Short answer: You can remove all empty lists from a list of lists by using the list comprehension statement [x for x in list if x] to filter the list.
In the following, you’ll learn about the two methods using list comprehension and the filter() function to remove all empty lists from a list of lists.
But before that, feel free to play with the code yourself:
Method 1: List Comprehension
How can you remove all empty lists from a list of lists? Say, you’ve got a list of lists
and you want all empty lists removed to obtain the list of lists
[[1, 2, 3], [1, 2], [1, 2, 3, 4]].
Solution: Use list comprehension[x for x in list if x] to filter the list and remove all lists that are empty.
lst = [[1, 2, 3], [1, 2], [], [], [], [1, 2, 3, 4], [], []]
print([x for x in lst if x])
# [[1, 2, 3], [1, 2], [1, 2, 3, 4]]
The condition if x evaluates to False only if the list x is empty. In all other cases, it evaluates to True and the element is included in the new list.
You can visualize the execution flow here by clicking the “Next” button:
Method 2: filter()
An alternative is to use the filter() function to remove all empty lists from a list of lists:
the filter decision function to check for each element whether it should be included in the filtered iterable (it returns a Boolean value), and
the iterable to be filtered.
As filter decision function, you use the identity function that just passes the list through. Why does this work? Because only an empty list will be evaluated to False. All other lists will be evaluated to True (and, thus, pass the filtering test).
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.
Short answer: To print a list of lists in Python without brackets and aligned columns, use the ''.join() function and a generator expression to fill each string with enough whitespaces so that the columns align:
# Create the list of lists
lst = [['Alice', 'Data Scientist', '121000'], ['Bob', 'Java Dev', '99000'], ['Ann', 'Python Dev', '111000']] # Find maximal length of all elements in list
n = max(len(x) for l in lst for x in l) # Print the rows
for row in lst: print(''.join(x.ljust(n + 2) for x in row))
I’ll explain this code (and simpler variants) in the following video:
As an exercise, you can also try it yourself in our interactive shell:
Step-By-Step Problem Formulation
Do you want to print a list of lists in Python so that the format doesn’t look like a total mess? In this tutorial, I’ll show you how to orderly print a list of lists in Python—without using any external library.
Before you start to think about the solution, you should understand the problem. What happens if you print a list of lists? Well, let’s try:
And maybe, you even want to see an output that aligns the three columns as well like this one:
Alice Data Scientist 121000 Bob Java Dev 99000 Ann Python Dev 111000
I’ll show you all those different ways of printing a list of lists next. So let’s start with the easiest one:
Print List of Lists With Newline
Problem: Given a list of lists, print it one row per line.
Example: Consider the following example list:
lst = [[1, 2, 3], [4, 5, 6]]
You want to print the list of lists with a newline character after each inner list:
1 2 3
4 5 6
Solution: Use a for loop and a simple print statement:
lst = [[1, 2, 3], [4, 5, 6]] for x in lst: print(*x)
The output has the desired form:
1 2 3
4 5 6
Explanation: The asterisk operator “unpacks” all values in the inner list x into the print statement. You must know that the print statement also takes multiple inputs and prints them, whitespace-separated, to the shell.
Alice 'Data Scientist', 121000], Bob 'Java Dev', 99000], Ann 'Python Dev', 111000]]
Solution: Use the following code snippet to print the list of lists and align all columns (no matter how many characters each string in the list of lists occupies).
# Create the list of lists
lst = [['Alice', 'Data Scientist', '121000'], ['Bob', 'Java Dev', '99000'], ['Ann', 'Python Dev', '111000']] # Find maximal length of all elements in list
n = max(len(x) for l in lst for x in l) # Print the rows
for row in lst: print(''.join(x.ljust(n + 2) for x in row))
The output is the desired:
Alice Data Scientist 121000 Bob Java Dev 99000 Ann Python Dev 111000
Explanation:
First, you determine the length n (in characters) of the largest string in the list of lists using the statement max(len(x) for l in lst for x in l). The code uses a nested for loop in a generator expression to achieve this.
Second, you iterate over each list in the list of lists (called row).
Third, you create a string representation with columns aligned by ‘padding’ each row element so that it occupies n+2 characters of space. The missing characters are filled with empty spaces.
You can see the code in action in the following memory visualizer. Just click “Next” to see which objects are created in memory if you run the code in Python:
Related articles: You may need to refresh your understanding of the following Python features used in the code:
Last but not least, I’ll show you my simple favorite: simply import the pandas library (the excel of Python coders) and print the DataFrame. Pandas takes care of pretty formatting:
The output looks beautiful—like a spreadsheet in your Python shell:
0 1 2
0 Alice Data Scientist 121000
1 Bob Java Dev 99000
2 Ann Python Dev 111000
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.
[20-SEC SUMMARY] Given a list of list stored in variable lst.
To flatten a list of lists, use the list comprehension statement [x for l in lst for x in l].
To modify all elements in a list of lists (e.g., increment them by one), use a list comprehension of list comprehensions [[x+1 for x in l] for l in lst].
List comprehension is a compact way of creating lists. The simple formula is [ expression + context ].
Expression: What to do with each list element?
Context: What list elements to select? It consists of an arbitrary number of for and if statements.
The example [x for x in range(3)] creates the list [0, 1, 2].
In this tutorial, you’ll learn three ways how to apply list comprehension to a list of lists:
to flatten a list of lists
to create a list of lists
to iterate over a list of lists
Additionally, you’ll learn how to apply nested list comprehension. So let’s get started!
Python List Comprehension Flatten List of Lists
Problem: Given a list of lists. How to flatten the list of lists by getting rid of the inner lists—and keeping their elements?
Example: You want to transform a given list into a flat list like here:
Solution: Use a nested list comprehension statement [x for l in lst for x in l] to flatten the list.
lst = [[2, 2], [4], [1, 2, 3, 4], [1, 2, 3]] # ... Flatten the list here ...
lst = [x for l in lst for x in l] print(lst)
# [2, 2, 4, 1, 2, 3, 4, 1, 2, 3]
Explanation: In the nested list comprehension statement [x for l in lst for x in l], you first iterate over all lists in the list of lists (for l in lst). Then, you iterate over all elements in the current list (for x in l). This element, you just place in the outer list, unchanged, by using it in the “expression” part of the list comprehension statement [x for l in lst for x in l].
Try It Yourself: You can execute this code snippet yourself in our interactive Python shell. Just click “Run” and test the output of this code.
Can you flatten a three-dimensional list (= a list of lists of lists)? Try it in the shell!
Python List Comprehension Create List of Lists
Problem: How to create a list of lists by modifying each element of an original list of lists?
Example: You’re given the list
[[1, 2, 3], [4, 5, 6], [7, 8, 9]]
You want to add one to each element and create a new list of lists:
[[2, 3, 4], [5, 6, 7], [8, 9, 10]]
Solution: Use two nested list comprehension statements, one to create the outer list of lists, and one to create the inner lists.
lst = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
new = [[x+1 for x in l] for l in lst]
print(new)
# [[2, 3, 4], [5, 6, 7], [8, 9, 10]]
Explanation: You’ll study more examples of two nested list comprehension statements later. The main idea is to use as “expression” of the outer list comprehension statement a list comprehension statement by itself. Remember, you can create any object you want in the expression part of your list comprehension statement.
Explore the code: You can play with the code in the interactive Python tutor that visualizes the execution step-by-step. Just click the “Next” button repeatedly to see what happens in each step of the code.
Let’s explore the third question: how to use list comprehension to iterate over a list of lists?
Python List Comprehension Over List of Lists
You’ve seen this in the previous example where you not only created a list of lists, you also iterated over each element in the list of lists. To summarize, you can iterate over a list of lists by using the statement [[modify(x) for x in l] for l in lst] using any statement or function modify(x) that returns an arbitrary object.
How Does Nested List Comprehension Work in Python?
After publishing the first version of this tutorial, many readers asked me to write a follow-up tutorial on nested list comprehension in Python. There are two interpretations of nested list comprehension:
Coming from a computer science background, I was assuming that “nested list comprehension” refers to the creation of a list of lists. In other words: How to create a nested list with list comprehension?
But after a bit of research, I learned that there is a second interpretation of nested list comprehension: How to use a nested for loop in the list comprehension?
How to create a nested list with list comprehension?
It is possible to create a nested list with list comprehension in Python. What is a nested list? It’s a list of lists. Here is an example:
## Nested List Comprehension
lst = [[x for x in range(5)] for y in range(3)]
print(lst)
# [[0, 1, 2, 3, 4], [0, 1, 2, 3, 4], [0, 1, 2, 3, 4]]
As you can see, we create a list with three elements. Each list element is a list by itself.
Everything becomes clear when we go back to our magic formula of list comprehension: [expression + context]. The expression part generates a new list consisting of 5 integers. The context part repeats this three times. Hence, each of the three nested lists has five elements.
If you are an advanced programmer (test your skills on the Finxter app), you may ask whether there is some aliasing going on here. Aliasing in this context means that the three list elements point to the same list [0, 1, 2, 3, 4]. This is not the case because each expression is evaluated separately, a new list is created for each of the three context executions. This is nicely demonstrated in this code snippet:
How to use a nested for loop in the list comprehension?
To be frank, the latter one is super-simple stuff. Do you remember the formula of list comprehension (= ‘[‘ + expression + context + ‘]’)?
The context is an arbitrary complex restriction construct of for loops and if restrictions with the goal of specifying the data items on which the expression should be applied.
In the expression, you can use any variable you define within a for loop in the context. Let’s have a look at an example.
Suppose you want to use list comprehension to make this code more concise (for example, you want to find all possible pairs of users in your social network application):
# BEFORE
users = ["John", "Alice", "Ann", "Zach"]
pairs = []
for x in users: for y in users: if x != y: pairs.append((x,y))
print(pairs)
#[('John', 'Alice'), ('John', 'Ann'), ('John', 'Zach'), ('Alice', 'John'), ('Alice', 'Ann'), ('Alice', 'Zach'), ('Ann', 'John'), ('Ann', 'Alice'), ('Ann', 'Zach'), ('Zach', 'John'), ('Zach', 'Alice'), ('Zach', 'Ann')]
Now, this code is a mess! How can we fix it? Simply use nested list comprehension!
# AFTER
pairs = [(x,y) for x in users for y in users if x!=y]
print(pairs)
# [('John', 'Alice'), ('John', 'Ann'), ('John', 'Zach'), ('Alice', 'John'), ('Alice', 'Ann'), ('Alice', 'Zach'), ('Ann', 'John'), ('Ann', 'Alice'), ('Ann', 'Zach'), ('Zach', 'John'), ('Zach', 'Alice'), ('Zach', 'Ann')]
As you can see, we are doing exactly the same thing as with un-nested list comprehension. The only difference is to write the two for loops and the if statement in a single line within the list notation [].
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.
You can convert a list of lists to a Pandas DataFrame that provides you with powerful capabilities such as the to_csv() method. This is the easiest method and it allows you to avoid importing yet another library (I use Pandas in many Python projects anyways).
You create a Pandas DataFrame—which is Python’s default representation of tabular data. Think of it as an Excel spreadsheet within your code (with rows and columns).
The DataFrame is a very powerful data structure that allows you to perform various methods. One of those is the to_csv() method that allows you to write its contents into a CSV file.
You set the index and header arguments of the to_csv() method to False because Pandas, per default, adds integer row and column indices 0, 1, 2, …. Again, think of them as the row and column indices in your Excel spreadsheet. You don’t want them to appear in the CSV file so you set the arguments to False.
If you want to customize the CSV output, you’ve got a lot of special arguments to play with. Check out this article for a comprehensive list of all arguments.
Feel free to play with alternative methods to convert a list of lists to a CSV file in our interactive code shell. Simply click the “Run” button and find the generated CSV files in the “Files” tab.
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.
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!
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.
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
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.
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!
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.
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.
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:
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.
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 '''
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.
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.
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:
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.
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):
Dictionary Comprehension
Generator Expression
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.
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.
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:
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.
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].
NoteTuple: 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.
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.
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.
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.
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]
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:
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
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!
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.
