Pandas DataFrame in Excel konvertieren
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Mit der Aspose.Cells for Python via .NET API können Sie Pandas DataFrame in Excel, OpenOffice, Pdf, Json und viele verschiedene Formate konvertieren.
Pandas DataFrame über JSON-Daten in Excel konvertieren
Hier ist ein Beispielcode, der zeigt, wie Sie Daten aus einem Pandas DataFrame in eine Excel-Datei mithilfe von Aspose.Cells for Python via .NET importieren können:
- Erstellen Sie Beispieldaten für ein Pandas DataFrame.
- Verwenden Sie die Pandas-Bibliothek, um die DataFrame-Daten in JSON-Daten zu konvertieren.
- Importieren Sie JSON-Daten unter Verwendung von Aspose.Cells for Python via .NET.
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import pandas as pd | |
from aspose.cells.utility import JsonUtility, JsonLayoutOptions | |
from aspose.cells import Workbook, Worksheet, Cells | |
# Create a sample pandas DataFrame | |
data = {'Name': ['Alice', 'Bob', 'Charlie'], | |
'Age': [25, 30, 35], | |
'City': ['New York', 'San Francisco', 'Los Angeles']} | |
df = pd.DataFrame(data) | |
# Convert pandas DataFrame to JSON | |
json_string = df.to_json(orient='records') | |
workbook = Workbook() | |
# Get the first worksheet | |
worksheet = workbook.worksheets[0] | |
# Get the cells | |
cells = worksheet.cells | |
options = JsonLayoutOptions() | |
unit = JsonUtility() | |
# Processes as table. | |
options.array_as_table = True | |
unit.import_data(json_string, cells, 0, 0, options) | |
workbook.save("out.xlsx") |
Pandas DataFrame direkt in Excel konvertieren
Hier ist ein Beispielcode, der zeigt, wie Sie Daten aus einem Pandas DataFrame in eine Excel-Datei mithilfe von Aspose.Cells for Python via .NET importieren können:
- Erstellen Sie Beispieldaten für ein Pandas DataFrame.
- Durchlaufen Sie das DataFrame und importieren Sie Daten mithilfe von Aspose.Cells for Python via .NET.
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import pandas as pd | |
import aspose.cells | |
from aspose.cells import Workbook, CellsHelper, License | |
workbook = Workbook() | |
# Get the first worksheet | |
worksheet = workbook.worksheets[0] | |
# Get the cells | |
cells = worksheet.cells | |
# create a sample DataFrame | |
data = {'name': ['Alice', 'Bob', 'Charlie', 'David'], | |
'age': [25, 32, 18, 47], | |
'city': ['New York', 'Paris', 'London', 'Berlin']} | |
df = pd.DataFrame(data) | |
rowindex = 0 | |
colindex = 0 | |
for column in df: | |
cell = cells.get(rowindex, colindex) | |
cell.put_value(df[column].name) | |
colindex += 1 | |
for index, row in df.iterrows(): | |
rowindex += 1 | |
colindex = 0 | |
cell = cells.get(rowindex, colindex) | |
cell.put_value(row["name"]) | |
colindex += 1 | |
cell = cells.get(rowindex, colindex) | |
cell.put_value(row["age"]) | |
colindex += 1 | |
cell = cells.get(rowindex, colindex) | |
cell.put_value(row["city"]) | |
colindex += 1 | |
workbook.save("out.xlsx") |