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]')

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

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)

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

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)

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

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.

Practice

1. What does the dtypes attribute in a pandas DataFrame tell you?

easy

A. The type of data stored in each column

B. The number of rows in the DataFrame

C. The names of the columns

D. The total memory used by the DataFrame

Solution

1. Step 1: Understand what dtypes represents

   The dtypes attribute shows the data type of each column in a DataFrame, such as integer, float, or object (text).

2. Step 2: Compare with other DataFrame attributes

   Other attributes like shape give row and column counts, and columns give column names, but dtypes specifically shows data types.

3. Final Answer:

   The type of data stored in each column -> Option A

4. Quick Check:

   dtypes = data types per column [OK]

Hint: Remember: dtypes = data types of columns [OK]

Common Mistakes:

• Confusing dtypes with number of rows
• Thinking dtypes shows column names
• Assuming dtypes shows memory usage

2. Which of the following is the correct way to check the data type of a single column named 'age' in a pandas DataFrame df?

easy

A. df.dtypes('age')

B. df['age'].dtype

C. df.dtype['age']

D. df.get_dtype('age')

Solution

1. Step 1: Recall syntax for accessing column dtype

   To get the data type of a single column, use df['column_name'].dtype.

2. Step 2: Check other options for correctness

   Options B, C, and D use incorrect syntax or methods that do not exist in pandas.

3. Final Answer:

   df['age'].dtype -> Option B

4. Quick Check:

   Single column dtype = df['column'].dtype [OK]

Hint: Use df['column'].dtype to check one column's type [OK]

Common Mistakes:

• Using parentheses with dtypes like a function
• Trying to access dtype as a dictionary
• Using non-existent methods like get_dtype

3. Given the following code:

import pandas as pd
data = {'name': ['Anna', 'Bob'], 'score': [85, 92]}
df = pd.DataFrame(data)
print(df.dtypes)

What will be the output?

medium

A. name object
score object
dtype: object

B. name int64
score int64
dtype: object

C. name float64
score int64
dtype: object

D. name object
score int64
dtype: object

Solution

1. Step 1: Identify data types of each column

   The 'name' column contains text, so pandas assigns it the 'object' dtype. The 'score' column contains integers, so it gets 'int64'.

2. Step 2: Understand pandas output format

   Printing df.dtypes shows each column name with its dtype, followed by 'dtype: object' indicating the Series type.

3. Final Answer:

   name object score int64 dtype: object -> Option D

4. Quick Check:

   Text = object, integers = int64 [OK]

Hint: Text columns show as object, numbers as int64 or float64 [OK]

Common Mistakes:

• Assuming text columns show as string dtype
• Confusing int64 with float64 for integers
• Expecting score to be object dtype

4. You run this code:

import pandas as pd
data = {'A': [1, 2], 'B': ['x', 'y']}
df = pd.DataFrame(data)
print(df['A'].dtypes())

What error will you get?

medium

A. AttributeError: 'Series' object has no attribute 'dtypes'

B. TypeError: 'dtype' object is not callable

C. SyntaxError: invalid syntax

D. No error, prints int64

Solution

1. Step 1: Identify the issue with the code

   A pandas Series (df['A']) has a .dtype attribute (singular), not .dtypes (plural, which is for DataFrames).

2. Step 2: Understand the error caused

   Accessing .dtypes on a Series raises AttributeError: 'Series' object has no attribute 'dtypes'. The parentheses would cause further issues if the attribute existed.

3. Final Answer:

   AttributeError: 'Series' object has no attribute 'dtypes' -> Option A

4. Quick Check:

   Series: .dtype | DataFrame: .dtypes [OK]

Hint: Series: .dtype | DataFrame: .dtypes [OK]

Common Mistakes:

• Confusing Series .dtype with DataFrame .dtypes
• Adding parentheses after attributes
• Expecting no error from wrong attribute

5. You have a DataFrame df with columns ['id', 'value', 'date']. The value column is stored as object dtype but contains numbers as strings. Which pandas command will convert value to numeric dtype safely, turning invalid parsing into NaN?

hard

A. df['value'] = df['value'].astype('float64')

B. df['value'] = df['value'].astype(int)

C. df['value'] = pd.to_numeric(df['value'], errors='coerce')

D. df['value'] = df['value'].convert_dtypes()

Solution

1. Step 1: Understand the problem with object dtype

   The 'value' column has numbers stored as strings, so converting directly with astype(int) or astype(float64) may fail if there are invalid strings.

2. Step 2: Use pd.to_numeric with errors='coerce'

   This function converts strings to numbers and replaces invalid parsing with NaN, preventing errors.

3. Step 3: Check other options

   df['value'] = df['value'].convert_dtypes() uses convert_dtypes() which infers types but does not coerce invalid strings safely.

4. Final Answer:

   df['value'] = pd.to_numeric(df['value'], errors='coerce') -> Option C

5. Quick Check:

   Use pd.to_numeric with errors='coerce' for safe conversion [OK]

Hint: Use pd.to_numeric with errors='coerce' to convert safely [OK]

Common Mistakes:

• Using astype(int) on strings causing errors
• Assuming convert_dtypes fixes string numbers
• Not handling invalid strings causing crashes