Reading Excel files (read_excel) in Data Analysis Python - Step-by-Step Execution

Concept Flow - Reading Excel files (read_excel)

Start

↓

Call pd.read_excel()

↓

Locate Excel file

↓

Open file and read sheet

↓

Convert sheet data to DataFrame

↓

Return DataFrame

↓

End

The process starts by calling read_excel, which opens the Excel file, reads the specified sheet, converts it into a DataFrame, and returns it.

Execution Sample

Data Analysis Python

import pandas as pd

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

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

Execution Table

Step	Action	Input/Parameter	Result/Output
1	Call pd.read_excel()	'data.xlsx'	Function called with file name
2	Locate Excel file	'data.xlsx'	File found in current directory
3	Open file and read sheet	Default sheet (first sheet)	Sheet data loaded
4	Convert sheet data to DataFrame	Sheet data	DataFrame created with rows and columns
5	Return DataFrame	DataFrame	DataFrame assigned to variable df
6	Print df.head()	df	First 5 rows of DataFrame printed

💡 Execution stops after printing the first 5 rows of the DataFrame.

Variable Tracker

Variable	Start	After read_excel	After print
df	None	DataFrame with Excel data	DataFrame with Excel data (unchanged)

Key Moments - 3 Insights

What happens if the Excel file is not in the current folder?

The code will raise a FileNotFoundError at step 2 in the execution table because it cannot locate the file.

Why does read_excel return a DataFrame?

Because pandas converts the Excel sheet data into a DataFrame format at step 4, which is easy to analyze and manipulate.

What does df.head() show?

At step 6, df.head() prints the first 5 rows of the DataFrame to give a quick look at the data.

Visual Quiz - 3 Questions

Look at the execution table, what is the output of step 4?

A A DataFrame created with rows and columns

B The Excel file is saved

C The file is closed

D An error message

💡 Hint

Check the 'Result/Output' column for step 4 in the execution table.

At which step is the Excel file located?

A Step 1

B Step 2

C Step 3

D Step 5

💡 Hint

Look at the 'Action' column in the execution table to find when the file is found.

If the file name is wrong, what will happen according to the execution flow?

A The DataFrame will be empty

B The code will print an empty DataFrame

C A FileNotFoundError will occur at step 2

D The code will read a default file

💡 Hint

Refer to the key moment about file location and step 2 in the execution table.

Concept Snapshot

pd.read_excel('file.xlsx')
- Reads Excel file into a DataFrame
- Default reads first sheet
- Returns DataFrame for analysis
- Raises error if file not found
- Use df.head() to preview data

Full Transcript

This visual execution shows how pandas reads an Excel file using read_excel. First, the function is called with the file name. Then pandas locates the file in the current folder. It opens the file and reads the default first sheet. The sheet data is converted into a DataFrame, which is returned and assigned to the variable df. Finally, df.head() prints the first 5 rows to show the data. If the file is missing, a FileNotFoundError occurs. This process helps you load Excel data easily for analysis.

Practice

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

easy

A. It reads data from an Excel file into a DataFrame.

B. It writes data from a DataFrame to an Excel file.

C. It converts a DataFrame to a CSV file.

D. It creates a new Excel file from scratch.

Solution

1. Step 1: Understand the purpose of read_excel

   The function pandas.read_excel() is designed to load data from Excel files into Python as a DataFrame.

2. Step 2: Differentiate from other file operations

   Writing data to Excel or converting to CSV are done by other functions like to_excel() or to_csv(), not read_excel().

3. Final Answer:

   It reads data from an Excel file into a DataFrame. -> Option A

4. Quick Check:

   read_excel loads Excel data [OK]

Hint: Remember: read_excel loads data, to_excel saves data [OK]

Common Mistakes:

• Confusing read_excel with to_excel
• Thinking it writes data instead of reading
• Mixing up CSV and Excel functions

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: Identify the correct parameter name

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

2. Step 2: Check case sensitivity and spelling

   Parameter names are case-sensitive and must be spelled exactly as documented. Also, sheet names are case-sensitive, so 'Sales' is correct if that is 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 exact parameter name 'sheet_name' to select sheets [OK]

Common Mistakes:

• Using 'sheet' instead of 'sheet_name'
• Misspelling 'sheet_name' as 'sheetname'
• Ignoring case sensitivity in sheet names

3. What will be the output of this code snippet?

import pandas as pd
df = pd.read_excel('data.xlsx', usecols='A:C')
print(df.columns.tolist())

Assuming columns A, B, C in the Excel file are named 'Name', 'Age', 'City'.

medium

A. ['A', 'B', 'C']

B. Error: Invalid usecols parameter

C. ['Unnamed: 0', 'Unnamed: 1', 'Unnamed: 2']

D. ['Name', 'Age', 'City']

Solution

1. Step 1: Understand usecols='A:C'

   The parameter usecols='A:C' selects columns A to C from the Excel file, which correspond to the columns named 'Name', 'Age', and 'City'.

2. Step 2: Check what df.columns contains

   After reading, df.columns contains the actual column names from the Excel file, so printing df.columns.tolist() outputs ['Name', 'Age', 'City'].

3. Final Answer:

   ['Name', 'Age', 'City'] -> Option D

4. Quick Check:

   usecols selects columns by letter, output shows column names [OK]

Hint: usecols='A:C' picks columns A to C by letter [OK]

Common Mistakes:

• Expecting column letters instead of names in output
• Confusing usecols syntax causing errors
• Assuming default numeric column indexing

4. You run this code but get an error:

import pandas as pd
df = pd.read_excel('data.xlsx', sheet_name=1, usecols=[0, 2, 5])

What is the most likely cause of the error?

medium

A. The file path 'data.xlsx' is incorrect or file missing.

B. The usecols list contains an invalid column index.

C. The sheet index 1 does not exist in the Excel file.

D. The parameter 'sheet_name' must be a string, not an integer.

Solution

1. Step 1: Check sheet_name parameter

   Using sheet_name=1 is valid to select the second sheet by index (0-based), so this is not an error cause.

2. Step 2: Validate usecols parameter

   The list [0, 2, 5] is valid to select columns by zero-based index, so this is not an error cause.

3. Step 3: Consider file path and existence

   If the file 'data.xlsx' does not exist or path is wrong, pandas raises a FileNotFoundError, which is a common cause of errors when reading Excel files.

4. Final Answer:

   The file path 'data.xlsx' is incorrect or file missing. -> Option A

5. Quick Check:

   FileNotFoundError usually means wrong file path [OK]

Hint: Check file path first if read_excel errors [OK]

Common Mistakes:

• Assuming sheet_name must be string only
• Thinking usecols list indexes are invalid
• Ignoring file existence or path errors

5. You want to read only the columns 'Date', 'Sales', and 'Profit' from the sheet named '2023' in an Excel file 'report.xlsx'. Which code snippet correctly does this?

hard

A. pd.read_excel('report.xlsx', sheet_name='2023', usecols='Date,Sales,Profit')

B. pd.read_excel('report.xlsx', sheet='2023', columns=['Date', 'Sales', 'Profit'])

C. pd.read_excel('report.xlsx', sheet_name='2023', usecols=['Date', 'Sales', 'Profit'])

D. pd.read_excel('report.xlsx', sheet_name='2023', usecols=[0,1,2])

Solution

1. Step 1: Use correct parameter names

   The parameter to select the sheet is sheet_name, and to select columns by name is usecols with a list of column names.

2. Step 2: Check option correctness

   pd.read_excel('report.xlsx', sheet_name='2023', usecols=['Date', 'Sales', 'Profit']) uses sheet_name='2023' and usecols=['Date', 'Sales', 'Profit'], which is the correct syntax to read only those columns from the specified sheet.

3. Step 3: Identify errors in other options

   pd.read_excel('report.xlsx', sheet='2023', columns=['Date', 'Sales', 'Profit']) uses wrong parameter names sheet and columns. pd.read_excel('report.xlsx', sheet_name='2023', usecols='Date,Sales,Profit') uses a string for usecols which is invalid for column names. pd.read_excel('report.xlsx', sheet_name='2023', usecols=[0,1,2]) selects columns by index, not by name.

4. Final Answer:

   pd.read_excel('report.xlsx', sheet_name='2023', usecols=['Date', 'Sales', 'Profit']) -> Option C

5. Quick Check:

   Use sheet_name and list in usecols for named columns [OK]

Hint: Use list of column names in usecols to select columns [OK]

Common Mistakes:

• Using wrong parameter names like 'sheet' or 'columns'
• Passing column names as a comma string instead of list
• Selecting columns by index when names are needed