Creating DataFrames (dict, list, CSV) in Data Analysis Python - Try It Yourself

Creating DataFrames from Dictionary, List, and CSV

📖 Scenario: You work as a data analyst for a small bookstore. You want to organize book information using tables to analyze sales and inventory easily.

🎯 Goal: You will create tables called DataFrames from different data sources: a dictionary, a list of lists, and a CSV file. This will help you understand how to start working with data in Python.

📋 What You'll Learn

Use the pandas library to create DataFrames

Create a DataFrame from a dictionary

Create a DataFrame from a list of lists

Create a DataFrame by reading a CSV file

Print the DataFrames to see their content

💡 Why This Matters

🌍 Real World

DataFrames are the main way to organize and analyze data in many fields like sales, finance, and research.

💼 Career

Knowing how to create DataFrames from various data sources is a key skill for data analysts and data scientists.

1

Create a DataFrame from a dictionary

Import the pandas library as pd. Then create a dictionary called book_dict with these exact entries: 'Title': ['Python Basics', 'Data Science 101', 'Machine Learning'], 'Author': ['John Doe', 'Jane Smith', 'Emily Davis'], and 'Price': [29.99, 39.99, 49.99]. Finally, create a DataFrame called df_dict from book_dict using pd.DataFrame().

Data Analysis Python

import pandas as pd
# Create the dictionary book_dict with the given entries
# Create the DataFrame df_dict from book_dict
# Your code here

Hint

Use pd.DataFrame() to convert the dictionary into a DataFrame.

2

Create a DataFrame from a list of lists

Create a list of lists called book_list with these exact rows: ['Python Basics', 'John Doe', 29.99], ['Data Science 101', 'Jane Smith', 39.99], and ['Machine Learning', 'Emily Davis', 49.99]. Then create a DataFrame called df_list from book_list using pd.DataFrame() and set the column names to ['Title', 'Author', 'Price'].

Data Analysis Python

import pandas as pd
book_dict = {
    'Title': ['Python Basics', 'Data Science 101', 'Machine Learning'],
    'Author': ['John Doe', 'Jane Smith', 'Emily Davis'],
    'Price': [29.99, 39.99, 49.99]
}
df_dict = pd.DataFrame(book_dict)
# Create the list of lists book_list with the given rows
# Create the DataFrame df_list from book_list with columns ['Title', 'Author', 'Price']
# Your code here

Hint

Use columns=[...] to name the columns when creating the DataFrame from the list.

3

Create a DataFrame by reading a CSV file

Create a CSV file named books.csv with these exact contents including the header:

Title,Author,Price
Python Basics,John Doe,29.99
Data Science 101,Jane Smith,39.99
Machine Learning,Emily Davis,49.99

Then read this CSV file into a DataFrame called df_csv using pd.read_csv().

Data Analysis Python

import pandas as pd
book_dict = {
    'Title': ['Python Basics', 'Data Science 101', 'Machine Learning'],
    'Author': ['John Doe', 'Jane Smith', 'Emily Davis'],
    'Price': [29.99, 39.99, 49.99]
}
df_dict = pd.DataFrame(book_dict)
book_list = [
    ['Python Basics', 'John Doe', 29.99],
    ['Data Science 101', 'Jane Smith', 39.99],
    ['Machine Learning', 'Emily Davis', 49.99]
]
df_list = pd.DataFrame(book_list, columns=['Title', 'Author', 'Price'])
# Create the CSV file books.csv with the given content
# Read the CSV file into a DataFrame called df_csv
# Your code here

Hint

Use open() with mode 'w' to create the CSV file, then pd.read_csv() to read it.

4

Print all three DataFrames

Print the DataFrames df_dict, df_list, and df_csv in this exact order, each on its own line.

Data Analysis Python

import pandas as pd
book_dict = {
    'Title': ['Python Basics', 'Data Science 101', 'Machine Learning'],
    'Author': ['John Doe', 'Jane Smith', 'Emily Davis'],
    'Price': [29.99, 39.99, 49.99]
}
df_dict = pd.DataFrame(book_dict)
book_list = [
    ['Python Basics', 'John Doe', 29.99],
    ['Data Science 101', 'Jane Smith', 39.99],
    ['Machine Learning', 'Emily Davis', 49.99]
]
df_list = pd.DataFrame(book_list, columns=['Title', 'Author', 'Price'])
with open('books.csv', 'w') as f:
    f.write('Title,Author,Price\n')
    f.write('Python Basics,John Doe,29.99\n')
    f.write('Data Science 101,Jane Smith,39.99\n')
    f.write('Machine Learning,Emily Davis,49.99\n')
df_csv = pd.read_csv('books.csv')
# Print df_dict, df_list, and df_csv each on its own line
# Your code here

Hint

Use three print() statements, one for each DataFrame.

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