Creating DataFrames (dict, list, CSV) in Data Analysis Python - Interactive Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a DataFrame from a dictionary.

Data Analysis Python

import pandas as pd

data = {'Name': ['Alice', 'Bob'], 'Age': [25, 30]}
df = pd.DataFrame([1])
print(df)

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

ADataFrame

Bdict

Cpd

Ddata

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'pd' instead of the data variable.

Trying to pass 'dict' or 'DataFrame' as argument instead of the data.

The pd.DataFrame() function takes a dictionary as input to create a DataFrame. Here, data is the dictionary holding the data.

2fill in blank

medium

Complete the code to create a DataFrame from a list of lists with column names.

Data Analysis Python

import pandas as pd

rows = [[1, 'Apple'], [2, 'Banana'], [3, 'Cherry']]
df = pd.DataFrame([1], columns=['ID', 'Fruit'])
print(df)

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

Arows

B['ID', 'Fruit']

Cpd.DataFrame

Dcolumns

Attempts:

3 left

💡 Hint

Common Mistakes

Passing the column names as the first argument instead of the data.

Using pd.DataFrame instead of the data variable.

The first argument to pd.DataFrame() is the data, here a list of lists named rows. The columns parameter names the columns.

3fill in blank

hard

Fix the error in the code to read a CSV file into a DataFrame.

Data Analysis Python

import pandas as pd

df = pd.read_csv([1])
print(df.head())

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

Acsv

Bdata.csv

C'data.csv'

Dpd.read_csv

Attempts:

3 left

💡 Hint

Common Mistakes

Forgetting to put quotes around the file name.

Passing the function name instead of the file path.

The file path must be a string, so it needs quotes around it. Without quotes, Python treats it as a variable which causes an error.

4fill in blank

hard

Fill both blanks to create a DataFrame from a dictionary with a condition filtering ages over 20.

Data Analysis Python

import pandas as pd

data = {'Name': ['Anna', 'Ben', 'Cara'], 'Age': [19, 22, 25]}
df = pd.DataFrame([1])
filtered = df[df['Age'] [2] 20]
print(filtered)

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

Adata

Bdataframe

C>

D<

Attempts:

3 left

💡 Hint

Common Mistakes

Using a wrong variable name instead of 'data'.

Using '<' instead of '>' for filtering.

First, create the DataFrame from the dictionary data. Then filter rows where the 'Age' column is greater than 20 using the operator >.

5fill in blank

hard

Fill all three blanks to create a DataFrame from a list of dictionaries and select rows where score is above 80.

Data Analysis Python

import pandas as pd

records = [{'name': 'Tom', 'score': 75}, {'name': 'Lucy', 'score': 85}, {'name': 'Jake', 'score': 90}]
df = pd.DataFrame([1])
high_scores = df[df[[2]] [3] 80]
print(high_scores)

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

Arecords

B'score'

C>

D'name'

Attempts:

3 left

💡 Hint

Common Mistakes

Using the wrong variable name instead of 'records'.

Using the wrong column name or missing quotes.

Using '<' instead of '>' for filtering.

Create the DataFrame from the list of dictionaries records. Then filter rows where the 'score' column is greater than 80 using >.

Practice

1. What is the main purpose of creating a DataFrame in data analysis?

easy

A. To organize data in a table format for easy analysis

B. To write data to a text file

C. To create a list of numbers

D. To perform calculations without storing data

Solution

1. Step 1: Understand what a DataFrame is

   A DataFrame is like a table with rows and columns, used to organize data clearly.

2. Step 2: Identify the purpose of DataFrames

   They help analyze data easily by structuring it in rows and columns.

3. Final Answer:

   To organize data in a table format for easy analysis -> Option A

4. Quick Check:

   DataFrame = Table for data analysis [OK]

Hint: DataFrames = tables for data, not just lists or files [OK]

Common Mistakes:

• Confusing DataFrame with a simple list
• Thinking DataFrame is for saving files only
• Believing DataFrame is just for calculations

2. Which of the following is the correct way to create a DataFrame from a dictionary in Python using pandas?

easy

A. pd.DataFrame(name=['Alice', 'Bob'], age=[25, 30])

B. pd.DataFrame({'name': ['Alice', 'Bob'], 'age': [25, 30]})

C. pd.DataFrame({'name': 'Alice', 'age': 25})

D. pd.DataFrame['name', 'age'] = ['Alice', 'Bob'], [25, 30]

Solution

1. Step 1: Recall pandas DataFrame creation syntax

   Use pd.DataFrame() with a dictionary where keys are column names and values are lists of column data.

2. Step 2: Check each option

   pd.DataFrame({'name': ['Alice', 'Bob'], 'age': [25, 30]}) correctly uses a dictionary with lists for columns. Others have syntax errors or wrong formats.

3. Final Answer:

   pd.DataFrame({'name': ['Alice', 'Bob'], 'age': [25, 30]}) -> Option B

4. Quick Check:

   Dictionary with lists = pd.DataFrame() [OK]

Hint: Use dictionary with lists inside pd.DataFrame() [OK]

Common Mistakes:

• Using pd.DataFrame with wrong brackets
• Passing single values instead of lists
• Trying to assign columns after creating DataFrame

3. What will be the output of this code?

import pandas as pd
data = [[1, 'Alice'], [2, 'Bob']]
df = pd.DataFrame(data, columns=['ID', 'Name'])
print(df)

medium

A. A DataFrame with columns 'ID' and 'Name' showing the two rows of data

B. A list of lists printed as is

C. An error because columns parameter is missing

D. A DataFrame with default numeric column names

Solution

1. Step 1: Understand DataFrame creation from list of lists

   Passing a list of lists with columns names creates a DataFrame with those columns and rows.

2. Step 2: Check the print output

   The DataFrame will show two rows with columns 'ID' and 'Name' filled with given data.

3. Final Answer:

   A DataFrame with columns 'ID' and 'Name' showing the two rows of data -> Option A

4. Quick Check:

   List + columns = DataFrame table [OK]

Hint: List + columns param creates labeled DataFrame [OK]

Common Mistakes:

• Expecting a list output instead of DataFrame
• Forgetting columns parameter causes default columns
• Confusing list of lists with dictionary input

4. Identify the error in this code that tries to create a DataFrame from a CSV file:

import pandas as pd
df = pd.read_csv('data.csv', header=2)
print(df.head())

medium

A. read_csv cannot read CSV files

B. The file name must be 'data.txt' not 'data.csv'

C. The header parameter should be 0 to read the first row as column names

D. print(df.head()) is incorrect syntax

Solution

1. Step 1: Understand header parameter in read_csv

   header=2 means pandas treats the third row as column names, which may be wrong if first row has headers.

2. Step 2: Identify correct header usage

   Usually header=0 is used to read the first row as column names. Using 2 can cause wrong column names or data misalignment.

3. Final Answer:

   The header parameter should be 0 to read the first row as column names -> Option C

4. Quick Check:

   header=0 reads first row as columns [OK]

Hint: Use header=0 to read first row as column names [OK]

Common Mistakes:

• Setting header to wrong row number
• Confusing file extensions
• Misunderstanding print(df.head()) usage

5. You have a CSV file with columns 'Name', 'Age', and 'Score'. You want to create a DataFrame but only keep rows where 'Score' is above 80. Which code correctly creates the DataFrame and filters it?

hard

A. import pandas as pd\ndf = pd.read_csv('file.csv', filter='Score > 80')\nprint(df)

B. import pandas as pd\ndf = pd.read_csv('file.csv')\ndf_filtered = df.where('Score' > 80)\nprint(df_filtered)

C. import pandas as pd\ndf = pd.read_csv('file.csv')\ndf_filtered = df.filter('Score > 80')\nprint(df_filtered)

D. import pandas as pd\ndf = pd.read_csv('file.csv')\ndf_filtered = df[df['Score'] > 80]\nprint(df_filtered)

Solution

1. Step 1: Read CSV file into DataFrame

   Use pd.read_csv('file.csv') to load all data.

2. Step 2: Filter rows where 'Score' > 80

   Use boolean indexing: df[df['Score'] > 80] to keep only rows with Score above 80.

3. Final Answer:

   import pandas as pd\ndf = pd.read_csv('file.csv')\ndf_filtered = df[df['Score'] > 80]\nprint(df_filtered) -> Option D

4. Quick Check:

   Boolean indexing filters rows [OK]

Hint: Use df[df['Score'] > 80] to filter rows [OK]

Common Mistakes:

• Trying to filter inside read_csv
• Using df.filter() incorrectly
• Using df.where() without proper condition