How can you drop a column named "age" from a data frame df?df$age <- NULLremove(df$age)df[-age]delete(df, age)
Question
How can you drop a column named "age" from a data frame df?dfage)df[-age]delete(df, age)
Solution
In R, you can drop a column named "age" from a data frame df using the following command:
df$age <- NULL
This command will remove the column "age" from the data frame df.
The other commands you mentioned are not correct for this task.
remove(df$age)is not a valid command becauseremove()function is used to remove objects from the workspace, not to remove columns from a data frame.df[-age]is not a valid command because-ageis not a valid index. If you want to remove a column by its index, you should use positive integer and don't forget to add comma before the index to specify that it's a column index.delete(df, age)is not a valid command because there is nodelete()function in base R that can be used to delete columns from a data frame.
Similar Questions
When using the drop command in python to remove columns from a dataframe, what should you remember?To either specify columns, or to specify axis = 1To either specify columns, or to specify axis = 2To either specify not rows, or to specify axis = 0To either specify columns, or to specify axis = 0
Copy df in df1a and calculate the scaled age for each row in df1a based on theirage relative to the range of ages in the dataset. Store the results in a new column namedScaled_Age.Hint: The formula for calculating scaled age is:Scaled_Age = (Age - MinimumAge) / (MaximumAge – MinimumAge)
Based on the dataframe generated in practice 1, use Loop to create a new columnnamed Age_Comparison indicating whether the age of a person is higher, lower or the same asthe average age in the dataset. (You can use Higher/Same/Lower)
Copy df in df1a and calculate the scaled age for each row in df1a based on theirage relative to the range of ages in the dataset. Store the results in a new column namedScaled_Age.Hint: The formula for calculating scaled age is:Scaled_Age = (Age - MinimumAge) / (MaximumAge – MinimumAge)Practice 2. Based on the dataframe generated in practice 1, use Loop to create a new columnnamed Age_Comparison indicating whether the age of a person is higher, lower or the same asthe average age in the dataset. (You can use Higher/Same/Lower).Practice 3: Based on the dataframe generated in practice 2, use the loc command to chooseonly rows from index 100 to index 500 and columns Age, Scaled_Age and Age_Comparison.Save the results in a new dataframe named df_quiz and reset the index.
How can you drop missing values in a Pandas DataFrame?Using the drop() methodUsing the dropna() methodUsing the fillna() methodUsing the isnull() method
Upgrade your grade with Knowee
Get personalized homework help. Review tough concepts in more detail, or go deeper into your topic by exploring other relevant questions.