Notebook 2.3: Files

Open and close files in Python.

  # Open a file object
  infile = open("./datafiles/data.txt", "r")

  # read data as a string
  data = infile.read()

  # close the file object
  infile.close()
        

  # A simpler alternative
  with open("./datafiles/data.txt", 'r') as infile:
      data = infile.read()
        

Notebook 2.3: Files

Open and close files in Python.

  # Open a file object for writing
  outfile = open("./datafiles/data.txt", "w")

  # write data as a string
  outfile.write("hello world")

  # close the file object
  outfile.close()
        

  # A simpler alternative
  with open("./datafiles/data.txt", 'w') as outfile:
      outfile.write("hello world")
        

Notebook 3.0: Dictionaries

Python objects.

  # Dictionaries are used to store key/value pairs
  {"a key": ["and value in a dictionary"]}
        

  # dicts can be created using curly brackets or the dict() func.
  dict([("a", 3), ("b", 4), ("c", 5)])
        

Notebook 3.0: Dictionaries

List comprehension methods: a shortcut.

  # list-comprehension example for list objects
  newlist = [i for i in range(10)]
  newlist          
        

  [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
        

  # list comprehension for a dictionary from a list of lists
  ddict = {i: j for (i, j) in [['a', 1], ['b', 2], ['c', 3]]}
  ddict
        

  {'a': 1, 'b': 2, 'c': 3}
        

Notebook 3.0: Dictionaries

Action 1: Add comments to the code below.

# comment: import the random library
import random

# comment: create ea list with 1000 random numbers between 0-10
integer_list = [random.randint(0, 10) for i in range(1000)]

# comment: create an empty dictionary
counter = {}

# comment: iterate over elements of the integer list
for item in integer_list:
    
    # comment: conditional True if item is not already in the dict keys
    if item not in counter:
        # comment: set the value to 1 for this key
        counter[item] = 1
    
    # comment: item is already in dict keys 
    else:
        # comment: increment value by 1 for this key
        counter[item] += 1
        

Notebook 3.1: Transcription & Translation

  GENCODE = {
      'ATA': 'I', 'ATC': 'I', 'ATT': 'I', 'ATG': 'M', 
      'ACA': 'T', 'ACC': 'T', 'ACG': 'T', 'ACT': 'T', 
      'AAC': 'N', 'AAT': 'N', 'AAA': 'K', 'AAG': 'K', 
      'AGC': 'S', 'AGT': 'S', 'AGA': 'R', 'AGG': 'R',                  
      'CTA': 'L', 'CTC': 'L', 'CTG': 'L', 'CTT': 'L', 
      'CCA': 'P', 'CCC': 'P', 'CCG': 'P', 'CCT': 'P', 
      'CAC': 'H', 'CAT': 'H', 'CAA': 'Q', 'CAG': 'Q', 
      'CGA': 'R', 'CGC': 'R', 'CGG': 'R', 'CGT': 'R', 
      'GTA': 'V', 'GTC': 'V', 'GTG': 'V', 'GTT': 'V', 
      'GCA': 'A', 'GCC': 'A', 'GCG': 'A', 'GCT': 'A', 
      'GAC': 'D', 'GAT': 'D', 'GAA': 'E', 'GAG': 'E', 
      'GGA': 'G', 'GGC': 'G', 'GGG': 'G', 'GGT': 'G', 
      'TCA': 'S', 'TCC': 'S', 'TCG': 'S', 'TCT': 'S', 
      'TTC': 'F', 'TTT': 'F', 'TTA': 'L', 'TTG': 'L', 
      'TAC': 'Y', 'TAT': 'Y', 'TAA': '_', 'TAG': '_', 
      'TGC': 'C', 'TGT': 'C', 'TGA': '_', 'TGG': 'W', 
  } 

  # get amino acid for a particular codon
  GENCODE["CTA"]
        

        
  "L"
        

Notebook 3.1: Transcription & Translation

Create a string of DNA with a coding block between two non-coding blocks.

  # use a random seed so we will all draw the same random values each time (repeatable) 
  random.seed(12345)

  # create a genomic region containing a coding region surrounded by non-coding DNA
  region = random_dna(300) + "ATG" + random_coding_dna(100) + "TGA" +  random_dna(300)
  region
        

        
  'TAGGCGTCGATGCCGATCCCACGGATGATAACCGATACTCGACATCCGTCACGACCGGCTGAAATATCAGCATAATGTCGACATCGCCCCGCAACATCAGTATTCCCAGGCTCCCTTGAATCCCCGGCAGTAGAACGAGTGTGTGGTTAGTACGCAAAACTTCGGCGGTAGGATCCACGCGTCACAAGTGACATCCGGCGAAACTACGCTTTAGATGAGTTAGGTGCTAATAACAAGCATTTATCCGCTCTCCCCTACAAAAGCCGCTGTTCTAAGCTTATTAGCTGTACCTGCAGATGCATGTCTGATCCAATCGGTCAACACAGATGGGCACTCAGGACGTGTTCTAGACTCTGTGCAATAATCAAAGCCGCGTCTTGGTCTAATCCGAGAAATTTAGACGACCCAGTCCTTATCAGACGACAATGTGGAGCGCAATCTGAAGATGGTGAGTTCCGCGCCGCACTGGTCCTTGTTACCGAGCGTTTGGGCCGTTGTGAAAAGTGCCGAACAGGGACTGGTTGCTTAAACCTGGAGCCCTATCAGGGTCAACGTACGCATGGCGAAAACTCACGAAGGGACATATCCCGGAAAGATATACCTTGACGCTCGGGTAGCTAGTTCGGCTTATGCTTCGTGCTGACCAATCGACCAAGGCGGGGTAATTGCGACGACCCGCGGAACCACAACTTTACCCTAGACAAGCGGCGCGTAGCGTCCTATCGCCGGGAGTCTAACTCAAATCATATGGCCCATCGCAGTGCGTGAGTTTTATTCAGCCCACCCCAACAAGAGATCGAAATAGTAATCTGTCTCTCTGCTATGATGAGACAATGTCCGTACACTCACTACTTGTTGTACAGTAGATATTCAACCTTAGTGGTTGGTACCTTAGGGTGGGCGAAT'
        

Notebook 3.1: Transcription & Translation

  # find the first occurrence 
  region.index("ATG")

  # 9
        

  # split on ATG and count items in the resulting list
  len(region.split("ATG"))

  # 16
        

  # best: get a list with every ATG start position
  starts = [i for (i, j) in enumerate(region) if region[i: i+3] == "ATG"]

  # [9, 24, 74, 214, 296, 300, 326, 425, 445, 559, 629, 747, 822, 825, 833]
        

Notebook 3.2: Numpy arrays

# create a 3-dimensional array
np.zeros((5, 3, 3))
        

        
array([[[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.]],

       [[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.]],

       [[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.]],

       [[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.]],

       [[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.]]])
        

Notebook 3.3: Pandas DataFrames

# create an array with randomly generated values 
df = pd.DataFrame({
    "column1": np.random.normal(0, 1, 10),
    "column2": np.random.choice(list("ACGT"), 10), 
    "column3": np.random.randint(0, 5, 10),
})

# return the dataframe
df
        

        

  column1   column2   column3
0   1.633686    T   4
1   0.071054    G   4
2   -0.201375   T   0
3   0.461895    A   3
4   -1.480207   G   2
5   -0.729692   C   0
6   -0.143699   A   4
7   -1.108858   G   2
8   -1.704039   A   1
9   -2.760000   A   1