dtypes for column data types 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'

Atype

Bdtype

Cinfo

Ddtypes

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'dtypes' instead of 'dtype' for a single column.

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

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

2fill in blank

medium

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

Pandas

import pandas as pd
data = {'height': [5.5, 6.0], 'weight': [130, 180]}
df = pd.DataFrame(data)
print(df.[1])

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

Adtypes

Bdtype

Ctype

Dinfo

Attempts:

3 left

💡 Hint

Common Mistakes

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

Using 'info' which prints summary but not a Series of dtypes.

The 'dtypes' attribute returns a Series with data types of all columns.

3fill in blank

hard

Fix the error in the code to print the data type of the 'score' column.

Pandas

import pandas as pd
data = {'score': [88, 92, 85]}
df = pd.DataFrame(data)
print(df.score.[1])

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

Ainfo

Bdtypes

Ctype

Ddtype

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'dtypes' on a Series causes an error.

Using 'type' returns Python type, not pandas dtype.

Accessing 'dtype' on a Series (a single column) returns its data type correctly.

4fill in blank

hard

Fill both blanks to create a dictionary of column names and their data types for columns with integer type.

Pandas

import pandas as pd
data = {'a': [1, 2], 'b': [3.5, 4.5], 'c': [5, 6]}
df = pd.DataFrame(data)
int_cols = {col: df[col].[1] for col in df.columns if df[col].[2] == 'int64'}
print(int_cols)

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

Adtype

Bdtypes

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'dtypes' for a single column which causes errors.

Comparing to wrong data type string.

Use 'dtype' to get the data type of each column and compare it to 'int64' to filter integer columns.

5fill in blank

hard

Fill all three blanks to create a dictionary with uppercase column names as keys and their data types as values for columns with float type.

Pandas

import pandas as pd
data = {'x': [1.1, 2.2], 'y': [3, 4], 'z': [5.5, 6.6]}
df = pd.DataFrame(data)
float_cols = [1]: df[[2]].[3] for [2] in df.columns if df[[2]].dtype == 'float64'}
print(float_cols)

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

Acol.upper()

Bcol

Cdtype

Ddtypes

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'dtypes' for single column data type.

Using the column name directly without uppercasing.

Use 'col.upper()' for uppercase keys, 'col' for iterating columns, and 'dtype' to get each column's data type.

Practice

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

easy

A. The names of the columns

B. The number of rows in the DataFrame

C. The total memory used by the DataFrame

D. The type of data stored in each column

Solution

1. Step 1: Understand what dtypes means

   The dtypes attribute shows the data type of each column in a DataFrame, like integer, float, or string.

2. Step 2: Compare options with the definition

   Only the type of data stored in each column correctly describes that dtypes tells the type of data stored in each column.

3. Final Answer:

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

4. Quick Check:

   dtypes = data type per column [OK]

Hint: Remember: dtypes show column data types, not counts or names [OK]

Common Mistakes:

• Confusing dtypes with DataFrame shape
• Thinking dtypes shows column names
• Assuming dtypes shows memory usage

2. Which of the following is the correct way to check the data types of columns in a pandas DataFrame named df?

easy

A. df.dtypes

B. df.types()

C. df.get_dtypes()

D. df.columns.types

Solution

1. Step 1: Recall the correct pandas syntax

   To check column data types, pandas uses the attribute dtypes without parentheses.

2. Step 2: Evaluate each option

   df.dtypes uses df.dtypes, which is correct. Options B and C use incorrect method names, and D is not a valid attribute.

3. Final Answer:

   df.dtypes -> Option A

4. Quick Check:

   Use df.dtypes to check column types [OK]

Hint: Use df.dtypes without parentheses to see column types [OK]

Common Mistakes:

• Adding parentheses after dtypes
• Using non-existent methods like get_dtypes()
• Confusing dtypes with columns attribute

3. Given the following code:

import pandas as pd
data = {'A': [1, 2, 3], 'B': [1.5, 2.5, 3.5], 'C': ['x', 'y', 'z']}
df = pd.DataFrame(data)
print(df.dtypes)

What will be the output?

medium

A. A object
B object
C object
dtype: object

B. A float64
B int64
C object
dtype: object

C. A int64
B float64
C object
dtype: object

D. A int32
B float32
C string
dtype: object

Solution

1. Step 1: Identify data types from the data dictionary

   Column 'A' has integers (1,2,3) so type is int64 by default. Column 'B' has floats (1.5,2.5,3.5) so type is float64. Column 'C' has strings ('x','y','z') which pandas treats as object type.

2. Step 2: Match with the printed output format

   The output shows each column name with its dtype, followed by 'dtype: object'. A int64 B float64 C object dtype: object matches this exactly.

3. Final Answer:

   A int64 B float64 C object dtype: object -> Option C

4. Quick Check:

   Integers=int64, floats=float64, strings=object [OK]

Hint: Integers default to int64, floats to float64, strings to object [OK]

Common Mistakes:

• Assuming strings show as 'string' dtype
• Confusing int32 with int64
• Mixing float and int types

4. You run this code:

import pandas as pd
df = pd.DataFrame({'X': [1, 2, 3], 'Y': ['a', 'b', 'c']})
print(df.dtypes())

But get an error. What is the problem?

medium

A. The DataFrame is empty

B. You should use df.dtypes without parentheses

C. Column names must be integers

D. You need to import numpy first

Solution

1. Step 1: Identify the error cause

   The code uses df.dtypes() with parentheses, but dtypes is an attribute, not a method.

2. Step 2: Correct the syntax

   Remove the parentheses and use df.dtypes to get the data types without error.

3. Final Answer:

   You should use df.dtypes without parentheses -> Option B

4. Quick Check:

   dtypes is attribute, not method [OK]

Hint: Remember: dtypes has no parentheses, it's not a function [OK]

Common Mistakes:

• Calling dtypes as a function
• Thinking DataFrame is empty causes error
• Believing numpy import is needed for dtypes

5. You have a DataFrame df with columns: age (integers), income (floats), and member (True/False). You want to check the data types and convert member to integer (1 for True, 0 for False). Which code correctly does this?

hard

A. print(df.dtypes)
df['member'] = df['member'].astype(int)

B. print(df.types)
df['member'] = int(df['member'])

C. print(df.dtypes())
df['member'] = df['member'].to_int()

D. print(df.columns)
df['member'] = df['member'].astype('bool')

Solution

1. Step 1: Check data types correctly

   Use df.dtypes (no parentheses) to print data types. Options B and C use incorrect syntax.

2. Step 2: Convert boolean column to integer

   Use astype(int) to convert True/False to 1/0. print(df.dtypes) df['member'] = df['member'].astype(int) does this correctly. print(df.columns) df['member'] = df['member'].astype('bool') converts to bool again, which is unnecessary.

3. Final Answer:

   print(df.dtypes) df['member'] = df['member'].astype(int) -> Option A

4. Quick Check:

   Use df.dtypes and astype(int) for boolean to int [OK]

Hint: Use df.dtypes and astype(int) to convert booleans to 0/1 [OK]

Common Mistakes:

• Calling dtypes as a method
• Trying to convert column with int() directly
• Converting boolean to bool again instead of int