Series vs DataFrame relationship in Pandas - Interactive Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a pandas Series from a list.

Pandas

import pandas as pd

my_list = [10, 20, 30]
my_series = pd.[1](my_list)
print(my_series)

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

Aarray

BDataFrame

CSeries

Dlist

Attempts:

3 left

💡 Hint

Common Mistakes

Using DataFrame instead of Series to create a single column.

Trying to use list() which is a Python built-in, not pandas.

We use pd.Series() to create a Series from a list.

2fill in blank

medium

Complete the code to create a DataFrame from two Series.

Pandas

import pandas as pd

s1 = pd.Series([1, 2, 3])
s2 = pd.Series([4, 5, 6])
df = pd.DataFrame({'A': s1, 'B': [1])
print(df)

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

As1

Bs2

C[4, 5, 6]

Dpd.Series

Attempts:

3 left

💡 Hint

Common Mistakes

Using s1 twice instead of s2 for the second column.

Passing a list instead of a Series for the second column.

To create a DataFrame with two columns, pass both Series as values in the dictionary.

3fill in blank

hard

Fix the error in the code to select a column from a DataFrame as a Series.

Pandas

import pandas as pd

df = pd.DataFrame({'X': [7, 8, 9], 'Y': [10, 11, 12]})
col = df[1]['X']
print(type(col))

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

A['X']

B.iloc

C[['X']]

D.loc

Attempts:

3 left

💡 Hint

Common Mistakes

Using .iloc which selects by position, not label.

Using double brackets which returns a DataFrame, not Series.

Using .loc with the column label selects a Series. Using double brackets returns a DataFrame.

4fill in blank

hard

Fill both blanks to create a DataFrame from a dictionary of Series and select a Series column.

Pandas

import pandas as pd

s1 = pd.Series([1, 2, 3])
s2 = pd.Series([4, 5, 6])
df = pd.DataFrame({'A': s1, 'B': s2})
selected = df[1]['[2]']
print(selected)

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

A.loc

B.iloc

CA

DB

Attempts:

3 left

💡 Hint

Common Mistakes

Using .iloc which selects by position, not label.

Selecting column 'A' instead of 'B'.

Use .loc to select the column labeled 'B' as a Series.

5fill in blank

hard

Fill all three blanks to create a DataFrame, add a new Series column, and select it.

Pandas

import pandas as pd

s1 = pd.Series([10, 20, 30])
s2 = pd.Series([40, 50, 60])
df = pd.DataFrame({'X': s1})
df['[1]'] = [2]
df_selected = df[3]['Y']
print(df_selected)

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

AY

Bs2

C.loc

D.iloc

Attempts:

3 left

💡 Hint

Common Mistakes

Using .iloc instead of .loc for label selection.

Using wrong column name when adding or selecting.

Add the Series s2 as a new column 'Y' to the DataFrame, then select it using .loc.

Practice

1.

What is a pandas.Series?

easy

A. A function to read CSV files

B. A table with multiple rows and columns

C. A method to plot graphs

D. A single labeled column of data

Solution

1. Step 1: Understand the definition of Series

   A Series in pandas is a one-dimensional labeled array, like a single column of data.

2. Step 2: Compare with other options

   Options A, B, and D describe file reading, DataFrame, and plotting, which are not Series.

3. Final Answer:

   A single labeled column of data -> Option D

4. Quick Check:

   Series = single column [OK]

Hint: Series is one column, DataFrame is many columns [OK]

Common Mistakes:

• Confusing Series with DataFrame
• Thinking Series is a function
• Mixing Series with plotting methods

2.

Which of the following is the correct way to get a Series from a DataFrame named df?

df = pd.DataFrame({"A": [1, 2], "B": [3, 4]})

easy

A. df["A"]

B. df.loc["A"]

C. df.iloc["A"]

D. df.get_series("A")

Solution

1. Step 1: Identify how to select a column as Series

   Using df["A"] selects the column labeled "A" as a Series.

2. Step 2: Check other options for correctness

   df.loc["A"] selects a row, df.iloc["A"] is invalid because iloc expects integer index, df.get_series() is not a pandas method.

3. Final Answer:

   df["A"] -> Option A

4. Quick Check:

   Column selection uses df["col"] [OK]

Hint: Use df["column_name"] to get a Series [OK]

Common Mistakes:

• Using loc with column name instead of row label
• Using iloc with string index
• Assuming get_series() exists

3.

What is the output of this code?

import pandas as pd

df = pd.DataFrame({"X": [10, 20], "Y": [30, 40]})
series = df["Y"]
print(type(series))

medium

A. <class 'pandas.core.frame.DataFrame'>

B. <class 'pandas.core.series.Series'>

C. <class 'list'>

D. TypeError

Solution

1. Step 1: Understand df["Y"] returns a Series

   Selecting a single column from a DataFrame returns a Series object.

2. Step 2: Check the printed type

   Printing type(series) shows <class 'pandas.core.series.Series'>.

3. Final Answer:

   <class 'pandas.core.series.Series'> -> Option B

4. Quick Check:

   Single column selection = Series type [OK]

Hint: df[column] returns Series, not DataFrame [OK]

Common Mistakes:

• Thinking df[column] returns DataFrame
• Confusing Series with list
• Expecting an error from this code

4.

Find the error in this code snippet:

import pandas as pd

df = pd.DataFrame({"A": [1, 2], "B": [3, 4]})
series = df.loc[:, "A"]
print(type(series))

medium

A. Using df.loc[:, "A"] returns a DataFrame, not Series

B. df.loc["A"] is the correct way to get Series

C. df.loc[:, "A"] returns a Series, no error here

D. df.loc[:, "A"] causes a syntax error

Solution

1. Step 1: Understand df.loc[:, "A"] selection

   df.loc[:, "A"] selects all rows and column "A", returning a Series.

2. Step 2: Check if this causes error

   This is valid syntax and returns a Series, so no error occurs.

3. Final Answer:

   df.loc[:, "A"] returns a Series, no error here -> Option C

4. Quick Check:

   df.loc with column label returns Series [OK]

Hint: df.loc[:, col] returns Series if col is single label [OK]

Common Mistakes:

• Thinking df.loc[:, col] returns DataFrame
• Confusing row and column selection
• Expecting syntax error on loc usage

5.

You have a DataFrame df with columns ['A', 'B', 'C']. You want to create a new DataFrame with only columns 'A' and 'C' as Series combined. Which code correctly does this?

hard

A. All of the above

B. new_df = df[["A", "C"]]

C. new_df = df.loc[:, ["A", "C"]]

D. new_df = pd.DataFrame({"A": df["A"], "C": df["C"]})

Solution

1. Step 1: Understand ways to select columns

   df[["A", "C"]] and df.loc[:, ["A", "C"]] both select columns as DataFrame.

2. Step 2: Creating DataFrame from Series

   pd.DataFrame({"A": df["A"], "C": df["C"]}) creates a new DataFrame from two Series.

3. Step 3: Check if all options are valid

   All three options produce a DataFrame with columns A and C.

4. Final Answer:

   All of the above -> Option A

5. Quick Check:

   Multiple ways to select columns as DataFrame [OK]

Hint: Selecting multiple columns: use list or dict of Series [OK]

Common Mistakes:

• Thinking only one method works
• Confusing Series with DataFrame in selection
• Using wrong syntax for multiple columns