将Excel转换为Pandas DataFrame

使用Aspose.Cells for Python via .NET API，您可以将Excel、TSV、CSV、Json和许多其他格式转换为pandas DataFrame。

通过json数据将Excel转换为Pandas DataFrame

以下是一个示例代码片段，演示如何使用Aspose.Cells for Python via .NET将excel数据通过json数据导出为pandas DataFrame：

1. 创建一个工作簿并添加一些值。
2. 将excel数据导出为JSON字符串。
3. 使用pandas库读取JSON数据。

import pandas as pd
from aspose.cells.utility import JsonUtility, JsonLayoutOptions
from aspose.cells import Workbook, Worksheet, Cells, JsonSaveOptions

# Create a new Aspose.Cells Workbook
workbook = Workbook()

# Get the first worksheet
worksheet = workbook.worksheets[0]

# Get the cells
cells = worksheet.cells

# Add some values
cells.get("A1").value = "Name"
cells.get("B1").value = "Age"
cells.get("C1").value = "City"
cells.get("A2").value = "Alice"
cells.get("B2").value = 25
cells.get("C2").value = "New York"
cells.get("A3").value = "Bob"
cells.get("B3").value = 30
cells.get("C3").value = "San Francisco"
cells.get("A4").value = "Charlie"
cells.get("B4").value = 35
cells.get("C4").value = "Los Angeles"

jsonSaveOptions = JsonSaveOptions()

# Save data to json string
json = JsonUtility.export_range_to_json(cells.max_display_range, jsonSaveOptions);

print(json)

# Read json string using pandas
dfData = pd.read_json(json)

print(dfData)

直接将 Pandas DataFrame 转换为 Excel

以下是一个示例代码片段，演示如何使用Aspose.Cells for Python via .NET将excel数据直接导出为pandas DataFrame：

1. 创建一个工作簿并添加一些值。
2. 遍历Excel数据并使用Aspose.Cells for Python via .NET将数据导出到Pandas DataFrame。

import pandas as pd
from aspose.cells.utility import JsonUtility, JsonLayoutOptions
from aspose.cells import Workbook, Worksheet, Cells, JsonSaveOptions

# Create a new Aspose.Cells Workbook
workbook = Workbook()

# Get the first worksheet
worksheet = workbook.worksheets[0]

# Get the cells
cells = worksheet.cells

# Add some values
cells.get("A1").value = "Name"
cells.get("B1").value = "Age"
cells.get("C1").value = "City"
cells.get("A2").value = "Alice"
cells.get("B2").value = 25
cells.get("C2").value = "New York"
cells.get("A3").value = "Bob"
cells.get("B3").value = 30
cells.get("C3").value = "San Francisco"
cells.get("A4").value = "Charlie"
cells.get("B4").value = 35
cells.get("C4").value = "Los Angeles"

rowCount = cells.max_data_row
columnCount = cells.max_data_column


columnDatas=[]
for c in range(columnCount + 1):
    currCell = cells.get_cell(0, c)
    columnDatas.append(currCell.value)

result = pd.DataFrame(columns=columnDatas, dtype=object)

for i in range(1, rowCount + 1):
    rowarray = []
    for j in range(columnCount + 1):
        currCell = cells.get_cell(i, j)
        rowarray.append(currCell.value)
    
    print(rowarray)
    result.loc[i - 1] = rowarray

print(result)