Reading Excel files with read_excel in Pandas - Step-by-Step Execution

Concept Flow - Reading Excel files with read_excel

Start

↓

Call pd.read_excel()

↓

File path given?

No→Error: File path needed

Yes↓

Read Excel file

↓

Load data into DataFrame

↓

Return DataFrame

↓

End

The process starts by calling pandas read_excel with a file path. It reads the Excel file and loads the data into a DataFrame, which is then returned.

Execution Sample

Pandas

import pandas as pd

df = pd.read_excel('data.xlsx')
print(df.head())

This code reads an Excel file named 'data.xlsx' into a DataFrame and prints the first 5 rows.

Execution Table

Step	Action	Input/Condition	Result	Output
1	Import pandas	N/A	pandas module loaded	N/A
2	Call pd.read_excel()	File path='data.xlsx'	Check if file exists	File found
3	Read Excel file	File found	Parse Excel content	Data loaded into DataFrame
4	Return DataFrame	Data loaded	DataFrame object created	DataFrame with Excel data
5	Print df.head()	DataFrame available	Show first 5 rows	Printed first 5 rows of data
6	End	N/A	Process complete	N/A

💡 Process ends after DataFrame is created and first 5 rows are printed.

Variable Tracker

Variable	Start	After pd.read_excel()	After print(df.head())	Final
df	undefined	DataFrame with Excel data	DataFrame unchanged	DataFrame unchanged

Key Moments - 3 Insights

What happens if the file path is incorrect or the file does not exist?

pandas will raise a FileNotFoundError at step 2 in the execution_table because it cannot find the file to read.

Does read_excel automatically show the data after reading?

No, read_excel only loads data into a DataFrame. You must explicitly print or display it, as shown in step 5.

Can read_excel read multiple sheets at once?

By default, read_excel reads the first sheet. To read multiple sheets, you must specify the sheet_name parameter.

Visual Quiz - 3 Questions

Look at the execution_table, what is the value of 'df' after step 3?

A A string with the file path

B Undefined

C A DataFrame containing the Excel data

D An error message

💡 Hint

Check the 'Result' and 'Output' columns at step 3 in the execution_table.

At which step does the program print the first 5 rows of the data?

A Step 5

B Step 2

C Step 4

D Step 6

💡 Hint

Look for the action involving print(df.head()) in the execution_table.

If the file 'data.xlsx' does not exist, what will happen at step 2?

A The file will be created automatically

B pandas will raise a FileNotFoundError

C The program will skip reading and continue

D An empty DataFrame will be returned

💡 Hint

Refer to key_moments about file path errors and step 2 in the execution_table.

Concept Snapshot

pandas.read_excel('file.xlsx')
- Reads Excel file into a DataFrame
- Requires valid file path
- By default reads first sheet
- Use df.head() to preview data
- Raises error if file missing

Full Transcript

This visual execution shows how pandas reads an Excel file using read_excel. First, pandas is imported. Then read_excel is called with the file path 'data.xlsx'. The program checks if the file exists. If found, it reads the Excel content and loads it into a DataFrame named df. Finally, df.head() prints the first 5 rows of the data. If the file is missing, an error occurs. This step-by-step trace helps beginners see how data moves from file to DataFrame.

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