dtypes and data type checking in Pandas - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to check the data type of the 'age' column in the DataFrame.

Pandas

import pandas as pd

data = {'name': ['Alice', 'Bob'], 'age': [25, 30]}
df = pd.DataFrame(data)
print(df['age'].[1])

Drag options to blanks, or click blank then click option'

Adtype

Bdtypes

Ctype

Dinfo

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'dtypes' which is for the whole DataFrame.

Using 'type' which returns the Python type of the object, not the data type of the column.

The dtype attribute shows the data type of a single column in a DataFrame.

2fill in blank

medium

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

Pandas

import pandas as pd

data = {'name': ['Alice', 'Bob'], 'age': [25, 30]}
df = pd.DataFrame(data)
print(df.[1])

Drag options to blanks, or click blank then click option'

Ainfo

Bdtype

Ctype

Ddtypes

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'dtype' which works only for a single column.

Using 'info' which prints summary but does not return data types directly.

The dtypes attribute returns a Series with the data types of all columns in the DataFrame.

3fill in blank

hard

Fix the error in the code to check if the 'score' column is of integer type.

Pandas

import pandas as pd

data = {'score': [88, 92, 79]}
df = pd.DataFrame(data)
print(df['score'].dtype == '[1]')

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Aint64

Binteger

Cint32

Dint

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'int' which is a Python type, not a pandas dtype.

Using 'integer' which is not a valid dtype string.

The dtype for integer columns in pandas is usually int64 on 64-bit systems.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps column names to their data types for columns with object type.

Pandas

import pandas as pd

data = {'name': ['Alice', 'Bob'], 'age': [25, 30], 'city': ['NY', 'LA']}
df = pd.DataFrame(data)
obj_cols = {col: df[col].[1] for col in df.columns if df[col].[2] == 'object'}
print(obj_cols)

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Adtype

Bdtypes

Cdtype.name

Ddtype.type

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'dtypes' which is for DataFrame, not Series.

Using 'dtype' alone which returns a dtype object, not a string.

Accessing dtype.name gives the string name of the data type, which can be compared to 'object'.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps column names to their data types for columns with numeric types.

Pandas

import pandas as pd
import numpy as np

data = {'height': [5.5, 6.0], 'weight': [130, 150], 'name': ['Alice', 'Bob']}
df = pd.DataFrame(data)
numeric_cols = {col: df[col].[1] for col in df.columns if np.[2](df[col].dtype, np.number) and df[col].dtype != '[3]'}
print(numeric_cols)

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Adtype

Bdtypes

Cissubdtype

Disnumeric

Eint64

Fobject

Gfloat64

Hint32

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'dtypes' instead of 'dtype' for Series.

Using 'isnumeric' which is not a numpy function.

Comparing dtype to 'int64' or 'float64' directly instead of excluding 'object'.

Use dtype to get the column's data type, np.issubdtype to check if it is numeric, and exclude 'object' dtype columns.