- \n",
- "
- Prompt for input \"FASTA sequence name: \"\n", - "
- Prompt for input \"What is the DNA sequence? (use ATGC or atgc) of at least 10 bases \"\n", + "
- Prompt for input \"FASTA sequence name. \"\n", + "
- Prompt for input \"What is the DNA sequence? (use ATGC or atgc) of at least 10 bases.\"\n", "
- Output \"The DNA fragment is: > (since > starts all FASTA sequences) then string together the fragment name, \"\\n\" to get a return and the DNA sequence.\n", - "
- Print just the last letter of the FASTA code (or any position(s) you choose?)\n", + "
- Print just the last letter of the FASTA code (or any position(s) you choose).\n", "
\n", - "With that foundation, we will can look at more advanced [data structures](./Submodule_1_Tutorial3_DataStructures.ipynb) which will be necessary for Bioinformatics." + "With that foundation, we will look at more advanced [data structures](./Submodule_1_Tutorial3_DataStructures.ipynb)." ] }, { @@ -919,7 +924,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.4" + "version": "3.12.9" } }, "nbformat": 4, diff --git a/Submodule_1/Submodule_1_Tutorial3_DataStructures.ipynb b/Submodule_1/Submodule_1_Tutorial3_DataStructures.ipynb index b2843ef..3ad3c06 100755 --- a/Submodule_1/Submodule_1_Tutorial3_DataStructures.ipynb +++ b/Submodule_1/Submodule_1_Tutorial3_DataStructures.ipynb @@ -6,9 +6,10 @@ "metadata": {}, "source": [ "# Overview: Complex Data Structures\n", - "-------------------------------------------------------------------------------------------------\n", "\n", - "In this tutorial we will move past simple variables to explore more complex and mutable data types. We have already explored basic data types (string, integer). However, these basic types often fall short of the kinds of structures that would be helpful for Python programming in general and bioinformatics processing in particular. Two key data types are provided by Python: will be common to Python (list & dictionaries) while two classes will be imported from Biopython (Seq & SeqRecord)" + "In this tutorial, we will move beyond simple variables to explore more complex and mutable data types. We have already looked at basic data types such as strings and integers. However, these basic types often fall short of the kinds of structures that are useful for Python programming in general—and bioinformatics processing in particular.\n", + "\n", + "Python provides two key data types that are commonly used: lists and dictionaries. Additionally, we will import two important classes from Biopython: Seq and SeqRecord." ] }, { @@ -18,8 +19,8 @@ "source": [ "## Prerequisites\n", "
- \n",
- "
- Submodule Lesson 1: \n", - "
- Submodule 1 lesson 2: Variables \n", + "
- Submodule 1- Tutorial 1: Python Overview \n", + "
- Submodule 1- Tutorial 2: Variables \n", "
\n", "
\n", "The contents of a list can be:\n", - "+ edited (they are mutable)\n", - "+ concatenated using $ + $\n", - "+ nested\n", - "+ indexed\n", + "+ edited (they are mutable).\n", + "+ concatenated using \"+\".\n", + "+ nested.\n", + "+ indexed.\n", "+ members can be accessed using [] brackets. Counting starts at 0 but indexing can be positive or negative (as we saw with strings.)" ] }, @@ -172,10 +173,9 @@ "id": "60a241ff-9ea0-4597-852b-23166d37d261", "metadata": {}, "source": [ - "Lists can be aldo be \"nested.\" In other words, a list can contain other lists, which can contain still more lists (or other variable types), and so on. \n", + "Lists can be also be \"nested.\" In other words, a list can contain other lists, which can contain still more lists (or other variable types), and so on. \n", "
\n", - "We will, in the code box, create a list with two elements\n", - "Each element is a list with a pair of words\n", + "We will, in the code box, create a list with two elements. Each element is a list with a pair of words.\n", "\n", "
Tip: Try to add another two-part element nested_list.
"
]
@@ -214,7 +214,7 @@
"id": "4edb6835-0dc3-4869-b66f-711f711c84be",
"metadata": {},
"source": [
- "We can append, insert, and remove list elements.
"
+ "We can append, insert, and remove list elements."
]
},
{
@@ -251,7 +251,7 @@
"So one element ends up holding an entire list, rather than just one element.\n",
"\n", "
\n", - "To fix this problem, use **extend**, rather than **append**:" + "To fix this problem, use **extend**, rather than **append**." ] }, { @@ -279,15 +279,11 @@ "source": [ "# Exercise\n", "\n", - "Test your skill:\n", - "
\n", - "- Create a list using the numbers 1 through 5\n", - "
\n", - "- Create a list using the letters a-e\n", - "
\n", - "- Add the lists together and print the result\n", - "
\n", - "- Try multiply operator *, what happens?" + "Test your skill: Create a list using the numbers 1 through 5\n", + "\n", + "- Create a list using the letters a-e\n", + "- Add the lists together and print the result\n", + "- Try multiply operator *, what happens?" ] }, { @@ -305,9 +301,7 @@ "source": [ "## Slicing\n", "\n", - "Slicing is a useful way to get a portion of a string or of a list. In bioinformatics, we might want to look at the first 3 bases or amino acids in each string in a FASTA file. \n", - "
\n", - "Slicing has 3 arguments.\n", + "Slicing is a useful way to get a portion of a string or of a list. In bioinformatics, we might want to look at the first 3 bases or amino acids in each string in a FASTA file. Slicing has 3 arguments.\n", "\n", "- [ start : stop : increment ]\n", "\n", @@ -315,7 +309,7 @@ "\n", "With slicing, start/stop can be *negative*, impying count from the end.\n", "\n", - "Increment default value is 1, but it can be any integer (including negative)." + "The increment default value is 1, but it can be any integer (including negative)." ] }, { @@ -403,7 +397,7 @@ "metadata": {}, "source": [ "# Test Your Knowledge\n", - "Now it’s your turn to apply what you have learned in the following maatching quiz exercise. You **should feel free** to write the code in a Python cell if you cannot predict the outcome. " + "Now it’s your turn to apply what you have learned in the following matching quiz exercise. You **should feel free** to write the code in a Python cell if you cannot predict the outcome. " ] }, { @@ -432,18 +426,19 @@ "metadata": {}, "source": [ "In bioinformatics, you often need lookup tools or to access parts of a long set of data, without having to know the data's position in a list. The ideal tool for this is a python **dictionary**. \n", - "
\n", + "\n", + "\n", "In Python, a dictionary is a data structure that stores data in **key-value pairs.** As you can see in the next code box, an example is a dictionary for the genetic code.\n", "\n", "The basic properties of a dictionary are:\n", " + Key-Value Pairs: Each item in a dictionary consists of a unique key and its corresponding value. Think of it like a real-world dictionary where words are the keys and their definitions are the value. Or, for the genetic code, where AUG is the key and Met or Methionine is the value\n", " + Unordered: Unlike lists, dictionaries don't maintain any inherent order for the elements\n", - " + Mutabile: You can add, remove, or modify items in a dictionary after its created\n", + " + Mutable: You can add, remove, or modify items in a dictionary after its created\n", " + Accessing Values: You retrieve values by using their associated keys\n", " + Creating dictionaries: use curly braces {}\n", "
\n", "\n", - "Let's examine a few common examples" + "Let's examine a few common examples." ] }, { @@ -485,7 +480,7 @@ "Other useful dictionary functions:\n", "\n", "- **del** (This is a keyword, for removing an element),\n", - "- **update()** (This is a method of adding new elements)\n", + "- **update** (This is a method of adding new elements)\n", "
\n", "
\n", "Let's see some examples:" @@ -622,23 +617,22 @@ "id": "dd897b29-c445-43c5-983b-3fdb59ed8d9a", "metadata": {}, "source": [ - "## SeqRecord\n", + "### The SeqRecord Object in Biopython\n", "\n", - "In Biopython, the SeqRecord object is used to hold a biological sequence along with its associated metadata.\n", + "In Biopython, the SeqRecord object is used to hold a biological sequence along with its associated metadata.\n", "\n", - "If you are familiar with GenBank or EMBL data structures, you can see that the SeqRecord is well-equipped to include the variety of informative data pieces that are used in addition to the simple DNA or Protein sequence alone.\n", + "If you are familiar with GenBank or EMBL data structures, you’ll see that SeqRecord is well-equipped to store the wide range of informative data often included in addition to the DNA or protein sequence itself.\n", "\n", - "A SeqRecord works a bit like a * dictionary * with some built-in keys that all files will have.\n", + "A SeqRecord behaves somewhat like a *dictionary*, with several built-in attributes that are commonly found across different biological file formats.\n", "\n", - "With a SeqRecord, the key attributes you can access directly are:\n", - "
- \n",
- "
- ID: the sequence identifier \n", - "
- name: the sequence name \n", - "
- description: a descriptive string \n", - "
- seq: The biological sequence, stored as a Seq object \n", - "
- features: A list of SeqFeature objects which provide structured information about features like gene locations, domains, or other relevant biological annotations on the sequence \n", - "
- dbxrefs: a list of database cross-references \n", - "
\n", - "The next tutorial will use tools to analyze and import dataset using [Functions](./Submodule_1_Tutorial4_Functions.ipynb)." + "# Conclusion\r", + "Inr thistTutorial, you've learned to work with several different data structures that are common to bioinformatic data sets.\n", + "
In thee next tutorial will use tools to analyze and import datasest using [Functions](./Submodule_1_Tutorial4_Functions.ipynb)." ] }, { @@ -688,7 +681,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.4" + "version": "3.12.9" } }, "nbformat": 4, diff --git a/Submodule_1/Submodule_1_Tutorial4_Functions.ipynb b/Submodule_1/Submodule_1_Tutorial4_Functions.ipynb index 8cd6437..ae0fcc5 100755 --- a/Submodule_1/Submodule_1_Tutorial4_Functions.ipynb +++ b/Submodule_1/Submodule_1_Tutorial4_Functions.ipynb @@ -16,15 +16,15 @@ "source": [ "## Learning Objectives\n", "In this lesson, you will:\n", - "
\n", "* Learn the structure of Python functions\n", "* Call a function\n", "* Utilize bioinformatics functions from BioPython\n", "\n", "## Prerequisites\n", "\n", - "- Some experience in Python or\n", - "- Tutorials 1, 2, and 3\n", + "- Submodule 1 - Tutorial 1: Python Overview\n", + "- Submodule 1 - Tutorial 2: Variables\n", + "- Submodule 1 - Tutorial 3: Data Structures\n", "\n", "## Getting Started\n", "Run the code box below to import the required libraries" @@ -51,19 +51,19 @@ "source": [ "## Functions\n", "\n", - "In Python, a **function** is a block of reusable code that performs a specific task. It takes input, processes it, and optionally returns a value. It only runs when you \"call\" it \n", + "In Python, a **function** is a block of reusable code that performs a specific task. It takes input, processes it, and optionally returns a value. It only runs when you \"call\" it.\n", "\n", - "Lets try to use a very common bioinformatics task to illustrate the strcture of FUNCTIONS in python. \n", - "
\n", - "They are created with the key word def (you are DEFining the function). The function may be named any way you want and is logical to you.\n", - "
\n", - "Parentheses surround the variables that will be provided by the user of the function. Within the function, many different jobs can be done, including calculations and perhaps returning a value to you, such as you've already seen with the len(string) function that returns to the console the length of the string \"sent\" to it in the parentheses.\n", - "
\n", - "In the Python code box below we define a function, \"Count_base\", which needs two pieces of information: some kind of sequence and the base to be counted in the sequence list. Calling that function will give back (return) a number that represents the count of the base you ask for in the sequence you provide. (count is a built-in function in Python that can be used with strings).\n", + "Let’s try using a common bioinformatics task to illustrate the structure of **functions** in Python.\n", + "\n", + "They are created with the keyword `def` (you are **defining** the function). The function may be named in any way that makes logical sense to you.\n", + "\n", + "Parentheses surround the variables that will be provided by the user of the function. Within the function, many different tasks can be performed, including calculations, and possibly returning a value—such as what you've already seen with the `len(string)` function, which returns the length of the string passed to it.\n", "\n", - "The last line uses the keyword 'return' to tell Python to print to the console the \"result\" of the tasks that it runs.\n", + "In the Python code box below, we define a function called `Count_base`, which needs two pieces of information: a sequence and the base to be counted in that sequence. Calling this function will return a number representing how many times that base appears in the sequence you provide. (`count` is a built-in Python function that works on strings.)\n", "\n", - "
Tip: Try changing the base or making the \"base\" multiple letters (e.g, \"aaa\") and running the Python code box again..
"
+ "The last line uses the keyword `return` to tell Python to print the result of the function’s operation to the console.\n",
+ "\n",
+ " Tip: Try changing the base or making the \"base\" multiple letters (e.g., \"aaa\") and running the Python code box again.
\n"
]
},
{
@@ -85,7 +85,7 @@
"id": "dbefcfe0-4bc9-4d9e-bb4d-02e8831e2525",
"metadata": {},
"source": [
- " Tip: Try this: Instead of just returning the number, edit the function so that it returns \"g=17\" or \"In seq, g=17\".
"
+ " Tip: Instead of just returning the number, edit the function so that it returns \"g=17\" or \"In seq, g=17\".
"
]
},
{
@@ -147,7 +147,7 @@
"id": "66a5efbb-8c5d-4fd8-a273-d247d4ad99bc",
"metadata": {},
"source": [
- "Functions can also call another function, though to use these routinely you will need to learn to save these. (see ___ in tutorial). For now, lets write another function that calls our count_base function to calculate the GC %"
+ "Functions can also call another function, though to use these routinely you will need to learn to save these. For now, lets write another function that calls our count_base function to calculate the GC%. "
]
},
{
@@ -178,7 +178,7 @@
"id": "9a75092f-c6c4-4835-9c1b-2ef56344d809",
"metadata": {},
"source": [
- "**Can you write your own tool that calculates the percentage of the time of all guanines that are found as the pairing AG?**"
+ "Can you write your own tool that calculates the percentage of the time of all guanines that are found as the pairing AG?"
]
},
{
@@ -194,15 +194,15 @@
"id": "b9065064-ed5d-4090-9312-278c0c40ab82",
"metadata": {},
"source": [
- "There are many other ways that we might want to manipulate, align, and evaluate bioinformatic sets (e.g., FASTA sequences, both DNA and protein.) Fortunately, many of these standard functions have already been written and are freely available to everyone in Biopython: \"A set of python tools for computational molecular biology.\" (biopython.org) \n",
+ "There are many ways we might want to manipulate, align, and evaluate bioinformatic data sets—such as FASTA sequences, both DNA and protein. Fortunately, many standard functions for these tasks have already been written and are freely available through **Biopython**: *“A set of Python tools for computational molecular biology.”* (biopython.org) \n",
"\n", - "We will start here using the tools developed for \"sequence input and output\" (SeqIO). \n", + "We will begin by using tools developed for **sequence input and output** (`SeqIO`). \n", "
\n", - "We import that set of functions and tools (\"objects\") from the whole Biopython toolset with the following syntax: \n", + "We import that specific set of functions and tools (also called \"objects\") from the full Biopython toolkit using the following syntax: \n", "
\n", - "from Bio import SeqIO\n", + "`from Bio import SeqIO` \n", "
\n", - "Here we'll use that to look at a provided file (glut_human.fasta) that contains 4 different protein FASTA sequences, something that would be rather challenging for a novice Python programmer without the BioPython tools." + "We’ll use this to examine a provided file, **`glut_human.fasta`**, which contains four different protein FASTA sequences. Analyzing a file like this manually would be quite challenging for a novice Python programmer—Biopython makes it much easier.\n" ] }, { @@ -226,7 +226,7 @@ "id": "77555b0a-dcfc-448a-b30d-47e644c19c31", "metadata": {}, "source": [ - "You should see the 4 different protein identifiers in the file-- in this case with PDB ID numbers. \n", + "You should see the 4 different protein identifiers in the file- in this case with PDB ID numbers. \n", "\n", "There is a lot of information besides just the ID in each of these records, but it is not convenient to access the pieces yet. But, we can load all of that information into a single variable called (here) record_glut. The specific format is as a python LIST. " ] @@ -288,10 +288,10 @@ "metadata": {}, "source": [ "We wrote a function above (count_base) that can now come in handy to determine how many of any amino acid was present in that sequence. Although we conceived of it as a nucleotide counter, the mini program accepts whatever information we submit to it. \n", - "\n", + "```\n", "def count_base(dna, base):\n", " return dna.count(base)\n", - "\n", + "```\n", "We can send it the FASTA sequence of the GLUT protein and count an amino acid, rather than a base. The function takes any sequence and will count the letter you give it in quotes. This helps us to see how these functions \"think\" about the material you provide to it." ] }, @@ -395,18 +395,21 @@ "id": "9bcdcc97-f4dc-411e-aca7-6ea24ca0c272", "metadata": {}, "source": [ - "Fetching records from NCBI using BioPython\r\n", - "The public databases of bioinformatics data have designed ways to access their extensive files without having to go through the GUI interfaces. We can collect the data to use in our analyses or comparisons in bioinformatics tasks.\r\n", - "\r\n", - "In biopython, the modules for doing so are found in Entrez. We must import those as well as the sequence input/output tools (SeqIO) to read and parse these complex files.\r\n", - "\r\n", - "The commands below will fetch and parse the specified genbank refseq file for the human insulin receptor 2 protein.\r\n", - "\r\n", - "(You can learn more about the different database names and how to use efetch from this book chapter: https://www.ncbi.nlm.nih.gov/books/NBK25499/#chapter4.EFetch)\r\n", - "\r\n", - "To run, you'll need to give a proper email address in to Entrez\r\n", - "\r\n", - "The data is read in (by convention) with the name \"handle\" but any variable name can be used. After reading in the information, one always closes the connection = handle.close()" + "Fetching Records from NCBI Using Biopython\n", + "\n", + "The public databases of bioinformatics data have built-in ways to access their extensive files **programmatically**, without needing to use graphical user interfaces (GUIs). This allows us to efficiently collect data for analysis and comparison in bioinformatics tasks. \n", + "
\n", + "In Biopython, the modules for accessing these databases are found in **Entrez**. We must import both `Entrez` (for data fetching) and `SeqIO` (for reading and parsing sequence files). \n", + "
\n", + "The commands below will **fetch and parse** a GenBank RefSeq file for the human *insulin receptor 2* protein. \n", + "
\n", + "🔗 You can learn more about database names and how to use `efetch` from this [NCBI book chapter](https://www.ncbi.nlm.nih.gov/books/NBK25499/#chapter4.EFetch). \n", + "
\n", + "**Note:** To run these commands, you must provide a valid email address to `Entrez`. \n", + "
\n", + "The data is conventionally read into a variable named `handle`, but any valid variable name can be used. Once the data is read, **you must close the connection** with: \n", + "`handle.close()`\n", + "ction = handle.close()" ] }, { @@ -436,12 +439,18 @@ "id": "9f35c7af-0412-413b-9d3d-636ce7c9eaec", "metadata": {}, "source": [ - "Reading this genbank file creates a variable of class SeqRecord (i.e, a \"Sequence record\") which is a bit like a \"list\" but it contains an ID, a sequence, and other identifying information. We can find out what \"things\" are in the file by asking for a directory of all the features.\n", + "Reading this GenBank file creates a variable of **class `SeqRecord`** (i.e., a *sequence record*), which behaves somewhat like a list—but also includes useful attributes such as an ID, a sequence, and other identifying information. \n", + "
\n", + "We can explore what components are included in the file by requesting a **directory of all the available attributes** using the `dir()` function.\n", "\n", - "We'll look at just a few parts of the sequence record in this less, so this will show just the attributes we are most likely to need. Look at what is in various aspects of the file by writing:\n", + "In this lesson, we'll focus on just a few key parts of the `SeqRecord`, highlighting the attributes most commonly used in bioinformatics workflows. \n", + "
\n", + "For example, to access the description field of the record, you can write:\n", "\n", + "```python\n", "humInsR2.description\n", - "for example. Any of the terms from the directory can be added after the . but only a few end up being useful to us." + "```\n", + "You can replace .description with any other attribute listed in the output of dir(humInsR2), although only a few are typically useful for common tasks." ] }, { @@ -545,7 +554,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.4" + "version": "3.12.9" } }, "nbformat": 4, diff --git a/Submodule_1/Submodule_1_Tutorial5_Project.ipynb b/Submodule_1/Submodule_1_Tutorial5_Project.ipynb index 94bf222..f35ee6c 100755 --- a/Submodule_1/Submodule_1_Tutorial5_Project.ipynb +++ b/Submodule_1/Submodule_1_Tutorial5_Project.ipynb @@ -7,14 +7,14 @@ "source": [ "# Project for Module 1:\n", "\n", - "Time to put all your new skills into practice! You will download 2 gene sequence files from the NCBI database using biopython, xyz, and align the two genes. \n", + "Time to put all your new skills into practice! You will download 2 gene sequence files from the NCBI database and align the two genes using the packages biopython and xyz.\n", "\n", "## Objectives\n", "\n", "You will compare the human alcohol dehydrogenase 1A gene (NM_000667.4) to a similar gene sequence from the American Mink (Neovison vison, XM_044226065.1), which is the E chain. \n", - "1. Determine the lengths of the two DNA sequences (hint: len(X) gives you the length of any file\n", - "2. Calculate the GC% in each sequence (hint: we wrote a tool called count that you could copy or recreate)\n", - "3. Perform a pairwise global alignment, obtaining the score (hint: tools within pairwise2 imported from bio)\n", + "1. Determine the lengths of the two DNA sequences (hint: len(X) gives you the length of any file.)\n", + "2. Calculate the GC% in each sequence (hint: we wrote a tool called count that you could copy or recreate.)\n", + "3. Perform a pairwise global alignment, obtaining the score (hint: tools within pairwise2 imported from bio.)\n", "\n", "
Tip: If you need help, you can jump to the solutions from the next box.
"
]
@@ -33,11 +33,10 @@
"metadata": {},
"source": [
"## Prerequisites\n",
- "Before taking on this project, you should either do the 4 tutorials in Submodule 1 \n",
- "\n", - "or\n", - "\n", - "be familiar with Python variables, data structures, and functions, including BioPython tools" + "- Submodule 1 - Tutorial 1: Python Overview\n", + "- Submodule 1 - Tutorial 2: Variables\n", + "- Submodule 1 - Tutorial 3: Data Structures\n", + "- Submodule 1 - Tutorial 4: Functions" ] }, { @@ -74,7 +73,7 @@ "id": "4de267c8-f853-4b86-a557-21bd2b2b6084", "metadata": {}, "source": [ - "Align the two sequences (suggestion: if you'd like to see that it's working, you might want to align and display only a smaller portion of the file, e.g., ...(hum.seq[0:50], mink.seq[100:200])" + "Align the two sequences (suggestion: if you'd like to see that it's working, you might want to align and display only a smaller portion of the file, e.g., `...(hum.seq[0:50], mink.seq[100:200])`" ] }, { @@ -95,7 +94,7 @@ "id": "0c29003d-13db-4f43-8c92-8d3a9655d802", "metadata": {}, "source": [ - "**Congrats on completing your first solo python sequence import and alignment!**" + "Congrats on completing your first solo python sequence import and alignment!" ] }, { @@ -105,12 +104,10 @@ "source": [ "# Conclusion\n", "\n", - "You have completed the first module of the Introduction to Python tutorial!\n", + "You have completed the first module of the Introduction to Python tutorial!\n", "\n", "The next module will help you to develop more advanced data handling skills with NumPy, Pandas, and their powerful data science tools.\n", "\n", - "Start with the Overview page in Submodule_2\n", - "\n", "## Clean up\n", "Be sure to end your compute session to avoid unnecessary charges." ] @@ -132,7 +129,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.4" + "version": "3.12.9" } }, "nbformat": 4, diff --git a/Submodule_1/Submodule_1_Tutorial5_Project_answers.ipynb b/Submodule_1/Submodule_1_Tutorial5_Project_answers.ipynb index 0abb268..8b780b3 100755 --- a/Submodule_1/Submodule_1_Tutorial5_Project_answers.ipynb +++ b/Submodule_1/Submodule_1_Tutorial5_Project_answers.ipynb @@ -21,12 +21,16 @@ "id": "35b54b8d-863b-41f7-9e10-4dc113b45525", "metadata": {}, "source": [ - "Its time to put all your new skills into practice! You will download 2 gene sequence files from the NCBI database using biopython, xyz, and align the two genes. \n", + "Time to put all your new skills into practice! You will download 2 gene sequence files from the NCBI database and align the two genes using the packages biopython and xyz.\n", + "\n", + "## Objectives\n", "\n", "You will compare the human alcohol dehydrogenase 1A gene (NM_000667.4) to a similar gene sequence from the American Mink (Neovison vison, XM_044226065.1), which is the E chain. \n", - "1. Determine the lengths of the two DNA sequences (hint: len(X) gives you the length of any file\n", - "2. Calculate the GC% in each sequence (hint: we wrote a tool called count that you could copy or recreate)\n", - "3. Perform a pairwise global alignment, obtaining the score (hint: tools within pairwise2 imported from bio)" + "1. Determine the lengths of the two DNA sequences (hint: len(X) gives you the length of any file.)\n", + "2. Calculate the GC% in each sequence (hint: we wrote a tool called count that you could copy or recreate.)\n", + "3. Perform a pairwise global alignment, obtaining the score (hint: tools within pairwise2 imported from bio.)\n", + "\n", + "
Tip: If you need help, you can jump to the solutions from the next box.
"
]
},
{
@@ -112,7 +116,7 @@
"id": "4de267c8-f853-4b86-a557-21bd2b2b6084",
"metadata": {},
"source": [
- "Align the two sequences (suggestion: if you'd like to see that it's working, you might want to align and display only a smaller portion of the file, e.g., ...(hum.seq[0:50], mink.seq[100:200])"
+ "Align the two sequences (suggestion: if you'd like to see that it's working, you might want to align and display only a smaller portion of the file, e.g., `...(hum.seq[0:50], mink.seq[100:200])`"
]
},
{
@@ -169,7 +173,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.12.4"
+ "version": "3.12.9"
}
},
"nbformat": 4,
diff --git a/Submodule_2/Submodule_2_Overview.ipynb b/Submodule_2/Submodule_2_Overview.ipynb
index 9d1eaa7..732cdc3 100755
--- a/Submodule_2/Submodule_2_Overview.ipynb
+++ b/Submodule_2/Submodule_2_Overview.ipynb
@@ -9,7 +9,7 @@
"\n",
"Module 2 takes us from introducing fundamental Python characteristics and functions towards data science. \n",
"\n",
- "The goal is to be able to use Python programming language to perform data analysis, manipulation, and visualization. This will enable you to use Python to extract meaningful insights from large datasets using the Python libraries of NumPy, Pandas, and Matplotlib.\n",
+ "The goal is to be able to use Python programming language to perform data analysis, manipulation, and visualization. This will enable you to use Python to extract meaningful insights from large datasets using the Python libraries NumPy, Pandas, and Matplotlib.\n",
"\n",
"The **NumPy** library expands Python's functionality, focusing on arrays of one or more dimensions. These are the foundation for scientific computing and processing of, especially, numerical data. \n",
"\n",
@@ -69,7 +69,7 @@
"source": [
"## Clean up\n",
"\n",
- "Remember to shut down your Jupyter Notebook instance when you are done for the day to avoid unnecessary charges you can do this by stopping the notebook instance in the **Cloud console**"
+ "Remember to shut down your Jupyter Notebook instance when you are done for the day to avoid unnecessary charges. You can do this by stopping the notebook instance in the **Cloud console**"
]
}
],
@@ -89,7 +89,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.12.4"
+ "version": "3.12.9"
}
},
"nbformat": 4,
diff --git a/Submodule_2/Submodule_2_Tutorial_1_NumPy.ipynb b/Submodule_2/Submodule_2_Tutorial_1_NumPy.ipynb
index d5acd82..febb209 100755
--- a/Submodule_2/Submodule_2_Tutorial_1_NumPy.ipynb
+++ b/Submodule_2/Submodule_2_Tutorial_1_NumPy.ipynb
@@ -5,9 +5,7 @@
"id": "e7cccaee",
"metadata": {},
"source": [
- "# Tutorial 1: Numerical Python (NumPy)\n",
- "\n",
- "-----------------------------------------------------------"
+ "# Tutorial 1: Numerical Python (NumPy)\n"
]
},
{
@@ -17,12 +15,12 @@
"source": [
"## Overview\n",
"\n",
- "Are you ready to supercharge your research and data analysis? As a biologist, you're swimming in data—DNA sequences, protein structures, population stats, or experimental results. Processing all that manually or with spreadsheets? That’s the slow lane. Let me introduce you to NumPy, your new powerhouse for scientific computing!\n",
+ "Are you ready to supercharge your research and data analysis? As a biologist, you're swimming in data— DNA sequences, protein structures, population stats, or experimental results. Processing all that manually or with spreadsheets? That’s the slow lane. Let me introduce you to NumPy, your new powerhouse for scientific computing!\n",
"\n",
"**Why Should a Biologist Care About NumPy?**\n",
"1. Handle Biological Data Like a Pro\n",
"\n",
- "Whether you’re analyzing gene expression data, creating protein matrices, or modeling population dynamics, NumPy makes it seamless to store, manipulate, and analyze huge datasets—faster and cleaner than Excel or vanilla Python.\n",
+ "Whether you’re analyzing gene expression data, creating protein matrices, or modeling population dynamics, NumPy makes it seamless to store, manipulate, and analyze huge datasets— faster and cleaner than Excel or vanilla Python.\n",
"\n",
"2. Speed Up Your Workflow\n",
"\n",
@@ -45,7 +43,7 @@
"- Dummy-code (one-hot-encode) categorical data into numerical data with a protein sequence\n",
"\n",
"## Prerequisites\n",
- "- basic python\n",
+ "- Basic python knowledge\n",
"\n",
"## Getting Started\n",
"- Run the next code box to prepare the quiz\n",
@@ -73,14 +71,6 @@
"print(\"done installing required packages\")"
]
},
- {
- "cell_type": "markdown",
- "id": "379832e3",
- "metadata": {},
- "source": [
- "----------------------------------------------------------------------------------\n"
- ]
- },
{
"cell_type": "markdown",
"id": "8dcebcfb",
@@ -88,7 +78,7 @@
"source": [
"## About Numpy\n",
"Numerical Python, or \"numpy\" is one of the most popular modules used in Python. Numpy is considered a foundational module for Python high-end scientific computing. \n",
- "\n", + "\n", "The common standard in Python is to import numpy with the alias **np**. You will see how often you need to use this alias!\n" ] }, @@ -112,12 +102,10 @@ "metadata": {}, "source": [ "## The Numpy Array \n", - "Among the many components it provides is the **ndarray**.\n", - "\n", - " - This is a high-performance array or vector and serves as one of the main classes for scientific computing.\n", - "\n", - " - Only holds one type of element, much like a standard array.\n", + "Among the many components it provides is the **ndarray**.\n", "\n", + " - This is a high-performance array or vector and serves as one of the main classes for scientific computing.\n", + " - Only holds one type of element, much like a standard array.\n", " - It is created with the **array()** function.\n" ] }, @@ -251,7 +239,7 @@ "\n", "\\[row number, column number]\n", "\n", - "You can also index a whole row or column using a colon, : , in place of the other dimension." + "You can also index a whole row or column using a colon,\":\" in place of the other dimension." ] }, { @@ -310,9 +298,9 @@ "# Test Your Knowledge\n", "(see the solution in the next markdown box)\n", "\n", - "> 1. Create an np array (\"let_arr\") of 10 elements (letters a-j). Note: there is no shortcut for a range of letters\n", - "> 2. Change the 2nd element to 'q' & print to the console\n", - "> 3. Change the 4th element to the string 'cat'. (does it work, what type is it?)" + "1. Create an np array (\"let_arr\") of 10 elements (letters a-j). Note: there is no shortcut for a range of letters.\n", + "2. Change the 2nd element to 'q' & print to the console.\n", + "3. Change the 4th element to the string 'cat'. Does it work, what type is it?" ] }, { @@ -367,8 +355,7 @@ "\n", "By reshaping your NumPy arrays, you adapt your data to fit specific tasks—statistical analysis, visualization, or preparing input for machine learning models. Instead of wasting time manually reorganizing data, you let NumPy do the heavy lifting so you can focus on your biology!\n", "\n", - "Numpy has built-in tools, as we've said, to work with arrays. A single array can be rearranged \n", - "We can reshape an array with **array.reshape()**" + "Numpy has built-in tools, as we've said, to work with arrays. A single array can be rearranged. We can reshape an array with **array.reshape()**" ] }, { @@ -394,10 +381,10 @@ "metadata": {}, "source": [ "## Test Your Knowledge\r", - " 1. Create a 2x5 array of the numbers 1-0\n", - "2. Transpose the array, change rows to columns, columns to rws\n", - "3. . Add the numbers 11 1 \n", - "4. 4. Reshape the array, make it a 2x2x3 array" + "1. Create a 2x5 array of the numbers 1-0.\n", + "2. Transpose the array, change rows to columns, columns to rws.\n", + "3. Add the numbers 11 and 1.\n", + "4. Reshape the array, make it a 2x2x3 array." ] }, { @@ -528,7 +515,8 @@ "metadata": {}, "source": [ "## Test your knowledge\n", - "> -Although you could easily look through the list yourself, use the gene_names and expression_levels arrays again to create a new clean_gene_name array that has removed all genes with zero expression. *This kind of filtering is commonly needed in biological data sets.*" + "\n", + "Although you could easily look through the list yourself, use the gene_names and expression_levels arrays again to create a new clean_gene_name array that has removed all genes with zero expression. *This kind of filtering is commonly needed in biological data sets.*" ] }, { @@ -638,7 +626,7 @@ "\n", "NumPy provides many functions you would expect in highly statistical applications such as data science. We have already been using some of them, such as enumerate and reshape\n", "\n", - "Operations that would take several lines of nested loops in Python can be done in one line with NumPy. Whether you're doing matrix multiplication, statistical analysis, or random sampling, NumPy's tools simplify and accelerate your workflow. We will focus here on ones you might need for bioinformatics. Once you see how these work, you will easily be able to edit and use other functions. The full list is in the [numpy documentation](https://numpy.org/doc/2.1/reference/routines.math.html)\n", + "Operations that would take several lines of nested loops in Python can be done in one line with NumPy. Whether you're doing matrix multiplication, statistical analysis, or random sampling, NumPy's tools simplify and accelerate your workflow. We will focus here on ones you might need for bioinformatics. Once you see how these work, you will easily be able to edit and use other functions. The full list is in the [numpy documentation](https://numpy.org/doc/2.1/reference/routines.math.html).\n", "\n", "- Mathematical functions\n", " * np.sum: Calculates the sum of elements along a specific axis\n", @@ -647,12 +635,11 @@ " * np.min / np.max: Find the minimum and maximum values in an array\n", " * np.std: Calculates the standard deviation\n", " * np.log10: Calculates the log of the value *commonly needed in biolinformatics*\n", - " * \n", "- Logical and Comparison Functions\n", " * np.where: Returns the indices of elements meeting a condition, or replaces elements based on a condition\n", " * np.any / np.all: Checks if any or all elements of an array meet a condition\n", - "- Tools to generate arrays\r\n", - " - np.random.normal\r\n", + "- Tools to generate arrays\r", + " * np.random.normal\r\n", "- For multidimensional arrays\r\n", " * row_means = np.mean(arr, axis=0)\r\n", " * column_means= np.mean(arr, axis =1)\r\n", @@ -729,9 +716,9 @@ "source": [ "
More on Arrays
\n",
"\n",
- "Arrays can also be split, i.e. taking a single array and breaking it up into multiple sub-arrays\n",
+ "Arrays can also be split, i.e. taking a single array and breaking it up into multiple sub-arrays.\n",
"\n",
- "The following code takes one array and splits it into 3 (equal parts)"
+ "The following code takes one array and splits it into 3 (equal parts)."
]
},
{
@@ -753,9 +740,9 @@
"id": "e22c5629",
"metadata": {},
"source": [
- "NumPy allows you to conduct array searches using a **where()** method\n",
+ "NumPy allows you to conduct array searches using a **where()** method.\n",
"\n",
- "The return value is an array of indexes where the search condition was satisfied"
+ "The return value is an array of indexes where the search condition was satisfied."
]
},
{
@@ -777,11 +764,9 @@
"id": "97c395d4",
"metadata": {},
"source": [
- "Finally you can sort arrays using the sort() method\n",
- "\n",
- " - The return value is a copy of the array sorted\n",
+ "Finally you can sort arrays using the sort() method. The return value is a copy of the array sorted.\n",
"\n",
- "Note the sort is only ascending. To do a descending sort you need to reverse the array using slicing"
+ "Note the sort is only ascending. To do a descending sort you need to reverse the array using slicing."
]
},
{
@@ -803,9 +788,10 @@
"metadata": {},
"source": [
"## Test Your Knowledge\r",
- "> - \n",
- "Use a version of our previous gene expression array (arr = np.array([\"GeneA\", \"GeneB\", \"GeneC\", \"GeneD\", \"GeneE\", \"GeneF\", \"GeneG\", 5.1, 0.3, 8.7, 1.2, 6.5, 0.0, 2.3]) to perform math function\n",
- "> - after you rearrange this into a 5x2 numpy array where the gene names are row 1. (you'll need to cope with the way that numpy interprets this single type array)"
+ " \n",
+ "Use a version of our previous gene expression array `arr = np.array([\"GeneA\", \"GeneB\", \"GeneC\", \"GeneD\", \"GeneE\", \"GeneF\", \"GeneG\", 5.1, 0.3, 8.7, 1.2, 6.5, 0.0, 2.3)` to perform math function.\n",
+ "\n",
+ "After you rearrange this into a 5x2 numpy array where the gene names are row 1. You'll need to cope with the way that numpy interprets this single type array."
]
},
{
@@ -921,13 +907,13 @@
"## Test your knowledge\n",
"\n",
"Can you make a similar tool for dummy coding a protein sequence? You'll use this to answer the quiz questions.\n",
- "> - Import a protein sequence from NCBI as your sequence (the quiz questions are based on the FASTA sequence human leptin from the **protein** database, id XP_005250397.1.... To fetch them, you can use the project solution from module 1.)\n",
- "> - Create a mapping matrix for the amino acids A,C,D,E,F,G,H,I,K,L,M,N,P,Q,R,S,T,V,W,Y\n",
- "> - Create a one-hot array for the protein sequence.\n",
- "> - Print the head or the tail of the one-hot matrix (the first 10 rows) for practice & to confirm\n",
- "> - Get the index of lysine (K) from the array you make of the amino acids.\n",
- "> - Count number of lysines for the whole sequence & display the value\n",
- "> - Check that answer by using the string.count(character) method from module 1"
+ "* Import a protein sequence from NCBI as your sequence (the quiz questions are based on the FASTA sequence human leptin from the **protein** database, id XP_005250397.1.... To fetch them, you can use the project solution from module 1.)\n",
+ "* Create a mapping matrix for the amino acids A,C,D,E,F,G,H,I,K,L,M,N,P,Q,R,S,T,V,W,Y\n",
+ "* Create a one-hot array for the protein sequence.\n",
+ "* Print the head or the tail of the one-hot matrix (the first 10 rows) for practice & to confirm\n",
+ "* Get the index of lysine (K) from the array you make of the amino acids.\n",
+ "* Count number of lysines for the whole sequence & display the value\n",
+ "* Check that answer by using the string.count(character) method from module 1"
]
},
{
@@ -1024,7 +1010,7 @@
"source": [
"# Conclusion\n",
"\n",
- "In this tutorial, you have learned the many functions associated with the NumPy array. \n",
+ "In this tutorial, you have learned the many functions associated with the NumPy array.\n",
"\n",
"The next module introduces you to [Pandas](./Submodule_2_Tutorial_2_Pandas.ipynb)"
]
@@ -1037,6 +1023,14 @@
"## Clean up\r\n",
"Remember to shut down your Jupyter Notebook instance when you are done for the day to avoid unnecessary charges. You can do this by stopping the noteboko **compute*k instance from the Cloud console."
]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "0983f205-7499-43b7-b051-c4e996785872",
+ "metadata": {},
+ "outputs": [],
+ "source": []
}
],
"metadata": {
@@ -1055,7 +1049,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.12.4"
+ "version": "3.12.9"
}
},
"nbformat": 4,
diff --git a/Submodule_2/Submodule_2_Tutorial_2_Pandas.ipynb b/Submodule_2/Submodule_2_Tutorial_2_Pandas.ipynb
index cfbfeb4..0881bd7 100755
--- a/Submodule_2/Submodule_2_Tutorial_2_Pandas.ipynb
+++ b/Submodule_2/Submodule_2_Tutorial_2_Pandas.ipynb
@@ -6,7 +6,7 @@
"metadata": {},
"source": [
"# Tutorial 2: Pandas\n",
- "-----------------------------------------------\n",
+ "\n",
"## Overview\n",
"NumPy is very fast at handling data. However, it is limited by allowing only ONE data type. Pandas adds the familiar data structure like a spreadsheet, with data organized in rows and columns with names. Why not just use Excel or Sheets, then? Because bioinformatics data sets, such as RNAseq results, are so large and you may need to do complex calculations on the data. Python tools are much faster, more powerful and more flexible than pre-packaged spreadsheet tools. Also, the data is typically not printed to the screen in a GUI interface so the processing speed is faster. If you process in the cloud, you can see substantial increases in speed of analysis.\n",
"\n",
@@ -54,10 +54,10 @@
"\n",
"The scripting in Python is good till now, but what about data organization or handling columns of tabular data with different types? \n",
"\n",
- "Pandas focuses on **data management** which can be combined with **analytics tools**\n",
+ "Pandas focuses on **data management** which can be combined with **analytics tools**.\n",
"\n",
"The core data type is a DataFrame. A DataFrame organizes data into rows and columns, making it easy to access, filter, and process.\n",
- "\n",
+ ".\n",
"\n",
"You can think of it like an Excel spreadsheet or (more appropriately) like a database table\n",
" - Tabular\n",
@@ -76,7 +76,7 @@
"1. Ease of Handling Tabular Data:\n",
" - Rows can represent biological samples, sequences, or variants.\n",
" - Columns can represent genes, features, or metadata.\n",
- " - Unlike Excel, Pandas does not attempt to display all the values all the time, so it is less demanding on computer memory-- especially for the large datasets common to bioinformatics\n",
+ " - Unlike Excel, Pandas does not attempt to display all the values all the time, so it is less demanding on computer memory- especially for the large datasets common to bioinformatics\n",
"\n",
"2. Data Analysis: Perform operations like filtering, grouping, and summarizing efficiently.\n",
" - Example: Find the top 10 most expressed genes in RNA-Seq data.\n",
@@ -97,11 +97,11 @@
"### Creating Data Frames\n",
"\n",
"A Pandas dataframe can be constructed in many ways. (see [pandas documentation](https://pandas.pydata.org/docs/user_guide/dsintro.html#dataframe) for more ways). Each column is called a **Series** and has it's own functions in Pandas [see more]\n",
- "We can create a dataframe by passing in a dictionary of equal length lists\n",
+ "We can create a dataframe by passing in a dictionary of equal length lists.\n",
"\n",
" * The dictionary keys will be column names\n",
"\n",
- "We can also create dataframes from file loads and queries\n",
+ "We can also create dataframes from file loads and queries.\n",
"\n",
"Here, you see how it is made with a dictionary as the data that you might assemble yourself. "
]
@@ -134,7 +134,7 @@
"\n",
"Probably the most common way to create a Pandas dataframe is to **import a CSV** for further analysis. \n",
"\n",
- "You can also import excel, JSON, and the clipboard. [All the data types and import methods](http://pandas.pydata.org/pandas-docs/stable/io.html)\n",
+ "You can also import excel, JSON, and the clipboard. [All the data types and import methods](http://pandas.pydata.org/pandas-docs/stable/io.html).\n",
"\n",
"Try the next box to import a portion of a large cancer dataset. "
]
@@ -207,11 +207,11 @@
"metadata": {},
"source": [
"### Summary Statistics\n",
- "Are the number of rows as expected? What about the column names? General range of continuous variables? It's easy to assess (much more so than with a spreadsheet!!)\n",
+ "Are the number of rows as expected? What about the column names? General range of continuous variables? It's easy to assess (much more so than with a spreadsheet!)\n",
"\n",
- "A Pandas dataframe has a method (df.describe()) that can easily summarize each column. *The summary information is itself a dataframe* To get summary statistics on a single column, just specify that with the name in quotes as shown.\n",
+ "A Pandas dataframe has a method (df.describe()) that can easily summarize each column. *The summary information is itself a dataframe*. To get summary statistics on a single column, just specify that with the name in quotes as shown.\n",
"\n",
- "Can you obtain these same statistics for the cancer dataset? Note: these values could be meaningful for normalizing gene expression (later tasks)"
+ "Can you obtain these same statistics for the cancer dataset? Note: these values could be meaningful for normalizing gene expression(later tasks)."
]
},
{
@@ -261,8 +261,9 @@
"metadata": {},
"source": [
"As with np arrays, you can reference different dimensions. **df.loc** attribute accesses a group of rows and columns by label(s) or a boolean array in the given DataFrame:\n",
- "df.loc [:, range] selects a set of columns\n",
- "df.loc[range,:] selects a set of rows\n",
+ "\n",
+ "- df.loc [:, range] selects a set of columns\n",
+ "- df.loc[range,:] selects a set of rows\n",
"\n",
"Thus, you can subset some rows and some columns based on columns. (next, we'll make a subset based on characteristics like size of the population.)\n",
"\n",
@@ -291,7 +292,7 @@
"source": [
"### Indexes\n",
"\n",
- "We can select portions of the data frame by indexes (rows and columns) in a variety of ways"
+ "We can select portions of the data frame by indexes (rows and columns) in a variety of ways."
]
},
{
@@ -358,8 +359,8 @@
"metadata": {},
"source": [
"### Test your Knowledge\n",
- "> 1. Efficiently determine how many states have a life expectancy between 70 and 71\n",
- "> 2. What is the average HS Graduate percentage for these states?"
+ "1. Efficiently determine how many states have a life expectancy between 70 and 71\n",
+ "2. What is the average HS Graduate percentage for these states?"
]
},
{
@@ -411,13 +412,7 @@
"source": [
"### Working with Text \n",
"\n",
- "Strings in Pandas are roughly the same as strings in Python\n",
- "\n",
- "We simply operate on series rather than a single object\n",
- "\n",
- "Pandas provides many of the same methods, such as **len()**, **lower()**, **upper()**, **split()** and others you have seen before\n",
- "\n",
- "Pandas methods also usually ignore missing NaN values"
+ "Strings in Pandas are roughly the same as strings in Python. We simply operate on series rather than a single object. Pandas provides many of the same methods, such as **len()**, **lower()**, **upper()**, **split()** and others you have seen before. Pandas methods also usually ignore missing NaN values."
]
},
{
@@ -536,9 +531,9 @@
"source": [
"### Adding/Deleting columns\n",
"\n",
- "We can add columns by naming a new column, then assigning a set of values. If the added column doesn't have enough values, Pandas will automatically add NaN\n",
+ "We can add columns by naming a new column, then assigning a set of values. If the added column doesn't have enough values, Pandas will automatically add 'NaN'.\n",
"\n",
- "Use the **del** keyword to delete a column\n",
+ "Use the **del** keyword to delete a column.\n",
"\n",
"Try this: You might have noticed that the first two columns of the cancer dataset are basically the same. Drop one of them & check the head to show you were successful.
" ] @@ -563,24 +558,25 @@ "id": "6d6e1af2", "metadata": {}, "source": [ - "### Adding/deleting ROWS\n", - "\n", + "### Adding/deleting Rows\n", + "\n", "The syntax is slightly more complex to add a row to a Pandas dataframe. \n", "\n", "You can make a new row, then **concat**, that is to concatenate the data. This row should be a single dictionary for one row, or multiple dictionaries to add multiple rows. Since it's a dictionary, you don't have to put the data in the 'correct' order of the existing dataframe. \n", - "\n", + "`\n", "new_row= pd.DataFrame(\\[{key:value, key1:value1, key2:value2}]\n", - "\n", + "`\n", "If you do not give a value for each of the columns, then Pandas will use NaN\n", "\n", "Then, add that row of new data by concatenation. \n", + "`\n", "df = pd.concat(\\[df, new_rows], ignore_index=True)\n", - "\n", + "`\n", "**To delete**, and to directly replace the dataframe using the inplace tag:\n", - "\n", + "`\n", "df.drop(index=\\[Rowindex1, Rowindex2], inplace=True)\n", - "\n", - "If you just drop one row, use index=#" + "`\n", + "If you just drop one row, use index=#. " ] }, { @@ -627,7 +623,9 @@ "\n", "You can use that to your advantage (remembering that US VI will always be index 51) or we can reindex:\n", "\n", - "**df.reset_index(drop=True, inplace=True)**\n" + "`\n", + "df.reset_index(drop=True, inplace=True)\n", + "`" ] }, { @@ -684,7 +682,6 @@ "id": "aac18a37", "metadata": {}, "source": [ - "-----------------------------------------------------------------------------------\n", "## Data wrangling\n", "\n", "A large challenge when working with real biological datasets is data \"cleaning.\" This removes values or samples with errors (negative read counts, participants over age 110) or missing data (NaN) that could skew downstream analysis. This process must be carried out systematically and carefully. The normal workflow for this is beyond this introductory module. Please look at the NIGMS Sandbox Machine Learning Module for a full treatment on this topic.\n", @@ -1202,17 +1199,15 @@ "id": "b9892700", "metadata": {}, "source": [ - "We often want to apply functions to groups within our dataset. \n", + "We often want to apply functions to groups within our dataset. \n", "\n", - " - What is the mean value within group?\n", + " - What is the mean value within group?\n", + " - What is the variance within group?\n", + " - Perform a linear regression within group, then get the slope estimates out.\n", "\n", - " - What is the variance within group?\n", + "The first step is to establish the groups (SPLIT). \n", "\n", - " - Perform a linear regression within group, then get the slope estimates out.\n", - "\n", - "The first step is to establish the groups (SPLIT)\n", - "\n", - "In Pandas, we can use the **.groupby()** method for this Use states, and group on the variable we just created \n", + "In Pandas, we can use the **.groupby()** method for this Use states, and group on the variable we just created \n", "\n", "Note: \"Groupby\" makes a new groupby pandas object. The **agg** function in a Pandas groupby object is used to apply one or more aggregation functions to grouped data. It allows you to compute summary statistics like mean, sum, min, max, and more, across multiple columns or for specific groups." ] @@ -1239,7 +1234,7 @@ "id": "1a1428dc", "metadata": {}, "source": [ - "We can index the group-by with a dynamically created value" + "We can index the group-by with a dynamically created value." ] }, { @@ -1260,11 +1255,9 @@ "source": [ "# Pandas Visuals \n", "\n", - "The next tutorial is all about python visuals (mainly using matplotlib), but because it is so important to data analytics, pandas provides visuals too\n", - "\n", - "Pandas supports many types of plots\n", + "The next tutorial is all about python visuals (mainly using matplotlib), but because it is so important to data analytics, pandas provides visuals too.\n", "\n", - " - Line, bar, area, box, histogram and scatter plots among others" + "Pandas supports many types of plots- line, bar, area, box, histogram and scatter plots among others. " ] }, { @@ -1360,9 +1353,9 @@ "metadata": {}, "source": [ "# Conclusion\n", - "You now have tools to do a LOT of manipulations of data frames with Pandas in Python. You are ready to work through a bioinformatics exercise. [Exploring ligand binding sites](./Submodule_2_Tutorial_2b_PDB_Pandas_Exercise.ipynb) in a pdb file (protein structure file) using pandas and biopandas\n", + "You now have tools to do a LOT of manipulations of data frames with Pandas in Python. You are ready to work through a bioinformatics exercise. [Exploring ligand binding sites](./Submodule_2_Tutorial_2b_PDB_Pandas_Exercise.ipynb) in a pdb file (protein structure file) using pandas and biopandas.\n", "\n", - "If you do not want to practice at this point, the next tutorial picks up with how to [visualize your data](./Submodule_2_Tutorial_3_VisualizingData.ipynb) with graphs in matplotlib\n" + "If you do not want to practice at this point, the next tutorial picks up with how to [visualize your data](./Submodule_2_Tutorial_3_VisualizingData.ipynb) with graphs in matplotlib.\n" ] }, { @@ -1371,7 +1364,7 @@ "metadata": {}, "source": [ "## Clean up\n", - "Remember to stop your Jupyter Notebook compute instance to avoid unnecessary charges.." + "Remember to stop your Jupyter Notebook compute instance to avoid unnecessary charges." ] } ], @@ -1391,7 +1384,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.4" + "version": "3.12.9" } }, "nbformat": 4, diff --git a/Submodule_2/Submodule_2_Tutorial_2b_PDB_Pandas_Exercise.ipynb b/Submodule_2/Submodule_2_Tutorial_2b_PDB_Pandas_Exercise.ipynb index e4ac61a..a736f2c 100755 --- a/Submodule_2/Submodule_2_Tutorial_2b_PDB_Pandas_Exercise.ipynb +++ b/Submodule_2/Submodule_2_Tutorial_2b_PDB_Pandas_Exercise.ipynb @@ -6,7 +6,7 @@ "metadata": {}, "source": [ "# Biopandas for PDB files\n", - "-----------------------------------------------------------------------------\n", + "\n", "## Overview\n", "If you are a structural biologist working with molecular structure files, a fantastic way to process pdb files is with Pandas dataframes. The tools are beyond the scope of this Introduction to Python course, but we include it here to give you a taste of how you can use traditional programming to query and calculate with these complex file types.\n", "\n", @@ -156,7 +156,7 @@ "### Step one: Get the file\n", "Assign the pdb file to the variable pdb PDB ID: 4AKE (adenylate kinase with a bound ligand). \n", "\n", - "As always, look at the first few lines of the file to make sure it is what you expected and to see the column names" + "As always, look at the first few lines of the file to make sure it is what you expected and to see the column names." ] }, { @@ -357,7 +357,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.4" + "version": "3.12.9" } }, "nbformat": 4, diff --git a/Submodule_2/Submodule_2_Tutorial_3_VisualizingData.ipynb b/Submodule_2/Submodule_2_Tutorial_3_VisualizingData.ipynb index 6e0866e..84e04ee 100755 --- a/Submodule_2/Submodule_2_Tutorial_3_VisualizingData.ipynb +++ b/Submodule_2/Submodule_2_Tutorial_3_VisualizingData.ipynb @@ -5,8 +5,7 @@ "id": "7c73d63b-45c6-4e52-bc17-d40186fc602b", "metadata": {}, "source": [ - "# Tutorial 3: Visualizing Data\n", - "------------------------------------------------------------------------------------" + "# Tutorial 3: Visualizing Data\n" ] }, { @@ -50,7 +49,7 @@ "metadata": {}, "source": [ "# Matplotlib\n", - "The main plotting set of libraries in Python is **Matplotlib** . It can do 2D and 3D plotting in \"Matlab\" style, and even [animate plots](https://matplotlib.org/stable/users/explain/animations/animations.html#sphx-glr-users-explain-animations-animations-py) (Although that is beyond the scope of this introduction). \n", + "The main plotting set of libraries in Python is **Matplotlib** . It can do 2D and 3D plotting in \"Matlab\" style, and even [animate plots](https://matplotlib.org/stable/users/explain/animations/animations.html#sphx-glr-users-explain-animations-animations-py) (although that is beyond the scope of this introduction). \n", "\n", "It is typical to import the libraries (matplotlib.pyplot) as plt to simplify it's use, much like we have been using np and pd as shorthand for NumPy and Pandas in this module. \n", "\n", @@ -59,10 +58,10 @@ "While it may seem easier to use Excel and its drop-down menus to make plots, there are some fantastic advantages with Matplotlib.\n", "1. You can print in a format with enough dots per inch (dpi) to make publication-qualitity images: fig.saveas('fig.tiff',dpi=600)\n", "2. While it takes a few more steps than excel:\n", - " - you CAN control all the characteristics\n", - " - the code is re-usable and can be applied to the next plot vs. having to start from scratch for each excel or even SigmaPlot figure.\n", - " - python is free\n", - " - you can plot much larger datasets\n" + " - you CAN control all the characteristics.\n", + " - the code is re-usable and can be applied to the next plot vs. having to start from scratch for each excel or even SigmaPlot figure.\n", + " - python is free.\n", + " - you can plot much larger datasets.\n" ] }, { @@ -72,9 +71,7 @@ "source": [ "## Making a plot with PyPlot\n", "\n", - "With Pyplot, we establish a figure, then annotate it and/or add all the necessary plotting elements. By default, the plot() function draws a line from point to point. \n", - "\n", - " 'Dose µM0, 0.1, 0.5, 1, 5, 10, 50, 100, 200, 5000 'Cell Viabilit drug 1y ()': [100, 98, 92, 85, 70, 60, 40, 25, 5Cell Viability with drug 2: 100, 99, 97, 94, 90, 85, 80, 75, 70, 65print(df)\r\n" + "With Pyplot, we establish a figure, then annotate it and/or add all the necessary plotting elements. By default, the plot() function draws a line from point to point. \r\n" ] }, { @@ -108,11 +105,11 @@ "\n", "![\"Anatomy](\"https://matplotlib.org/stable/_images/anatomy.png\")
\n",
"\n",
- "At the top of the hierarchy is the **Figure** object, holding one or more **Axes**\n",
+ "At the top of the hierarchy is the **Figure** object, holding one or more **Axes**.\n",
"\n",
- "Below that are individual lines, grids, legends and text boxes, ticks and labels\n",
+ "Below that are individual lines, grids, legends and text boxes, ticks and labels.\n",
"\n",
- "This gives us a fine granularity and level of control over the plot\n"
+ "This gives us a fine granularity and level of control over the plot.\n"
]
},
{
@@ -196,25 +193,35 @@
"source": [
"## Histograms\n",
"\n",
- "A histogram is a type of bar plot where the X-axis represents the bin ranges while the Y-axis gives information about frequency\n",
+ "A **histogram** is a type of bar plot where the X-axis represents bin ranges, and the Y-axis shows the frequency of data points within those ranges.\n",
+ "\n",
+ "Histograms are appropriate for **continuous numerical data**. They provide insight into the **distribution** (or *density*) of data points.\n",
+ "\n",
+ "### Key Components of a Histogram:\n",
+ "\n",
+ "1. **Bins**: The range of values is divided into intervals called *bins*. The height of each bin represents the number of data points that fall within that interval.\n",
+ "2. **X-axis**: Represents the values or ranges of the data being plotted.\n",
+ "3. **Y-axis**: Represents the **frequency** (or count) of data points within each bin.\n",
+ "\n",
+ "---\n",
+ "\n",
+ "### Generating Histograms with NumPy & Matplotlib\n",
"\n",
- "Data that is appropriate for a histogram is continuous. Histograms show us the overall distribution of numerical data, so you are displaying the *density* of data points. \n",
+ "Histograms are typically created from arrays of data. In this first example, we’ll use **NumPy** to generate random distributions and **Matplotlib** to plot them.\n",
"\n",
- "The key variables of histograms that affect their appearance are \n",
- "1. Bins: The range of values is divided into a set of intervals called bins. The height of each bin represents the frequency of data points falling within that interval\n",
- "2. \n",
- "X-axis: Represents the values or ranges of the data being plotetd.\n",
- "3.\r\n",
- "Y-axis: Represents the frequency or count of data points within eachin \n",
+ "Matplotlib will automatically choose the number of bins, but you **can** specify the number or size of bins manually. Try adjusting the number of bins to see how it affects the shape of the histogram. You can also explore different data distributions, such as exponential vs. normal.\n",
"\n",
+ "---\n",
"\n",
- "The histogram plots an array of data. In this first demonstration, we will just create rayars os random distributions me bady NumPy\n",
+ "### Customizing Histogram Appearance\n",
"\n",
- "Matplotlib will automatically create bins, but you CAN choose the size. Play around with the # of bins and look at the alternative (exponential) array\n",
+ "You have many options to control how your histogram looks—bin count, bin width, color, edge styling, transparency, and more.\n",
"\n",
- "You have a lot of options for how your histogram could look. Here are some examples: \n",
- "\n",
+ "---\n",
"\n",
+ "### Example Histogram Types:\n",
+ "\n",
+ "\n",
"\n"
]
},
@@ -303,8 +310,9 @@
"source": [
"### Histograms in Pandas\n",
"The central wrapper is DataFrame.plot() The default value is line plots You can change this with the kind argument: ‘bar’, ‘scatter’, ‘pie’ and others Thus, you can call for a histogram directly (the other plots require X and Y)\n",
- "\n",
- "df.plot.typeofplot\n"
+ "```\n",
+ "df.plot.typeofplot\n",
+ "```"
]
},
{
@@ -342,7 +350,8 @@
"source": [
"### Histogram Styles\n",
"We can add arguments to plt.hist() for different styles.\n",
- "Check help for plt.hist() "
+ "\n",
+ "Check help for plt.hist()."
]
},
{
@@ -392,10 +401,10 @@
"id": "6063ce47-bfba-46e3-9e5b-0db905d0532c",
"metadata": {},
"source": [
- "The central wrapper is DataFrame.plot()\n",
- "The default value is line plots\n",
- "You can change this with the kind argument: ‘bar’, ‘scatter’, ‘pie’ and others\n",
- "You can also call hist directly "
+ "The central wrapper is DataFrame.plot().\n",
+ "The default value is line plots.\n",
+ "You can change this with the kind argument: ‘bar’, ‘scatter’, ‘pie’ and others.\n",
+ "You can also call hist directly ."
]
},
{
@@ -422,6 +431,7 @@
"metadata": {},
"source": [
"Pandas - Annotate Title\n",
+ "\n",
"Set the blank canvas in order to annotate it."
]
},
@@ -445,8 +455,7 @@
"2. Optional Enhancements:\n",
" * Additional arguments for customization, such as color (c), size (s), labels, etc.\n",
"\n",
- "**If you use pandas** you use df.plot(x='Column', y='Column, kind='scatter')\n",
- "Pandas uses the column labels for X & Y axis.\n",
+ "**If you use pandas** you use df.plot(x='Column', y='Column, kind='scatter'). Pandas uses the column labels for X & Y axis.\n",
"\n",
"See this use for our states data."
]
@@ -552,14 +561,12 @@
"\n",
"There are two main ways to create subplots\n",
"1. plt.subplot() (Simple Grid-Based Subplots):\n",
- "\n",
- " - Specify a grid layout (e.g., 2x2) and plot in a specific cell.\n",
- " - Example: plt.subplot(2, 2, 1) means a 2x2 grid, and the plot is in the first cell.\n",
+ " - Specify a grid layout (e.g., 2x2) and plot in a specific cell.\n",
+ " - Example: plt.subplot(2, 2, 1) means a 2x2 grid, and the plot is in the first cell.\n",
"\n",
"2. plt.subplots() (More Flexible and Modern):\n",
- "\n",
- " - Creates a grid layout and returns figure and axes objects for better control.\n",
- " - Use axes[i, j] to reference individual subplots."
+ " - Creates a grid layout and returns figure and axes objects for better control.\n",
+ " - Use axes[i, j] to reference individual subplots."
]
},
{
@@ -600,7 +607,7 @@
"\n",
"The shape and location of the box and whiskers in a boxplot provide insights into the distribution of the data:\n",
"\n",
- "1. **Box (Middle 50% of Data):**The box represents the interquartile range (IQR), which is the range between the 25th percentile (Q1) and the 75th percentile (Q3). A wide box indicates high variability in the middle 50% of the data, while a narrow box suggests low variability.\n",
+ "1. **Box (Middle 50% of Data):** The box represents the interquartile range (IQR), which is the range between the 25th percentile (Q1) and the 75th percentile (Q3). A wide box indicates high variability in the middle 50% of the data, while a narrow box suggests low variability.\n",
"2. **Line Inside the Box (Median):** The line inside the box shows the median (50th percentile), giving the central tendency of the data.\n",
"If the median is closer to one end of the box, it indicates a skewed distribution.\n",
"3. **Whiskers:** The whiskers extend from the box to the smallest and largest data points within 1.5 times the IQR.\n",
@@ -717,7 +724,7 @@
"metadata": {},
"source": [
"## Clean up\n",
- "Remember to shut down your Jupyter Notebook compute instance when you are done for the day to avoid unnecessary charges. "
+ "Remember to shut down your Jupyter Notebook compute instance when you are done to avoid unnecessary charges. "
]
},
{
@@ -792,7 +799,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.12.4"
+ "version": "3.12.9"
}
},
"nbformat": 4,
diff --git a/Submodule_2/Submodule_2_Tutorial_4_InferentialStatistics.ipynb b/Submodule_2/Submodule_2_Tutorial_4_InferentialStatistics.ipynb
index 3ec8342..7570bb5 100755
--- a/Submodule_2/Submodule_2_Tutorial_4_InferentialStatistics.ipynb
+++ b/Submodule_2/Submodule_2_Tutorial_4_InferentialStatistics.ipynb
@@ -5,8 +5,7 @@
"id": "ed5c8c48-8bae-4d86-ae10-8b83bc60b812",
"metadata": {},
"source": [
- "# Tutorial 4: Introduction to Inferential Statistics in Python\n",
- "------------------------------------------------------------------------------------------------------------------"
+ "# Tutorial 4: Introduction to Inferential Statistics in Python\n"
]
},
{
@@ -1801,7 +1800,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.12.4"
+ "version": "3.12.9"
}
},
"nbformat": 4,
diff --git a/Submodule_2/Submodule_2_Tutorial_5_PandasGuidedExercise.ipynb b/Submodule_2/Submodule_2_Tutorial_5_PandasGuidedExercise.ipynb
index 1c8dd0b..494649c 100755
--- a/Submodule_2/Submodule_2_Tutorial_5_PandasGuidedExercise.ipynb
+++ b/Submodule_2/Submodule_2_Tutorial_5_PandasGuidedExercise.ipynb
@@ -6,7 +6,7 @@
"metadata": {},
"source": [
"# Pandas RNA-seq exercise\n",
- "--------------------------------------------------------------------------------\n",
+ "\n",
"## Overview\n",
"\n",
"## Learning Objectives\n",
@@ -47,9 +47,9 @@
"metadata": {},
"source": [
"## Pandas Exercise: Analyzing RNA-seq Data\n",
- "Scenario: You have obtained RNA-seq data from NCBI,and you need to analyze it using Pandas. The dataset contains information about gene expression levels across different samples. Your tasks involve data cleaning, normalization, and exploratory analysis.\n",
+ "Scenario: You have obtained RNA-seq data from NCBI,and you need to analyze it using Pandas. The dataset contains information about gene expression levels across different samples. Your tasks involve data cleaning, normalization, and exploratory analysis.\n",
"\n",
- "*you should be able to use other datasets, including your own!*\n",
+ "*You should be able to use other datasets, including your own!*\n",
"\n",
"**Recommended Dataset:**\n",
"GEO Series Accession: GSE198050\n",
@@ -73,9 +73,7 @@
"\n",
"Some later steps will use techniques we have not yet covered in the tutorials.\n",
"\n",
- "If you get stuck, all the required coding can be found at the end. BUT, you should focus on **trying it yourself.**\n",
- "\n",
- "-----------------------------------------------------------------------------------------------"
+ "If you get stuck, all the required coding can be found at the end. BUT, you should focus on **trying it yourself.**\n"
]
},
{
@@ -183,7 +181,7 @@
"1. Create a variable to hold the sum of each column (df.sum())\n",
"2. Divide the data (axis=1) by these count values & multiply by 1 million\n",
"\n",
- "The only challenge is that the first column (or 2?) is gene names. Remove the gene name columns, merging them back onto the dataframe after doing the math "
+ "The only challenge is that the first column (or 2?) is gene names. Remove the gene name columns, merging them back onto the dataframe after doing the math. "
]
},
{
@@ -267,8 +265,6 @@
"id": "59c4e3a9-a9ca-4b8b-851e-49a89a13be2b",
"metadata": {},
"source": [
- "---------------------------------------------------------------------------------------\n",
- "\n",
"## Advanced steps\n",
"The rest of the steps are provided below. You will need to adjust your variable names (or the ones below) to enable you to demonstrate the remaining steps!"
]
@@ -516,7 +512,7 @@
"source": [
"## Conclusion\n",
"\n",
- "This exercise demonstrated some of the power of Python libraries (NumPy, Pandas, matplotlib, & statsmodel) to perform complex bioinformatics tasks and protein visualizations. After this guided exercise, it is time to tackle the [submodule 2 bioinformatics project](./Submodule_2_Tutorial_6_Project.ipynb) using the many skills you've learned in this module! (or jump to the [solved version](./Submodule_2_Tutorial_7_ProjectSolutions.ipynb) of the project)\n",
+ "This exercise demonstrated some of the power of Python libraries (NumPy, Pandas, matplotlib, & statsmodel) to perform complex bioinformatics tasks and protein visualizations. After this guided exercise, it is time to tackle the [submodule 2 bioinformatics project](./Submodule_2_Tutorial_6_Project.ipynb) using the many skills you've learned in this module (or jump to the [solved version](./Submodule_2_Tutorial_7_ProjectSolutions.ipynb) of the project)!\n",
"\n",
"### Clean up\n",
"\n",
@@ -540,7 +536,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.12.4"
+ "version": "3.12.9"
}
},
"nbformat": 4,
diff --git a/Submodule_2/Submodule_2_Tutorial_6_Project.ipynb b/Submodule_2/Submodule_2_Tutorial_6_Project.ipynb
index b7a8a90..7a7064c 100755
--- a/Submodule_2/Submodule_2_Tutorial_6_Project.ipynb
+++ b/Submodule_2/Submodule_2_Tutorial_6_Project.ipynb
@@ -5,8 +5,7 @@
"id": "3c16d197-b287-4bf2-af0c-d1712bc0f655",
"metadata": {},
"source": [
- "# Submodule 2 Project: Predictors of Diabetes\n",
- "------------------------------------------------------------"
+ "# Submodule 2 Project: Predictors of Diabetes"
]
},
{
@@ -28,7 +27,7 @@
"\n",
"## Prerequisites\n",
"\n",
- "You should have completed all the tutorials in Module 2 and developed some level of comfort with the tools\n",
+ "You should have completed all the tutorials in Module 2 and developed some level of comfort with the tools.\n",
"\n",
"## Getting Started\n",
"The 5 tasks are described below. Solutions for each part are provided in a [solved version](./Submodule_2_Tutorial6_ProjectSolutions.ipynb) of this notebook"
@@ -210,7 +209,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.12.4"
+ "version": "3.12.9"
}
},
"nbformat": 4,
diff --git a/Submodule_3/Submodule_3_Overview.ipynb b/Submodule_3/Submodule_3_Overview.ipynb
index 7b2e8cd..3fab620 100755
--- a/Submodule_3/Submodule_3_Overview.ipynb
+++ b/Submodule_3/Submodule_3_Overview.ipynb
@@ -5,8 +5,7 @@
"id": "71797b6e-6170-4f01-9578-0e16390e75c8",
"metadata": {},
"source": [
- "# Submodule 3 Overview: Python and Object-oriented programming\n",
- "---------------------------------------------------------------------------------"
+ "# Submodule 3 Overview: Python and Object-oriented programming"
]
},
{
@@ -57,7 +56,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.12.4"
+ "version": "3.12.9"
}
},
"nbformat": 4,
diff --git a/Submodule_3/Submodule_3_Tutorial_1_OOP.ipynb b/Submodule_3/Submodule_3_Tutorial_1_OOP.ipynb
index c3d6458..d38f30b 100755
--- a/Submodule_3/Submodule_3_Tutorial_1_OOP.ipynb
+++ b/Submodule_3/Submodule_3_Tutorial_1_OOP.ipynb
@@ -5,8 +5,7 @@
"id": "81a299a1",
"metadata": {},
"source": [
- "# Submodule 3 Tutorial 1: Object-Oriented Programming in Python\n",
- "---------------------------------------------------------------"
+ "# Submodule 3 Tutorial 1: Object-Oriented Programming in Python"
]
},
{
@@ -18,9 +17,9 @@
"Objects in Python (and in Object-Oriented Programming) allow for modular, efficient, and scalable code by organizing data and functions into reusable units. This is especially useful in bioinformatics, where handling large datasets and complex analyses efficiently is critical.\n",
"\n",
"## Learning Outcomes\n",
- "*AFter this tutorial, you should be able to:*\n",
- "- Define class, object, methods, attributes, inheritance\n",
- "- Write or edit these elements of a Python class "
+ "* After this tutorial, you should be able to:*\n",
+ " - Define class, object, methods, attributes, inheritance\n",
+ " - Write or edit these elements of a Python class "
]
},
{
@@ -262,7 +261,7 @@
"id": "4dc69382-963c-4c1f-b55f-5c741989bed2",
"metadata": {},
"source": [
- "---------------------------------------------------------------------------------\n",
+ "\n",
" After the overview, now we delve deeper into the individual pieces.
"
]
},
@@ -387,12 +386,12 @@
"metadata": {},
"source": [
" Tip: Try these in the above mutation class code block:\n",
- "
\n", + "\n", " - make a second mutation object (mut2) \n", " - querry other pieces of mut1 and mut2 such as the position.\n", " - see what happens if you do NOT give all expected/required pieces of information. \n", " - add a new element of the variable (\"disease_associated\")
\n",
- "\n", + "\n", " - make a second mutation object (mut2) \n", " - querry other pieces of mut1 and mut2 such as the position.\n", " - see what happens if you do NOT give all expected/required pieces of information. \n", " - add a new element of the variable (\"disease_associated\")
\n", + "\n", "*You should be able to tell that Python is using known classes to deal with the values entered, such as integers or strings*\n" ] }, @@ -640,15 +639,16 @@ "source": [ "## Conclusion\n", "\n", - "In this tutorial, you have learned:\n", - "- Some OOP vocabulary\n", - "- Defined a new class\n", - "- Written your own object\n", + "In this tutorial, you have:\n", + "- learned some OOP vocabulary.\n", + "- defined a new class.\n", + "- written your own object.\n", "\n", "You may be ready to try some advanced techniques in OOP in [Tutorial 2: OOP2](Submodule_3_Tutorial_2_OOP2.ipynb)\n", - "
\n", + "\n", "OR\n", - "Work with [modules and packages](Submodule_3_Tutorial_3_Modules&Packages.ipynb)" + "\n", + "Work with [modules and packages](Submodule_3_Tutorial_3_Modules&Packages.ipynb)." ] }, { @@ -677,7 +677,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.4" + "version": "3.12.9" } }, "nbformat": 4, diff --git a/Submodule_3/Submodule_3_Tutorial_2_OOP2.ipynb b/Submodule_3/Submodule_3_Tutorial_2_OOP2.ipynb index 7f02f18..68e5ebd 100755 --- a/Submodule_3/Submodule_3_Tutorial_2_OOP2.ipynb +++ b/Submodule_3/Submodule_3_Tutorial_2_OOP2.ipynb @@ -433,11 +433,11 @@ "We will examine two types of **error checking**: in the constructor and in a decorator.\n", "\n", "Error checking in the constructor (__init__) ensures that invalid data is caught immediately when an object is created. \n", - "
\n", + "\n", "Let's examine how to validate that the diagnosis is from an allowed list.\n", - "
\n", - "You can see that **before** we store the provided diagnosis value in self.diagnosis, we first eliminate issues with capitalization (the interpretation of strings is VERY literal, as you remember, such that diabetes is not Diabetes)\n", - "
\n", + "\n", + "You can see that **before** we store the provided diagnosis value in self.diagnosis, we first eliminate issues with capitalization (the interpretation of strings is VERY literal, as you remember, such that diabetes is not Diabetes).\n", + "\n", "We also check that the provided diagnosis is in our short list of options. **If** it is, then we can store it as self._diagnosis\n" ] }, @@ -949,7 +949,7 @@ "id": "b58d4914", "metadata": {}, "source": [ - "Because functions are an object like any other variable, Python allows us to pass them just as we would any other varaible.\n", + "Because functions are an object like any other variable, Python allows us to pass them just as we would any other variable.\n", "- We have already seen functions used as paramters in closures, but it is even simpler than that. You can just create functions and pass them to any other function:" ] }, @@ -1058,7 +1058,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.16" + "version": "3.12.9" } }, "nbformat": 4, diff --git a/Submodule_3/Submodule_3_Tutorial_3_Project.ipynb b/Submodule_3/Submodule_3_Tutorial_3_Project.ipynb index 34661f2..13212e4 100755 --- a/Submodule_3/Submodule_3_Tutorial_3_Project.ipynb +++ b/Submodule_3/Submodule_3_Tutorial_3_Project.ipynb @@ -18,15 +18,14 @@ "- Build scripts that load, filter, and summarize real-world datasets.\n", "\n", "## Prerequisites\n", - "- Submodule 1\n", - "- Submodule 2 (especially Pandas so you can import the dataset\n", + "- Submodule 1 Tutorials\n", + "- Submodule 2 Tutorials (especially Pandas so you can import the dataset)\n", "- Submodule 3 Tutorials\n", "\n", "## Getting Started\n", "\n", "Below, you will find a task prompt that will require you to define a new Class which can handle a dataset. You can attempt the task on your own, or use the guided prompts which are each followed by a \"fill-in-the-blank\" model. You can copy each of those sections, edit them, and use each to build a class then write a script. If you get stuck, the entire solution is in the next tutorial.\n", - "\n", - "-----------------------------------------" + "\n" ] }, { @@ -328,7 +327,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.4" + "version": "3.12.9" } }, "nbformat": 4,