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To replace current values with different values dynamically, replace is very helpful. To demonstrate this, if you wanted to replace the zeroes and ones in the current dataset with boolean values, true or false, you could do this using replace. In df, replace 0 with False and 1 with True. Store this output in df_bool.

To replace current values with different values dynamically, replace is very helpful. To demonstrate this, if you wanted to replace the zeroes and ones in the current dataset with boolean values, true or false, you could do this using replace. In df, replace 0 with False and 1 with True. Store this output in df_bool.

Database System Concepts
Database System Concepts
7th Edition
ISBN:9780078022159
Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Chapter1: Introduction
Section: Chapter Questions
Problem 1PE
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To replace current values with different values dynamically, replace is very helpful.

To demonstrate this, if you wanted to replace the zeroes and ones in the current dataset with boolean values, true or false, you could do this using replace.

In df, replace 0 with False and 1 with True.

Store this output in df_bool.

 

To replace current values with different values dynamically, replace is very helpful. To demonstrate this, if you wanted to replace the zeroes and ones in the current dataset with boolean values, true or false, you could do this using replace. In df , replace o with False and 1 with True . Store this output in df_bool. In [ ]: # YOUR CODE HERE df bool = In [ ]: assert df_bool.shape df.shape == assert df_bool.loc[1,'chocolate'] assert df_bool.iloc[0,0] assert type (df_bool.loc[1,'chocolate']) != np.int64 True == '100 Grand' In [ ]: df_bool
Transcribed Image Text:To replace current values with different values dynamically, replace is very helpful. To demonstrate this, if you wanted to replace the zeroes and ones in the current dataset with boolean values, true or false, you could do this using replace. In df , replace o with False and 1 with True . Store this output in df_bool. In [ ]: # YOUR CODE HERE df bool = In [ ]: assert df_bool.shape df.shape == assert df_bool.loc[1,'chocolate'] assert df_bool.iloc[0,0] assert type (df_bool.loc[1,'chocolate']) != np.int64 True == '100 Grand' In [ ]: df_bool
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