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Multiple sheets of an Excel workbook into different dataframes using Pandas

I have a Excel workbook which has 5 sheets containing data.
I want each sheet to be a different dataframe.
I tried using the below code for one sheet of my Excel Sheet
df = pd.read_excel("path",sheet_name = ['Product Capacity'])
df
But this returns the sheet as a dictionary of the sheet, not a dataframe.
I need a data frame.
Please suggest the code that will return a dataframe

If you want separate dataframes without dictionary, you have to read individual sheets:
with pd.ExcelFile('data.xlsx') as xlsx:
prod_cap = pd.read_excel(xlsx, sheet_name='Product Capacity')
load_cap = pd.read_excel(xlsx, sheet_name='Load Capacity')
# and so on
But you can also load all sheets and use a dict:
dfs = pd.read_excel('data.xlsx', sheet_name=None)
# dfs['Product Capacity']
# dfs['Load Capacity']

how to use element as dataframe name when looping over a list

I need to read data from several sheets in a xlsx file, and save data as a dataframe with the same name as sheet name. Here is the code I use. It can read data from different sheets, however, all dataframes are named as temp. How should I change it. Thanks.
import pandas as pd
sheet_name_list = ['sheet1','sheet2','sheet3']
for temp in sheet_name_list:
temp = pd.read_excel("data_spreadsheet.xlsx", sheet_name = temp)

You can use dictionary:
pd_dict = {}
for temp in sheet_name_list:
pd_dict[temp] = pd.read_excel("data_spreadsheet.xlsx", sheet_name=temp)

Read CSV sheet data and created new one

I have a CSV file which have multiple sheets in it. Want to read it sheet by sheet and filter some data and want to create csv file in same format. how can I do that. Please suggest. I was trying it though pandas.ExcelReader but its not working for CSV file.

you can use the following code for this may help!
import pandas as pd
def read_excel_sheets(xls_path):
"""Read all sheets of an Excel workbook and return a single DataFrame"""
print(f'Loading {xls_path} into pandas')
xl = pd.ExcelFile(xls_path)
df = pd.DataFrame()
columns = None
for idx, name in enumerate(xl.sheet_names):
print(f'Reading sheet #{idx}: {name}')
sheet = xl.parse(name)
if idx == 0:
# Save column names from the first sheet to match for append
columns = sheet.columns
sheet.columns = columns
# Assume index of existing data frame when appended
df = df.append(sheet, ignore_index=True)
return df
the resource for this code is the link below:
click here
and for converting it back to csv you can follow the post which link is
attached here

How to read separate Excel sheets into separate DataFrames?

I have an Excel file with 13 tabs, and I want to write a function that takes specified sheets from the file, converts them into separate DataFrames, then bundles them into a list of DataFrames. In this case, I want to take the sheets labeled 'tblProviderDetails', 'tblSubmissionStatus', and 'Data Validation Ref Data', convert them into DataFrames and make a list. The reason I want the dfs in a list, is because I want to eventually want to take the input dfs and return a dictionary which will then be used to create a YAML file.
This is ultimately what I want:
dfs = [ 'tblProviderDetails', 'tblSubmissionStatus', 'Data Validation Ref Data']
The reason that I want to use a user-defined function is that I want the flexibility to call any sheet and any number of sheets into a list.
I was able to write a function that converts single specified sheets to dataframes, but I'm not sure how to call any number of sheets in the Excel file or create a list within the function. This is as far as I've gotten:
def read_excel(path, sheet_name, header):
dfs = pd.read_excel(path, sheet_name=sheet_name, header=header)
return dfs
df1 = read_excel(path=BASEDIR, sheet_name='tblProviderDetails', header=2)
df2 = read_excel(path=BASEDIR, sheet_name='tblSubmissionStatus', header=2)
df3 = read_excel(path=BASEDIR, sheet_name='Data Validation Ref Data', header=2)
Thank you for your help.

There are multiple ways to do this but perhaps the simplest way is to first get all the sheet names and then in a loop for every sheet name, load the result in a data frame and append it to the required list.
dfList = []
def read_excel(path, h):
xls = pd.ExcelFile(path)
# Now you can access all sheetnames in the file
sheetsList = xls.sheet_names
# ['sheet1', 'sheet2', ...]
for sheet in sheetsList:
dfList.append(pd.read_excel(path, sheet_name=sheet, header
=h))
read_excel('book.xlsx',2)
print(dfList)

You can pass the a list of sheet names and\or sheet number to parameter sheet_name.
def read_excel(path, sheet_name, header):
sheet_name = ['tblProviderDetails','tblSubmissionStatus','Data Validation
Ref Data']
dfs = pd.read_excel(path, sheet_name=sheet_name, header=header)
return dfs

Convert excel file with many sheets (with spaces in the name of the shett) in pandas data frame

I would like to convert an excel file to a pandas dataframe. All the sheets name have spaces in the name, for instances, ' part 1 of 22, part 2 of 22, and so on. In addition the first column is the same for all the sheets.
I would like to convert this excel file to a unique dataframe. However I dont know what happen with the name in python. I mean I was hable to import them, but i do not know the name of the data frame.
The sheets are imported but i do not know the name of them. After this i would like to use another 'for' and use a pd.merge() in order to create a unique dataframe
for sheet_name in Matrix.sheet_names:
sheet_name = pd.read_excel(Matrix, sheet_name)
print(sheet_name.info())

Using only the code snippet you have shown, each sheet (each DataFrame) will be assigned to the variable sheet_name. Thus, this variable is overwritten on each iteration and you will only have the last sheet as a DataFrame assigned to that variable.
To achieve what you want to do you have to store each sheet, loaded as a DataFrame, somewhere, a list for example. You can then merge or concatenate them, depending on your needs.
Try this:
all_my_sheets = []
for sheet_name in Matrix.sheet_names:
sheet_name = pd.read_excel(Matrix, sheet_name)
all_my_sheets.append(sheet_name)
Or, even better, using list comprehension:
all_my_sheets = [pd.read_excel(Matrix, sheet_name) for sheet_name in Matrix.sheet_names]
You can then concatenate them into one DataFrame like this:
final_df = pd.concat(all_my_sheets, sort=False)

You might consider using the openpyxl package:
from openpyxl import load_workbook
import pandas as pd
wb = load_workbook(filename=file_path, read_only=True)
all_my_sheets = wb.sheetnames
# Assuming your sheets have the same headers and footers
n = 1
for ws in all_my_sheets:
records = []
for row in ws._cells_by_row(min_col=1,
min_row=n,
max_col=ws.max_column,
max_row=n):
rec = [cell.value for cell in row]
records.append(rec)
# Make sure you don't duplicate the header
n = 2
# ------------------------------
# Set the column names
records = records[header_row-1:]
header = records.pop(0)
# Create your df
df = pd.DataFrame(records, columns=header)

It may be easiest to call read_excel() once, and save the contents into a list.
So, the first step would look like this:
dfs = pd.read_excel(["Sheet 1", "Sheet 2", "Sheet 3"])
Note that the sheet names you use in the list should be the same as those in the excel file. Then, if you wanted to vertically concatenate these sheets, you would just call:
final_df = pd.concat(dfs, axis=1)
Note that this solution would result in a final_df that includes column headers from all three sheets. So, ideally they would be the same. It sounds like you want to merge the information, which would be done differently; we can't help you with the merge without more information.
I hope this helps!