Why flexible I/O handles real-world data in Data Analysis Python - Challenge Your Understanding

Challenge - 5 Problems

❓ Predict Output

intermediate

What is the output of this code reading a CSV with missing values?

Consider this Python code that reads a CSV file with missing values using pandas. What will be the output of the df.isnull().sum() line?

Data Analysis Python

import pandas as pd
from io import StringIO

data = '''name,age,score
Alice,30,85
Bob,,90
Charlie,25,
,40,70
'''

# Simulate reading CSV from string
csv_data = StringIO(data)
df = pd.read_csv(csv_data)

missing_counts = df.isnull().sum()
print(missing_counts)

A {'name': 1, 'age': 1, 'score': 1}

B {'name': 0, 'age': 1, 'score': 1}

C {'name': 1, 'age': 0, 'score': 1}

D {'name': 1, 'age': 1, 'score': 0}

💡 Hint

Check how many missing values are in each column by counting empty cells.

The CSV has one missing value in each column: 'name' missing in last row, 'age' missing in Bob's row, and 'score' missing in Charlie's row. So each column has exactly one missing value.

❓ data_output

intermediate

How many rows are read when using flexible I/O with different separators?

Given two CSV strings with different separators, what is the number of rows in the DataFrame after reading each with pandas?

Data Analysis Python

import pandas as pd
from io import StringIO

csv_comma = 'id,name\n1,Alice\n2,Bob'
csv_semicolon = 'id;name\n3;Charlie\n4;David'

df_comma = pd.read_csv(StringIO(csv_comma))
df_semicolon = pd.read_csv(StringIO(csv_semicolon), sep=';')

rows_comma = len(df_comma)
rows_semicolon = len(df_semicolon)
print(rows_comma, rows_semicolon)

A 2, 1

B 2, 2

C 1, 2

D 1, 1

💡 Hint

Count the number of data rows in each CSV string after reading.

Both CSV strings have two data rows each. Using the correct separator ensures pandas reads both rows correctly.

❓ visualization

advanced

Which plot shows correct handling of missing data after flexible I/O?

After reading a CSV with missing values, which plot correctly shows the count of missing values per column?

Data Analysis Python

import pandas as pd
import matplotlib.pyplot as plt
from io import StringIO

data = '''A,B,C
1,,3
4,5,
,7,9
'''

df = pd.read_csv(StringIO(data))
missing_counts = df.isnull().sum()

plt.bar(missing_counts.index, missing_counts.values)
plt.title('Missing Values per Column')
plt.ylabel('Count')
plt.show()

A Bar chart with bars for A=1, B=1, C=1

B Line chart with points for A=1, B=1, C=1

C Bar chart with bars for A=0, B=0, C=0

D Pie chart with equal slices for A, B, C

💡 Hint

Look for a bar chart showing counts of missing values per column.

The code counts missing values per column and plots a bar chart. Each column has one missing value, so bars show height 1.

🔧 Debug

advanced

What error does this code raise when reading a malformed CSV?

What error will this code raise when trying to read a CSV string with inconsistent columns?

Data Analysis Python

import pandas as pd
from io import StringIO

data = 'id,name\n1,Alice\n2'

try:
    df = pd.read_csv(StringIO(data))
except Exception as e:
    print(type(e).__name__)

A TypeError

B ValueError

C ParserError

D No error

💡 Hint

Check what pandas does when rows have fewer columns than header.

Pandas raises a ParserError when the number of fields in a row does not match the header.

🚀 Application

expert

How to handle mixed data types in flexible I/O for real-world data?

You have a CSV file where a column contains numbers and text mixed together. Which pandas option helps read this column without errors?

A Use <code>na_filter=False</code> in <code>read_csv</code>

B Use <code>dtype={'column': 'int'}</code> in <code>read_csv</code>

C Use <code>skiprows=1</code> in <code>read_csv</code>

D Use <code>dtype={'column': 'object'}</code> in <code>read_csv</code>

💡 Hint

Think about how to allow mixed types in one column.

Setting the column dtype to 'object' allows pandas to read mixed types as strings without error.