python - 从 Pandas 写入 Excel 时设置默认数字格式

Question

我希望在从 Pandas 数据框写入 Excel 时设置默认数字格式。这可能吗？

我可以使用以下设置默认日期/datetime_format，但找不到设置默认数字格式的方法。

writer = pd.ExcelWriter(f'{file_variable}.xlsx', engine='xlsxwriter',datetime_format='MM/DD/YYYY')

否则，我假设我必须将工作表分配给变量并循环遍历指定列的行以设置数字格式。

Answer 1

我得到了这种格式， float 到小数点后一位。

data = {'A Prime': {0: 3.26,  1: 3.24,  2: 3.22,  3: 3.2,  4: 3.18,  5: 3.16,
  6: 3.14,  7: 1.52,  8: 1.5,  9: 1.48,  10: 1.46,  11: 1.44,  12: 1.42},
 'B': {0: 0.16608,  1: 0.16575,  2: 0.1654,  3: 0.16505999999999998,  4: 0.1647,  5: 0.16434,  6: 0.16398,  7: 0.10759,  8: 0.10687,  9: 0.10614000000000001,
  10: 0.10540999999999999,  11: 0.10469,  12: 0.10396}, 'Proto Name': {0: 'Alpha',
  1: 'Alpha',  2: 'Alpha', 3: 'Alpha',  4: 'Alpha',  5: 'Alpha',  6: 'Alpha',  7: 'Bravo',  8: 'Bravo',  9: 'Bravo',  10: 'Bravo',  11: 'Bravo',  12: 'Bravo'}}

import pandas as pd
df = pd.DataFrame(data)


    A Prime B       Proto Name
0   3.26    0.16608 Alpha
1   3.24    0.16575 Alpha
2   3.22    0.16540 Alpha
3   3.20    0.16506 Alpha
4   3.18    0.16470 Alpha
5   3.16    0.16434 Alpha
6   3.14    0.16398 Alpha
7   1.52    0.10759 Bravo
8   1.50    0.10687 Bravo
9   1.48    0.10614 Bravo
10  1.46    0.10541 Bravo
11  1.44    0.10469 Bravo
12  1.42    0.10396 Bravo

writer = pd.ExcelWriter(r'c:\temp\output.xlsx')
# This method will truncate the data past the first decimal point
df.to_excel(writer,'Sheet1',float_format = "%0.1f")
writer.save()

但遗憾的是，这可能不是所有情况 - 说更大的数字和千位分隔符并不令人高兴

df.to_excel(writer,'Sheet1',float_format = ":,")

但是以下似乎有效

data = {'A Prime': {0: 326000,  1: 3240000}}
df = pd.DataFrame(data)

    A Prime
0   326000
1   3240000

writer = pd.ExcelWriter(r'c:\temp\output.xlsx')
df.to_excel(writer,'Sheet1')
workbook  = writer.book
worksheet = writer.sheets['Sheet1']
format1 = workbook.add_format({'num_format': '#,##0.00'})
worksheet.set_column('B', 18, format1)
#Alternatively, you could specify a range of columns with 'B:D' and 18 sets the column width
writer.save()

全部取自此处:http://xlsxwriter.readthedocs.io/working_with_pandas.html