CODEWITHSUNDEEP
pythonpandasnumpyopenpyxl
Lynn
Lynn

Reputation: 4408

Iterate through different tabs of excel, extract data and put into a dataframe

I have a single excel workbook, df, that contains two tabs, Sheet1 and Sheet2. I would like to extract values from both tabs and create a new dataframe using openpyxl/Pandas.

Sheet1

      2021    2021
      q1      q2
ID    1       1
ID2   3       3
name  A       A

Sheet2

  2021    2021
      q1      q2
ID    2       2
ID2   2       2
name  B       B

enter image description here

Desired

quarter year ID   ID2   name

q1     2021  1    3     A
q1     2021  2    2     B

Doing

#Load openpyxl

import openpyxl

wb = openpyxl.load_workbook("df.xlsx")
ws1 = wb.worksheets[0]
ws2 = wb.worksheets[1]


#create loop that will iterate over the first row and end at 2nd column for each sheet

for row in ws1.iter_rows(min_row = 0, max_col = 1, max_row = 3, min_col = 0 
                            for cell in row:
                                 print(cell.value, end="")
                            print()


for row in ws2.iter_rows(min_row = 0, max_col = 1,  max_row = 3, min_col = 0 
                            for cell in row:
                                 print(cell.value, end="")
                            print()

I am having trouble with creating a new dataframe from the values collected. Any suggestion or input is appreciated. I am still troubleshooting this.

Upvotes: 2

Views: 847

Answers (1)

Andreas
Andreas

Reputation: 9207

pd.read_excel can read a specific sheet or multiple, like shown below:

import pandas as pd
dict_dfs = pd.read_excel("df.xlsx", sheet_name=[0,1])

df = pd.concat(dict_dfs)

Afterwards you can iterate over the dictionary of dataframes or combine them directly if the format of the excel files already allows it.

Example, after loading with sample data:

import pandas as pd
df1 = pd.DataFrame({'2021': {'_': 'q1', 'ID': '2', 'ID2': '2', 'name': 'B'},
 '2021.1': {'_': 'q2', 'ID': '2', 'ID2': '2', 'name': 'B'}})
df2 = pd.DataFrame({'2021': {'_': 'q1', 'ID': '1', 'ID2': '3', 'name': 'A'},
 '2021.1': {'_': 'q2', 'ID': '1', 'ID2': '3', 'name': 'A'}})


df = pd.concat([df1.T,df2.T])
df.index = df.index.str.split(".").str[0]
print(df)
#        _ ID ID2 name
# 2021  q1  2   2    B
# 2021  q2  2   2    B
# 2021  q1  1   3    A
# 2021  q2  1   3    A

The .Tgives you the transposed dataframe.

Upvotes: 1

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