Advanced read/write in Excel

Formulas

Write formula

No problem to create Excel formulas with Pandas, but use these tips below.

• As you will use simple and double quote in your formulas, frame your string with triple quotes.
• Translate in english all Excel keywords (IF, CHAR, SUBSTITUTE...).
• Use a simple comma to separate your parameters (english...).
• Re-sort your dataframe and reset your index before the formula:

df.sort_values(by=['Your Id'], ascending=True, inplace=True)
df = df.reset_index(drop=True)

• Start the index with a special value (to start with 2, as usual in Excel):

df.index = df.index + 2

• Then use the index as a string in your formula:

'''...''' + df.index.astype(str) + '''...'''

• Do not add other sorts after the formulas creation, it causes index issues and impacts your formulas.

Read formula

Sometime you want read formula as string. If the Excel file was created by Excel himself, no problem, just use data_only=True:

wb = openpyxl.load_workbook('My file.xlsx', data_only = True)
ws = wb['My tab']
df = pd.DataFrame(ws.values)
print(tabulate(df.head(5), headers='keys', tablefmt='psql', showindex=False))

import xlwings
...
excel_app = xlwings.App(visible=False)
excel_book = excel_app.books.open('My file.xlsx')
excel_book.save()
excel_book.close()
excel_app.quit()

Conditional formatting

If a number is equal to, in just one cell

Here we add in one cell (B3) a rule to fill it in blue if it equals to 2:

import openpyxl
from openpyxl import styles, formatting
 
myFile = 'C:/Path/test.xlsx'
 
myDF.to_excel(myFile, index=False, sheet_name='Tab 1')
 
workbook = openpyxl.load_workbook(myFile)
worksheet = workbook['Sheet1']
 
myBlue = styles.PatternFill(start_color='0088cc', end_color='0088cc', fill_type='solid')
worksheet.conditional_formatting.add('B3', formatting.rule.CellIsRule(operator='equal', formula=['2'], fill=myBlue))
 
workbook.save(myFile)

If a number is greater than, in whole column

Same as above, we just change the operator with greaterThan and we target the whole column B as Excel did, with  $1:$1048576:

import openpyxl
from openpyxl import styles, formatting
 
myFile = 'C:/Path/test.xlsx'
 
myDF.to_excel(myFile, index=False, sheet_name='Tab 1')
 
workbook = openpyxl.load_workbook(myFile)
worksheet = workbook['Sheet1']
 
myBlue = styles.PatternFill(start_color='0088cc', end_color='0088cc', fill_type='solid')
worksheet.conditional_formatting.add('B1:B1048576', formatting.rule.CellIsRule(operator='greaterThan', formula=['2'], fill=myBlue))
 
workbook.save(myFile)

Operators

The above method supports several operators:

{'beginsWith', 'endsWith', 'notBetween', 'notContains', 'lessThan', 'greaterThanOrEqual', 'notEqual', 'between', 'lessThanOrEqual', 'containsText', 'greaterThan', 'equal'}

If contains a string

For match with strings it is a little specific, with redundant code and building the column_slot ourself, using the size of our dataframe +1 :

Here for example we fill in red the cells from A column when contain the string CSS.

import pandas as pd
import openpyxl
from openpyxl import styles, formatting
from openpyxl.styles.differential import DifferentialStyle
from openpyxl.formatting.rule import Rule
 
myFile = 'C:/Path/test.xlsx'
 
myDF.to_excel(myFile, index=False, sheet_name='Tab 1')
 
workbook = openpyxl.load_workbook(myFile)
worksheet = workbook['Sheet1']
 
myRed = styles.PatternFill(start_color='ffc7ce', end_color='ffc7ce', fill_type='solid')
 
column_slot = 'A2:A'+str(myDF.shape[0]+1)
 
styleRed = DifferentialStyle(fill=myRed)
rule = Rule(type='containsText', operator='containsText', text='css', dxf=styleRed)
rule.formula = ['SEARCH("css",' + column_slot + ")"]
worksheet.conditional_formatting.add(column_slot, rule)
 
workbook.save(myFile)

Fix Excel percentage

When your data comes from an Excel file and contains percentages, something like dtype (search it in Miscellaneous chapter) will be non-efficient, because Excel stores percentages as numbers (indices between 0 and 1). The displaying with % is just a view. Actually 35% will be stored as 0.3 for example.

In addition, it is possible that these percentages are melted with true strings or numbers in the same columns, and sometimes with or without decimal ... Argghhh 🥵

But just relax:

import re
...
df[MyField] = df[MyField].apply(lambda x: str(round(float(x) * 100, 2)).rstrip('0').rstrip('.') + ' %' if re.search(r'^0\.\d+$|^0$|^1$',x) else x)

Attention

If the Excel file is wrong builded, melting true numbers between 0 and 1 with percentages, they will be converted. So maybe add red alerts to manage these special cases.

Liens ou pièces jointes

France-Departements-Deformation.zip	[France-Departements-Deformation]	335 Ko
simple_countries.zip	[simple_countries]	1880 Ko