Konvertera NumPy till Excel

Introduktion till NumPy

NumPy (Numerical Python) är en öppen källkods-numerisk datortillägg till Python. Detta verktyg kan användas för att lagra och bearbeta stora matriser, vilket är mycket mer effektivt än Pythons nästlade liststruktur (som också kan användas för att representera matriser). Det stöder ett stort antal dimensionella matriser och matrisoperationer, och tillhandahåller också ett stort antal matematiska funktionsbibliotek för matrisoperationer.

NumPys huvudfunktioner:

  1. Ndarray, en flerdimensionell matrisobjekt, är en snabb, flexibel och platsbesparande datastruktur.
  2. Linjär algebraoperationer, inklusive matrismultiplikation, transposition, invertering, etc.
  3. Fouriertansformation, utföra en snabb fouriertansformation på en matris.
  4. Snabb operation av flyttalsmatriser.
  5. Integrera C-språkkod i Python för att få det att köra snabbare.

Genom att använda Aspose.Cells för Python via .NET API kan du konvertera NumPy ndarray till Excel, OpenOffice, Pdf, Json och många olika format.

Hur man konverterar NumPy ndarray till Excel-arbetsbok

Här är ett exempel på kodsnutt för att visa hur man importerar data från en NumPy-matris till en Excel-fil med hjälp av Aspose.Cells för Python via .NET:

  1. Skapa ett exempel på NumPy array data.
  2. Traversera NumPy array och importera data med hjälp av Aspose.Cells för Python via .NET.
import numpy as np
import pandas as pd
import aspose.cells
from aspose.cells import Workbook, Worksheet, Range, CellsHelper
def put_cell_value(cells, raw_value, row , column):
cell = cells.get(row , column)
dtype = type(raw_value)
match dtype:
case np.bool_ :
value = bool(raw_value)
case np.int_ :
value = int(raw_value)
case np.intc :
value = int(raw_value)
case np.intp :
value = int(raw_value)
case np.int8 :
value = int(raw_value)
case np.int16 :
value = int(raw_value)
case np.int32 :
value = int(raw_value)
case np.int64 :
value = int(raw_value)
case np.uint8 :
value = int(raw_value)
case np.uint16 :
value = int(raw_value)
case np.uint32 :
value = int(raw_value)
case np.uint64 :
value = int(raw_value)
case np.float_:
value = int(raw_value)
case np.float16:
value = float(raw_value)
case np.float32:
value = float(raw_value)
case np.float64:
value = float(raw_value)
case np.single:
value = float(raw_value)
case np.double:
value = float(raw_value)
case np.datetime64 :
ts = pd.to_datetime(str(raw_value))
value = ts.strftime('%Y.%m.%d')
case _:
value = raw_value
cell.put_value(value)
pass
def import_table_data_into_cells(cells,table_data,row_index,column_index,is_vertical):
table_row_index = row_index
table_column_index = column_index
row_count = len( table_data)
column_count = 0
for table_row in table_data:
for table_cell in table_row:
column_count =len( table_row)
put_cell_value(cells,table_cell,table_row_index,table_column_index)
if is_vertical :
table_row_index = table_row_index + 1
else:
table_column_index = table_column_index + 1
if is_vertical :
table_row_index = row_index
table_column_index = table_column_index + 1
else:
table_column_index = column_index
table_row_index = table_row_index + 1
if is_vertical :
end_row_index = row_index + row_count - 1
end_column_index = table_column_index - 1
else:
end_row_index = table_row_index - 1
end_column_index = column_index + column_count - 1
return (row_index,column_index,end_row_index,end_column_index)
excel_data = np.array([[['City', 'Region', 'Store'], ['Chicago', 'Central', 3055], ['New York', 'East', 3036], ['Detroit', 'Central', 3074]],
[['City2', 'Region2', 'Store3'], ['Seattle', 'West', 3000], ['philadelph', 'East', 3082], ['Detroit', 'Central', 3074]], [['City3', 'Region3', 'Store3'], ['Seattle', 'West', 3166], ['New York', 'East', 3090], ['Chicago', 'Central', 3055]]] )
# Create a new Aspose.Cells Workbook
workbook = Workbook()
begin_row_index = 0
begin_column_index = 0
is_vertical = False
sheet_index = 1
for table_data in excel_data:
curr_sheet = workbook.worksheets.add("Sheet_data_" + str(sheet_index))
curr_cells = curr_sheet.cells
import_table_data_into_cells(curr_cells , table_data , begin_row_index,begin_column_index,is_vertical)
sheet_index += 1
workbook.save("out.xlsx")
view raw Numpy-import-ndarray-to-excel.py hosted with ❤ by GitHub

Utmatningsresultat:

Hur man konverterar NumPy ndarray till arbetsblad

Här är ett exempel på kodsnutt för att visa hur man importerar data från en NumPy-matris till en Excel-fil med hjälp av Aspose.Cells för Python via .NET:

  1. Skapa ett exempel på NumPy array data.
  2. Traversera NumPy array och importera data med hjälp av Aspose.Cells för Python via .NET.
import numpy as np
import pandas as pd
import aspose.cells
from aspose.cells import Workbook, Worksheet, Range, CellsHelper
def put_cell_value(cells, raw_value, row , column):
cell = cells.get(row , column)
dtype = type(raw_value)
match dtype:
case np.bool_ :
value = bool(raw_value)
case np.int_ :
value = int(raw_value)
case np.intc :
value = int(raw_value)
case np.intp :
value = int(raw_value)
case np.int8 :
value = int(raw_value)
case np.int16 :
value = int(raw_value)
case np.int32 :
value = int(raw_value)
case np.int64 :
value = int(raw_value)
case np.uint8 :
value = int(raw_value)
case np.uint16 :
value = int(raw_value)
case np.uint32 :
value = int(raw_value)
case np.uint64 :
value = int(raw_value)
case np.float_:
value = int(raw_value)
case np.float16:
value = float(raw_value)
case np.float32:
value = float(raw_value)
case np.float64:
value = float(raw_value)
case np.single:
value = float(raw_value)
case np.double:
value = float(raw_value)
case np.datetime64 :
ts = pd.to_datetime(str(raw_value))
value = ts.strftime('%Y.%m.%d')
case _:
value = raw_value
cell.put_value(value)
pass
def import_table_data_into_cells(cells,table_data,row_index,column_index,is_vertical):
table_row_index = row_index
table_column_index = column_index
row_count = len( table_data)
column_count = 0
for table_row in table_data:
for table_cell in table_row:
column_count =len( table_row)
put_cell_value(cells,table_cell,table_row_index,table_column_index)
if is_vertical :
table_row_index = table_row_index + 1
else:
table_column_index = table_column_index + 1
if is_vertical :
table_row_index = row_index
table_column_index = table_column_index + 1
else:
table_column_index = column_index
table_row_index = table_row_index + 1
if is_vertical :
end_row_index = row_index + row_count - 1
end_column_index = table_column_index - 1
else:
end_row_index = table_row_index - 1
end_column_index = column_index + column_count - 1
return (row_index,column_index,end_row_index,end_column_index)
# Create a sample NumPy array
data = np.array([['City', 'Region', 'Store'], ['Chicago', 'Central', 3055], ['New York', 'East', 3036], ['Detroit', 'Central', 3074]])
# Create a new Aspose.Cells Workbook
workbook = Workbook()
# Access the first (default) worksheet
worksheet = workbook.worksheets[0]
# Get the cells
cells = worksheet.cells
begin_row_index = 0
begin_column_index = 0
is_vertical = False
import_table_data_into_cells(cells , data , begin_row_index,begin_column_index,is_vertical)
workbook.save("out.xlsx")
view raw Numpy-import-ndarray-to-worksheet.py hosted with ❤ by GitHub

Utmatningsresultat:

Hur man konverterar NumPy ndarray till ListObject

Här är ett exempel på kodsnutt för att visa hur man importerar data från en NumPy-matris till en ListObject med hjälp av Aspose.Cells för Python via .NET:

  1. Skapa ett exempel på NumPy array data.
  2. Traversera NumPy array och importera data med hjälp av Aspose.Cells för Python via .NET.
  3. Skapa ListObject-objekt med hjälp av provdata.
import numpy as np
import pandas as pd
import aspose.cells
from aspose.cells import Workbook, Worksheet, Range, CellsHelper
def put_cell_value(cells, raw_value, row , column):
cell = cells.get(row , column)
dtype = type(raw_value)
match dtype:
case np.bool_ :
value = bool(raw_value)
case np.int_ :
value = int(raw_value)
case np.intc :
value = int(raw_value)
case np.intp :
value = int(raw_value)
case np.int8 :
value = int(raw_value)
case np.int16 :
value = int(raw_value)
case np.int32 :
value = int(raw_value)
case np.int64 :
value = int(raw_value)
case np.uint8 :
value = int(raw_value)
case np.uint16 :
value = int(raw_value)
case np.uint32 :
value = int(raw_value)
case np.uint64 :
value = int(raw_value)
case np.float_:
value = int(raw_value)
case np.float16:
value = float(raw_value)
case np.float32:
value = float(raw_value)
case np.float64:
value = float(raw_value)
case np.single:
value = float(raw_value)
case np.double:
value = float(raw_value)
case np.datetime64 :
ts = pd.to_datetime(str(raw_value))
value = ts.strftime('%Y.%m.%d')
case _:
value = raw_value
cell.put_value(value)
pass
def import_table_data_into_cells(cells,table_data,row_index,column_index,is_vertical):
table_row_index = row_index
table_column_index = column_index
row_count = len( table_data)
column_count = 0
for table_row in table_data:
for table_cell in table_row:
column_count =len( table_row)
put_cell_value(cells,table_cell,table_row_index,table_column_index)
if is_vertical :
table_row_index = table_row_index + 1
else:
table_column_index = table_column_index + 1
if is_vertical :
table_row_index = row_index
table_column_index = table_column_index + 1
else:
table_column_index = column_index
table_row_index = table_row_index + 1
if is_vertical :
end_row_index = row_index + row_count - 1
end_column_index = table_column_index - 1
else:
end_row_index = table_row_index - 1
end_column_index = column_index + column_count - 1
return (row_index,column_index,end_row_index,end_column_index)
# Create a sample NumPy array
data = np.array([['City', 'Region', 'Store'], ['Chicago', 'Central', 3055], ['New York', 'East', 3036], ['Detroit', 'Central', 3074]])
# Create a new Aspose.Cells Workbook
workbook = Workbook()
# Access the first (default) worksheet
worksheet = workbook.worksheets[0]
# Get the cells
cells = worksheet.cells
begin_row_index = 0
begin_column_index = 0
is_vertical = False
# ndarray to listobject
(begin_row_index,begin_column_index,end_row_index,end_column_index) = import_table_data_into_cells(cells , data , begin_row_index,begin_column_index,is_vertical)
start_cell = CellsHelper.cell_index_to_name(begin_row_index, begin_column_index)
end_cell = CellsHelper.cell_index_to_name(end_row_index, end_column_index)
index = worksheet.list_objects.add(start_cell, end_cell,True)
workbook.save("out.xlsx")
view raw Numpy-import-ndarray-to-listobject.py hosted with ❤ by GitHub

Utmatningsresultat:

Hur man konverterar NumPy ndarray till Range

Här är ett exempel på kodsnutt för att visa hur man importerar data från en NumPy-matris till ett område med hjälp av Aspose.Cells för Python via .NET:

  1. Skapa ett exempel på NumPy array data.
  2. Traversera NumPy array och importera data med hjälp av Aspose.Cells för Python via .NET.
  3. Skapa Range-objekt med hjälp av exempeldata.
import numpy as np
import pandas as pd
import aspose.cells
from aspose.cells import Workbook, Worksheet, Range, CellsHelper
def put_cell_value(cells, raw_value, row , column):
cell = cells.get(row , column)
dtype = type(raw_value)
match dtype:
case np.bool_ :
value = bool(raw_value)
case np.int_ :
value = int(raw_value)
case np.intc :
value = int(raw_value)
case np.intp :
value = int(raw_value)
case np.int8 :
value = int(raw_value)
case np.int16 :
value = int(raw_value)
case np.int32 :
value = int(raw_value)
case np.int64 :
value = int(raw_value)
case np.uint8 :
value = int(raw_value)
case np.uint16 :
value = int(raw_value)
case np.uint32 :
value = int(raw_value)
case np.uint64 :
value = int(raw_value)
case np.float_:
value = int(raw_value)
case np.float16:
value = float(raw_value)
case np.float32:
value = float(raw_value)
case np.float64:
value = float(raw_value)
case np.single:
value = float(raw_value)
case np.double:
value = float(raw_value)
case np.datetime64 :
ts = pd.to_datetime(str(raw_value))
value = ts.strftime('%Y.%m.%d')
case _:
value = raw_value
cell.put_value(value)
pass
def import_table_data_into_cells(cells,table_data,row_index,column_index,is_vertical):
table_row_index = row_index
table_column_index = column_index
row_count = len( table_data)
column_count = 0
for table_row in table_data:
for table_cell in table_row:
column_count =len( table_row)
put_cell_value(cells,table_cell,table_row_index,table_column_index)
if is_vertical :
table_row_index = table_row_index + 1
else:
table_column_index = table_column_index + 1
if is_vertical :
table_row_index = row_index
table_column_index = table_column_index + 1
else:
table_column_index = column_index
table_row_index = table_row_index + 1
if is_vertical :
end_row_index = row_index + row_count - 1
end_column_index = table_column_index - 1
else:
end_row_index = table_row_index - 1
end_column_index = column_index + column_count - 1
return (row_index,column_index,end_row_index,end_column_index)
# Create a sample NumPy array
data = np.array([['City', 'Region', 'Store'], ['Chicago', 'Central', 3055], ['New York', 'East', 3036], ['Detroit', 'Central', 3074]])
# Create a new Aspose.Cells Workbook
workbook = Workbook()
# Access the first (default) worksheet
worksheet = workbook.worksheets[0]
# Get the cells
cells = worksheet.cells
begin_row_index = 0
begin_column_index = 0
is_vertical = False
# ndarray to range
(begin_row_index,begin_column_index,end_row_index,end_column_index) = import_table_data_into_cells(cells , data , begin_row_index,begin_column_index,is_vertical)
start_cell = CellsHelper.cell_index_to_name(begin_row_index, begin_column_index)
end_cell = CellsHelper.cell_index_to_name(end_row_index, end_column_index)
range_obj = cells.create_range(start_cell, end_cell)
print("row count: " + str(range_obj.row_count))
print("column count: " + str(range_obj.column_count))
workbook.save("out.xlsx")
view raw Numpy-import-ndarray-to-range.py hosted with ❤ by GitHub

Utmatningsresultat:

row count: 4
column count: 3

Hur man konverterar NumPy ndarray till Namn

Här är ett exempel på kodsnutt som visar hur man importerar data från en NumPy array till ett Namn med hjälp av Aspose.Cells för Python via .NET:

  1. Skapa ett exempel på NumPy array data.
  2. Traversera NumPy array och importera data med hjälp av Aspose.Cells för Python via .NET.
  3. Skapa Namn-objekt med hjälp av exempeldata.
import numpy as np
import pandas as pd
import aspose.cells
from aspose.cells import Workbook, Worksheet, Range, CellsHelper
def put_cell_value(cells, raw_value, row , column):
cell = cells.get(row , column)
dtype = type(raw_value)
match dtype:
case np.bool_ :
value = bool(raw_value)
case np.int_ :
value = int(raw_value)
case np.intc :
value = int(raw_value)
case np.intp :
value = int(raw_value)
case np.int8 :
value = int(raw_value)
case np.int16 :
value = int(raw_value)
case np.int32 :
value = int(raw_value)
case np.int64 :
value = int(raw_value)
case np.uint8 :
value = int(raw_value)
case np.uint16 :
value = int(raw_value)
case np.uint32 :
value = int(raw_value)
case np.uint64 :
value = int(raw_value)
case np.float_:
value = int(raw_value)
case np.float16:
value = float(raw_value)
case np.float32:
value = float(raw_value)
case np.float64:
value = float(raw_value)
case np.single:
value = float(raw_value)
case np.double:
value = float(raw_value)
case np.datetime64 :
ts = pd.to_datetime(str(raw_value))
value = ts.strftime('%Y.%m.%d')
case _:
value = raw_value
cell.put_value(value)
pass
def import_table_data_into_cells(cells,table_data,row_index,column_index,is_vertical):
table_row_index = row_index
table_column_index = column_index
row_count = len( table_data)
column_count = 0
for table_row in table_data:
for table_cell in table_row:
column_count =len( table_row)
put_cell_value(cells,table_cell,table_row_index,table_column_index)
if is_vertical :
table_row_index = table_row_index + 1
else:
table_column_index = table_column_index + 1
if is_vertical :
table_row_index = row_index
table_column_index = table_column_index + 1
else:
table_column_index = column_index
table_row_index = table_row_index + 1
if is_vertical :
end_row_index = row_index + row_count - 1
end_column_index = table_column_index - 1
else:
end_row_index = table_row_index - 1
end_column_index = column_index + column_count - 1
return (row_index,column_index,end_row_index,end_column_index)
# Create a sample NumPy array
data = np.array([['City', 'Region', 'Store'], ['Chicago', 'Central', 3055], ['New York', 'East', 3036], ['Detroit', 'Central', 3074]])
# Create a new Aspose.Cells Workbook
workbook = Workbook()
# Access the first (default) worksheet
worksheet = workbook.worksheets[0]
# Get the cells
cells = worksheet.cells
begin_row_index = 0
begin_column_index = 0
is_vertical = False
# ndarray to Name
(begin_row_index,begin_column_index,end_row_index,end_column_index) = import_table_data_into_cells(cells , data , begin_row_index,begin_column_index,is_vertical)
start_cell = CellsHelper.cell_index_to_name(begin_row_index, begin_column_index)
end_cell = CellsHelper.cell_index_to_name(end_row_index, end_column_index)
name_text = "Name_test"
name_refers_to = "=" + worksheet.name + "!$" + start_cell + ":$" + end_cell
name_index = worksheet.workbook.worksheets.names.add(name_text);
name = worksheet.workbook.worksheets.names[name_index]
name.refers_to = name_refers_to
workbook.save("out.xlsx")
view raw Numpy-import-ndarray-to-name.py hosted with ❤ by GitHub

Utmatningsresultat: