Long to wide format conversion in Pandas - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is the purpose of converting data from long to wide format?

Converting data from long to wide format reshapes the data so that each unique value in a specified column becomes a new column. This makes it easier to compare values side by side, like turning a list of measurements into a table with separate columns for each measurement type.

beginner

Which pandas function is commonly used to convert data from long to wide format?

The pandas function pivot() or pivot_table() is used to convert data from long to wide format. They rearrange the data by setting one column as the new columns and another as the values.

intermediate

What is the difference between pivot() and pivot_table() in pandas?

pivot() requires unique index/column pairs and will fail if duplicates exist. pivot_table() can handle duplicates by aggregating values using a function like mean or sum.

beginner

Given a DataFrame with columns ['Date', 'City', 'Temperature'] in long format, how would you convert it to wide format with cities as columns?

Use df.pivot(index='Date', columns='City', values='Temperature'). This makes each city a column and rows indexed by date, showing temperatures side by side.

intermediate

Why might you need to reset the index after pivoting a DataFrame?

Pivoting often sets one or more columns as the index. Resetting the index with reset_index() turns the index back into regular columns, which can be easier to work with or export.

Which pandas function would you use to convert a DataFrame from long to wide format when there are duplicate entries for the same index and column?

A melt()

B pivot()

C pivot_table()

D groupby()

pivot_table() can handle duplicates by aggregating values, while pivot() cannot.

In a long format DataFrame, which column typically becomes the new columns in wide format after pivoting?

A Value column

B Column specified in <code>columns=</code> parameter

C Index column

D Any numeric column

The column specified in the columns= parameter becomes the new columns in the wide format.

What happens if you try to use pivot() on data with duplicate index-column pairs?

A It raises an error

B It converts duplicates to NaN

C It ignores duplicates

D It automatically aggregates duplicates

pivot() requires unique index-column pairs and will raise an error if duplicates exist.

After pivoting, why might you want to use reset_index()?

A To convert index back to columns

B To remove duplicate rows

C To sort the DataFrame

D To rename columns

reset_index() converts the index back into regular columns for easier access.

Which of these is NOT a typical use case for converting long to wide format?

A Comparing values side by side

B Preparing data for plotting

C Creating summary tables

D Reducing the number of columns

Converting long to wide format usually increases the number of columns, not reduces them.

Explain how to convert a DataFrame from long to wide format using pandas. Include the key function and parameters.

Think about which columns become rows, columns, and values.

Describe the difference between pivot() and pivot_table() in pandas and when to use each.

Consider how duplicates in data affect the choice.