How to Read Excel Files in Python using Pandas
Python
03.10.2024
Reading time: 8 min
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Excel files are commonly used to organize, sort, and analyze data in a tabular format with rows and columns. They are widely applied in industries like data analysis, finance, and reporting.
Using Python, the pandas library allows for efficient manipulation of Excel files, enabling operations like reading and writing data. This article will cover how to use the read_excel function from pandas to read Excel files.
Installing Pandas
To begin, install pandas by running the following command:
pip install pandasThis will install pandas along with the required dependencies in your work environment. Additionally, the openpyxl module is needed for reading .xlsx files.
Why OpenPyXL?
Excel files come in different formats and extensions. To ensure compatibility when working with these files, pandas allows you to specify the engine you want to use. Below is a list of supported engines for reading Excel files:
OpenPyXL: Used for reading and writing.xlsxfiles (Excel 2007+).XlsxWriter: Primarily used for writing.xlsxfiles.xlrd: Used for reading older.xlsfiles (Excel 97-2003).Pyxlsb: Used for reading.xlsb(binary Excel format) files.
OpenPyXL also supports Excel-specific features, such as formatting and formulas. OpenPyXL is already installed as a dependency of pandas, but you can install it using the following command:
pip install openpyxlWhile OpenPyXL can be used on its own to read Excel files, it is also integrated as an engine within pandas for reading and writing .xlsx files.
We will work with an Excel file that you can download here. Download the file and move it into your working environment.
Basic Usage of read_excel Function
The Excel file we are working with has the following structure:
It also has three worksheets: Orders, Returns, and Users.
To read this file, the read_excel function from pandas will be used.
The read_excel function in pandas is used to import data from Excel files into a pandas DataFrame, a powerful structure for analyzing and manipulating data. This function is highly versatile, allowing users to read data from specific sheets, columns, or ranges.
Here is how to use this function while specifying the engine:
import pandas as pd
df = pd.read_excel('SuperStoreUS-2015.xlsx')
print(df)
This code imports the pandas library and uses the read_excel function to read the SuperStoreUS-2015.xlsx Excel file into a pandas DataFrame. The print(df) statement outputs the DataFrame contents, displaying the data from the Excel file. Below is the resulting output:
Row ID Order Priority Discount Unit Price Shipping Cost ... Ship Date Profit Quantity ordered new Sales Order ID
0 20847 High 0.01 2.84 0.93 ... 2015-01-08 4.5600 4 13.01 88522
1 20228 Not Specified 0.02 500.98 26.00 ... 2015-06-15 4390.3665 12 6362.85 90193
2 21776 Critical 0.06 9.48 7.29 ... 2015-02-17 -53.8096 22 211.15 90192
3 24844 Medium 0.09 78.69 19.99 ... 2015-05-14 803.4705 16 1164.45 86838
4 24846 Medium 0.08 3.28 2.31 ... 2015-05-13 -24.0300 7 22.23 86838
The read_excel function is highly flexible and can be adapted to various usage scenarios. Next, we will explore how to use it for reading specific sheets and columns.
Reading Specific Sheets and Columns
Excel files can come with multiple sheets and as many columns as possible. The read_excel function takes the sheet_name argument to tell pandas which sheet to read. By default, read_excel will load all worksheets. Here is how you can use the sheet_name argument:
df = pd.read_excel('SuperStoreUS-2015.xlsx', sheet_name="Returns")
print(df)
This will read the Returns sheet, and here is an example output:
Order ID Status
0 65 Returned
1 612 Returned
2 614 Returned
3 678 Returned
4 710 Returned
... ... ...
1629 182681 Returned
1630 182683 Returned
1631 182750 Returned
1632 182781 Returned
1633 182906 Returned
[1634 rows x 2 columns]
The sheet_name argument also takes integers that are used in zero-indexed sheet positions. For instance, using pd.read_excel('SuperStoreUS-2015.xlsx', sheet_name=1) will load the Returns sheet as well.
You can also choose to read specific columns from the Excel file. The read_excel function allows for selective column reading using the usecols parameter. It accepts various formats:
- A string for Excel column letters or ranges (e.g., "A:C").
- A list of integers for column positions.
- A list of column names.
Here is an example using column names:
import pandas as pd
df = pd.read_excel('SuperStoreUS-2015.xlsx', usecols=['Row ID', 'Sales'])
print(df)
In this case, the usecols parameter specifies that only columns Row ID and Sales from the Excel file should be imported into the DataFrame. The code below does the same thing, but using Excel column letters:
import pandas as pd
df = pd.read_excel('SuperStoreUS-2015.xlsx', usecols='A,X')
print(df)
Here is the output:
Row ID Sales
0 20847 13.01
1 20228 6362.85
2 21776 211.15
3 24844 1164.45
4 24846 22.23
... ... ...
1947 19842 207.31
1948 19843 143.12
1949 26208 59.98
1950 24911 135.78
1951 25914 506.50
You can also use range selection to read columns by their position. In the code below, we are reading from Order Priority to Customer ID.
df = pd.read_excel('SuperStoreUS-2015.xlsx', usecols='B:F')
Here is an example output when reading columns B to F:
Order Priority Discount Unit Price Shipping Cost Customer ID
0 High 0.01 2.84 0.93 3
1 Not Specified 0.02 500.98 26.00 5
2 Critical 0.06 9.48 7.29 11
3 Medium 0.09 78.69 19.99 14
4 Medium 0.08 3.28 2.31 14
Additionally, you can provide a callable that evaluates column names, reading only those for which the function returns True.
Handling Missing Data in Excel Files
In Excel files, missing data refers to values that are absent, often represented by empty cells. When reading an Excel file into a pandas DataFrame, missing data is automatically identified and handled as NaN (Not a Number), which is pandas placeholder for missing values.
Pandas offers several methods to handle missing data, such as:
dropna(): Removes rows or columns with missing values.fillna(): Replaces missing values with a specified value (e.g., 0 or the mean of the column).isna(): Detects missing values and returns a booleanDataFrame.
For example, using fillna on our Excel file will replace all missing values with 0:
df = pd.read_excel('SuperStoreUS-2015.xlsx')
df_cleaned = df.fillna(0)
Handling missing data is essential to ensure accurate analysis and prevent errors or biases in data-driven decisions.
Reading and Analyzing an Excel File in Pandas
Let’s make a pragmatic use of the notion we have learned. In this practical example, we will walk through reading an Excel file, performing some basic analysis, and exporting the manipulated data into various formats.
Specifically, we’ll calculate the sum, maximum, and minimum values for the Profit column for the year 2015, and export the results to CSV, JSON, and a Python dictionary.
Step 1: Loading the Excel File
The first step is to load the Excel file using the read_excel function from pandas:
import pandas as pd
df = pd.read_excel('SuperStoreUS-2015.xlsx', usecols=['Ship Date', 'Profit'])
print(df.head())
This code reads the SuperStoreUS-2015.xlsx file into a pandas DataFrame and displays the first few rows, including the Ship Date and Profit columns.
Step 2: Calculating Profit for June 2015
Next, we will filter the data to include only records from June 2015 and calculate the total, maximum, and minimum profit for that month. Since the date format in the dataset is MM/DD/YYYY, we will convert the Ship Date column to a datetime format and filter by the specific month:
df['Ship Date'] = pd.to_datetime(df['Ship Date'], format='%m/%d/%Y')
df_june_2015 = df[(df['Ship Date'].dt.year == 2015) & (df['Ship Date'].dt.month == 6)]
# Calculate the sum, max, and min for the Profit column
profit_sum = df_june_2015['Profit'].sum()
profit_max = df_june_2015['Profit'].max()
profit_min = df_june_2015['Profit'].min()
print(f"Total Profit in June 2015: {profit_sum}")
print(f"Maximum Profit in June 2015: {profit_max}")
print(f"Minimum Profit in June 2015: {profit_min}")
The output will be something like:
print(f"Total Profit in June 2015: {round(profit_sum, ndigits=2)}")
print(f"Maximum Profit in June 2015: {round(profit_max, ndigits=2)}")
print(f"Minimum Profit in June 2015: {round(profit_min, ndigits=2)}")
Step 3: Exporting the Manipulated Data
Once the profit for June 2015 has been calculated, we can export the filtered data to different formats, including CSV, JSON, and a Python dictionary.
# Export to CSV
df_june_2015.to_csv('SuperStoreUS_June2015_Profit.csv', index=False)
# Export to JSON
df_june_2015.to_json('SuperStoreUS_June2015_Profit.json', orient='records')
# Convert to Dictionary
data_dict = df_june_2015.to_dict(orient='records')
print(data_dict[:5])
In this step, the data is first exported to a CSV file and then to a JSON file. Finally, the DataFrame is converted into a Python dictionary, with each row represented as a dictionary.
Conclusion
In this article, we have learned how to use the read_excel function from pandas to read and manipulate Excel files. This is a powerful function with the ability to simplify data filtering for a better focus on the rows or columns we want.
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Python
03.10.2024
Reading time: 8 min
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Python
Python Static Method
A static method in Python is bound to the class itself rather than any instance of that class. So, you can call it without first creating an object and without access to instance data (self).
To create a static method we need to use a decorator, specifically @staticmethod. It will tell Python to call the method on the class rather than an instance.
Static methods are excellent for utility or helper functions that are logically connected to the class but don't need to access any of its properties.
When To Use & Not to Use a Python Static Method
Static methods are frequently used in real-world code for tasks like input validation, data formatting, and calculations—especially when that logic naturally belongs with a class but doesn't need its state.
Here's an example from a User class that checks email format:
class User:
@staticmethod
def is_valid_email(email):
return "@" in email and "." in email
This method doesn't depend on any part of the User instance, but conceptually belongs in the class. It can be used anywhere as User.is_valid_email(email), keeping your code cleaner and more organized.
If the logic requires access to or modification of instance attributes or class-level data, avoid using a static method as it won't help here. For instance, if you are working with user settings or need to monitor object creation, you will require a class method or an instance method instead.
class Counter:
count = 0
@classmethod
def increment(cls):
cls.count += 1
In this example, using a static method would prevent access to cls.count, making it useless for this kind of task.
Python Static Method vs Class Method
Though they look similar, class and static methods in Python have different uses; so, let's now quickly review their differences.
Defined inside a class, a class method is connected to that class rather than an instance. Conventionally called cls, the class itself is the first parameter; so, it can access and change class-level data. Factory patterns, alternate constructors, or any activity applicable to the class as a whole and not individual instances are often implemented via class methods.
Conversely, a static method is defined within a class but does not start with either self or cls parameters. It is just a regular function that “lives” inside a class but doesn’t interact with the class or its instances. For utility tasks that are conceptually related to the class but don’t depend on its state, static methods are perfect.
Here's a quick breakdown of the Python class/static methods differences:
Feature
Class Method
Static Method
Binding
Bound to the class
Not bound to class or instance
First parameter
cls (class itself)
None (no self or cls)
Access to class/instance data
Yes
No
Common use cases
Factory methods, class-level behavior
Utility/helper functions
Decorator
@classmethod
@staticmethod
Python Static Method vs Regular Functions
You might ask: why not just define a function outside the class instead of using a static method? The answer is structure. A static method keeps related logic grouped within the class, even if it doesn't interact with the class or its instances.
# Regular function
def is_even(n):
return n % 2 == 0
# Static method inside a class
class NumberUtils:
@staticmethod
def is_even(n):
return n % 2 == 0
Both functions do the same thing, but placing is_even inside NumberUtils helps keep utility logic organized and easier to find later.
Let’s proceed to the hands-on Python static method examples.
Example #1
Imagine that we have a MathUtils class that contains a static method for calculating the factorial:
class MathUtils:
@staticmethod
def factorial(n):
if n == 0:
return 1
else:
return n * MathUtils.factorial(n-1)
Next, let's enter:
print(MathUtils.factorial(5))120
We get the factorial of 5, which is 120. Here, the factorial static method does not use any attributes of the class instance, only the input argument n. And we called it using the MathUtils.factorial(n) syntax without creating an instance of the MathUtils class.
In Python, static methods apply not only in classes but also in modules and packages. The @staticmethod decorator marks a function you define inside a class if it does not interact with instance-specific data. The function exists on its own; it is related to the class logically but is independent of its internal state.
Managed solution for Backend development
Example #2
Let's say we're working with a StringUtils module with a static method for checking if a string is a palindrome. The code will be:
def is_palindrome(string): return string == string[::-1]
This function doesn't rely on any instance-specific data — it simply performs a check on the input. That makes it a good candidate for a static method. To organize it within a class and signal that it doesn't depend on the class state, we can use the @staticmethod decorator like this:
class StringUtils: @staticmethod def is_palindrome(string): return string == string[::-1]
Let's enter for verification:
print(StringUtils.is_palindrome("deed"))True
print(StringUtils.is_palindrome("deer"))False
That's correct, the first word is a palindrome, so the interpreter outputs True, but the second word is not, and we get False.
So, we can call the is_palindrome method through the StringUtils class using the StringUtils.is_palindrome(string) syntax instead of importing the is_palindrome function and calling it directly.
-
Python static method and class instance also differ in that the static cannot affect the state of an instance. Since they do not have access to the instance, they cannot alter attribute values, which makes sense. Instance methods are how one may modify the instance state of a class.
Example #3
Let's look at another example. Suppose we have a Person class that has an age attribute and a static is_adult method that checks the value against the age of majority:
class Person: def __init__(self, age): self.age = age @staticmethod def is_adult(age): return age >= 21
Next, let's create an age variable with a value of 24, call the is_adult static method from the Person class with this value and store its result in the is_adult variable, like this:
age = 24is_adult = Person.is_adult(age)
Now to test this, let's enter:
print(is_adult)True
Since the age matches the condition specified in the static method, we get True. In the example, the is_adult static method serves as an auxiliary tool—a helper function—accepting the age argument but without access to the age attribute of the Person class instance.
Conclusion
Static methods improve code readability and make it possible to reuse it. They are also more convenient when compared to standard Python functions. Static methods are convenient as, unlike functions, they do not call for a separate import. Therefore, applying Python class static methods can help you streamline and work with your code greatly. And, as you've probably seen from the examples above, they are quite easy to master.
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16 April 2025 · 6 min to read
Python
Input in Python
Python provides interactive capabilities through various tools, one of which is the input() function. Its primary purpose is to receive user input. This function makes Python programs meaningful because without user interaction, applications would have limited utility.
How the Python Input Works
This function operates as follows:
user_name = input('Enter your name: ')
user_age = int(input('How old are you? '))
First, the user is asked to enter their name, then their age. Both inputs are captured using a special operator that stores the entered values in the variables user_name and user_age. These values can then be used in the program.
For example, we can create an age-based access condition for a website (by converting the age input to an integer using int()) and display a welcome message using the entered name:
if user_age < 18:
print('Sorry, access is restricted to adults only')
else:
print('Welcome to the site,', user_name, '!')
So, what happens when int() receives an empty value? If the user presses Enter without entering anything, let's see what happens by extending the program:
user_name = input('Enter your name: ')
user_age = int(input('How old are you? '))
if user_age < 18:
print('Sorry, access is restricted to adults only')
else:
print('Welcome to the site,', user_name, '!')
input('Press Enter to go to the menu')
print('Welcome to the menu')
Pressing Enter moves the program to the next line of code. If there is no next line, the program exits. The last line can be written as:
input('Press Enter to exit')
If there are no more lines in the program, it will exit. Here is the complete version of the program:
user_name = input('Enter your name: ')
user_age = int(input('How old are you? '))
if user_age < 18:
print('Sorry, access is restricted to adults only')
else:
print('Welcome to the site,', user_name, '!')
input('Press Enter to go to the menu')
print('Welcome to the menu')
input('Press Enter to exit')
input('Press Enter to exit')
If the user enters an acceptable age, they will see the message inside the else block. Otherwise, they will see only the if block message and the final exit prompt. The input() function is used four times in this program, and in the last two cases, it does not store any values but serves to move to the next part of the code or exit the program.
input() in the Python Interpreter
The above example is a complete program, but you can also execute it line by line in the Python interpreter. However, in this case, you must enter data immediately to continue:
>>> user_name = input('Enter your name: ')
Enter your name: Jamie
>>> user_age = int(input('How old are you? '))
How old are you? 18
The code will still execute, and values will be stored in variables. This method allows testing specific code blocks. However, keep in mind that values are retained only until you exit the interactive mode. It is recommended to save your code in a .py file.
Input Conversion Methods: int(), float(), split()
Sometimes, we need to convert user input into a specific data type, such as an integer, a floating-point number, or a list.
Integer conversion (int())
We've already seen this in a previous example:
user_age = int(input('How old are you? '))
The int() function converts input into an integer, allowing Python to process it as a numeric type. By default, numbers entered by users are treated as strings, so Python requires explicit conversion. A more detailed approach would be:
user_age = input('How old are you? ')
user_age = int(user_age)
The first method is shorter and more convenient, but the second method is useful for understanding function behavior.
Floating-point conversion (float())
To convert user input into a floating-point number, use float():
height = float(input('Enter your height (e.g., 1.72): '))
weight = float(input('Enter your weight (e.g., 80.3): '))
Or using a more detailed approach:
height = input('Enter your height (e.g., 1.72): ')
height = float(height)
weight = input('Enter your weight (e.g., 80.3): ')
weight = float(weight)
Now, the program can perform calculations with floating-point numbers.
Converting Input into a List (split())
The split() method converts input text into a list of words:
animals = input('Enter your favorite animals separated by spaces: ').split()
print('Here they are as a list:', animals)
Example output:
Enter your favorite animals separated by spaces: cat dog rabbit fox bear
Here they are as a list: ['cat', 'dog', 'rabbit', 'fox', 'bear']
Handling Input Errors
Users often make mistakes while entering data or may intentionally enter incorrect characters. In such cases, incorrect input can cause the program to crash:
>>> height = float(input('Enter your height (e.g., 1.72): '))
Enter your height (e.g., 1.72): 1m72
Traceback (most recent call last):
File "<pyshell#2>", line 1, in <module>
height = float(input('Enter your height (e.g., 1.72): '))
ValueError: could not convert string to float: '1m72'
The error message indicates that Python cannot convert the string into a float. To prevent such crashes, we use the try-except block:
try:
height = float(input('Enter your height (e.g., 1.72): '))
except ValueError:
height = float(input('Please enter your height in the correct format: '))
We can also modify our initial age-input program to be more robust:
try:
user_age = int(input('How old are you? '))
except ValueError:
user_age = int(input('Please enter a number: '))
However, the program will still crash if the user enters incorrect data again. To make it more resilient, we can use a while loop:
while True:
try:
height = float(input('Enter your height (e.g., 1.72): '))
break
except ValueError:
print('Let’s try again.')
continue
print('Thank you!')
Here, we use a while loop with break and continue. The program works as follows:
If the input is correct, the loop breaks, and the program proceeds to the final message: print('Thank you!').
If the program cannot convert input to a float, it catches an exception (ValueError) and displays the message "Let’s try again."
The continue statement prevents the program from crashing and loops back to request input again.
Now, the user must enter valid data before proceeding.
Here is the complete code for a more resilient program:
user_name = input('Enter your name: ')
while True:
try:
user_age = int(input('How old are you? '))
break
except ValueError:
print('Are you sure?')
continue
if user_age < 18:
print('Sorry, access is restricted to adults only')
else:
print('Welcome to the site,', user_name, '!')
input('Press Enter to go to the menu')
print('Welcome to the menu')
input('Press Enter to exit')
This program still allows unrealistic inputs (e.g., 3 meters tall or 300 years old). To enforce realistic values, additional range checks would be needed, but that is beyond the scope of this article.
08 April 2025 · 6 min to read
Python
Operators in Python
Python operators are tools used to perform various actions with variables, as well as numerical and other values called operands—objects on which operations are performed. There are several types of Python operators:
Arithmetic
Comparison
Assignment
Identity
Membership
Logical
Bitwise
This article will examine each of them in detail and provide examples.
Arithmetic Operators
For addition, subtraction, multiplication, and division, we use the Python operators +, -, *, and / respectively:
>>> 24 + 28
52
>>> 24 - 28
-4
>>> 24 * 28
672
>>> 24 / 28
0.8571428571428571
For exponentiation, ** is used:
>>> 5 ** 2
25
>>> 5 ** 3
125
>>> 5 ** 4
625
For floor division (integer division without remainder), // is used:
>>> 61 // 12
5
>>> 52 // 22
2
>>> 75 // 3
25
>>> 77 // 3
25
The % operator returns the remainder (modulo division):
>>> 62 % 6
2
>>> 65 % 9
2
>>> 48 % 5
3
>>> 48 % 12
0
Comparison Operators
Python has six comparison operators: >, <, >=, <=, ==, !=.
Note that equality in Python is written as ==, because a single = is used for assignment. The != operator is used for "not equal to." When comparing values, Python will return True or False depending on whether the expressions are true or false.
>>> 26 > 58
False
>>> 26 < 58
True
>>> 26 >= 26
True
>>> 58 <= 57
False
>>> 50 == 50
True
>>> 50 != 50
False
>>> 50 != 51
True
Assignment Operators
A single = is used for assigning values to variables:
>>> b = 5
>>> variants = 20
Python also provides convenient shorthand operators that combine arithmetic operations with assignment: +=, -=, *=, /=, //=, %=. For example:
>>> cars = 5
>>> cars += 7
>>> cars
12
This is equivalent to:
>>> cars = cars + 7
>>> cars
12
The first version is more compact. Other assignment operators work similarly:
>>> train = 11
>>> train -= 2
>>> train
9
>>> moto = 3
>>> moto *= 7
>>> moto
21
>>> plain = 8
>>> plain /= 4
>>> plain
2.0
Notice that in the last case, the result is a floating-point number (float), not an integer (int).
Identity Operators
Python has two identity operators: is and is not. The results are True or False, similar to comparison operators.
>>> 55 is 55
True
>>> 55 is 56
False
>>> 55 is not 55
False
>>> 55 is not 56
True
>>> 55 is '55'
False
>>> '55' is "55"
True
In the last two examples:
55 is '55' returned False because an integer and a string were compared.
'55' is "55" returned True because both operands are strings.
Python does not differentiate between single and double quotes, so the identity check was successful.
Membership Operators
There are only two membership operators in Python: in and not in. They check whether a certain value exists within a sequence. For example:
>>> wordlist = ('assistant', 'streetcar', 'fraudster', 'dancer', 'heat', 'blank', 'compass', 'commerce', 'judgment', 'approach')
>>> 'house' in wordlist
False
>>> 'assistant' in wordlist
True
>>> 'assistant' and 'streetcar' in wordlist
True
In the last case, a logical operator (and) was used, which leads us to the next topic.
Logical Operators
Python has three logical operators: and, or, and not.
and returns True only if all operands are true. It can process any number of values. Using an example from the previous section:
>>> wordlist = ('assistant', 'streetcar', 'fraudster', 'dancer', 'heat', 'blank', 'compass', 'commerce', 'judgment', 'approach')
>>> 'assistant' and 'streetcar' in wordlist
True
>>> 'fraudster' and 'dancer' and 'heat' and 'blank' in wordlist
True
>>> 'fraudster' and 'dancer' and 'heat' and 'blank' and 'house' in wordlist
False
Since 'house' is not in the sequence, the result is False. These operations also work with numerical values:
>>> numbers = 54 > 55 and 22 > 21
>>> print(numbers)
False
One of the expressions is false, and and requires all conditions to be true.
or works differently: it returns True if at least one operand is true. If we replace and with or in the previous example, we get:
>>> numbers = 54 > 55 or 22 > 21
>>> print(numbers)
True
Here, 22 > 21 is true, so the overall expression evaluates to True, even though 54 > 55 is false.
not reverses logical values:
>>> first = True
>>> second = False
>>> print(not first)
False
>>> print(not second)
True
As seen in the example, not flips True to False and vice versa.
Bitwise Operators
Bitwise operators are used in Python to manipulate objects at the bit level. There are five of them (shift operators belong to the same type, as they differ only in shift direction):
& (AND)
| (OR)
^ (XOR)
~ (NOT)
<< and >> (shift operators)
Bitwise operators are based on Boolean logic principles and work as follows:
& (AND) returns 1 if both operands contain 1; otherwise, it returns 0:
>>> 1 & 1
1
>>> 1 & 0
0
>>> 0 & 1
0
>>> 0 & 0
0
| (OR) returns 1 if at least one operand contains 1, otherwise 0:
>>> 1 | 1
1
>>> 1 | 0
1
>>> 0 | 1
1
>>> 0 | 0
0
^ (XOR) returns 1 if the operands are different and 0 if they are the same:
>>> 1 ^ 1
0
>>> 1 ^ 0
1
>>> 0 ^ 1
1
>>> 0 ^ 0
0
~ (NOT) inverts bits, turning positive values into negative ones with a shift of one:
>>> ~5
-6
>>> ~-5
4
>>> ~7
-8
>>> ~-7
6
>>> ~9
-10
>>> ~-9
8
<< and >> shift bits by a specified number of positions:
>>> 1 << 1
2
>>> 1 >> 1
0
To understand shifts, let’s break down values into bits:
0 = 00
1 = 01
2 = 10
Shifting 1 left by one bit gives 2, while shifting right results in 0. What happens if we shift by two positions?
>>> 1 << 2
4
>>> 1 >> 2
0
1 = 001
2 = 010
4 = 100
Shifting 1 two places to the left results in 4 (100 in binary). Shifting right always results in zero because bits are discarded.
For more details, refer to our article on bitwise operators.
Difference Between Operators and Functions
You may have noticed that we have included no functions in this overview. The confusion between operators and functions arises because both perform similar actions—transforming objects. However:
Functions are broader and can operate on strings, entire blocks of code, and more.
Operators work only with individual values and variables.
In Python, a function can consist of a block of operators, but operators can never contain functions.
08 April 2025 · 6 min to read
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