I'm trying to read in an Excel file with multiple sheets (s.t that all columns are strings). The below code works for that but it doen't get the correct sheet names. So my dic_excel which is a dictionary with all sheet names and the corresponding data has the following keys: 'Sheet1', 'Sheet2', 'Sheet3', etc. But the actual names of the sheets are different. How do I get the actual names of the sheets?
dic_excel={}
excel = pd.ExcelFile(excel_path)
for sheet in excel.sheet_names:
print(sheet)
columns = excel.parse(sheet).columns
converters = {col: str for col in columns}
dic_excel[sheet] = excel.parse(sheet, converters=converters)
Here is two ways to get the real names of your Excel sheets:
By using pandas.DataFrame.keys with pandas
import pandas as pd
excel = pd.read_excel(excel_path, sheet_name=None)
dic_excel = df.keys()
This will return a dictionnary of the sheetnames
By using Workbook.sheetname with openpyxl
import openpyxl
wb = openpyxl.load_workbook(excel_path)
list_excel = wb.sheetnames
This will return a list of the sheetnames
Related
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']
I am having trouble updating an Excel Sheet using pandas by writing new values in it. I already have an existing frame df1 that reads the values from MySheet1.xlsx. so this needs to either be a new dataframe or somehow to copy and overwrite the existing one.
The spreadsheet is in this format:
I have a python list: values_list = [12.34, 17.56, 12.45]. My goal is to insert the list values under Col_C header vertically. It is currently overwriting the entire dataframe horizontally, without preserving the current values.
df2 = pd.DataFrame({'Col_C': values_list})
writer = pd.ExcelWriter('excelfile.xlsx', engine='xlsxwriter')
df2.to_excel(writer, sheet_name='MySheet1')
workbook = writer.book
worksheet = writer.sheets['MySheet1']
How to get this end result? Thank you!
Below I've provided a fully reproducible example of how you can go about modifying an existing .xlsx workbook using pandas and the openpyxl module (link to Openpyxl Docs).
First, for demonstration purposes, I create a workbook called test.xlsx:
from openpyxl import load_workbook
import pandas as pd
writer = pd.ExcelWriter('test.xlsx', engine='openpyxl')
wb = writer.book
df = pd.DataFrame({'Col_A': [1,2,3,4],
'Col_B': [5,6,7,8],
'Col_C': [0,0,0,0],
'Col_D': [13,14,15,16]})
df.to_excel(writer, index=False)
wb.save('test.xlsx')
This is the Expected output at this point:
In this second part, we load the existing workbook ('test.xlsx') and modify the third column with different data.
from openpyxl import load_workbook
import pandas as pd
df_new = pd.DataFrame({'Col_C': [9, 10, 11, 12]})
wb = load_workbook('test.xlsx')
ws = wb['Sheet1']
for index, row in df_new.iterrows():
cell = 'C%d' % (index + 2)
ws[cell] = row[0]
wb.save('test.xlsx')
This is the Expected output at the end:
In my opinion, the easiest solution is to read the excel as a panda's dataframe, and modify it and write out as an excel. So for example:
Comments:
Import pandas as pd.
Read the excel sheet into pandas data-frame called.
Take your data, which could be in a list format, and assign it to the column you want. (just make sure the lengths are the same). Save your data-frame as an excel, either override the old excel or create a new one.
Code:
import pandas as pd
ExcelDataInPandasDataFrame = pd.read_excel("./YourExcel.xlsx")
YourDataInAList = [12.34,17.56,12.45]
ExcelDataInPandasDataFrame ["Col_C"] = YourDataInAList
ExcelDataInPandasDataFrame .to_excel("./YourNewExcel.xlsx",index=False)
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
I have an Excel workbook that I have already loaded and put all the sheets together, now I would like to add a column where I have the name of each original sheet, I don't know if I have to do it before I put everything together, and if that's how I could do it , I am using pandas. This is my code so far, I want the sheet name or number to be in the "Week" column.
xlsx= pd.ExcelFile('archivo.xlsx')
hojas=[]
for hojaslibro in xlsx.sheet_names:
hojas.append(xlsx.parse(hojaslibro))
estado=pd.concat(hojas,ignore_index=True)
estado['Week']=0
This should work:
xl = pd.ExcelFile('archvio.xlsx')
df_combined = pd.DataFrame()
for sheet_name in xl.sheet_names:
df = xl.parse(sheet_name)
df['Week'] = sheet_name # this adds `sheet_name` into the column `Week`
df_combined = df_combined.append(df)
I want to merge multiple excel sheets to one and to have a new column with the name of the original sheet
I'm using the following code:
list_of_sheets = list(df.keys())
cdf = pd.concat(df[sheet] for sheet in list_of_sheets)
# tried
cdf = pd.concat(df[sheet]["Brand"] for sheet in list_of_sheets)
# and
list_of_sheets = list(df.keys())
for sheet in list_of_sheets:
df[sheet]["Brand"] = sheet
cdf = pd.concat(df[sheet])
but none of them works
Does this accomplish what you want?
import pandas as pd
pd.concat(pd.read_excel("my_excel_file.xlsx", sheet_name=None))
The sheet's names will be the index of the dataframe.
First read the file:
xl = pd.ExcelFile(file)
Which should produce the following:
<pandas.io.excel.ExcelFile at 0x12cad0860>
Then iterate over the sheets, append the sheet name as a separate column and store all dfs in a list:
dfs = []
for sheet in xl.sheet_names:
df = xl.parse(sheet)
df['sheet_name'] = sheet
dfs.append(df)
In order to concat them at last:
pd.concat(dfs)