replace() for value substitution in Data Analysis Python - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does the replace() method do in pandas DataFrames?

The replace() method substitutes specified values in a DataFrame or Series with new values. It helps clean or modify data by changing unwanted or incorrect values.

beginner

How do you replace all occurrences of the value 0 with 100 in a pandas DataFrame column named 'score'?

You can use: <br>df['score'] = df['score'].replace(0, 100)<br>This changes every 0 in 'score' to 100.

intermediate

Can replace() handle multiple values at once? How?

Yes! You can pass a dictionary to replace() where keys are old values and values are new ones. For example:<br>df.replace({0: 100, -1: 999}) replaces 0 with 100 and -1 with 999 in the whole DataFrame.

intermediate

What happens if you use replace() on a DataFrame without assigning it back or using inplace=True?

The original DataFrame stays the same because replace() returns a new DataFrame with changes. You must assign it back or use inplace=True to modify the original.

beginner

Is it possible to replace values only in specific columns using replace()?

Yes. You can select columns first, then apply replace(). For example:<br>df['col1'] = df['col1'].replace(old, new) changes values only in 'col1'.

What does df.replace(0, 100) do?

A Replaces 100 with 0

B Replaces all 0 values with 100 in the entire DataFrame

C Adds 100 to all 0 values

D Deletes all rows with 0

The replace() method substitutes all 0 values with 100.

How do you replace multiple values at once in pandas?

A Using a dictionary mapping old to new values

B Using a list of values

C Using a tuple of values

D Using a string pattern

Passing a dictionary like {old_value: new_value} replaces multiple values.

What must you do to keep changes after using replace()?

A Nothing, changes are automatic

B Call <code>save()</code> method

C Assign the result back or use <code>inplace=True</code>

D Restart the kernel

By default, replace() returns a new object; assign it or use inplace=True to modify original.

Can replace() be used on a single column?

A Only on string columns

B No, only on entire DataFrames

C Only on numeric columns

D Yes, by selecting the column first

You select the column first, then apply replace() to it.

Which of these is a correct way to replace -1 with NaN in pandas?

A df.replace(-1, np.nan)

B df.replace(-1, None)

C df.replace(-1, 'NaN')

D df.replace(-1, 0)

Use np.nan to represent missing values in pandas.

Explain how to use replace() to change multiple values in a DataFrame.

Think about passing a map of old to new values.

Describe what happens if you call replace() without assignment or inplace=True.

Consider how pandas methods handle changes by default.