Category: Programming

Pandas is an open-source Python library that is powerful and flexible for data analysis. If there is something you want to do with data, the chances are it will be possible in pandas. There are a vast number of possibilities within pandas, but most users find themselves using the same methods time after time. In this article, we compiled the best cheat sheets from across the web, which show you these core methods at a glance.

The primary data structure in pandas is the DataFrame used to store two-dimensional data, along with a label for each corresponding column and row. If you are familiar with Excel spreadsheets or SQL databases, you can think of the DataFrame as being the pandas equivalent. If we take a single column from a DataFrame, we have one-dimensional data. In pandas, this is called a Series. DataFrames can be created from scratch in your code, or loaded into Python from some external location, such as a CSV. This is often the first stage in any data analysis task. We can then do any number of things with our DataFrame in Pandas, including removing or editing values, filtering our data, or combining this DataFrame with another DataFrame. Each line of code in these cheat sheets lets you do something different with a DataFrame. Also, if you are coming from an Excel background, you will enjoy the performance pandas has to offer. After you get over the learning curve, you will be even more impressed with the functionality.

Whether you are already familiar with pandas and are looking for a handy reference you can print out, or you have never used pandas and are looking for a resource to help you get a feel for the library- there is a cheat sheet here for you!

1. The Most Comprehensive Cheat Sheet

https://pandas.pydata.org/Pandas_Cheat_Sheet.pdf

This one is from the pandas guys, so it makes sense that this is a comprehensive and inclusive cheat sheet. It covers the vast majority of what most pandas users will ever need to do to a DataFrame. Have you already used pandas for a little while? And are you looking to up your game? This is your cheat sheet! However, if you are newer to pandas and this cheat sheet is a bit overwhelming, don’t worry! You definitely don’t need to understand everything in this cheat sheet to get started. Instead, check out the next cheat sheet on this list.

2. The Beginner’s Cheat Sheet

https://www.dataquest.io/blog/pandas-cheat-sheet/

Dataquest is an online platform that teaches Data Science using interactive coding challenges. I love this cheat sheet they have put together. It has everything the pandas beginner needs to start using pandas right away in a friendly, neat list format. It covers the bare essentials of each stage in the data analysis process:

• Importing and exporting your data from an Excel file, CSV, HTML table or SQL database
• Cleaning your data of any empty rows, changing data formats to allow for further analysis or renaming columns
• Filtering your data or removing anomalous values
• Different ways to view the data and see it’s dimensions
• Selecting any combination of columns and rows within the DataFrame using loc and iloc
• Using the .apply method to apply a formula to a particular column in the DataFrame
• Creating summary statistics for columns in the DataFrame. This includes the median, mean and standard deviation
• Combining DataFrames

3. The Excel User’s Cheat Sheet

https://www.shanelynn.ie/using-pandas-dataframe-creating-editing-viewing-data-in-python/

Ok, this isn’t quite a cheat sheet, it’s more of an entire manifesto on the pandas DataFrame! If you have a little time on your hands, this will help you get your head around some of the theory behind DataFrames. It will take you all the way from loading in your trusty CSV from Microsoft Excel to viewing your data in Jupyter and handling the basics. The article finishes off by using the DataFrame to create a histogram and bar chart. For migrating your spreadsheet work from Excel to pandas, this is a fantastic guide. It will teach you how to perform many of the Excel basics in pandas. If you are also looking for how to perform the pandas equivalent of a VLOOKUP in Excel, check out Shane’s article on the merge method.

4. The Most Beautiful Cheat Sheet

https://www.enthought.com/wp-content/uploads/Enthought-Python-Pandas-Cheat-Sheets-1-8-v1.0.2.pdf

If you’re more of a visual learner, try this cheat sheet! Many common pandas tasks have intricate, color-coded illustrations showing how the operation works. On page 3, there is a fantastic section called ‘Computation with Series and DataFrames’, which provides an intuitive explanation for how DataFrames work and shows how the index is used to align data when DataFrames are combined and how element-wise operations work in contrast to operations which work on each row or column. At 8 pages long, it’s more of a booklet than a cheat sheet, but it can still make for a great resource! 

5. The Best Machine Learning Cheat Sheet

https://elitedatascience.com/python-cheat-sheet

Much like the other cheat sheets, there is comprehensive coverage of the pandas basic in here. So, that includes filtering, sorting, importing, exploring, and combining DataFrames. However, where this Cheat Sheet differs is that it finishes off with an excellent section on scikit-learn, Python’s machine learning library. In this section, the DataFrame is used to train a machine learning model. This cheat sheet will be perfect for anybody who is already familiar with machine learning and is transitioning from a different technology, such as R.

6. The Most Compact Cheat Sheet

http://datacamp-community-prod.s3.amazonaws.com/dbed353d-2757-4617-8206-8767ab379ab3

Data Camp is an online platform that teaches Data Science with videos and coding exercises. They have made cheat sheets on a bunch of the most popular Python libraries, which you can also check out here. This cheat sheet nicely introduces the DataFrame, and then gives a quick overview of the basics. Unfortunately, it doesn’t provide any information on the various ways you can combine DataFrames, but it does all fit on one page and looks great. So, if you are looking to stick a pandas cheat sheet on your bedroom wall and nail home the basics, this one might be for you! The cheat sheet finishes with a small section introducing NaN values, which come from NumPy. These indicate a null value and arise when the indices of two Series don’t quite match up in this case.

7. The Best Statistics Cheat Sheet

https://www.webpages.uidaho.edu/~stevel/504/pandas%20dataframe%20notes.pdf

While there aren’t any pictures to be found in this sheet, it is an incredibly detailed set of notes on the pandas DataFrame. This cheat shines with its complete section on time series and statistics. There are methods for calculating covariance, correlation, and regression here. So, if you are using pandas for some advanced statistics or any kind of scientific work, this is going to be your cheat sheet.

Where to go from here?

For just automating a few tedious tasks at work, or using pandas to replace your crashing Excel spreadsheet, everything covered in these cheat sheets should be entirely sufficient for your purposes. 

If you are looking to use pandas for Data Science, then you are only going to be limited by your knowledge of statistics and probability. This is the area that most people lack when they try to enter this field. I highly recommend checking out Think Stats by Allen B Downey, which provides an introduction to statistics using Python.

For those a little more advanced, looking to do some machine learning, you will want to start taking a look at the scikit-learn library. Data Camp has a great cheat sheet for this. You will also want to pick up a linear algebra textbook to understand the theory of machine learning. For something more practical, perhaps give the famous Kaggle Titanic machine learning competition.

Learning about pandas has many uses, and can be interesting simply for its own sake. However, Python is massively in demand right now, and for that reason, it is a high-income skill. At any given time, there are thousands of people searching for somebody to solve their problems with Python. So, if you are looking to use Python to work as a freelancer, then check out the Finxter Python Freelancer Course. This provides the step by step path to go from nothing to earning a full-time income with Python in a few months, and gives you the tools to become a six-figure developer!

Problem: You’re given a list of lists. Your goal is to convert it into a Pandas Dataframe.

Example: Say, you want to compare salary data of different companies and job descriptions. You’ve obtained the following salary data set as a list of list:

salary = [['Google', 'Machine Learning Engineer', 121000], ['Google', 'Data Scientist', 109000], ['Google', 'Tech Lead', 129000], ['Facebook', 'Data Scientist', 103000]]

How can you convert this into a Pandas Dataframe?

DataFrame()

Solution: The straight-forward solution is to use the pandas.DataFrame() constructor that creates a new Dataframe object from different input types such as NumPy arrays or lists.

Here’s how to do it for the given example:

import pandas as pd salary = [['Google', 'Machine Learning Engineer', 121000], ['Google', 'Data Scientist', 109000], ['Google', 'Tech Lead', 129000], ['Facebook', 'Data Scientist', 103000]] df = pd.DataFrame(salary)

This results in the following Dataframe:

print(df) ''' 0 1 2
0 Google Machine Learning Engineer 121000
1 Google Data Scientist 109000
2 Google Tech Lead 129000
3 Facebook Data Scientist 103000 '''

Try It Yourself: Run this code in our interactive Python shell by clicking the “Run” button.

DataFrame.from_records()

An alternative is the pandas.DataFrame.from_records() method that generates the same output:

import pandas as pd salary = [['Company', 'Job', 'Salary($)'], ['Google', 'Machine Learning Engineer', 121000], ['Google', 'Data Scientist', 109000], ['Google', 'Tech Lead', 129000], ['Facebook', 'Data Scientist', 103000]] df = pd.DataFrame.from_records(salary)
print(df) ''' 0 1 2
0 Google Machine Learning Engineer 121000
1 Google Data Scientist 109000
2 Google Tech Lead 129000
3 Facebook Data Scientist 103000 '''

Try It Yourself: Run this code in our interactive Python shell by clicking the “Run” button.

Column Names

If you want to add column names to make the output prettier, you can also pass those as a separate argument:

import pandas as pd salary = [['Google', 'Machine Learning Engineer', 121000], ['Google', 'Data Scientist', 109000], ['Google', 'Tech Lead', 129000], ['Facebook', 'Data Scientist', 103000]] df = pd.DataFrame(salary, columns=['Company', 'Job', 'Salary($)'])
print(df) ''' Company Job Salary($)
0 Google Machine Learning Engineer 121000
1 Google Data Scientist 109000
2 Google Tech Lead 129000
3 Facebook Data Scientist 103000 '''

Try It Yourself: Run this code in our interactive Python shell by clicking the “Run” button.

If the first list of the list of lists contains the column name, use slicing to separate the first list from the other lists:

import pandas as pd salary = [['Company', 'Job', 'Salary($)'], ['Google', 'Machine Learning Engineer', 121000], ['Google', 'Data Scientist', 109000], ['Google', 'Tech Lead', 129000], ['Facebook', 'Data Scientist', 103000]] df = pd.DataFrame(salary[1:], columns=salary[0])
print(df) ''' Company Job Salary($)
0 Google Machine Learning Engineer 121000
1 Google Data Scientist 109000
2 Google Tech Lead 129000
3 Facebook Data Scientist 103000 '''

Slicing is a powerful Python feature and before you can master Pandas, you need to master slicing. To refresh your Python slicing skills, download my ebook “Coffee Break Python Slicing” for free.

Summary: To convert a list of lists into a Pandas DataFrame, use the pd.DataFrame() constructor and pass the list of lists as an argument. An optional columns argument can help you structure the output.

Where to Go From Here?

Enough theory, let’s get some practice!

To become successful in coding, you need to get out there and solve real problems for real people. That’s how you can become a six-figure earner easily. And that’s how you polish the skills you really need in practice. After all, what’s the use of learning theory that nobody ever needs?

Practice projects is how you sharpen your saw in coding!

Do you want to become a code master by focusing on practical code projects that actually earn you money and solve problems for people?

Then become a Python freelance developer! It’s the best way of approaching the task of improving your Python skills—even if you are a complete beginner.

Join my free webinar “How to Build Your High-Income Skill Python” and watch how I grew my coding business online and how you can, too—from the comfort of your own home.

Join the free webinar now!

Short answer: Convert a list of lists—let’s call it l—to a NumPy array by using the standard np.array(l) function. This works even if the inner lists have a different number of elements.

Convert List of Lists to 2D Array

Problem: Given a list of lists in Python. How to convert it to a 2D NumPy array?

Example: Convert the following list of lists

[[1, 2, 3], [4, 5, 6]]

into a NumPy array

[[1 2 3] [4 5 6]]

Solution: Use the np.array(list) function to convert a list of lists into a two-dimensional NumPy array. Here’s the code:

# Import the NumPy library
import numpy as np # Create the list of lists
lst = [[1, 2, 3], [4, 5, 6]] # Convert it to a NumPy array
a = np.array(lst) # Print the resulting array
print(a) '''
[[1 2 3] [4 5 6]] '''

Try It Yourself: Here’s the same code in our interactive code interpreter:

<iframe height="700px" width="100%" src="https://repl.it/@finxter/numpylistoflists?lite=true" scrolling="no" frameborder="no" allowtransparency="true" allowfullscreen="true" sandbox="allow-forms allow-pointer-lock allow-popups allow-same-origin allow-scripts allow-modals"></iframe>

Hint: The NumPy method np.array() takes an iterable as input and converts it into a NumPy array.

Convert a List of Lists With Different Number of Elements

Problem: Given a list of lists. The inner lists have a varying number of elements. How to convert them to a NumPy array?

Example: Say, you’ve got the following list of lists:

[[1, 2, 3], [4, 5], [6, 7, 8]]

What are the different approaches to convert this list of lists into a NumPy array?

Solution: There are three different strategies you can use. (source)

(1) Use the standard np.array() function.

# Import the NumPy library
import numpy as np # Create the list of lists
lst = [[1, 2, 3], [4, 5], [6, 7, 8]] # Convert it to a NumPy array
a = np.array(lst) # Print the resulting array
print(a) '''
[list([1, 2, 3]) list([4, 5]) list([6, 7, 8])] '''

This creates a NumPy array with three elements—each element is a list type. You can check the type of the output by using the built-in type() function:

>>> type(a)
<class 'numpy.ndarray'>

(2) Make an array of arrays.

# Import the NumPy library
import numpy as np # Create the list of lists
lst = [[1, 2, 3], [4, 5], [6, 7, 8]] # Convert it to a NumPy array
a = np.array([np.array(x) for x in lst]) # Print the resulting array
print(a) '''
[array([1, 2, 3]) array([4, 5]) array([6, 7, 8])] '''

This is more logical than the previous version because it creates a NumPy array of 1D NumPy arrays (rather than 1D Python lists).

(3) Make the lists equal in length.

# Import the NumPy library
import numpy as np # Create the list of lists
lst = [[1, 2, 3], [4, 5], [6, 7, 8, 9]] # Calculate length of maximal list
n = len(max(lst, key=len)) # Make the lists equal in length
lst_2 = [x + [None]*(n-len(x)) for x in lst]
print(lst_2)
# [[1, 2, 3, None], [4, 5, None, None], [6, 7, 8, 9]] # Convert it to a NumPy array
a = np.array(lst_2) # Print the resulting array
print(a) '''
[[1 2 3 None] [4 5 None None] [6 7 8 9]] '''

You use list comprehension to “pad” None values to each inner list with smaller than maximal length.

Where to Go From Here?

Enough theory, let’s get some practice!

To become successful in coding, you need to get out there and solve real problems for real people. That’s how you can become a six-figure earner easily. And that’s how you polish the skills you really need in practice. After all, what’s the use of learning theory that nobody ever needs?

Practice projects is how you sharpen your saw in coding!

Do you want to become a code master by focusing on practical code projects that actually earn you money and solve problems for people?

Then become a Python freelance developer! It’s the best way of approaching the task of improving your Python skills—even if you are a complete beginner.

Join my free webinar “How to Build Your High-Income Skill Python” and watch how I grew my coding business online and how you can, too—from the comfort of your own home.

Join the free webinar now!

For some applications, it’s quite useful to convert a list of lists into a dictionary.

• Databases: List of list is table where the inner lists are the database rows and you want to assign each row to a primary key in a new dictionary.
• Spreadsheet: List of list is two-dimensional spreadsheet data and you want to assign each row to a key (=row name).
• Data Analytics: You’ve got a two-dimensional matrix (=NumPy array) that’s initially represented as a list of list and you want to obtain a dictionary to ease data access.

There are three main ways to convert a list of lists into a dictionary in Python (source):

1. Dictionary Comprehension
2. Generator Expression
3. For Loop

Let’s dive into each of those.

1. Dictionary Comprehension

Problem: Say, you’ve got a list of lists where each list represents a person and consists of three values for the person’s name, age, and hair color. For convenience, you want to create a dictionary where you use a person’s name as a dictionary key and the sublist consisting of the age and the hair color as the dictionary value.

Solution: You can achieve this by using the beautiful (but, surprisingly, little-known) feature of dictionary comprehension in Python.

persons = [['Alice', 25, 'blonde'], ['Bob', 33, 'black'], ['Ann', 18, 'purple']] persons_dict = {x[0]: x[1:] for x in persons}
print(persons_dict)
# {'Alice': [25, 'blonde'],
# 'Bob': [33, 'black'],
# 'Ann': [18, 'purple']}

Explanation: The dictionary comprehension statement consists of the expression x[0]: x[1:] that assigns a person’s name x[0] to the list x[1:] of the person’s age and hair color. Further, it consists of the context for x in persons that iterates over all “data rows”.

Exercise: Can you modify the code in our interactive code shell so that each hair color is used as a key and the name and age are used as the values?

Modify the code and click the “run” button to see if you were right!

2. Generator Expression

A similar way of achieving the same thing is to use a generator expression in combination with the dict() constructor to create the dictionary.

persons = [['Alice', 25, 'blonde'], ['Bob', 33, 'black'], ['Ann', 18, 'purple']] persons_dict = dict((x[0], x[1:]) for x in persons)
print(persons_dict)
# {'Alice': [25, 'blonde'],
# 'Bob': [33, 'black'],
# 'Ann': [18, 'purple']}

This code snippet is almost identical to the one used in the “list comprehension” part. The only difference is that you use tuples rather than direct mappings to fill the dictionary.

3. For Loop

Of course, there’s no need to get fancy here. You can also use a regular for loop and define the dictionary elements one by one within a simple for loop. Here’s the alternative code:

persons = [['Alice', 25, 'blonde'], ['Bob', 33, 'black'], ['Ann', 18, 'purple']] persons_dict = {}
for x in persons: persons_dict[x[0]] = x[1:] print(persons_dict)
# {'Alice': [25, 'blonde'],
# 'Bob': [33, 'black'],
# 'Ann': [18, 'purple']}

Again, you map each person’s name to the list consisting of its age and hair color.

Where to Go From Here?

Enough theory, let’s get some practice!

To become successful in coding, you need to get out there and solve real problems for real people. That’s how you can become a six-figure earner easily. And that’s how you polish the skills you really need in practice. After all, what’s the use of learning theory that nobody ever needs?

Practice projects is how you sharpen your saw in coding!

Do you want to become a code master by focusing on practical code projects that actually earn you money and solve problems for people?

Then become a Python freelance developer! It’s the best way of approaching the task of improving your Python skills—even if you are a complete beginner.

Join my free webinar “How to Build Your High-Income Skill Python” and watch how I grew my coding business online and how you can, too—from the comfort of your own home.

Join the free webinar now!

Problem: Given a Python tuple with n elements. How to convert it into a list with the same n elements?

Examples:

• Convert tuple (1, 2, 3, 4, 5) into list [1, 2, 3, 4, 5].
• Convert tuple ('Alice', 'Bob', 'Ann') into list ['Alice', 'Bob', 'Ann'].
• Convert tuple (1,) into list [1].

Note Tuple: Tuples are similar to lists—with the difference that you cannot change the tuple values (tuples are immutable) and you use parentheses rather than square brackets.

Solution: Use the built-in Python list() function to convert a list into a tuple. You don’t need to import any external library.

Code: The following code converts the three given tuples into lists.

tuple_1 = (1, 2, 3, 4, 5)
print(list(tuple_1))
# [1, 2, 3, 4, 5] tuple_2 = ('Alice', 'Bob', 'Ann')
print(list(tuple_2))
# ['Alice', 'Bob', 'Ann'] tuple_3 = (1,)
print(list(tuple_3))
# [1]

Try It Yourself: With our interactive code shell, you can try it yourself. As a small exercise, try to convert the empty tuple () into a list and see what happens.

Explanation: You can see that converting a tuple with one element leads to a list with one element. The list() function is the easiest way to convert a tuple into a list. Note that the values in the tuple are not copied—only a new reference to the same element is created:

The graphic also shows how to convert a tuple back to a list by using the tuple() function (that’s also a Python built-in function). Thus, calling list(tuple(lst)) on a list lst will result in a new list with the same elements.

Related articles:

• List copy
• List complete guide
• Convert list to tuple
• Convert list to dict

Try to execute this code with the interactive Python tutor:

Summing up a list of numbers appears everywhere in coding. Fortunately, Python provides the built-in sum() function to sum over all elements in a Python list—or any other iterable for that matter. (Official Docs)

The syntax is sum(iterable, start=0):

Argument	Description
iterable	Sum over all elements in the iterable. This can be a list, a tuple, a set, or any other data structure that allows you to iterate over the elements. Example: sum([1, 2, 3]) returns 1+2+3=6.
start	(Optional.) The default start value is 0. If you define another start value, the sum of all values in the iterable will be added to this start value. Example: sum([1, 2, 3], 9) returns 9+1+2+3=15.

Check out the Python Freelancer Webinar and KICKSTART your coding career!

Code: Let’s check out a practical example!

lst = [1, 2, 3, 4, 5, 6] print(sum(lst))
# 21 print(sum(lst, 10))
# 31

Exercise: Try to modify the sequence so that the sum is 30 in our interactive Python shell:

Let’s explore some important details regarding the sum() function in Python.

Errors

A number of errors can happen if you use the sum() function in Python.

TypeError: Python will throw a TypeError if you try to sum over elements that are not numerical. Here’s an example:

# Demonstrate possible execeptions
lst = ['Bob', 'Alice', 'Ann'] # WRONG:
s = sum(lst)

If you run this code, you’ll get the following error message:

Traceback (most recent call last): File "C:\Users\xcent\Desktop\code.py", line 3, in <module> s = sum(lst)
TypeError: unsupported operand type(s) for +: 'int' and 'str'

Python tries to perform string concatenation using the default start value of 0 (an integer). Of course, this fails. The solution is simple: sum only over numerical values in the list.

If you try to “hack” Python by using an empty string as start value, you’ll get the following exception:

# Demonstrate possible execeptions
lst = ['Bob', 'Alice', 'Ann'] # WRONG:
s = sum(lst, '')

Output:

Traceback (most recent call last): File "C:\Users\xcent\Desktop\code.py", line 5, in <module> s = sum(lst, '')
TypeError: sum() can't sum strings [use ''.join(seq) instead]

You can get rid of all those errors by summing only over numerical elements in the list.

(For more information about the join() method, check out this blog article.)

Python Sum List Time Complexity

The time complexity of the sum() function is linear in the number of elements in the iterable (list, tuple, set, etc.). The reason is that you need to go over all elements in the iterable and add them to a sum variable. Thus, you need to “touch” every iterable element once.

Python Sum List of Strings

Problem: How can you sum a list of strings such as ['python', 'is', 'great']? This is called string concatenation.

Solution: Use the join() method of Python strings to concatenate all strings in a list. The sum() function works only on numerical input data.

Code: The following example shows how to “sum” up (i.e., concatenate) all elements in a given list of strings.

# List of strings
lst = ['Bob', 'Alice', 'Ann'] print(''.join(lst))
# BobAliceAnn print(' '.join(lst))
# Bob Alice Ann

Python Sum List of Lists

Problem: How can you sum a list of lists such as [[1, 2], [3, 4], [5, 6]] in Python?

Solution: Use a simple for loop with a helper variable to concatenate all lists.

Code: The following code concatenates all lists into a single list.

# List of lists
lst = [[1, 2], [3, 4], [5, 6]] s = []
for x in lst: s.extend(x)
print(s)
# [1, 2, 3, 4, 5, 6]

The extend() method is little-known in Python—but it’s very effective to add a number of elements to a Python list at once. Check out my detailed tutorial on this Finxter blog.

Python Sum List While Loop

Problem: How can you sum over all list elements using a while loop (without sum())?

Solution: Create an aggregation variable and iteratively add another element from the list.

Code: The following code shows how to sum up all numerical values in a Python list without using the sum() function.

# list of integers
lst = [1, 2, 3, 4, 5] # aggregation variable
s = 0 # index variable
i = 0 # sum everything up
while i<len(lst): s += lst[i] i += 1 # print the result
print(s)
# 15

This is not the prettiest way but it’s readable and it works (and, you didn’t want to use the sum() function, right?).

Python Sum List For Loop

Problem: How can you sum over all list elements using a for loop (without sum())?

Solution: Create an aggregation variable and iteratively add another element from the list.

Code: The following code shows how to sum up all numerical values in a Python list without using the sum() function.

# list of integers
lst = [1, 2, 3, 4, 5] # aggregation variable
s = 0 # sum everything up
for x in lst: s += x # print the result
print(s)
# 15

This is a bit more readable than the previous version with the while loop because you don’t have to keep track about the current index.

Python Sum List with List Comprehension

List comprehension is a powerful Python features that allows you to create a new list based on an existing iterable. Can you sum up all values in a list using only list comprehension?

The answer is no. Why? Because list comprehension exists to create a new list. Summing up values is not about creating a new list. You want to get rid of the list and aggregate all values in the list into a single numerical “sum”.

Python Sum List of Tuples Element Wise

Problem: How to sum up a list of tuples, element-wise?

Example: Say, you’ve got list [(1, 1), (2, 0), (0, 3)] and you want to sum up the first and the second tuple values to obtain the “summed tuple” (1+2+0, 1+0+3)=(3, 4).

Solution: Unpack the tuples into the zip function to combine the first and second tuple values. Then, sum up those values separately. Here’s the code:

# list of tuples
lst = [(1, 1), (2, 0), (0, 3)] # aggregate first and second tuple values
zipped = list(zip(*lst))
# result: [(1, 2, 0), (1, 0, 3)] # calculate sum of first and second tuple values
res = (sum(zipped[0]), sum(zipped[1])) # print result to the shell
print(res)
# result: (3, 4)

Need a refresher of the zip() function and unpacking? Check out these articles on the Finxter blog:

• Zip refresher
• Unpacking refresher

Python Sum List Slice

Problem: Given a list. Sum up a slice of the original list between the start and the step indices (and assuming the given step size as well).

Example: Given is list [3, 4, 5, 6, 7, 8, 9, 10]. Sum up the slice lst[2:5:2] with start=2, stop=5, and step=2.

Solution: Use slicing to access the list. Then, apply the sum() function on the result.

Code: The following code computes the sum of a given slice.

# create the list
lst = [3, 4, 5, 6, 7, 8, 9, 10] # create the slice
slc = lst[2:5:2] # calculate the sum
s = sum(slc) # print the result
print(s)
# 12 (=5+7)

Let’s examine an interesting problem: to sum up conditionally!

Python Sum List Condition

Problem: Given is a list. How to sum over all values that meet a certain condition?

Example: Say, you’ve got the list lst = [5, 8, 12, 2, 1, 3] and you want to sum over all values that are larger than 4.

Solution: Use list comprehension to filter the list so that only the elements that satisfy the condition remain. Then, use the sum() function to sum over the remaining values.

Code: The following code sums over all values that satisfy a certain condition (e.g., x>4).

# create the list
lst = [5, 8, 12, 2, 1, 3] # filter the list
filtered = [x for x in lst if x>4]
# remaining list: [5, 8, 12] # sum over the filtered list
s = sum(filtered) # print everything
print(s)
# 25

Need a refresher on list comprehension? Check out my in-depth tutorial on the Finxter blog.

Python Sum List Ignore None

Problem: Given is a list of numerical values that may contain some values None. How to sum over all values that are not the value None?

Example: Say, you’ve got the list lst = [5, None, None, 8, 12, None, 2, 1, None, 3] and you want to sum over all values that are not None.

Solution: Use list comprehension to filter the list so that only the elements that satisfy the condition remain (that are different from None). You see, that’s a special case of the previous paragraph that checks for a general condition. Then, use the sum() function to sum over the remaining values.

Code: The following code sums over all values that are not None.

# create the list
lst = [5, None, None, 8, 12, None, 2, 1, None, 3] # filter the list
filtered = [x for x in lst if x!=None]
# remaining list: [5, 8, 12, 2, 1, 3] # sum over the filtered list
s = sum(filtered) # print everything
print(s)
# 31

A similar thing can be done with the value Nan that can disturb your result if you aren’t careful.

Python Sum List Ignore Nan

Problem: Given is a list of numerical values that may contain some values nan (=”not a number”). How to sum over all values that are not the value nan?

Example: Say, you’ve got the list lst = [1, 2, 3, float("nan"), float("nan"), 4] and you want to sum over all values that are not nan.

Solution: Use list comprehension to filter the list so that only the elements that satisfy the condition remain (that are different from nan). You see, that’s a special case of the previous paragraph that checks for a general condition. Then, use the sum() function to sum over the remaining values.

Code: The following code sums over all values that are not nan.

# for checking isnan(x)
import math # create the list
lst = [1, 2, 3, float("nan"), float("nan"), 4] # forget to ignore 'nan'
print(sum(lst))
# nan # ignore 'nan'
print(sum([x for x in lst if not math.isnan(x)]))
# 10

Phew! Quite some stuff. Thanks for reading through this whole article! I hope you’ve learned something out of this tutorial and remain with the following recommendation:

Where to Go From Here?

Enough theory, let’s get some practice!

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Practice projects is how you sharpen your saw in coding!

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