{"action":"created","comment":{"url":"https://api.github.com/repos/amazingvince/Real-Estate-Data-Cleaner/pulls/comments/72506791","id":72506791,"diff_hunk":"@@ -8,113 +8,160 @@\n import pandas as pd\n import numpy as np\n \n+# aea: argparse here\n input_file = 'Real Estate Model - Candler Park.csv'\n output_file = 'cleaned_data_cp5.csv'\n-#lists of keywords to make dummy vars of if they are present in an observation's description  \n+\n+\n+# aea: better as a map features = { 'Exterior' : [...], 'BasementDesc' : [...], ... }\n+# aea: this solves capitalization as well (mention PEP8)\n+# lists of keywords to make dummy vars of if they are present in an observation's description\n Exterior_features = ['Porch','Patio','Deck','Fenced','Out-Building']\n BasementDesc_features = ['Crawl Space','Slab', 'Basement']\n ParkingDesc_features = ['Driveway','Carport', '1 Car Garage', '2 Car Garage', '3 Car Garage']\n Stories_features = ['1 Story', '2 Story', 'More']\n \n def make_YearBuilt_bins(df, size):\n-\t'''Divides YearBuilt into bins and creates new column named YearBuiltBins\n+    \"\"\"Divides YearBuilt into bins and creates new column named YearBuiltBins\n \n     Args:\n-        df (pandas DataFrame): The DataFrame to make bins on \n+        df (pandas DataFrame): The DataFrame to make bins on\n         size (int): The size of each bin\n \n     Returns:\n         pandas DataFrame: with an additional column named YearBuiltBins\n-    '''\n-\n-\tbin_cutoffs = df['YearBuilt'].min()\n-\tbins = []\n-\twhile bin_cutoffs < df['YearBuilt'].max()+size:\n-\t    bins.append(bin_cutoffs)\n-\t    bin_cutoffs += size\n-\tdf['YearBuiltBins'] = pd.cut(df['YearBuilt'], bins)\n-\treturn df\n+    \"\"\"\n+\n+    bin_cutoffs = df['YearBuilt'].min()\n+    bins = []\n+    while bin_cutoffs < df['YearBuilt'].max()+size:\n+        bins.append(bin_cutoffs)\n+        bin_cutoffs += size\n+    df['YearBuiltBins'] = pd.cut(df['YearBuilt'], bins)\n+    return df\n \n def concatenate_address(df):\n-\t'''Takes all pieces of address and concatenates them into a unique key column named Address\n+    \"\"\"Takes all pieces of address and concatenates them into a unique key column named Address\n \n     Args:\n-        df (pandas DataFrame): The DataFrame to concatenate address on \n+        df (pandas DataFrame): The DataFrame to concatenate address on\n \n     Returns:\n         pandas DataFrame: with an additional column named Address\n-    '''\n-\t\n-\tdf['Address'] = df[['StreetNumber', 'StreetName', 'City', 'CountyOrParish', \n+    \"\"\"\n+\n+    df['Address'] = df[['StreetNumber', 'StreetName', 'City', 'CountyOrParish',\n                     'StateOrProvince', 'PostalCode']].apply(lambda x: ' '.join(x.map(str)), axis=1)\n-\treturn df\n+    return df\n \n def Basement_cleanup(x):\n-    #Recodes BasementDesc into three categories mapped onto a DataFrame\t\n+    # Recodes BasementDesc into three categories mapped onto a DataFrame\n \n     x = str(x)\n-    if 'Crawl Space' in x: \n+    if 'Crawl Space' in x:\n         return 'Crawl Space'\n-    elif 'Slab' in x: \n+    elif 'Slab' in x:\n         return 'Slab'\n     else:\n         return 'Basement'\n-    \n+\n def Stories_cleanup(x):\n-    #Recodes Stories into three categories mapped onto a DataFrame\n-    \n+    # Recodes Stories into three categories mapped onto a DataFrame\n+\n     x = str(x)\n-    if '+' in x: \n+    if '+' in x:\n         return 'More'\n-    elif '1 Story' in x: \n+    elif '1 Story' in x:\n         return '1 Story'\n     else:\n         return '2 Story'\n \n def extract_dummies(df, features, col):\n-    '''Takes a list and makes a column for each phrase in that list\n-    it places a 1 in that column if that phrase is present in the description else it places a 0 \n+    \"\"\"Takes a list and makes a column for each phrase in that list\n+    it places a 1 in that column if that phrase is present in the description else it places a 0\n \n     Args:\n         df (pandas DataFrame): The DataFrame containing the descriptions\n         features (list): The list of phrases that are being looked for in descriptions\n-        col (str): name of column which contains the descriptions  \n+        col (str): name of column which contains the descriptions\n \n     Returns:\n-        pandas DataFrame: without the column which contains descriptions plus \n-        additional columns one for each word in features \n-        \n-    '''\n-    \n+        pandas DataFrame: without the column which contains descriptions plus\n+        additional columns one for each word in features\n+\n+    \"\"\"\n+\n     for feature in features:\n         df[feature] = np.where((df[col].str.contains(feature)),1,0)\n     df.drop([col], axis=1, inplace=True)\n     return df\n \n-def main():\n-\tdf = pd.read_csv(input_file)\n-\tdf.drop(df[df.SqFtTotal == 0].index, inplace=True)\n-\tdf['CloseDate']  = pd.to_datetime(df['CloseDate'])\n-\tdf = concatenate_address(df)\n-\tdf = extract_dummies(df, Exterior_features, col='Exterior')\n-\tdf['BasementDesc'] = df['BasementDesc'].apply(Basement_cleanup)\n-\tdf = extract_dummies(df, BasementDesc_features, col='BasementDesc')\n-\tdf = extract_dummies(df, ParkingDesc_features, col='ParkingDesc')\n-\tdf['Stories'] = df['Stories'].apply(Stories_cleanup) \n-\tdf = extract_dummies(df, Stories_features, col='Stories')\n-\tdf = make_YearBuilt_bins(df, size=10)\n-\tdf['Year'] = pd.DatetimeIndex(df['CloseDate']).year\n-\tdf['Age'] = df['Year'] - df['YearBuilt']\n-\tdf['AssociationFee'] = np.where((df['AssociationFee']> 0),1,0)\n-\tdf['PoolonProperty'] = np.where((df['PoolonProperty'] !=  'None'),1,0)\n-\tdf['Bedrooms'] = df['UpperBedrooms'] + df['MainBedrooms']\n-\tdf['FullBaths'] = df['UpperFullBaths'] + df['MainFullBaths']\n-\tdf['HalfBaths'] = df['UpperHalfBaths'] + df['MainHalfBaths']\n-\tdf.drop(['StreetNumber', 'StreetName', 'City', 'CountyOrParish', 'StateOrProvince', \n-\t\t\t 'PostalCode','UpperBedrooms', 'MainBedrooms', 'UpperFullBaths', 'MainFullBaths', \n-\t\t\t 'UpperHalfBaths', 'MainHalfBaths'], axis=1, inplace=True)\n-\tdf.to_csv(output_file)\n+def add_upper_and_main(data_frame, desired):\n+    \"\"\"\n+    Look for the \"Upper\" and \"Main\" values for the desired column.  Sum these together and\n+    create a new column named desired.  Finally, remove the \"Upper\" and \"Main\" columns\n+\n+    e.g. \"Bedrooms\" is the sum of the \"UpperBedrooms\" and \"MainBedrooms\" values\n+\n+    :param data_frame: callback frame object to manipulate\n+    :param desired: string denoting the desired domain\n+    :return: None\n+    \"\"\"\n+    upper = \"Upper{}\".format(desired)\n+    main = \"Main{}\".format(desired)\n+\n+    data_frame[desired] = data_frame[upper] + data_frame[main]\n+\n+    data_frame.drop([upper, main], axis=1, inplace=True)\n+\n+def main(df):\n+    # method does too much - change to be testable (see example)\n+\n+    # df is an unclear name.  I'm not familiar with pandas, and it took me awhile to figure out","path":"RealEstateDataCleaning.py","position":170,"original_position":170,"commit_id":"5d9c0d80ba17da734ef72308a411f2e6e322cacb","original_commit_id":"5d9c0d80ba17da734ef72308a411f2e6e322cacb","user":{"login":"hkz-aarvesen","id":4959721,"avatar_url":"https://avatars.githubusercontent.com/u/4959721?v=3","gravatar_id":"","url":"https://api.github.com/users/hkz-aarvesen","html_url":"https://github.com/hkz-aarvesen","followers_url":"https://api.github.com/users/hkz-aarvesen/followers","following_url":"https://api.github.com/users/hkz-aarvesen/following{/other_user}","gists_url":"https://api.github.com/users/hkz-aarvesen/gists{/gist_id}","starred_url":"https://api.github.com/users/hkz-aarvesen/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/hkz-aarvesen/subscriptions","organizations_url":"https://api.github.com/users/hkz-aarvesen/orgs","repos_url":"https://api.github.com/users/hkz-aarvesen/repos","events_url":"https://api.github.com/users/hkz-aarvesen/events{/privacy}","received_events_url":"https://api.github.com/users/hkz-aarvesen/received_events","type":"User","site_admin":false},"body":"After reading through the `pandas` docs, I realize that `DataFrame` is a common thing.\r\n\r\nThere are two audiences for your code.  The first is you, right now, reading it.  You have a lot of context as to what is happening, so 'df' may mean something to you right now.  The second audience is someone maintaining this code at a future date.  This person will not have all the current context and will have to struggle to rebuild it.  This person is likely to be you in the future :)\r\n\r\nWhen naming things, consider both current you and future you.  It's a miserable feeling to open a piece of code after six months and think, \"man I have no idea what this thing does.\" \r\n\r\nSo I would rename \"df\" to \"data_frame\" or at least \"frame\". :)","created_at":"2016-07-27T19:29:50Z","updated_at":"2016-07-27T19:29:50Z","html_url":"https://github.com/amazingvince/Real-Estate-Data-Cleaner/pull/1#discussion_r72506791","pull_request_url":"https://api.github.com/repos/amazingvince/Real-Estate-Data-Cleaner/pulls/1","_links":{"self":{"href":"https://api.github.com/repos/amazingvince/Real-Estate-Data-Cleaner/pulls/comments/72506791"},"html":{"href":"https://github.com/amazingvince/Real-Estate-Data-Cleaner/pull/1#discussion_r72506791"},"pull_request":{"href":"https://api.github.com/repos/amazingvince/Real-Estate-Data-Cleaner/pulls/1"}}},"pull_request":{"url":"https://api.github.com/repos/amazingvince/Real-Estate-Data-Cleaner/pulls/1","id":79075404,"html_url":"https://github.com/amazingvince/Real-Estate-Data-Cleaner/pull/1","diff_url":"https://github.com/amazingvince/Real-Estate-Data-Cleaner/pull/1.diff","patch_url":"https://github.com/amazingvince/Real-Estate-Data-Cleaner/pull/1.patch","issue_url":"https://api.github.com/repos/amazingvince/Real-Estate-Data-Cleaner/issues/1","number":1,"state":"open","locked":false,"title":"Several suggestions","user":{"login":"hkz-aarvesen","id":4959721,"avatar_url":"https://avatars.githubusercontent.com/u/4959721?v=3","gravatar_id":"","url":"https://api.github.com/users/hkz-aarvesen","html_url":"https://github.com/hkz-aarvesen","followers_url":"https://api.github.com/users/hkz-aarvesen/followers","following_url":"https://api.github.com/users/hkz-aarvesen/following{/other_user}","gists_url":"https://api.github.com/users/hkz-aarvesen/gists{/gist_id}","starred_url":"https://api.github.com/users/hkz-aarvesen/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/hkz-aarvesen/subscriptions","organizations_url":"https://api.github.com/users/hkz-aarvesen/orgs","repos_url":"https://api.github.com/users/hkz-aarvesen/repos","events_url":"https://api.github.com/users/hkz-aarvesen/events{/privacy}","received_events_url":"https://api.github.com/users/hkz-aarvesen/received_events","type":"User","site_admin":false},"body":"Hey Vince,\r\n\r\nI made several changes and suggestions.  I'll put greater detail in the code.\r\n\r\nSome top level suggestions:\r\n\r\n- python is sensitive to formatting, and pythonistas are even more sensitive.  Take a gander through `PEP8` to see what all they want you to do.  I applied some minor \"fixes\" to the code (such as replacing tabs with spaces) to bring it in line with PEP8.  This is an annoying feature of python development but, hey, you will encounter it.\r\n- I added a unit test as an example.\r\n- I added the barebones necessary for a virtual environments.  Venvs are a great feature in python.  They let you avoid polluting your work machine with a bunch of junk you need for just one projet.\r\n- Added a `.gitignore` to avoid checking in python cache files.\r\n\r\nYou asked specifically about methods.  I assume you're also talking about the functions you're using here.  Proper decomposition is one of the hardest things to get right, so do not despair!\r\n\r\nFunctions do a few main things for you.  One is that they package up repeatable code.  This reduces defects since it's very common to make slight mistakes when copying code.  The second is that they allow us limited humans to comprehend what the code is trying to do.  A thousand line function is hard to understand; a function split up into something like\r\n\r\n```\r\nfilename = get_dictionary_filename()\r\nwords = read_dictionary_from_disk(filename)\r\nresults = []\r\nfor word in words:\r\n    if is_palindrome(word):\r\n        results.append(word)\r\nreturn results\r\n```\r\n\r\nis pretty clean on what it is doing.\r\n\r\nA third thing, which is not obvious, is that writing enough functions makes code testable.  This is because when you write a function you are constraining the state being passed to the function: it should be whatever your arguments are (with the obvious exception of objects that have internal state).  When you can control the state like that, things get a lot easier to test.  You can see how I pushed the `add_upper_and_main` code into a function, which just takes a data frame and a column name.  Then I can easily create a data frame in my test code and manipulate it rather than having to read in a csv file that I need to keep on disk.  One csv file isn't too bad, but you can imagine as you have more tests you have more csv files.\r\n\r\nYou'll develop a nose for your code and being to notice \"smells\".  Code smells are things that don't seem quite right in the code.  Honing this sense will help you decide when something should be a function.\r\n\r\nSome rules of thumb:\r\n- if you need to run the same code multiple times, consider a function\r\n- if you need a small testable unit of work, consider a function\r\n- if your current bit of code is more than a screen or two long, consider breaking it up into smaller functions (more than two pages of code is hard to keep in your head at once)\r\n- if you are passing in variables to one function that are only used once, or just used at the start or end of the function, consider if they belong in a different function (e.g. I refactored your `main` function to be ignorant of csv files, and do that just outside in the main body of the script)\r\n  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