Pandas and NumPy connection - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of NumPy array converted to Pandas Series

What is the output of this code snippet?

Pandas

import numpy as np
import pandas as pd
arr = np.array([10, 20, 30, 40])
series = pd.Series(arr)
print(series)

A [10 20 30 40]

B

0    10
1    20
2    30
3    40
dtype: int64

C

0    10
1    20
2    30
3    40
Name: arr, dtype: int64

D TypeError: cannot convert array to Series

💡 Hint

Think about how pandas.Series displays numpy arrays with default index.

When a NumPy array is passed to pd.Series, it creates a Series with default integer index starting at 0. The output shows the index and values with dtype.

❓ data_output

intermediate

Shape of NumPy array from Pandas DataFrame values

What is the shape of the NumPy array obtained from the DataFrame values below?

Pandas

import pandas as pd
import numpy as np
df = pd.DataFrame({'A': [1, 2], 'B': [3, 4], 'C': [5, 6]})
arr = df.values
print(arr.shape)

A (6,)

B (3, 2)

C (2, 3)

D (3,)

💡 Hint

Remember rows and columns in DataFrame translate to shape (rows, columns).

The DataFrame has 2 rows and 3 columns, so the NumPy array shape is (2, 3).

🔧 Debug

advanced

Error when assigning NumPy array to DataFrame column

What error does this code raise?

Pandas

import pandas as pd
import numpy as np
df = pd.DataFrame({'X': [1, 2, 3]})
arr = np.array([4, 5])
df['Y'] = arr

A ValueError: Length of values (2) does not match length of index (3)

B TypeError: Cannot assign array of shape (2,) to column of shape (3,)

C KeyError: 'Y'

D No error, DataFrame updated successfully

💡 Hint

Check if the length of the array matches the DataFrame rows.

Assigning a NumPy array with length 2 to a DataFrame column with 3 rows causes a ValueError due to length mismatch.

❓ visualization

advanced

Plotting NumPy data from Pandas DataFrame

Which option produces a line plot of the 'values' column from the DataFrame?

Pandas

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data = {'values': np.array([1, 3, 2, 5, 4])}
df = pd.DataFrame(data)
# Plot code here

A

plt.plot(df.values)
plt.show()

B

plt.plot(np.array(df))
plt.show()

C

plt.plot(df['values'].values)
plt.show()

D

plt.plot(df['values'])
plt.show()

💡 Hint

Use the DataFrame column directly for plotting.

Plotting df['values'] directly produces a line plot with index on x-axis and values on y-axis. Other options either plot entire array or cause errors.

🧠 Conceptual

expert

Effect of modifying NumPy array on Pandas DataFrame

Given this code, what is the value of df.loc[0, 'A'] after modifying arr[0]?

Pandas

import pandas as pd
import numpy as np
df = pd.DataFrame({'A': [10, 20, 30]})
arr = df.values
arr[0, 0] = 99
print(df.loc[0, 'A'])

A 99

B None

C 10

D Raises ValueError

💡 Hint

Consider if df.values returns a view or a copy of the data.

df.values returns a view of the underlying data. Modifying arr modifies the DataFrame data, so df.loc[0, 'A'] becomes 99.