Pipe for method chaining in Data Analysis Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to use the pipe method to apply a function to the DataFrame.

Data Analysis Python

result = df.pipe([1], arg1)

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Atransform

Bpipe

Capply_func

Dgroupby

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'pipe' as the function argument instead of the function name.

Using 'groupby' which is not a function to pass here.

The pipe method takes a function as its first argument. Here, apply_func is the function to apply.

2fill in blank

medium

Complete the code to chain methods using pipe and reset_index.

Data Analysis Python

df.pipe(clean_data).[1](drop=True)

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Agroupby

Breset_index

Csort_values

Dapply

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'groupby' which changes grouping, not index.

Using 'apply' which is for functions, not resetting index.

After using pipe, you can chain other DataFrame methods like reset_index to reset the index.

3fill in blank

hard

Fix the error in the pipe usage by filling the blank with the correct argument.

Data Analysis Python

df.pipe([1]).head()

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Aclean_data.apply

Bclean_data()

Cclean_data(arg)

Dclean_data

Attempts:

3 left

💡 Hint

Common Mistakes

Passing the function call with parentheses, causing immediate execution.

Using an attribute like 'apply' which is not a function here.

The pipe method expects a function object, not a function call. So you pass clean_data without parentheses.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that applies a function to each column using pipe.

Data Analysis Python

result = {col: df[[col]].pipe([1]) for col in df.columns if df[col].[2](0).any()}

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Aclean_data

Bgt

Clt

Dsum

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'lt' which means less than, opposite of intended.

Using 'sum' which is a function, not a comparison method.

The dictionary comprehension applies clean_data to each column using pipe. The condition uses gt (greater than) to filter columns with values greater than 0.

5fill in blank

hard

Fill all three blanks to create a filtered dictionary applying a function with pipe and a condition.

Data Analysis Python

filtered = {col[1]: df[[col]].pipe([2]) for col in df.columns if df[col].[3](10).any()}

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A.upper()

Bprocess_data

Cgt

Dlower

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'lower' instead of 'upper' for keys.

Using 'lt' instead of 'gt' for the condition.

The dictionary keys are column names converted to uppercase using .upper(). The function process_data is applied with pipe. The condition uses gt to filter columns with values greater than 10.