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How to read specif cell with pandas library?

I want to read from excel sheet a specific cell: h6. So I try it like this:
import pandas as pd
excel_file = './docs/fruit.xlsx'
df = pd.read_excel(excel_file,'Overzicht')
sheet = df.active
x1 = sheet['H6'].value
print(x1)
But then I get this error:
File "C:\Python310\lib\site-packages\pandas\core\generic.py", line 5575, in __getattr__
return object.__getattribute__(self, name)
AttributeError: 'DataFrame' object has no attribute 'active'
So my questiion is: How to read specif cell from sheet from excelsheet?
Thank you
Oke, I tried with openpyxl:
import openpyxl
path = "./docs/fruit.xlsx"
wb_obj = openpyxl.load_workbook(path)
sheet_obj = wb_obj.active
cell_obj = sheet_obj.cell(row = 6, column = 9)
print(cell_obj.value)
But then the formula is printed. Like this:
=(H6*1000)/F6/G6
and not the value: 93

You can do this using openpyxl directly or pandas (which internally uses openpyxl behind the scene)...
Using Openpyxl
You will need to use data_only=True when you open the file. Also, make sure you know the row and column number. To read the data in H6, row would be 6 and 8 would be H
import openpyxl
path = "./docs/Schoolfruit.xlsx"
wb_obj = openpyxl.load_workbook(path, data_only=True)
sheet_obj = wb_obj.active ## Or use sheet_obj = wb_obj['Sheet1'] if you know sheet name
val = sheet_obj.cell(row = 6, column = 8).value
print(val)
Using Pandas
The other option is to use pandas read_excel() which will read the whole sheet into a dataframe. You can use iloc() or at() to read the specific cell. Note that this is probably the less optimal solution if you need to read just one cell...
Another point to note here is that, once you have read the data into a dataframe, the row 1 will be considered as the header and the first row would now be 0. So the row number would be 4 instead of 6. Similarly, the first column would now be 0 and not 1, which would change the position to [4,7]
import pandas as pd
path = "./docs/Schoolfruit.xlsx"
df = pd.read_excel(path, 'Sheet1')
print(df.iloc[4,7])

I found a solution and hope, it works for you.
import pandas as pd
excel_file = './docs/Schoolfruit.xlsx'
df = pd.read_excel(excel_file, sheet_name='active' ,header=None, skiprows=1)
print(df[7][4])
7: Hth column
4: 6th row (skipped first row and index is began from 0)

How to combine value in multiple cell references in Excel by Python?

I am trying to do something like grabbing all values in each cell while they are referencing one by one. Maybe an example help illustration.
Example:
A
B
C
=B2
='I am' & C2
'Peter
Example2 - in term of number:
A
B
C
D
=B2
=C2*D2
12
56
So I want to get a concat string 'I am Peter' or 672 (from 12*56) when I reading the cell A2
Code I tried:
from openpyxl import load_workbook
import pandas as pd
wb = load_workbook(filename = 'new.xlsx')
sheet_names = wb.get_sheet_names()
name = sheet_names[0]
sheet_ranges = wb[name]
df = pd.DataFrame(sheet_ranges.values)
print(df)
The formula will become 'NaN'
Any suggestion to achieve it? Thanks!

If you want to have the actual values of the cells, you have to use data_only=True
wb = load_workbook(filename = 'new.xlsx', data_only=True)
Look here: Read Excel cell value and not the formula computing it -openpyxl
Anyway, as you use pandas, it would be way easier to go directly:
import pandas as pd
df = pd.read_excel('new.xlsx')
print(df)
which grabs the first sheet (but could be specified) and gives the values as output.

openpyxl supports either the formula or the value of the formula. You can select which using the data_only parameter when loading a workbook.
You can change your code like below:
from openpyxl import load_workbook
import pandas as pd
wb = load_workbook(filename='new.xlsx', data_only=True)
sheet_names = wb.get_sheet_names()
name = sheet_names[0]
sheet_ranges = wb[name]
df = pd.DataFrame(sheet_ranges.values)
print(df)

Pandas: Remove rows from the dataframe that begin with a letter and save CSV

Here is a sample CSV I'm working with
Here is my code:
import numpy as np
import pandas as pd
def deleteSearchTerm(inputFile):
#(1) Open the file
df = pd.read_csv(inputFile)
#(2) Filter every row where the first letter is 's' from search term
df = df[df['productOMS'].str.contains('^[a-z]+')]
#REGEX to filter anything that would ^ (start with) a letter
inputFile = inputFile
deleteSearchTerm(inputFile)
What I want to do:
Anything in the column ProductOMS that begins with a letter would be a row that I don't want. So I'm trying to delete them based on a condition and I was also trying would regular expressions just so I'd get a little bit more comfortable with them.
I tried to do that with:
df = df[df['productOMS'].str.contains('^[a-z]+')]
where if any of the rows starts with any lower case letter I would drop it (I think)
Please let me know if I need to add anything to my post!
Edit:
Here is a link to a copy of the file I'm working with.
https://drive.google.com/file/d/1Dsw2Ana3WVIheNT43Ad4Dv6C8AIbvAlJ/view?usp=sharing
Another Edit: Here is the dataframe I'm working with
productNum,ProductOMS,productPrice
2463448,1002623072,419.95,
2463413,1002622872,289.95,
2463430,1002622974,309.95,
2463419,1002622908,329.95,
2463434,search?searchTerm=2463434,,
2463423,1002622932,469.95,
New Edit:
Here's some updated code using an answer
import numpy as np
import pandas as pd
def deleteSearchTerm(inputFile):
#(1) Open the file
df = pd.read_csv(inputFile)
print(df)
#(2) Filter every row where the first letter is 's' from search term
df = df[~pd.to_numeric(df['ProductOMS'],errors='coerce').isnull()]
print(df)
inputFile = inputFile
deleteSearchTerm(inputFile)
When I run this code and print out the dataframes this gets rid of the rows that start with 'search'. However my CSV file is not updating

The issue here is that you're most likely dealing with mixed data types.
if you just want numeric values you can use pd.to_numeric
df = pd.DataFrame({'A' : [0,1,2,3,'a12351','123a6']})
df[~pd.to_numeric(df['A'],errors='coerce').isnull()]
A
0 0
1 1
2 2
3 3
but if you only want to test the first letter then :
df[~df['A'].astype(str).str.contains('^[a-z]')==True]
A
0 0
1 1
2 2
3 3
5 123a6
Edit, it seems the first solution works, but you need to write this back to your csv?
you need to use the to_csv method, i'd recommend you read 10 minutes to pandas here
As for your function, lets edit it a little to take a source csv file and throw out an edited version, it will save the file to the same location with _edited added on. feel free to edit/change.
from pathlib import Path
def delete_search_term(input_file, column):
"""
Takes in a file and removes any strings from a given column
input_file : path to your file.
column : column with strings that you want to remove.
"""
file_path = Path(input_file)
if not file_path.is_file():
raise Exception('This file path is not valid')
df = pd.read_csv(input_file)
#(2) Filter every row where the first letter is 's' from search term
df = df[~pd.to_numeric(df[column],errors='coerce').isnull()]
print(f"Creating file as:\n{file_path.parent.joinpath(f'{file_path.stem}_edited.csv')}")
return df.to_csv(file_path.parent.joinpath(f"{file_path.stem}_edited.csv"),index=False)
Solution:
import numpy as np
import pandas as pd
def deleteSearchTerm(inputFile):
df = pd.read_csv(inputFile)
print(df)
#(2) Filter every row where the first letter is 's' from search term
df = df[~pd.to_numeric(df['ProductOMS'],errors='coerce').isnull()]
print(df)
return df.to_csv(inputFile)
inputFile = filePath
inputFile = deleteSearchTerm(inputFile)

Data from the source csv as shared at the google drive location:
'''
productNum,ProductOMS,productPrice,Unnamed: 3
2463448,1002623072,419.95,
2463413,1002622872,289.95,
2463430,1002622974,309.95,
2463419,1002622908,329.95,
2463434,search?searchTerm=2463434,,
2463423,1002622932,469.95,
'''
import pandas as pd
df = pd.read_clipboard()
Output:
productNum ProductOMS productPrice Unnamed: 3
0 2463448 1002623072 419.95 NaN
1 2463413 1002622872 289.95 NaN
2 2463430 1002622974 309.95 NaN
3 2463419 1002622908 329.95 NaN
4 2463434 search?searchTerm=2463434 NaN NaN
5 2463423 1002622932 469.95 NaN
.
df1 = df.loc[df['ProductOMS'].str.isdigit()]
print(df1)
Output:
productNum ProductOMS productPrice Unnamed: 3
0 2463448 1002623072 419.95 NaN
1 2463413 1002622872 289.95 NaN
2 2463430 1002622974 309.95 NaN
3 2463419 1002622908 329.95 NaN
5 2463423 1002622932 469.95 NaN

I hope it helps you:
df = pd.read_csv(filename)
df = df[~df['ProductOMS'].str.contains('^[a-z]+')]
df.to_csv(filename)

For the most part your function is fine but you seem to have forgotten to save the CSV, which is done by df.to_csv() method.
Let me rewrite the code for you:
import pandas as pd
def processAndSaveCSV(filename):
# Read the CSV file
df = pd.read_csv(filename)
# Retain only the rows with `ProductOMS` being numeric
df = df[df['ProductOMS'].str.contains('^\d+')]
# Save CSV File - Rewrites file
df.to_csv(filename)
Hope this helps :)

It looks like a scope problem to me.
First we need to return df:
def deleteSearchTerm(inputFile):
#(1) Open the file
df = pd.read_csv(inputFile)
print(df)
#(2) Filter every row where the first letter is 's' from search term
df = df[~pd.to_numeric(df['ProductOMS'],errors='coerce').isnull()]
print(df)
return df
Then replace the line
DeleteSearchTerm(InputFile)
with:
InputFile = DeleteSearchTerm(InputFile)
Basically your function is not returning anything.
After you fix that you just need to redefine your inputFile variable to the new dataframe your function is returning.
If you already defined df earlier in your code and you're trying to manipulate it, then the function is not actually changing your existing global df variable. Instead it's making a new local variable under the same name.
To fix this we first return the local df and then re-assign the global df to the local one.
You should be able to find more information about variable scope at this link:
https://www.geeksforgeeks.org/global-local-variables-python/
It also appears you never actually update your original file.
Try adding this to the end of your code:
df.to_csv('CSV file name', index=True)
Index just says whether you want to have a line index.

Import all excel sheets from a excel file into pandas [duplicate]

I am trying to read an excel file this way :
newFile = pd.ExcelFile(PATH\FileName.xlsx)
ParsedData = pd.io.parsers.ExcelFile.parse(newFile)
which throws an error that says two arguments expected, I don't know what the second argument is and also what I am trying to achieve here is to convert an Excel file to a DataFrame, Am I doing it the right way? or is there any other way to do this using pandas?

Close: first you call ExcelFile, but then you call the .parse method and pass it the sheet name.
>>> xl = pd.ExcelFile("dummydata.xlsx")
>>> xl.sheet_names
[u'Sheet1', u'Sheet2', u'Sheet3']
>>> df = xl.parse("Sheet1")
>>> df.head()
Tid dummy1 dummy2 dummy3 dummy4 dummy5 \
0 2006-09-01 00:00:00 0 5.894611 0.605211 3.842871 8.265307
1 2006-09-01 01:00:00 0 5.712107 0.605211 3.416617 8.301360
2 2006-09-01 02:00:00 0 5.105300 0.605211 3.090865 8.335395
3 2006-09-01 03:00:00 0 4.098209 0.605211 3.198452 8.170187
4 2006-09-01 04:00:00 0 3.338196 0.605211 2.970015 7.765058
dummy6 dummy7 dummy8 dummy9
0 0.623354 0 2.579108 2.681728
1 0.554211 0 7.210000 3.028614
2 0.567841 0 6.940000 3.644147
3 0.581470 0 6.630000 4.016155
4 0.595100 0 6.350000 3.974442
What you're doing is calling the method which lives on the class itself, rather than the instance, which is okay (although not very idiomatic), but if you're doing that you would also need to pass the sheet name:
>>> parsed = pd.io.parsers.ExcelFile.parse(xl, "Sheet1")
>>> parsed.columns
Index([u'Tid', u'dummy1', u'dummy2', u'dummy3', u'dummy4', u'dummy5', u'dummy6', u'dummy7', u'dummy8', u'dummy9'], dtype=object)

This is much simple and easy way.
import pandas
df = pandas.read_excel(open('your_xls_xlsx_filename','rb'), sheetname='Sheet 1')
# or using sheet index starting 0
df = pandas.read_excel(open('your_xls_xlsx_filename','rb'), sheetname=2)
Check out documentation full details.
FutureWarning: The sheetname keyword is deprecated for newer Pandas versions, use sheet_name instead.

Thought i should add here, that if you want to access rows or columns to loop through them, you do this:
import pandas as pd
# open the file
xlsx = pd.ExcelFile("PATH\FileName.xlsx")
# get the first sheet as an object
sheet1 = xlsx.parse(0)
# get the first column as a list you can loop through
# where the is 0 in the code below change to the row or column number you want
column = sheet1.icol(0).real
# get the first row as a list you can loop through
row = sheet1.irow(0).real
Edit:
The methods icol(i) and irow(i) are deprecated now. You can use sheet1.iloc[:,i] to get the i-th col and sheet1.iloc[i,:] to get the i-th row.

I think this should satisfy your need:
import pandas as pd
# Read the excel sheet to pandas dataframe
df = pd.read_excel("PATH\FileName.xlsx", sheet_name=0) #corrected argument name

Here is an updated method with syntax that is more common in python code. It also prevents you from opening the same file multiple times.
import pandas as pd
sheet1, sheet2 = None, None
with pd.ExcelFile("PATH\FileName.xlsx") as reader:
sheet1 = pd.read_excel(reader, sheet_name='Sheet1')
sheet2 = pd.read_excel(reader, sheet_name='Sheet2')
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_excel.html

You just need to feed the path to your file to pd.read_excel
import pandas as pd
file_path = "./my_excel.xlsx"
data_frame = pd.read_excel(file_path)
Checkout the documentation to explore parameters like skiprows to ignore rows when loading the excel

import pandas as pd
data = pd.read_excel (r'**YourPath**.xlsx')
print (data)

Loading an excel file without explicitly naming a sheet but instead giving the number of the sheet order (often one will simply load the first sheet) goes like:
import pandas as pd
myexcel = pd.ExcelFile("C:/filename.xlsx")
myexcel = myexcel.parse(myexcel.sheet_names[0])
Since .sheet_names returns a list of sheet names, it is easy to load one or more sheets by simply calling the list element(s).

All of these works for me
In [1]: import pandas as pd
In [2]: df = pd.read_excel('FileName.xlsx') # If there is only one sheet in the excel file
In [3]: df = pd.read_excel('FileName.xlsx', sheet_name=0)
In [4]: In [20]: df = pd.read_excel('FileName.xlsx', sheet_name='Sheet 1')

#load pandas library
import pandas as pd
#set path where the file is
path = "./myfile.xlsx"
#load the file into dataframe df
df = pd.read_excel(path)
#check the first 5 rows
df.head(5)

Accessing Excel columns by names in xlwings

In pandas Excel columns can be accessed using names that are assigned in the first row of the sheet. How can this be achieved in xlwings?

You can use Pandas as a converter as of xlwings 0.7.0. for an example workbook like this:
A B C
1 4 7
2 5 8
3 6 9
This code will read the table in and allow you to access the data via column headers. The key is the .options(pd.DataFrame, index=False) bit. That particular call will return a Pandas DataFrame, with a default index.
More info on xlwings converters here.
import xlwings as xw
import pandas as pd
def calc():
# Create a reference to the calling Excel xw.Workbook
wb = xw.Workbook.caller()
table = xw.Range('A1').table.options(pd.DataFrame, index=False).value
# Access columns as attributes of the Pandas DataFrame
print table.A
print table.B
# Access columns as column labels of the Pandas DataFrame
print table['A']
print table['B']
if __name__ == '__main__':
path = "test.xlsm"
xw.Workbook.set_mock_caller(path)
calc()

You can use square brackets to access the columns, as suggested here:
import xlwings as xw
wb = xw.Workbook.active()
xw.Range('TableName[ColumnName]').value