Creating DataFrame from NumPy array in Pandas - Try It Yourself

Creating DataFrame from NumPy array

📖 Scenario: You work in a small shop and have sales data stored as numbers in a NumPy array. You want to organize this data into a table with labels for each column to understand it better.

🎯 Goal: Create a pandas DataFrame from a given NumPy array and add column names to make the data easy to read.

📋 What You'll Learn

Create a NumPy array named sales_data with the exact values [[10, 20, 30], [15, 25, 35], [20, 30, 40]]

Create a list named columns with the exact values ['Monday', 'Tuesday', 'Wednesday']

Create a pandas DataFrame named df from sales_data with columns set to columns

Print the DataFrame df to display the table

💡 Why This Matters

🌍 Real World

Organizing raw numerical data into tables helps businesses analyze sales, inventory, or customer information clearly.

💼 Career

Data scientists and analysts often convert arrays or raw data into DataFrames to prepare for analysis and visualization.

1

Create the NumPy array with sales data

Import NumPy as np and create a NumPy array called sales_data with these exact values: [[10, 20, 30], [15, 25, 35], [20, 30, 40]]

Pandas

import numpy as np
# Create the NumPy array sales_data with the given values
# Your code here

Need a hint?

Use np.array() to create the array with the exact nested list of numbers.

2

Create the list of column names

Create a list called columns with these exact strings: 'Monday', 'Tuesday', and 'Wednesday'

Pandas

import numpy as np
sales_data = np.array([[10, 20, 30], [15, 25, 35], [20, 30, 40]])
# Create the list columns with the given day names
# Your code here

Need a hint?

Use square brackets [] to create the list with the exact day names as strings.

3

Create the pandas DataFrame from the NumPy array

Import pandas as pd and create a DataFrame called df from sales_data using columns=columns to set the column names

Pandas

import numpy as np
sales_data = np.array([[10, 20, 30], [15, 25, 35], [20, 30, 40]])
columns = ['Monday', 'Tuesday', 'Wednesday']
# Import pandas and create the DataFrame df from sales_data with columns
# Your code here

Need a hint?

Use pd.DataFrame() with the columns parameter to add column names.

4

Print the DataFrame to see the table

Write a print statement to display the DataFrame df

Pandas

import numpy as np
sales_data = np.array([[10, 20, 30], [15, 25, 35], [20, 30, 40]])
columns = ['Monday', 'Tuesday', 'Wednesday']
import pandas as pd
df = pd.DataFrame(sales_data, columns=columns)
# Print the DataFrame df
# Your code here

Need a hint?

Use print(df) to show the table in the output.