Reading Excel files with read_excel in Pandas - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to read an Excel file named 'data.xlsx' into a DataFrame.

Pandas

import pandas as pd

df = pd.read_excel([1])

Drag options to blanks, or click blank then click option'

A'data.xlsx'

B'data.csv'

C'data.txt'

D'data.json'

Attempts:

3 left

💡 Hint

Common Mistakes

Using a CSV or text file name instead of an Excel file.

Forgetting to put the file name inside quotes.

The read_excel function reads Excel files. The filename must end with .xlsx to read an Excel file.

2fill in blank

medium

Complete the code to read the Excel file 'report.xlsx' and select the sheet named 'Sales'.

Pandas

df = pd.read_excel('report.xlsx', sheet_name=[1])

Drag options to blanks, or click blank then click option'

A'Sheet1'

B'Summary'

C'Data'

D'Sales'

Attempts:

3 left

💡 Hint

Common Mistakes

Using the wrong sheet name.

Not putting the sheet name inside quotes.

The sheet_name parameter lets you choose which sheet to read. Here, the sheet is named 'Sales'.

3fill in blank

hard

Fix the error in the code to read only columns 'A' and 'C' from 'data.xlsx'.

Pandas

df = pd.read_excel('data.xlsx', usecols=[1])

Drag options to blanks, or click blank then click option'

A['A', 'C']

B['A:C']

C'A,C'

D'A:C'

Attempts:

3 left

💡 Hint

Common Mistakes

Using a list instead of a string for usecols.

Using a range string like 'A:C' which selects all columns between A and C.

The usecols parameter accepts a string like 'A,C' to select columns A and C.

4fill in blank

hard

Fill both blanks to read 'file.xlsx' selecting sheet 2 and skipping the first 3 rows.

Pandas

df = pd.read_excel([1], sheet_name=[2], skiprows=3)

Drag options to blanks, or click blank then click option'

A'file.xlsx'

B'file.csv'

C1

D2

Attempts:

3 left

💡 Hint

Common Mistakes

Using the wrong file extension.

Confusing sheet index with sheet name.

The file name must be 'file.xlsx'. Sheet 2 is selected by index 1 (0-based index means sheet 0 is first, so 1 is second sheet).

5fill in blank

hard

Fill all three blanks to read 'data.xlsx', parse dates in column 'Date', and set 'ID' as index.

Pandas

df = pd.read_excel([1], parse_dates=[[2]], index_col=[3])

Drag options to blanks, or click blank then click option'

A'data.csv'

B'Date'

C'ID'

D'data.xlsx'

Attempts:

3 left

💡 Hint

Common Mistakes

Using a CSV file name instead of Excel.

Not putting the date column name inside a list.

Not setting the correct index column.

The file name is 'data.xlsx'. The parse_dates parameter takes a list of column names to convert to dates. The index_col sets the index column.

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