day1/2 Strings And Methods (solutions).ipynb

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   "source": [
    "# Intro to Methods and Polymorphism\n",
    "## With Strings and DNA Sequences"
   ]
  },
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    "`string` objects in Python represent text.  They can be created in several ways:\n",
    "\n",
    "```python\n",
    ">>> 'Hello'  # with apostrophes (\"single-quotes\")\n",
    "'Hello'\n",
    "\n",
    ">>> \"Hello\"  # with quotation marks (\"double-quotes\")\n",
    "\"Hello\"\n",
    "\n",
    ">>> \"\"\"Hello,     \n",
    "... my name is\n",
    "... Nick\"\"\"   # with triple-double-quotes (a \"docstring\", used for multi-line text)\n",
    "'Hello,\\nmy name is\\nNick'  \n",
    "\n",
    ">>> str(32)  # using the str() function to change into a string\n",
    "'32'\n",
    "```"
   ]
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    "Nucleotide sequences are often represented as strings:\n",
    "\n",
    "```python\n",
    ">>> seq = 'GCATTGGCT'\n",
    "```"
   ]
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    "## Built-In String Operations, Functions, and Methods\n",
    "\n",
    "Modify the dna sequences below in a single line of code to match what's asked for.  Functions and methods that may be used are:\n",
    "\n",
    "### Operations\n",
    "\n",
    "The same operations we've used on numbers and lists work on strings!\n",
    "\n",
    "  - `'GTC' * 3  # Repeats a string N times`\n",
    "  - `'GTC' + 'GTC'  # Concatenates two strings`\n",
    "  - `'GTC'[0]`  \n",
    "  - `'GTC'[-1]`\n",
    "  - `'GTC'[1:]`\n",
    "  - `'GTC'[:-1]`\n",
    "  - `'GTC'[::-1]   # Reverses the sequence`\n",
    "  - `'GTC' == 'GTC'  # If they are the same, then True`\n",
    "  - `'GTC' != 'GTC'  # If they are different, then True`\n",
    "\n",
    "\n",
    "### Built-In Functions for Strings and their Methods\n",
    "\n",
    "Strings also contain their own functions.  Functions inside types are called **\"Methods\"**, and they are a way to automatically put the string into the function.\n",
    "\n",
    "| Function | Method Syntax Equivalent |\n",
    "| :--           |  :---- |\n",
    "| `str.count('GTC', 'A')` |`'GTC'.count('A')` |\n",
    "| `str.upper('GtC')` | `'GtC'.upper()` |\n",
    "| `str.lower('GTc')` |  `'GTc'.lower()` |\n",
    "| `str.isdigit('GTC')` |`'GTC'.isdigit()` |\n",
    "| `str.index('GTC', 'T')` | `'GTC'.index('T')` |\n",
    "| `str.replace('GTC', 'G', 'C')` |  `'GTC'.replace('G', 'C')` |\n",
    "|  `str.split('GTC-CCA', '-')` | `'GTC-CCA'.split('-')` |\n",
    "| `len('GTC')` |  -None- |\n"
   ]
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   "source": [
    "**Exercises**"
   ]
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   "source": [
    "**Example**: Count the Number of \"G\" in the following sequence:"
   ]
  },
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   "cell_type": "code",
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    "seq = \"GTGTCAGTCCCCATGAATCGATAG\"\n",
    "seq.count('G')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Count the Number of \"C\" in the following sequence:"
   ]
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   "execution_count": 4,
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   "source": [
    "seq = \"GTGTCAGTCCCCATGAATCGATAG\""
   ]
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   "execution_count": 5,
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    "seq = \"GTGTCAGTCCCCATGAATCGATAG\"\n",
    "seq.count('C')"
   ]
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  {
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   "source": [
    "Count the number of \"AT\" repeats in the following sequence:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "seq = \"GTGTCAGTCCCCATGAATCGATAG\""
   ]
  },
  {
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   "execution_count": 3,
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     "exercise"
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     "execution_count": 3,
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   "source": [
    "seq = \"GTGTCAGTCCCCATGAATCGATAG\"\n",
    "seq.count('AT')"
   ]
  },
  {
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   "source": [
    "Concatenate the following two sequences (i.e. combine them into one sequence)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "seq1 = \"GTGTCAGT\"\n",
    "seq2 = \"TGAATCGATAG\""
   ]
  },
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   "execution_count": 6,
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    {
     "data": {
      "text/plain": [
       "'GTGTCAGTTGAATCGATAG'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seq1 = \"GTGTCAGT\"\n",
    "seq2 = \"TGAATCGATAG\"\n",
    "seq1 + seq2"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "How long is the following sequence?"
   ]
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   "source": [
    "seq = \"GTGTCAGTCCCCATGAATCGATAG\""
   ]
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  {
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   "execution_count": 7,
   "metadata": {
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       "24"
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     "execution_count": 7,
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   "source": [
    "seq = \"GTGTCAGTCCCCATGAATCGATAG\"\n",
    "len(seq)"
   ]
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  {
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   "source": [
    "What is the 2nd nucleotide in this sequence?"
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   "source": [
    "seq = \"GTGTCAGTCCCCATGAATCGATAG\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "tags": [
     "exercise"
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "'T'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "seq = \"GTGTCAGTCCCCATGAATCGATAG\"\n",
    "seq[1]"
   ]
  },
  {
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    "deepnote_cell_type": "markdown",
    "tags": []
   },
   "source": [
    "What is the 3rd-from-the-last nucleotide in this sequence?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "cell_id": "00016-740974fb-b373-443e-9079-25e9a29b1f99",
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   "source": [
    "seq = \"GTGTCAGTCCCCATGAATCGATAG\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "tags": [
     "exercise"
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "'T'"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seq = \"GTGTCAGTCCCCATGAATCGATAG\"\n",
    "seq[-3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "cell_id": "00010-cba5a2d8-f65c-4394-946c-4a98eefd7630",
    "deepnote_cell_type": "markdown",
    "tags": []
   },
   "source": [
    "Repeat the following sequence 13 times"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "cell_id": "00013-93c65027-89e2-4a78-a619-dc5b0daf8f83",
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    "execution_millis": 0,
    "execution_start": 1634079205489,
    "source_hash": "e8325bd0",
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   "outputs": [],
   "source": [
    "gc = \"GC\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "tags": [
     "exercise"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'GCGCGCGCGCGCGCGCGCGCGCGCGC'"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gc = \"GC\"\n",
    "gc * 13"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "cell_id": "00013-0473c5b5-e6a6-4e56-8e4a-54d887bdf5a7",
    "deepnote_cell_type": "markdown",
    "tags": []
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   "source": [
    "Replace the incorrect letter with an empty string (i.e. delete the letter)  (Hint: an empty string is just a pair of quotes, like `''` or `\"\"`)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "cell_id": "00014-21679293-a7e9-4f78-ab6a-5ed90cec9782",
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    "execution_millis": 1,
    "execution_start": 1634079290023,
    "source_hash": "4bf1e4b3",
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   "outputs": [],
   "source": [
    "seq = \"GTGXXGTXCCXCCATGXAATCGXATA\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "tags": [
     "exercise"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'GTGGTCCCCATGAATCGATA'"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seq = \"GTGXXGTXCCXCCATGXAATCGXATA\"\n",
    "seq.replace('X', '')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "cell_id": "00021-8331d446-a336-4950-ab3e-3584761b9945",
    "deepnote_cell_type": "markdown",
    "tags": []
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   "source": [
    "Keep only the first six nucleotides in this sequence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "cell_id": "00022-3536ebf8-08f8-4e96-9faf-6a8b5a304013",
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    "execution_millis": 5,
    "execution_start": 1634081214827,
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   "outputs": [],
   "source": [
    "seq = \"GTGTCAGTCCCCATGAATCGATAG\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "tags": [
     "exercise"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'GTGTCA'"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seq = \"GTGTCAGTCCCCATGAATCGATAG\"\n",
    "seq[:6]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "cell_id": "00014-d7405393-4e09-4348-b3eb-73f3d5c508f4",
    "deepnote_cell_type": "markdown",
    "tags": []
   },
   "source": [
    "Standardize the formatting of this sequence by either upper- or lower-casing the letters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "cell_id": "00017-9265e569-65b1-4e0e-a7f1-03f600501257",
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    "execution_millis": 1,
    "execution_start": 1634079427930,
    "source_hash": "5ceb027e",
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   },
   "outputs": [],
   "source": [
    "seq = \"GtCGAaaCCgTaGcTAgc\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "tags": [
     "exercise"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'GTCGAAACCGTAGCTAGC'"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seq = \"GtCGAaaCCgTaGcTAgc\"\n",
    "seq.upper()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "cell_id": "00017-2115d821-3cf9-4b10-9a11-3b91c7d18d06",
    "deepnote_cell_type": "markdown",
    "tags": []
   },
   "source": [
    "Split the following string around the empty space into a list of sequences (Hint: the string for a space is quotes with a space between them, like `' '` or `\" \"`)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "cell_id": "00017-cd7e0ba1-54d1-4833-a336-5be1d6e9d7ec",
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    "execution_millis": 1,
    "execution_start": 1634079573701,
    "source_hash": "140b9420",
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   },
   "outputs": [],
   "source": [
    "seqs = \"GTTCGAAAG GACCTGATTATAG AACCGATTTA\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "tags": [
     "exercise"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['GTTCGAAAG', 'GACCTGATTATAG', 'AACCGATTTA']"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seqs = \"GTTCGAAAG GACCTGATTATAG AACCGATTTA\"\n",
    "seqs.split(' ')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "cell_id": "00027-a018e06b-da15-4a11-9acc-1a09569311bd",
    "deepnote_cell_type": "markdown",
    "tags": []
   },
   "source": [
    "Reverse this sequence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "cell_id": "00027-44bc90e1-94c1-4dd5-acb0-6eead8708776",
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    "execution_millis": 4,
    "execution_start": 1634082719763,
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   "source": [
    "seq = \"GTGTCAGTCCCCATGAATCGATAG\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "tags": [
     "exercise"
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "'GATAGCTAAGTACCCCTGACTGTG'"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "seq = \"GTGTCAGTCCCCATGAATCGATAG\"\n",
    "seq[::-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "cell_id": "00019-ff7565ff-ad94-4e5c-93da-0cfda3494505",
    "deepnote_cell_type": "markdown",
    "tags": []
   },
   "source": [
    "What percentage of strong nucleotides (G and C) are there in this sequence?  (Hint: count the Gs and Cs, then divide by the total number of nucleotides)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "cell_id": "00020-a614801b-0e9e-4aec-899e-371d4dab1e91",
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    "execution_start": 1634082723387,
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   "source": [
    "seq = \"GTGTCAGTCCCCATGAATCGATAG\""
   ]
  },
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   "cell_type": "code",
   "execution_count": 22,
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    {
     "data": {
      "text/plain": [
       "0.5"
      ]
     },
     "execution_count": 22,
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   ],
   "source": [
    "seq = \"GTGTCAGTCCCCATGAATCGATAG\"\n",
    "(seq.count(\"G\") + seq.count(\"C\")) / len(seq)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "cell_id": "00031-9f1903c4-213b-46ae-870b-3b78d6cb6967",
    "deepnote_cell_type": "markdown",
    "tags": []
   },
   "source": [
    "Is this sequence the same forwards and backwards (i.e. a palindrome)?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "cell_id": "00032-e853c1a7-4ed8-4f0a-96f8-01f45a530915",
    "deepnote_cell_type": "code",
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 1,
    "execution_start": 1634083069195,
    "source_hash": "e9788ffd",
    "tags": []
   },
   "outputs": [],
   "source": [
    "seq = \"TCGATCTAGCGCGAATATCGGAGAAGAGGCTATAAGCGCGATCTAGCT\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "tags": [
     "exercise"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seq = \"TCGATCTAGCGCGAATATCGGAGAAGAGGCTATAAGCGCGATCTAGCT\"\n",
    "seq[::-1] == seq"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "cell_id": "00018-839a5e65-599d-476a-ab1c-fe84fc113e9f",
    "deepnote_cell_type": "markdown",
    "tags": []
   },
   "source": [
    "## Text Files\n",
    "\n",
    "### Writing Strings to Files\n",
    "\n",
    "Strings can be saved to text files by making a `File` object with the `open()` function and writing the string to it.  Here are two ways to do it:\n",
    "\n",
    "```python\n",
    "my_file = open('myfile.txt', 'w')  # get a file object open in 'write' mode\n",
    "my_file.write('This is my text')  # call the file.write() method\n",
    "my_file.close()  # call the file.close() method\n",
    "```\n",
    "\n",
    "\n",
    "### Reading Strings from Files\n",
    "\n",
    "Reading works in a similar way\n",
    "\n",
    "```python\n",
    "my_file = open('myfile.txt')\n",
    "text = my_file.read()\n",
    "my_file.close()\n",
    "```\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "cell_id": "00034-f7dafb88-24f7-4417-97be-30f1d0e06dd3",
    "deepnote_cell_type": "markdown",
    "tags": []
   },
   "source": [
    "**Exercises**:\n",
    "\n",
    "Write the following sequence to a text file named \"sequence.txt\":"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "cell_id": "00035-fdba6b56-4f40-4bb9-9b4d-0431c64ea010",
    "deepnote_cell_type": "code",
    "tags": []
   },
   "outputs": [],
   "source": [
    "seq = \"GTGTCAGTCCCCATGAATCGATAG\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "tags": [
     "exercise"
    ]
   },
   "outputs": [],
   "source": [
    "file = open('sequence.ignore.txt', 'w')\n",
    "file.write(seq)\n",
    "file.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "cell_id": "00036-b63fba54-258e-4382-b6a8-342d4eb8635c",
    "deepnote_cell_type": "markdown",
    "tags": []
   },
   "source": [
    "Read the sequence from the file back into Python."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "cell_id": "00037-fc9d7632-cbd3-4ebf-8532-0b1e82d4c84f",
    "deepnote_cell_type": "code",
    "tags": [
     "parameters",
     "exercise"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'TCGATCTAGCGCGAATATCGGAGAAGAGGCTATAAGCGCGATCTAGCT'"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file = open('sequence.ignore.txt')\n",
    "seq = file.read()\n",
    "file.close()\n",
    "seq"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "cell_id": "00030-84725862-0db5-4314-946d-d1c2e457794c",
    "deepnote_cell_type": "markdown",
    "tags": []
   },
   "source": [
    "### Online Text\n",
    "\n",
    "For getting text data from the internet, we can use the `urllib` package, which contains multiple subpackages for handling web requests and responses.\n",
    "\n",
    "```python\n",
    "from urllib.request import urlopen\n",
    "url = \"https://docs.python-requests.org/en/master/\"\n",
    "data = urlopen(url).read()\n",
    "text = data.decode()\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "cell_id": "00032-ca9af187-e068-4870-ba1c-650da235a862",
    "deepnote_cell_type": "markdown",
    "tags": []
   },
   "source": [
    "**Exercises**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "cell_id": "00033-887069d7-4bd8-4a90-ba05-3e389ecf5e1f",
    "deepnote_cell_type": "markdown",
    "tags": []
   },
   "source": [
    "Roughly how many letters are in William Shakespeare's play \"Romeo and Juliet\"?  Use the following link to the play:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "cell_id": "00035-8cea9996-e0c1-4e00-9666-8854e88dea7c",
    "deepnote_cell_type": "code",
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 142,
    "execution_start": 1634080828800,
    "source_hash": "ba84b533",
    "tags": []
   },
   "outputs": [],
   "source": [
    "url = \"https://raw.githubusercontent.com/cgovella/learning/master/edx-python/case%20studies/gutenverg/Books/English/shakespeare/Romeo%20and%20Juliet.txt\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "tags": [
     "exercise"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "178981"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url = \"https://raw.githubusercontent.com/cgovella/learning/master/edx-python/case%20studies/gutenverg/Books/English/shakespeare/Romeo%20and%20Juliet.txt\"\n",
    "from urllib.request import urlopen\n",
    "text = urlopen(url).read().decode()\n",
    "len(text)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "cell_id": "00037-2ec7639b-5370-4f67-829a-0e83f2a092fb",
    "deepnote_cell_type": "markdown",
    "tags": []
   },
   "source": [
    "Does the word \"Romeo\" or \"Juliet\" appear more often in the text?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "cell_id": "00038-bbb31d33-f40e-46c1-b5b2-633cdc3aab8b",
    "deepnote_cell_type": "code",
    "tags": [
     "exercise"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(159, 69)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "text.count(\"Romeo\"), text.count(\"Juliet\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "cell_id": "00036-4b7eb191-8d0b-4988-9ffa-f42ff5558292",
    "deepnote_cell_type": "markdown",
    "tags": []
   },
   "source": [
    "Write the play into a text file called \"romeo_and_juliet.txt\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "tags": [
     "exercise"
    ]
   },
   "outputs": [],
   "source": [
    "file = open('romeo_and_juliet.ignore.txt', 'w')\n",
    "file.write(text)\n",
    "file.close()"
   ]
  }
 ],
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