame.to_excel()<\/code>\u65b9\u6cd5\uff0c\u7528\u6237\u53ef\u4ee5\u6307\u5b9a\u6587\u4ef6\u540d\u548c\u683c\u5f0f\uff0c\u9009\u62e9\u662f\u5426\u5305\u542b\u7d22\u5f15\u3001\u5217\u540d\u7b49\u4fe1\u606f\u3002\u5bf9\u4e8e\u8f83\u5927\u7684\u6570\u636e\u96c6\uff0c.xlsx<\/code>\u683c\u5f0f\u901a\u5e38\u662f\u9996\u9009\uff0c\u56e0\u4e3a\u5b83\u652f\u6301\u66f4\u5927\u7684\u6570\u636e\u91cf\u548c\u66f4\u590d\u6742\u7684\u683c\u5f0f\u3002<\/p>\n\u4f7f\u7528Python\u64cd\u4f5cExcel\u6587\u4ef6\u65f6\uff0c\u662f\u5426\u9700\u8981\u5b89\u88c5Excel\u8f6f\u4ef6\uff1f<\/strong>
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