Exporting to CSV in Data Analysis Python - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

What is the output of this CSV export code?

Consider the following Python code that exports a DataFrame to a CSV file. What will be the content of the CSV file?

Data Analysis Python

import pandas as pd

data = {'Name': ['Alice', 'Bob'], 'Age': [25, 30]}
df = pd.DataFrame(data)
df.to_csv('output.csv', index=False)

A "Name,Age\nAlice,25\nBob,30\n"

B "0,Name,Age\n0,Alice,25\n1,Bob,30\n"

C "Name,Age\n0,Alice\n1,Bob\n"

D "Name,Age\nAlice,25\nBob,30"

💡 Hint

Check the effect of index=False in to_csv method.

The parameter index=False tells pandas not to write row numbers to the CSV. So the CSV contains only the column headers and the data rows.

❓ data_output

intermediate

How many rows will the exported CSV contain?

Given this DataFrame export code, how many data rows (excluding header) will the CSV file have?

Data Analysis Python

import pandas as pd

data = {'City': ['NY', 'LA', 'Chicago'], 'Population': [8000000, 4000000, 2700000]}
df = pd.DataFrame(data)
df.to_csv('cities.csv', index=False)

A 1

B 4

C 2

D 3

💡 Hint

Count the number of rows in the DataFrame.

The DataFrame has 3 rows, so the CSV will have 3 data rows plus 1 header row.

🔧 Debug

advanced

Why does this CSV export include row numbers?

This code exports a DataFrame to CSV but the CSV includes row numbers as the first column. Why?

Data Analysis Python

import pandas as pd

data = {'Product': ['Pen', 'Pencil'], 'Price': [1.5, 0.5]}
df = pd.DataFrame(data)
df.to_csv('products.csv')

A Because index=True by default, so row numbers are included.

B Because the DataFrame has a custom index that is exported.

C Because the to_csv method requires index=False to include row numbers.

D Because the CSV format always includes row numbers.

💡 Hint

Check the default value of the index parameter in to_csv.

By default, to_csv includes the DataFrame index as the first column. To exclude it, you must set index=False.

🚀 Application

advanced

How to export only specific columns to CSV?

You have a DataFrame with columns 'A', 'B', 'C'. You want to export only columns 'A' and 'C' to a CSV file. Which code does this correctly?

Data Analysis Python

import pandas as pd

data = {'A': [1,2], 'B': [3,4], 'C': [5,6]}
df = pd.DataFrame(data)

A df.to_csv('subset.csv', columns=['A', 'C'], index=False)

B df[['A', 'C']].to_csv('subset.csv', index=False)

C df.to_csv('subset.csv', index=False).filter(['A', 'C'])

D df.to_csv('subset.csv', index=False).loc[:, ['A', 'C']]

💡 Hint

Check the columns parameter in to_csv method.

The to_csv method accepts a columns parameter to select columns to export. Option A uses this correctly. Option B also works by selecting columns before exporting.

🧠 Conceptual

expert

What happens if you export a DataFrame with missing values to CSV?

You export a DataFrame containing some missing values (NaN) to CSV using to_csv with default settings. How are missing values represented in the CSV?

A Missing values are replaced by zeros in the CSV.

B Missing values are saved as empty fields in the CSV.

C Missing values cause an error during export.

D Missing values are saved as the string 'NaN' in the CSV.

💡 Hint

Check the default na_rep parameter in to_csv.

By default, missing values are written as empty fields in the CSV file.

Practice

1. What does the to_csv() method do in pandas?

easy

A. Deletes a CSV file from disk

B. Reads a CSV file into a DataFrame

C. Converts a CSV file to Excel format

D. Exports a DataFrame to a CSV file

Solution

1. Step 1: Understand the purpose of to_csv()

   The to_csv() method is used to save or export a pandas DataFrame to a CSV file format.

2. Step 2: Differentiate from other methods

   Methods like read_csv() are for reading CSV files, not exporting. to_csv() specifically writes data to CSV.

3. Final Answer:

   Exports a DataFrame to a CSV file -> Option D

4. Quick Check:

   to_csv() exports data [OK]

Hint: Remember: 'to' means export/save, 'read' means import/load [OK]

Common Mistakes:

• Confusing to_csv() with read_csv()
• Thinking it deletes files
• Assuming it converts file formats

2. Which of the following is the correct syntax to export a DataFrame named df to a CSV file called data.csv without saving the index?

easy

A. df.to_csv('data.csv', index=False)

B. df.to_csv('data.csv', index=True)

C. df.to_csv('data.csv', save_index=False)

D. df.export_csv('data.csv', index=False)

Solution

1. Step 1: Identify correct method and parameters

   The method to export is to_csv(). To exclude the index, use index=False.

2. Step 2: Check each option

   df.to_csv('data.csv', index=True) saves index (wrong). df.to_csv('data.csv', index=False) correctly uses index=False. df.to_csv('data.csv', save_index=False) uses wrong parameter name. df.export_csv('data.csv', index=False) uses a non-existent method.

3. Final Answer:

   df.to_csv('data.csv', index=False) -> Option A

4. Quick Check:

   Use index=False to skip index [OK]

Hint: Use index=False to skip row numbers [OK]

Common Mistakes:

• Using index=True when index should be excluded
• Using wrong parameter names like save_index
• Calling non-existent methods like export_csv

3. Given the code below, what will be the content of the file output.csv?

import pandas as pd
data = {'Name': ['Anna', 'Bob'], 'Age': [28, 34]}
df = pd.DataFrame(data)
df.to_csv('output.csv', index=False)

medium

A. Name,Age
0,Anna,28
1,Bob,34

B. "",Name,Age
0,Anna,28
1,Bob,34

C. Name,Age
Anna,28
Bob,34

D. 43,boB
82,annA
egA,emaN

Solution

1. Step 1: Understand index=False effect

   Setting index=False means row numbers are not saved in the CSV file.

2. Step 2: Analyze CSV content

   The CSV will have header row "Name,Age" and data rows "Anna,28" and "Bob,34" without any index column.

3. Final Answer:

   Name,Age Anna,28 Bob,34 -> Option C

4. Quick Check:

   Index excluded means no row numbers in CSV [OK]

Hint: Index=False means no row numbers saved in CSV [OK]

Common Mistakes:

• Expecting index column to be saved despite index=False
• Confusing CSV header with index column
• Misreading CSV format with extra commas

4. What is wrong with this code snippet?

import pandas as pd
data = {'A': [1, 2], 'B': [3, 4]}
df = pd.DataFrame(data)
df.to_csv('file.csv', index=No)

medium

A. The filename must be a full path, not just 'file.csv'

B. The parameter index should be a boolean, not 'No'

C. The DataFrame is empty and cannot be saved

D. The method to_csv does not exist

Solution

1. Step 1: Check parameter types

   The index parameter expects a boolean value: True or False.

2. Step 2: Identify the error

   Using index=No is invalid because 'No' is not a boolean and will cause a NameError.

3. Final Answer:

   The parameter index should be a boolean, not 'No' -> Option B

4. Quick Check:

   Use True or False for index [OK]

Hint: Boolean parameters must be True or False, not words [OK]

Common Mistakes:

• Using strings like 'No' or 'Yes' instead of booleans
• Misspelling method names
• Assuming filename must be absolute path

5. You have a DataFrame df with columns ['Name', 'Score']. You want to export only the Name column to a CSV file named names.csv without the index. Which code snippet achieves this?

hard

A. df[['Name']].to_csv('names.csv', index=False)

B. df.to_csv('names.csv', columns='Name', index=False)

C. df.to_csv('names.csv', index=False, columns=['name'])

D. df.to_csv('names.csv', index=False, usecols=['Name'])

Solution

1. Step 1: Select only the 'Name' column

   Using df[['Name']] creates a DataFrame with just the 'Name' column.

2. Step 2: Export without index

   Calling to_csv() with index=False saves the file without row numbers.

3. Step 3: Check other options

   Options A, C, and D use incorrect parameters for selecting columns in to_csv().

4. Final Answer:

   df[['Name']].to_csv('names.csv', index=False) -> Option A

5. Quick Check:

   Select columns first, then export [OK]

Hint: Select columns first with df[['Name']], then export [OK]

Common Mistakes:

• Using columns='Name' (string instead of list)
• Using usecols which is for reading, not writing
• Exporting whole DataFrame without selecting columns