Reading Excel files with read_excel in Pandas - Cheat Sheet & Quick Revision

Recall & Review

beginner

What function in pandas is used to read Excel files?

The function read_excel() is used to read Excel files into a pandas DataFrame.

beginner

How do you specify which sheet to read from an Excel file using read_excel()?

Use the sheet_name parameter to specify the sheet name or index. For example, sheet_name='Sheet1' or sheet_name=0.

beginner

What type of object does read_excel() return?

read_excel() returns a pandas DataFrame, which is a table-like data structure.

intermediate

How can you read multiple sheets at once using read_excel()?

Set sheet_name=None to read all sheets. It returns a dictionary with sheet names as keys and DataFrames as values.

intermediate

What parameter would you use to skip rows at the start of an Excel sheet when reading it?

Use the skiprows parameter to skip a number of rows at the start. For example, skiprows=2 skips the first two rows.

Which pandas function reads an Excel file into a DataFrame?

A read_json()

B read_csv()

C read_excel()

D read_table()

read_excel() is the correct function to read Excel files.

How do you read the second sheet of an Excel file using read_excel()?

A sheet_name=1

B sheet_name='second'

C sheet=2

D sheet='Sheet2'

Sheets are zero-indexed, so sheet_name=1 reads the second sheet.

What does read_excel() return when sheet_name=None?

A A NumPy array

B A list of DataFrames

C A single DataFrame

D A dictionary of DataFrames

It returns a dictionary with sheet names as keys and DataFrames as values.

Which parameter skips rows at the start of the sheet when reading Excel?

A header

B skiprows

C skipfooter

D index_col

skiprows skips rows at the beginning of the sheet.

If you want to read only columns A and C from an Excel sheet, which parameter helps?

A usecols='A,C'

B columns=['A','C']

C cols='A,C'

D select_cols='A,C'

The usecols parameter selects specific columns by name or letter.

Explain how to read an Excel file with multiple sheets using pandas.

Think about how to get all sheets at once.

Describe how to skip the first 3 rows and read only specific columns from an Excel sheet.

Focus on parameters that control rows and columns.

Practice

1. What does the pandas.read_excel() function do?

easy

A. Saves a pandas DataFrame to an Excel file

B. Creates a new Excel file

C. Loads data from an Excel file into a pandas DataFrame

D. Converts Excel formulas into Python code

Solution

1. Step 1: Understand the purpose of read_excel

   The function pandas.read_excel() is designed to read data from Excel files.

2. Step 2: Identify the output type

   It loads the Excel data into a pandas DataFrame, which is a table-like structure for data analysis.

3. Final Answer:

   Loads data from an Excel file into a pandas DataFrame -> Option C

4. Quick Check:

   read_excel loads Excel data [OK]

Hint: Remember: read_excel reads, not writes Excel files [OK]

Common Mistakes:

• Confusing read_excel with to_excel
• Thinking it creates Excel files
• Assuming it converts formulas automatically

2. Which of the following is the correct syntax to read the sheet named 'Sales' from an Excel file 'data.xlsx' using pandas?

easy

A. pd.read_excel('data.xlsx', sheet='Sales')

B. pd.read_excel('data.xlsx', sheet_name='Sales')

C. pd.read_excel('data.xlsx', sheetname='Sales')

D. pd.read_excel('data.xlsx', sheetname=Sales)

Solution

1. Step 1: Recall the correct parameter name

   The correct parameter to specify the sheet is sheet_name, not sheet or sheetname.

2. Step 2: Check the syntax and quotes

   pd.read_excel('data.xlsx', sheet_name='Sales') uses sheet_name='Sales' correctly with quotes around the sheet name.

3. Final Answer:

   pd.read_excel('data.xlsx', sheet_name='Sales') -> Option B

4. Quick Check:

   Use sheet_name to select sheet [OK]

Hint: Use sheet_name, not sheet or sheetname [OK]

Common Mistakes:

• Using sheet instead of sheet_name
• Missing quotes around sheet name
• Passing sheetname without quotes causing error

3. Given the Excel file 'report.xlsx' with two sheets: '2023' and '2024'. The '2023' sheet has columns 'Month', 'Sales', 'Profit'. What will be the output of this code?

import pandas as pd
df = pd.read_excel('report.xlsx', sheet_name='2023', usecols=['Month', 'Profit'])
print(df.head())

medium

A. A DataFrame with columns 'Month' and 'Profit' showing first 5 rows

B. A DataFrame with all columns from '2023' sheet

C. Error because usecols expects column indexes, not names

D. Empty DataFrame because 'Profit' column is missing

Solution

1. Step 1: Understand usecols parameter

   The usecols parameter accepts a list of column names to read only those columns.

2. Step 2: Check the sheet and columns requested

   The code reads sheet '2023' and selects columns 'Month' and 'Profit'. It then prints the first 5 rows with head().

3. Final Answer:

   A DataFrame with columns 'Month' and 'Profit' showing first 5 rows -> Option A

4. Quick Check:

   usecols selects columns by name [OK]

Hint: usecols can take column names list [OK]

Common Mistakes:

• Thinking usecols only accepts indexes
• Expecting all columns without usecols
• Assuming head() prints all rows

4. What is wrong with this code snippet to read only the first 10 rows from 'data.xlsx'?

import pandas as pd
df = pd.read_excel('data.xlsx', nrows=10)
print(df)

medium

A. Nothing is wrong; the code correctly reads the first 10 rows

B. The file path is incorrect

C. Missing sheet_name parameter causes error

D. The print statement is incorrect

Solution

1. Step 1: Check if nrows is supported by read_excel

   The read_excel function supports the nrows parameter to limit the number of rows read.

2. Step 2: Verify other components

   The import is correct, file path is standard, sheet_name is optional (uses first sheet), and print(df) displays the DataFrame.

3. Final Answer:

   Nothing is wrong; the code correctly reads the first 10 rows -> Option A

4. Quick Check:

   nrows supported in read_excel [OK]

Hint: read_excel supports nrows parameter [OK]

Common Mistakes:

• Thinking nrows is not supported by read_excel
• Assuming sheet_name is mandatory
• Thinking print causes error

5. You have an Excel file 'sales_data.xlsx' with multiple sheets named by year (e.g., '2021', '2022'). You want to read only the sheets for years 2021 and 2022 and combine them into one DataFrame. Which code snippet correctly does this?

hard

A. df = pd.read_excel('sales_data.xlsx', sheet_name='2021,2022')

B. df1 = pd.read_excel('sales_data.xlsx', sheet_name='2021')
df2 = pd.read_excel('sales_data.xlsx', sheet_name='2022')
df_combined = df1.append(df2)

C. df = pd.read_excel('sales_data.xlsx', sheet_name=['2021', '2022'])
df_combined = df.concat()

D. df = pd.read_excel('sales_data.xlsx', sheet_name=['2021', '2022'])
df_combined = pd.concat(df.values())

Solution

1. Step 1: Read multiple sheets using sheet_name list

   Using sheet_name=['2021', '2022'] reads both sheets and returns a dictionary of DataFrames.

2. Step 2: Combine the DataFrames from dictionary values

   pd.concat(df.values()) concatenates all DataFrames in the dictionary into one DataFrame.

3. Final Answer:

   df = pd.read_excel('sales_data.xlsx', sheet_name=['2021', '2022']) df_combined = pd.concat(df.values()) -> Option D

4. Quick Check:

   Use sheet_name list + concat dict values [OK]

Hint: Read sheets as dict, then concat values [OK]

Common Mistakes:

• Using sheet_name string with comma
• Trying to call concat on dict
• Using deprecated append method