Learn Python with zero knowledge: Your 101 guide

A 2025 Stack Overflow technology survey revealed that Python remains one of the most used programming languages, with usage climbing by nearly seven percentage points compared to 2024. This growth is hardly surprising as Python is often regarded as the most versatile programming language, with wide adoption in web development, data science, machine learning, automation, and even game development.

This versatility means that learning Python equips you with the ability to build solutions across a wide range of domains, and it’s not just for developers. Whether you're a student, data analyst, marketer, or anyone looking to boost productivity, learning Python can open up new possibilities. If you’ve been wondering whether it’s worth learning, the answer is a definite yes. Here’s the big question: how long will it take to learn Python? Well, we’d get into that in a bit.

Another advantage of Python’s popularity is the sheer wealth of resources available to beginners. However, this abundance can sometimes feel overwhelming, especially for users who want to move quickly from learning to producing real results. Imagine if you could start applying Python to solve problems without having to master every detail first.

That’s where Quadratic comes in. With native Python support and AI-powered assistance, Quadratic allows you to write Python code directly within your spreadsheet. This means you can perform advanced data analysis and automate tasks seamlessly, even without deep Python expertise. Quadratic is also a great place to learn Python and SQL.

This blog post is for readers who want to learn Python with zero knowledge. We’ll walk through the fundamentals of Python and demonstrate how Quadratic makes it easier to apply Python in practical, results-driven ways right inside your spreadsheet.

How long does it take to learn Python?

Is Python hard to learn? Well, no. Python is actually considered the easiest programming language to learn because its syntax is simple and resembles natural English. This makes it easy for beginners to quickly grasp core programming concepts without feeling overwhelmed.

That said, the time it takes to become proficient in Python varies. Factors such as how often you practice, the quality of learning resources you use, and whether you choose a learning approach that fits your style all play a role. On average, it takes about 2 to 6 months to build a solid understanding of Python basics, while achieving true mastery can take longer. The best way to learn Python is to figure out what learning approach works best for you, and act on it accordingly.

The good news is, thanks to modern AI-powered tools like Quadratic, you don’t need to be a Python expert before you can start creating solutions. Quadratic allows you to apply Python for real-world problem-solving right away, helping you build and automate faster without waiting until you’ve mastered every detail. We’ll explore how this works shortly

Traditional methods of spreadsheet data analysis

For years, spreadsheets like Excel and Google Sheets have relied on formulas as the primary way to generate insights. While effective, complex formulas can quickly become difficult to write, maintain, and debug, especially when working with large datasets. Remembering the exact syntax often slows users down, and small mistakes can lead to frustrating errors. These challenges have driven many analysts and professionals to learn Python for data analysis.

Python empowers data analysis by making it easier to uncover patterns, identify trends, and extract meaningful insights from raw data. Its rich ecosystem of libraries also supports Python exploratory data analysis (EDA) and data visualization, enabling analysts to quickly explore datasets, test hypotheses, and present findings in clear formats.

While Python streamlines data analysis, achieving comprehensive results often requires a certain level of technical expertise. This is where AI tools for data analysis come in. These tools bridge the gap by enabling non-technical users, technical users, and citizen developers to self-serve analytics and generate insights without deep programming knowledge.

Fundamental concepts in Python

In this section, we’ll cover the fundamental Python concepts that form the foundation for uncovering insights from your data.

Variables

A variable is a name that refers to a memory location where data is stored. Think of it as a label that points to an object in your program. Variables usually hold data that can be changed (mutable), while data that cannot be changed is referred to as a constant.

Variable names in Python can be made up of letters, numbers, and underscores. However, there are a few important rules to follow when naming them:

• A variable name must begin with a letter (A-Z, a-z) or an underscore (_), it cannot start with a number.
• They can only contain alphanumeric characters and underscores; special characters are not allowed.
• They are case-sensitive (age, Age, and AGE would be treated as three different variables).
• You cannot use Python’s reserved keywords (True, False, and, return, continue, etc) as variable names.

Data types

Data types define the kind of values a variable can store, instructing the computer on how to handle that data. Python offers several built-in data types that analysts can work with. Here’s a breakdown of the different data types in Python:

• String: Used to store a sequence of characters.

x = "Quadratic" # x is of string type

• Integer: Used to store whole numbers.

x = 12 # x is of integer type

• Float: Used to store decimal numbers.

x = 20.6 # x is of float type

• Complex: Used to store complex numbers.

z = 4 + 3j # x is of complex type

• List: An ordered and mutable collection of items. Used to store multiple items in a single variable.

x = ["Quadratic", "AI", "Code", "Connections"] # x is of list type

• Tuple: An ordered and immutable collection of items. It is also used to store multiple items in a single variable. Unlike Lists, once a tuple is created, it cannot be modified.

x = ("Quadratic", "AI", "Code", "Connections") # x is of tuple type

• Dict: Used to store data in key-value pairs.

x = {"name": "Quadratic AI", "description": "Spreadsheet with AI"} # x is of dict type

• Set: An unordered collection of unique items. It is also used to store multiple items in a single variable.

x = {"AI", "Code", "Connections"} # x is of set type

• Bool: Used to represent true or false

x: True

Operators

Operators are used to perform operations on values (also called operands). They are symbols used in various operations. Let’s explore the different types of Python operators:

• Arithmetic operators: These are used to perform arithmetic operations on values. They include addition, subtraction, multiplication, and division. Here’s a code sample that shows arithmetic operators in use:

x = 10
y = 2
add = x + y # addition: output is 12

sub = x - y # subtraction: output is 8

mul = x * y # multiplication: output is 20

mod = x % y # modulus: output is 0

E = x ** y # exponent: output is  100 

• Relational/Comparison operators: Relational operators are used to compare two values. The result is a Boolean value (either true or false) depending on the values and operands used. Consider this code sample that shows how relational operators in Python work:

x = 10
y = 2

print(x == y)   # returns False since 10 is not equal to 2
print(x != y)   #  returns True since 10 is not equal to 2
print(x > y)    # returns True since 10 is greater than 2
print(x < y)    # returns True since 10 is not less than 2
print(x >= y)   # returns True, 10 is greater than or equal to 2
print(x <= y)   # False, 10 is not less than or equal to 2

• Assignment operators: Assignment Operators are used to assign values to variables. The simple equals sign (=) is the most commonly used, but Python also provides several compound assignment operators that let you perform an operation and assign the result in a single step. Let’s explore them:

x = 5
y = x # assigns the value of y to x, output is 5

y += x # adds y to x then assigns the value, output is 10
y -= x # subtracts x from y, then assigns the value, output is 0
y *= x # multiplies x and y, then assigns the value, output is 25
y <<= x # performs left bitwise shift operation, then assigns the value, output is 160

• Logical operators: Logical operators are mostly used to combine conditional statements. A conditional statement depends on some conditions to be met before execution. Python operators offer the Logical AND, Logical OR, and Logical NOT operations. Let’s see an example:

x = True
y = False
print(x and y) # returns true only if both conditions are true, so output is False
print(x or y) # returns true if at least one condition is true, output is True
print(not x) # reverses the condition, output is False

• Bitwise operators: Bitwise operators are used to perform operations on binary numbers. It performs bit by bit operation. Here:

x = 10
y = 2
print(x & y) # returns one only if both bits are 1, output is 2
print(x | y) # returns one if either bit is 1, output is 10
print(~x) # flips all bits and stores the number in two's complement for negative, output is -11
print(x ^ y) # returns 1 only if the bits are different, output is 8
print(x >> 2) # shifts all bits 2 places to the right, output is 2
print(x << 2) # shifts all bits 2 places to the left, output is 40

Loops

Loops in Python are control flow statements that are used to streamline repetitive tasks. They are used to iterate over a block of code based on a specified condition. Python primarily provides two types of loops: for loop and while loop. Let’s explore them in detail:

• for loop: for loops are used for iterating over instances like strings, lists, sets, or ranges. They are ideal in cases where you know the exact number of iterations before implementation. They execute the block of code once for each iteration. Here’s an example:

# Iterating over a list
descriptions = ["AI", "Code", "Connections"]
for description in descriptions:
print(description) # Outputs all the items line by line

# Iterating using range()
for i in range(1, 5):
    print(i) # This will iterate from 1 to 4

• while loop: while loops are used to repeatedly execute a block of code as long as a condition remains true. In comparison to for loops, the number of iterations in while loops is unknown. Let’s see how we can iterate under the condition that the number is less than or equal to 4:

count = 1
while count <= 4:
    print(count) # This will iterate from 1 to 4
    count += 1 

Functions

Functions are blocks of reusable code designed to perform a specific task. They make your code more modular, reusable, and easier to read. Python provides many built-in functions (such as print(), len(), and input()) to simplify common operations. In addition, you can define custom functions (known as user-defined functions) using the def keyword. Let’s create a simple Python function that adds two numbers:

# Function to add two numbers
def add_numbers(a, b):
    return a + b

# usage
result = add_numbers(10, 2)
print("The sum is:", result) # Output: The sum is 12

Rather than explicitly perform the addition operation on every instance, you can simply call the add_numbers function and input the parameters.

Common spreadsheet operations

In this section, we’ll see how to perform some spreadsheet operations using Python and how we would perform the same operation using Quadratic. Let’s dive in:

Finding duplicates in data using Python

When dealing with large datasets, the likelihood of having duplicate data increases. Having duplicate data present in your spreadsheet can distort analysis and lead to inaccurate results.

The first stage in handling duplicate data is identifying them. Manually scanning through rows in a spreadsheet to spot duplicates can be frustrating and time-consuming. Thankfully, Python (with the help of Pandas) streamlines this process. Consider the messy dataset below:

In the image above, we have duplicate data on lines 5 and 17. While this can be easily spotted through manual means on a small sample of data, large datasets come with more difficulty. With Python, we can find duplicates in our data more efficiently. Here:

import pandas as pd
df = pd.read_csv('Data.csv')
df.duplicated()

The .dupliicated function scans the dataset and returns true for all occurrences of duplicate values. Here:

It returns true in line 17, meaning that line is occurring multiple times in the dataset. The ideal action after identifying duplicate values in a dataset is to drop them. Python (using Pandas) also provides a function to help with this. Here:

df.drop_duplicates()
print(df)

This automatically drops all duplicate values in the dataset.

Finding duplicate data using Quadratic

If you’d prefer to find and clean duplicate data without writing code or relying on data cleaning tools, then Quadratic is the best choice for you. With simple text prompts, you can describe exactly how you want your data cleaned, and Quadratic will generate a refined dataset in seconds. Here’s how duplicate data can be handled using Quadratic:

In the example above, we loaded our data into Quadratic and simply prompted its built-in AI to “Identify duplicate values in this dataset and drop them.” In seconds, Quadratic produced a clean version of the dataset with duplicates removed.

Even better, it highlights the dropped values, giving users full visibility into what changes were made and ensuring transparency in the cleaning process.

Quadratic lets you write and execute code directly inside the spreadsheet, with real-time visibility into how the code is generated and applied to your analysis. This interactive approach makes it easy to connect blocks of code with results. For visual and hands-on learners, Quadratic is the best place to learn Python.

Generating insights using Python

Python makes it easy to uncover valuable insights from your dataset. For instance, let’s find the top 5 products by revenue. In this example, we’ll load the data and use Google Colab as our IDE for data analysis. Here’s how:

import pandas as pd
df = pd.read_csv('Data.csv')

Then we ensure the Quantity and Price columns are numeric:

df['Quantity'] = pd.to_numeric(df['Quantity'])
df['Price'] = pd.to_numeric(df['Price'])

Next, we calculate the revenue for each transaction and group by product:

df['Revenue'] = df['Quantity'] * df['Price']
product_revenue = df.groupby('Product')['Revenue'].sum().reset_index()

Finally, sort by revenue to get the top 5 and format the currency accordingly:

top_products = product_revenue.sort_values('Revenue', ascending=False).head(5)

top_products['Revenue'] = top_products['Revenue'].map('${:,.2f}'.format)

top_products

Here’s the result:

Generating insights using Quadratic

Just like we did when identifying duplicates, Quadratic also makes it easy to generate insights by using LLMs for data analysis. Instead of writing code, you simply ask a question, and Quadratic handles the heavy lifting in the background. Let’s see how we can get the top 5 products by revenue. Remember, we ask:

We asked Quadratic AI: “Can you show me the top 5 products by revenue from the sales data?” In seconds, it generated a separate table with the breakdown of the top 5 products. The results matched exactly what we achieved earlier with Python code, but this time it was much faster, since all we had to do was ask a simple question.

Visualizing data using Python

To create comprehensive visualizations, you’ll need Python data visualization libraries. In this example, we’ll visualize the distribution of sales by region with a pie chart. The first step is to import the necessary libraries: Pandas, Matplotlib, and Seaborn. Here:

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

Specify the width and height of the pie chart:

plt.figure(figsize=[6,6])

Next, get the percentage count of each region and give labels to each slice of the pie chart:

data = df["Region"].value_counts(normalize=True)
labels = ["North","South","East","West"]

Set a color palette and add further properties to draw the pie chart:

colors = sns.color_palette('pastel')
plt.pie(data,labels=labels,colors=colors, autopct='%.0f%%')

Add a title and render the pie chart:

plt.title("Distribution of Sales by Region")
plt.show()

Here’s the result:

Visualizing data using Quadratic

Being one of the best data visualization software tools, Quadratic offers different chart types that make data visualization simple. Its AI spreadsheet analysis technology automatically generates interactive charts based on user prompts. All you need to do is describe how you’d like to visualize your data, and Quadratic instantly brings it to life. Let’s create a pie chart to show the distribution of sales by region using Quadratic:

Here, we asked Quadratic AI to “Create a pie chart that shows the distribution of sales by region” and it generated the pie chart as expected.

Conclusion

Python’s versatility makes it one of the most valuable programming languages to learn, with applications spanning data analysis, machine learning, web development, and beyond. While coding may feel intimidating at first, building a solid foundation in the fundamentals opens the door to endless possibilities.

In this blog post, we established the foundation for users to learn Python with zero knowledge. We explored essential concepts such as variables, data types, operators, loops, and functions, and demonstrated how these fundamentals can be applied to common spreadsheet tasks.

Finally, we saw how Quadratic, an AI-powered software for data analysis, simplifies these processes even further by allowing both beginners and experts to analyze and visualize data using simple text prompts. Users who want to learn Python for data analytics will find this tool very helpful. Try Quadratic out for free today.