info() for column types in Data Analysis Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to display the summary info of the DataFrame.

Data Analysis Python

df.[1]()

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Ainfo

Bdescribe

Chead

Dtail

Attempts:

3 left

💡 Hint

Common Mistakes

Using describe() which shows statistics, not column types.

Using head() or tail() which show rows, not info.

The info() method shows a summary of the DataFrame including column types.

2fill in blank

medium

Complete the code to print the data types of all columns in the DataFrame.

Data Analysis Python

print(df.[1])

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Acolumns

Bdtypes

Cshape

Dindex

Attempts:

3 left

💡 Hint

Common Mistakes

Using df.columns which lists column names, not types.

Using df.shape which shows DataFrame size.

The dtypes attribute shows the data type of each column.

3fill in blank

hard

Fix the error in the code to show DataFrame info with memory usage.

Data Analysis Python

df.info(memory_[1]=True)

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Ause

Bdisplay

Cshow

Dusage

Attempts:

3 left

💡 Hint

Common Mistakes

Using incorrect parameter names like memory_use or memory_show.

Forgetting the underscore between memory and usage.

The correct parameter name is memory_usage to show memory details.

4fill in blank

hard

Fill both blanks to create a dictionary of column names and their data types.

Data Analysis Python

col_types = {col: df[col].[1] for col in df.[2]

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Adtype

Bcolumns

Cdtypes

Dindex

Attempts:

3 left

💡 Hint

Common Mistakes

Using df.dtypes in the loop which returns a Series, not column names.

Using df.index which lists row labels, not columns.

Use dtype to get a column's type and columns to loop over column names.

5fill in blank

hard

Fill all three blanks to filter columns with numeric data types using info() output.

Data Analysis Python

numeric_cols = [col for col in df.[1] if df.[2][col] in ['int64', [3]]]

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Acolumns

Bdtypes

C'float64'

D'object'

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'object' instead of 'float64' for numeric types.

Looping over df.dtypes which gives types, not column names.

Loop over columns, check each column's dtypes, and include 'float64' as numeric type.