Numeric types (int64, float64) in Pandas - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of dtype after arithmetic operation

What is the dtype of the 'result' column after adding an int64 and a float64 column in pandas?

Pandas

import pandas as pd

df = pd.DataFrame({
    'A': pd.Series([1, 2, 3], dtype='int64'),
    'B': pd.Series([0.1, 0.2, 0.3], dtype='float64')
})
df['result'] = df['A'] + df['B']
print(df['result'].dtype)

A object

B int64

C float64

D int32

💡 Hint

Think about what happens when you add integers and floats in pandas columns.

When you add an int64 column to a float64 column, pandas upcasts the result to float64 to preserve decimal values.

❓ data_output

intermediate

Count of float64 values after conversion

After converting a pandas Series of integers to float64, how many values remain float64?

Pandas

import pandas as pd

s = pd.Series([10, 20, 30], dtype='int64')
s = s.astype('float64')
print(s.dtype)
print(s.count())

A dtype: float64, count: 3

B dtype: int64, count: 3

C dtype: float64, count: 0

D dtype: object, count: 3

💡 Hint

Check the dtype after astype conversion and count of non-null values.

The Series is converted to float64 dtype and all 3 values remain non-null, so count is 3.

🔧 Debug

advanced

Identify the error in dtype conversion

What error will this code raise when converting a string column with non-numeric values to int64?

Pandas

import pandas as pd

df = pd.DataFrame({'col': ['1', '2', 'three']})
df['col'] = df['col'].astype('int64')

A TypeError: cannot convert float NaN to integer

B ValueError: invalid literal for int() with base 10: 'three'

C KeyError: 'col'

D No error, conversion succeeds

💡 Hint

Check what happens when a non-numeric string is converted to int.

The string 'three' cannot be converted to int64, so pandas raises a ValueError.

❓ visualization

advanced

Visualizing dtype distribution in DataFrame

Which code snippet correctly plots the count of int64 and float64 columns in a DataFrame?

Pandas

import pandas as pd
import matplotlib.pyplot as plt

df = pd.DataFrame({
    'int_col': pd.Series([1, 2, 3], dtype='int64'),
    'float_col': pd.Series([1.1, 2.2, 3.3], dtype='float64'),
    'str_col': ['a', 'b', 'c']
})

A df.value_counts().plot(kind='bar'); plt.show()

B df.dtypes.plot(kind='bar'); plt.show()

C df.select_dtypes(include=['int64', 'float64']).plot(kind='bar'); plt.show()

D df.dtypes.value_counts().loc[['int64', 'float64']].plot(kind='bar'); plt.show()

💡 Hint

Use dtypes and value_counts to count column types before plotting.

Option D counts the number of columns by dtype and plots only int64 and float64 counts correctly.

🧠 Conceptual

expert

Memory usage difference between int64 and float64

Which statement about memory usage of int64 vs float64 columns in pandas is correct?

A int64 and float64 columns use the same amount of memory per element

B float64 columns use twice the memory of int64 columns per element

C int64 columns use less memory because integers are stored more efficiently than floats

D Memory usage depends only on the number of elements, not the dtype

💡 Hint

Check the size in bytes of int64 and float64 types.

Both int64 and float64 use 8 bytes per element, so their memory usage per element is the same.