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.