diff --git a/src/notebooks/mainNb.ipynb b/src/notebooks/mainNb.ipynb index 48b36ab..f97c05f 100644 --- a/src/notebooks/mainNb.ipynb +++ b/src/notebooks/mainNb.ipynb @@ -18,7 +18,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 2, "id": "d11a2343", "metadata": {}, "outputs": [], @@ -142,7 +142,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 4, "id": "15c0e5af", "metadata": {}, "outputs": [ @@ -152,7 +152,7 @@ "dict_keys(['ARC_Enrollments', 'ARC_Application', 'All_demographics_and_programs'])" ] }, - "execution_count": 16, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -172,7 +172,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 5, "id": "5875ef3e", "metadata": {}, "outputs": [ @@ -314,7 +314,7 @@ "4 NaN NaN Tech Louisville 21-22 " ] }, - "execution_count": 18, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -334,7 +334,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "id": "c3c755a4", "metadata": {}, "outputs": [], @@ -346,7 +346,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 7, "id": "fa63b693", "metadata": {}, "outputs": [ @@ -599,7 +599,7 @@ "[32230 rows x 12 columns]" ] }, - "execution_count": 21, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -608,6 +608,76 @@ "All_demographics_and_programs" ] }, + { + "cell_type": "markdown", + "id": "b83d9a39", + "metadata": {}, + "source": [ + "Should we switch to this rather than the 2 step process above?\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3a009686", + "metadata": {}, + "outputs": [], + "source": [ + "def load_data_folder(\n", + " folder_path: Union[str, os.PathLike] = \"../../data\",\n", + " safe_names: bool = False\n", + ") -> Dict[str, pd.DataFrame]:\n", + " \"\"\"\n", + " Load all CSV/XLS/XLSX files in a folder into pandas DataFrames.\n", + " ...\n", + " safe_names : bool, optional\n", + " If True, replace spaces in filenames with underscores for dict keys.\n", + " \"\"\"\n", + " path = Path(folder_path)\n", + " if not path.exists():\n", + " raise FileNotFoundError(f\"Folder not found: {path.resolve()}\")\n", + "\n", + " dataframes: Dict[str, pd.DataFrame] = {}\n", + " for p in path.iterdir():\n", + " if not p.is_file():\n", + " continue\n", + "\n", + " ext = p.suffix.lower()\n", + " if ext == \".csv\":\n", + " df = pd.read_csv(p)\n", + " elif ext in {\".xlsx\", \".xls\"}:\n", + " df = pd.read_excel(p)\n", + " else:\n", + " continue\n", + "\n", + " key = p.stem.replace(\" \", \"_\") if safe_names else p.stem\n", + " dataframes[key] = df\n", + "\n", + " return dataframes" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ce7ffc41", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "dict_keys(['ARC_Enrollments', 'ARC_Application', 'All_demographics_and_programs'])" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dfs = load_data_folder(safe_names=True)\n", + "dfs.keys()" + ] + }, { "cell_type": "markdown", "id": "fe6f5506", @@ -640,26 +710,88 @@ "source": [ "class DataCleaner:\n", " \"\"\"\n", - " General-purpose cleaner for multiple WORC datasets\n", - " (Employment, Enrollments, Demographics).\n", + " A utility class for cleaning and standardizing tabular datasets.\n", + "\n", + " This class wraps a pandas DataFrame and provides a set of \n", + " convenience methods for common data cleaning tasks such as:\n", "\n", - " Uses try/except for safety (does not break if col missing).\n", - " Keeps all rows (no drops), but fills/fixes when possible.\n", + " - Dropping unnecessary columns.\n", + " - Filling missing values with specified defaults.\n", + " - Replacing or normalizing categorical values.\n", + " - Converting data types safely (including datetime).\n", + " - Standardizing demographic fields (e.g., gender, race).\n", + " - Parsing and normalizing salary values.\n", + "\n", + " All methods are designed to fail gracefully:\n", + " - If a target column does not exist, it is skipped.\n", + " - If an operation fails due to incompatible data, a warning \n", + " is printed and the DataFrame remains unchanged.\n", + "\n", + " Most methods return `self`, enabling method chaining:\n", + "\n", + " Example\n", + " -------\n", + " >>> cleaner = DataCleaner(df)\n", + " >>> clean_df = (\n", + " ... cleaner\n", + " ... .drop_columns([\"UnusedCol\"])\n", + " ... .fillna({\"Age\": 0, \"City\": \"Unknown\"})\n", + " ... .normalize_gender()\n", + " ... .normalize_race()\n", + " ... .clean_salary()\n", + " ... .finalize()\n", + " ... )\n", " \"\"\"\n", "\n", " def __init__(self, df: pd.DataFrame):\n", " self.df = df.copy()\n", "\n", - " def safe_drop_columns(self, cols_to_drop):\n", - " \"\"\"Drop columns if they exist, otherwise ignore.\"\"\"\n", + " def drop_columns(self, cols_to_drop) -> \"Self\":\n", + " \"\"\"\n", + " Drop one or more columns from the DataFrame safely.\n", + "\n", + " This method attempts to drop the specified columns. If a column \n", + " does not exist, it is ignored (no error is raised). If dropping \n", + " fails due to another issue (e.g., invalid argument type), a \n", + " warning is printed and the DataFrame is left unchanged.\n", + "\n", + " Parameters\n", + " ----------\n", + " cols_to_drop : str or list of str\n", + " Column name or list of column names to drop.\n", + "\n", + " Returns\n", + " -------\n", + " Self\n", + " The current instance, allowing method chaining.\n", + " \"\"\"\n", " try:\n", " self.df = self.df.drop(columns=cols_to_drop, errors='ignore')\n", " except Exception as e:\n", " print(f\"[Warning] Failed dropping columns: {e}\")\n", " return self\n", "\n", - " def safe_fillna(self, fill_map: dict):\n", - " \"\"\"Fill NaN values for specific columns safely.\"\"\"\n", + " def fillna(self, fill_map: dict) -> \"Self\":\n", + " \"\"\"\n", + " Fill missing (NaN) values in specified columns safely.\n", + "\n", + " For each column provided in the mapping, this method replaces \n", + " NaN values with the specified fill value. Columns not present \n", + " in the DataFrame are skipped. If filling fails for a column \n", + " (e.g., due to incompatible data types), a warning is printed \n", + " and that column is left unchanged.\n", + "\n", + " Parameters\n", + " ----------\n", + " fill_map : dict\n", + " A dictionary mapping {column_name: fill_value} pairs.\n", + " Example: {\"age\": 0, \"city\": \"Unknown\"}\n", + "\n", + " Returns\n", + " -------\n", + " Self\n", + " The current instance, allowing method chaining.\n", + " \"\"\"\n", " for col, val in fill_map.items():\n", " try:\n", " if col in self.df.columns:\n", @@ -668,8 +800,31 @@ " print(f\"[Warning] Failed filling NaN for {col}: {e}\")\n", " return self\n", "\n", - " def safe_replace(self, col, replacements: dict):\n", - " \"\"\"Replace values in a column safely.\"\"\"\n", + " def replace_column_values(self, col: str, replacements: dict) -> \"Self\":\n", + " \"\"\"\n", + " Replace values in a specified DataFrame column using a mapping dictionary.\n", + "\n", + " This method attempts to apply the given replacements safely. \n", + " If the column exists, it replaces matching values based on the \n", + " provided mapping. If an error occurs during replacement \n", + " (e.g., invalid mapping or data type mismatch), a warning \n", + " is printed and the DataFrame is left unchanged.\n", + "\n", + " Parameters\n", + " ----------\n", + " col : str\n", + " The name of the column in the DataFrame to modify.\n", + " replacements : dict\n", + " A mapping of {old_value: new_value} pairs to replace.\n", + "\n", + " Returns\n", + " -------\n", + " Self\n", + " The current instance, allowing method chaining.\n", + " Sample usage:\n", + " >>> cleaner = DataCleaner(df)\n", + " >>> cleaner.replace_column_values(\"status\", {\"yes\": 1, \"no\": 0})\n", + " \"\"\"\n", " try:\n", " if col in self.df.columns:\n", " self.df[col] = self.df[col].replace(replacements)\n", @@ -677,8 +832,41 @@ " print(f\"[Warning] Failed replacing values in {col}: {e}\")\n", " return self\n", "\n", - " def safe_convert_dtype(self, col, dtype, errors=\"ignore\"):\n", - " \"\"\"Convert column dtype safely.\"\"\"\n", + " def convert_datetime(self, col, dtype, errors=\"ignore\"):\n", + " \"\"\"\n", + " Convert a column to a specified dtype, with special handling for datetimes.\n", + "\n", + " Parameters\n", + " ----------\n", + " col : str\n", + " Name of the column to convert.\n", + " dtype : str or type\n", + " Target dtype. If the string contains \"datetime\", the method will use\n", + " `pandas.to_datetime` for conversion. Otherwise, it uses `.astype()`.\n", + " errors : {\"ignore\", \"raise\", \"coerce\"}, default \"ignore\"\n", + " Error handling behavior:\n", + " - \"ignore\": invalid parsing will return the original input.\n", + " - \"raise\": raises an exception on invalid parsing.\n", + " - \"coerce\": invalid parsing will be set as NaT (for datetime) or NaN.\n", + "\n", + " Returns\n", + " -------\n", + " self : DataFrameCleaner\n", + " The instance with the modified DataFrame, allowing for method chaining.\n", + "\n", + " Notes\n", + " -----\n", + " - For datetime conversion, the method forces `errors=\"coerce\"` to ensure\n", + " invalid values are converted to NaT instead of raising.\n", + " - For non-datetime conversions, the provided `errors` argument is passed\n", + " directly to `.astype()`.\n", + " - If the column does not exist, no action is taken.\n", + "\n", + " Examples\n", + " --------\n", + " >>> cleaner.convert_datetime(\"StartDate\", \"datetime64[ns]\")\n", + " >>> cleaner.convert_datetime(\"Age\", \"int\", errors=\"coerce\")\n", + " \"\"\"\n", " try:\n", " if col in self.df.columns:\n", " if \"datetime\" in str(dtype):\n", @@ -690,8 +878,25 @@ " print(f\"[Warning] Failed dtype conversion on {col}: {e}\")\n", " return self\n", "\n", - " def normalize_gender(self):\n", - " \"\"\"Unify transgender categories safely.\"\"\"\n", + " def normalize_gender(self) -> \"Self\":\n", + " \"\"\"\n", + " Standardize gender labels in the DataFrame.\n", + "\n", + " This method looks for a column named \"Gender\" and replaces \n", + " specific transgender categories with the unified label \n", + " \"Transgender\". If the column does not exist or the replacement \n", + " fails (e.g., due to unexpected data types), the method prints a \n", + " warning and leaves the DataFrame unchanged.\n", + "\n", + " Replacements performed:\n", + " - \"Transgender male to female\" → \"Transgender\"\n", + " - \"Transgender female to male\" → \"Transgender\"\n", + "\n", + " Returns\n", + " -------\n", + " Self\n", + " The current instance, allowing method chaining.\n", + " \"\"\"\n", " try:\n", " if \"Gender\" in self.df.columns:\n", " self.df[\"Gender\"] = self.df[\"Gender\"].replace({\n", @@ -702,18 +907,35 @@ " print(f\"[Warning] Failed gender normalization: {e}\")\n", " return self\n", "\n", - " def split_race(self):\n", - " \"\"\"Split Race column into Race_1, Race_2, etc., if it exists.\"\"\"\n", + " def normalize_race(self) -> \"Self\":\n", + " \"\"\"\n", + " Normalize the 'Race' column so that multi-value entries are \n", + " collapsed into a single category \"Two or More Races\".\n", + "\n", + " Behavior\n", + " --------\n", + " - Single race values are kept as-is.\n", + " - Multi-value entries separated by \";\" or \",\" are replaced with\n", + " \"Two or More Races\".\n", + "\n", + " Example\n", + " -------\n", + " Original: \"White;Asian\" → \"Two or More Races\"\n", + " Original: \"White,Asian\" → \"Two or More Races\"\n", + "\n", + " Returns\n", + " -------\n", + " Self\n", + " The current instance, allowing method chaining.\n", + " \"\"\"\n", " try:\n", " if \"Race\" in self.df.columns:\n", - " splitting = self.df[\"Race\"].astype(\n", - " str).str.split(\";\", expand=True)\n", - " splitting.columns = [\n", - " f\"Race_{i+1}\" for i in range(splitting.shape[1])]\n", - " self.df = pd.concat(\n", - " [self.df.drop(columns=[\"Race\"]), splitting], axis=1)\n", + " self.df[\"Race\"] = self.df[\"Race\"].astype(str).apply(\n", + " lambda x: \"Two or More Races\" if (\n", + " \";\" in x or \",\" in x) else x\n", + " )\n", " except Exception as e:\n", - " print(f\"[Warning] Failed race splitting: {e}\")\n", + " print(f\"[Warning] Failed race normalization: {e}\")\n", " return self\n", "\n", " def clean_salary(self, hours_per_year: int = 2080):\n", @@ -721,13 +943,14 @@ " Clean and standardize salary values in the DataFrame.\n", "\n", " Steps performed:\n", - " 1. Remove currency symbols, commas, and shorthand (e.g., \"$50k\" → \"50000\").\n", + " 1. Remove currency symbols, commas, and shorthand (e.g., \"$50k\" → 50000).\n", " 2. Handle ranges by converting them to the average value \n", - " (e.g., \"50,000-70,000\" → 60000).\n", - " 3. Convert values to numeric, coercing invalid entries to NaN.\n", - " 4. Treat values < 200 as hourly wages and convert to annual salaries \n", - " (multiplied by `hours_per_year`).\n", - " 5. Drop unrealistic values greater than 1,000,000 (set to NaN).\n", + " (e.g., \"50,000–70,000\" → 60000).\n", + " 3. Handle shorthand \"M\" (e.g., \"$1.5M\" → 1,500,000).\n", + " 4. Convert values to numeric, coercing invalid entries to NaN.\n", + " 5. Treat values <= 200 as hourly wages and convert to annual salaries \n", + " (multiplied by `hours_per_year`).\n", + " 6. Drop unrealistic values greater than 1,000,000 (set to NaN).\n", "\n", " Parameters\n", " ----------\n", @@ -745,10 +968,15 @@ "\n", " def parse_salary(val: str):\n", " val = val.strip()\n", + " if not val or val.lower() in {\"nan\", \"none\"}:\n", + " return None\n", + "\n", + " # Normalize dash types (hyphen, en dash, em dash \"-\")\n", + " val = re.sub(r\"[–—]\", \"-\", val)\n", "\n", - " # Handle range like \"50k-70k\" or \"50,000–70,000\"\n", - " if \"-\" in val or \"–\" in val:\n", - " parts = re.split(r\"[-–]\", val)\n", + " # Handle range like \"50k-70k\" or \"50,000-70,000\"\n", + " if \"-\" in val:\n", + " parts = val.split(\"-\")\n", " nums = [parse_salary(p) for p in parts if p.strip()]\n", " nums = [n for n in nums if n is not None]\n", " return sum(nums) / len(nums) if nums else None\n", @@ -756,12 +984,17 @@ " # Remove $, commas, spaces\n", " val = re.sub(r\"[\\$,]\", \"\", val)\n", "\n", - " # Handle shorthand k/K (e.g., 50k -> 50000)\n", - " match = re.match(r\"(\\d+(\\.\\d+)?)([kK])\", val)\n", - " if match:\n", - " return float(match.group(1)) * 1000\n", + " # Handle shorthand k/K (e.g., \"50k\" → 50000)\n", + " match_k = re.match(r\"^(\\d+(\\.\\d+)?)[kK]$\", val)\n", + " if match_k:\n", + " return float(match_k.group(1)) * 1000\n", + "\n", + " # Handle shorthand M (e.g., \"1.5M\" → 1500000)\n", + " match_m = re.match(r\"^(\\d+(\\.\\d+)?)[mM]$\", val)\n", + " if match_m:\n", + " return float(match_m.group(1)) * 1_000_000\n", "\n", - " # Convert plain number if possible\n", + " # Plain number (integer or float)\n", " try:\n", " return float(val)\n", " except ValueError:\n", @@ -771,7 +1004,11 @@ " self.df[\"Salary\"] = self.df[\"Salary\"].apply(parse_salary)\n", "\n", " # Convert small numbers (hourly) to annual\n", +<<<<<<< HEAD + " self.df.loc[self.df[\"Salary\"] <=\n", +======= " self.df.loc[self.df[\"Salary\"] <\n", +>>>>>>> b474bc9b8d9e2498709a076e39a06c76a80d998c " 200, \"Salary\"] *= hours_per_year\n", "\n", " # Drop unrealistic salaries\n", @@ -782,13 +1019,224 @@ "\n", " return self\n", "\n", - " def finalize(self):\n", - " \"\"\"Return cleaned dataframe.\"\"\"\n", + " def finalize(self) -> pd.DataFrame:\n", + " \"\"\"\n", + " Finalize and return the cleaned DataFrame.\n", + "\n", + " This method should be called at the end of a cleaning pipeline \n", + " to retrieve the fully processed DataFrame after all applied \n", + " transformations.\n", + "\n", + " Returns\n", + " -------\n", + " pd.DataFrame\n", + " The cleaned and transformed DataFrame.\n", + " \"\"\"\n", " return self.df" ] }, { "cell_type": "markdown", +<<<<<<< HEAD + "id": "d84a5b95", + "metadata": {}, + "source": [ + "# example usage of each method\n" +======= + "id": "3eb6373f", + "metadata": {}, + "source": [ + "### Sample use of the clean_salary function.\n" +>>>>>>> b474bc9b8d9e2498709a076e39a06c76a80d998c + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "329da719", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/2d/yt4_w6zn5pbfjg_jx5sdmm180000gn/T/ipykernel_89024/677290603.py:130: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", + " self.df[col] = pd.to_datetime(\n" + ] + }, + { + "data": { + "text/html": [ + "
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Auto IdGenderRaceEthnicity Hispanic/LatinoOutcomeVeteranEx-OffenderJustice InvolvedSingle ParentProgram: Program Name
0NaTMaleBlack or African AmericanUnknownUnknown0UnknownUnknownUnknownReimage 21-22
1NaTMaleBlack or African AmericanUnknownUnknown0UnknownUnknownUnknownReimage 21-22
2NaTMaleBlack or African AmericanUnknownUnknown0UnknownUnknownUnknownReimage 21-22
3NaTMaleAsianUnknownSuccessfully Completed0UnknownNoUnknownTech Louisville 21-22
4NaTMaleBlack or African AmericanUnknownUnknown-1UnknownUnknownUnknownTech Louisville 21-22
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" + ], + "text/plain": [ + " Auto Id Gender Race Ethnicity Hispanic/Latino \\\n", + "0 NaT Male Black or African American Unknown \n", + "1 NaT Male Black or African American Unknown \n", + "2 NaT Male Black or African American Unknown \n", + "3 NaT Male Asian Unknown \n", + "4 NaT Male Black or African American Unknown \n", + "\n", + " Outcome Veteran Ex-Offender Justice Involved Single Parent \\\n", + "0 Unknown 0 Unknown Unknown Unknown \n", + "1 Unknown 0 Unknown Unknown Unknown \n", + "2 Unknown 0 Unknown Unknown Unknown \n", + "3 Successfully Completed 0 Unknown No Unknown \n", + "4 Unknown -1 Unknown Unknown Unknown \n", + "\n", + " Program: Program Name \n", + "0 Reimage 21-22 \n", + "1 Reimage 21-22 \n", + "2 Reimage 21-22 \n", + "3 Tech Louisville 21-22 \n", + "4 Tech Louisville 21-22 " + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cleaner = DataCleaner(all_demo)\n", + "\n", + "clean_df = (\n", + " cleaner\n", + " # 1. Drop unneeded columns\n", + " .drop_columns([\"First Name\", \"Last Name\"])\n", + "\n", + " # 2. Fill missing values\n", + " .fillna({\n", + " \"Outcome\": \"Unknown\",\n", + " \"Veteran\": \"Unknown\",\n", + " \"Ex-Offender\": \"Unknown\",\n", + " \"Justice Involved\": \"Unknown\",\n", + " \"Single Parent\": \"Unknown\",\n", + " \"Ethnicity Hispanic/Latino\": \"Unknown\"\n", + " })\n", + "\n", + " # 3. Replace specific column values\n", + " .replace_column_values(\"Veteran\", {\"No\": 0, \"Yes\": 1, \"Unknown\": -1})\n", + "\n", + " # 4. Convert a column to datetime (pretend Auto Id is a date code)\n", + " .convert_datetime(\"Auto Id\", \"datetime64[ns]\") # will fail gracefully\n", + "\n", + " # 5. Normalize gender labels\n", + " .normalize_gender()\n", + "\n", + " # 6. Normalize race column (collapse multi-value)\n", + " .normalize_race()\n", + "\n", + " # 7. Clean salary column\n", + " .clean_salary()\n", + "\n", + " # 8. Finalize and return cleaned DataFrame\n", + " .finalize()\n", + ")\n", + "\n", + "clean_df.head()" + ] + }, + { + "cell_type": "markdown", "id": "3eb6373f", "metadata": {}, "source": [ @@ -797,7 +1245,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 11, "id": "182eac4a", "metadata": {}, "outputs": [ @@ -813,7 +1261,7 @@ "4 75000.0\n", "5 100000.0\n", "6 150000.0\n", - "7 200.0\n", + "7 416000.0\n", "8 3000.0\n", "9 NaN\n", "10 NaN\n", @@ -840,7 +1288,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 12, "id": "82806fc9", "metadata": {}, "outputs": [ @@ -858,7 +1306,7 @@ "6 NaN\n", "7 NaN\n", "8 NaN\n", - "9 NaN\n" + "9 75000.0\n" ] } ], @@ -1196,7 +1644,7 @@ ], "metadata": { "kernelspec": { - "display_name": ".venv", + "display_name": "venv (3.12.2)", "language": "python", "name": "python3" }, @@ -1210,7 +1658,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.13.1" + "version": "3.12.2" } }, "nbformat": 4, diff --git a/src/notebooks/cleaning.ipynb b/src/notebooks/old_notebooks/cleaning.ipynb similarity index 100% rename from src/notebooks/cleaning.ipynb rename to src/notebooks/old_notebooks/cleaning.ipynb diff --git a/src/old_files/nick_demographicscleaning.ipynb b/src/notebooks/old_notebooks/nick_demographicscleaning.ipynb similarity index 100% rename from src/old_files/nick_demographicscleaning.ipynb rename to src/notebooks/old_notebooks/nick_demographicscleaning.ipynb diff --git a/src/notebooks/visualization_examples.ipynb b/src/notebooks/old_notebooks/visualization_examples.ipynb similarity index 100% rename from src/notebooks/visualization_examples.ipynb rename to src/notebooks/old_notebooks/visualization_examples.ipynb diff --git a/src/notebooks/worc_cleaning.ipynb b/src/notebooks/old_notebooks/worc_cleaning.ipynb similarity index 100% rename from src/notebooks/worc_cleaning.ipynb rename to src/notebooks/old_notebooks/worc_cleaning.ipynb diff --git a/src/notebooks/old_notebooks/worc_employment_plots.ipynb b/src/notebooks/old_notebooks/worc_employment_plots.ipynb new file mode 100644 index 0000000..e5a0e3f --- /dev/null +++ b/src/notebooks/old_notebooks/worc_employment_plots.ipynb @@ -0,0 +1,626 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Best Practice\n", + "\n", + "Be sure to set up a virtual environment as there were lots of imports in this project\n", + "| Command | Linux/Mac | GitBash |\n", + "| ------- | --------- | ------- |\n", + "| Create | python3 -m venv venv | python -m venv venv |\n", + "| Activate | source venv/bin/activate | source venv/Scripts/activate |\n", + "| Install | pip install -r requirements.txt or pip install packages | pip install -r requirements.txt or pip install packages|\n", + "| Deactivate | deactivate | deactivate |" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "# import openpyxl\n", + "from Carmen_WORCEmployment import load_and_clean\n", + "\n", + "\n", + "# sys.path.append(os.path.abspath(\"..\"))\n", + "file_path = \"../../data/WORC Employment.xlsx\"\n", + "worc = pd.read_excel(file_path)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "ename": "ValueError", + "evalue": "Invalid file path or buffer object type: ", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mValueError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[25]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m worc = \u001b[43mload_and_clean\u001b[49m\u001b[43m(\u001b[49m\u001b[43mworc\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 2\u001b[39m worc\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/Desktop/codeyou/CodeYouDataProject/src/Carmen_WORCEmployment.py:14\u001b[39m, in \u001b[36mload_and_clean\u001b[39m\u001b[34m(file_path)\u001b[39m\n\u001b[32m 4\u001b[39m \u001b[38;5;250m\u001b[39m\u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m 5\u001b[39m \u001b[33;03mLoads and cleans the WORC Employment dataset.\u001b[39;00m\n\u001b[32m 6\u001b[39m \u001b[33;03m\u001b[39;00m\n\u001b[32m (...)\u001b[39m\u001b[32m 11\u001b[39m \u001b[33;03m pd.DataFrame: Cleaned DataFrame.\u001b[39;00m\n\u001b[32m 12\u001b[39m \u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m 13\u001b[39m \u001b[38;5;66;03m# Load data\u001b[39;00m\n\u001b[32m---> \u001b[39m\u001b[32m14\u001b[39m worc = \u001b[43mpd\u001b[49m\u001b[43m.\u001b[49m\u001b[43mread_excel\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfile_path\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 16\u001b[39m \u001b[38;5;66;03m# Drop columns we don't need\u001b[39;00m\n\u001b[32m 17\u001b[39m cols_to_drop = [\u001b[33m'\u001b[39m\u001b[33mEmployment History Name\u001b[39m\u001b[33m'\u001b[39m]\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/Desktop/codeyou/CodeYouDataProject/venv/lib/python3.12/site-packages/pandas/io/excel/_base.py:495\u001b[39m, in \u001b[36mread_excel\u001b[39m\u001b[34m(io, sheet_name, header, names, index_col, usecols, dtype, engine, converters, true_values, false_values, skiprows, nrows, na_values, keep_default_na, na_filter, verbose, parse_dates, date_parser, date_format, thousands, decimal, comment, skipfooter, storage_options, dtype_backend, engine_kwargs)\u001b[39m\n\u001b[32m 493\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(io, ExcelFile):\n\u001b[32m 494\u001b[39m should_close = \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m495\u001b[39m io = \u001b[43mExcelFile\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 496\u001b[39m \u001b[43m \u001b[49m\u001b[43mio\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 497\u001b[39m \u001b[43m \u001b[49m\u001b[43mstorage_options\u001b[49m\u001b[43m=\u001b[49m\u001b[43mstorage_options\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 498\u001b[39m \u001b[43m \u001b[49m\u001b[43mengine\u001b[49m\u001b[43m=\u001b[49m\u001b[43mengine\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 499\u001b[39m \u001b[43m \u001b[49m\u001b[43mengine_kwargs\u001b[49m\u001b[43m=\u001b[49m\u001b[43mengine_kwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 500\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 501\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m engine \u001b[38;5;129;01mand\u001b[39;00m engine != io.engine:\n\u001b[32m 502\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[32m 503\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mEngine should not be specified when passing \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 504\u001b[39m \u001b[33m\"\u001b[39m\u001b[33man ExcelFile - ExcelFile already has the engine set\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 505\u001b[39m )\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/Desktop/codeyou/CodeYouDataProject/venv/lib/python3.12/site-packages/pandas/io/excel/_base.py:1550\u001b[39m, in \u001b[36mExcelFile.__init__\u001b[39m\u001b[34m(self, path_or_buffer, engine, storage_options, engine_kwargs)\u001b[39m\n\u001b[32m 1548\u001b[39m ext = \u001b[33m\"\u001b[39m\u001b[33mxls\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 1549\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m-> \u001b[39m\u001b[32m1550\u001b[39m ext = \u001b[43minspect_excel_format\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 1551\u001b[39m \u001b[43m \u001b[49m\u001b[43mcontent_or_path\u001b[49m\u001b[43m=\u001b[49m\u001b[43mpath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstorage_options\u001b[49m\u001b[43m=\u001b[49m\u001b[43mstorage_options\u001b[49m\n\u001b[32m 1552\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 1553\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m ext \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m 1554\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[32m 1555\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mExcel file format cannot be determined, you must specify \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 1556\u001b[39m \u001b[33m\"\u001b[39m\u001b[33man engine manually.\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 1557\u001b[39m )\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/Desktop/codeyou/CodeYouDataProject/venv/lib/python3.12/site-packages/pandas/io/excel/_base.py:1402\u001b[39m, in \u001b[36minspect_excel_format\u001b[39m\u001b[34m(content_or_path, storage_options)\u001b[39m\n\u001b[32m 1399\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(content_or_path, \u001b[38;5;28mbytes\u001b[39m):\n\u001b[32m 1400\u001b[39m content_or_path = BytesIO(content_or_path)\n\u001b[32m-> \u001b[39m\u001b[32m1402\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[43mget_handle\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 1403\u001b[39m \u001b[43m \u001b[49m\u001b[43mcontent_or_path\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mrb\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstorage_options\u001b[49m\u001b[43m=\u001b[49m\u001b[43mstorage_options\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mis_text\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\n\u001b[32m 1404\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mas\u001b[39;00m handle:\n\u001b[32m 1405\u001b[39m stream = handle.handle\n\u001b[32m 1406\u001b[39m stream.seek(\u001b[32m0\u001b[39m)\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/Desktop/codeyou/CodeYouDataProject/venv/lib/python3.12/site-packages/pandas/io/common.py:728\u001b[39m, in \u001b[36mget_handle\u001b[39m\u001b[34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[39m\n\u001b[32m 725\u001b[39m codecs.lookup_error(errors)\n\u001b[32m 727\u001b[39m \u001b[38;5;66;03m# open URLs\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m728\u001b[39m ioargs = \u001b[43m_get_filepath_or_buffer\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 729\u001b[39m \u001b[43m \u001b[49m\u001b[43mpath_or_buf\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 730\u001b[39m \u001b[43m \u001b[49m\u001b[43mencoding\u001b[49m\u001b[43m=\u001b[49m\u001b[43mencoding\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 731\u001b[39m \u001b[43m \u001b[49m\u001b[43mcompression\u001b[49m\u001b[43m=\u001b[49m\u001b[43mcompression\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 732\u001b[39m \u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m=\u001b[49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 733\u001b[39m \u001b[43m \u001b[49m\u001b[43mstorage_options\u001b[49m\u001b[43m=\u001b[49m\u001b[43mstorage_options\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 734\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 736\u001b[39m handle = ioargs.filepath_or_buffer\n\u001b[32m 737\u001b[39m handles: \u001b[38;5;28mlist\u001b[39m[BaseBuffer]\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/Desktop/codeyou/CodeYouDataProject/venv/lib/python3.12/site-packages/pandas/io/common.py:472\u001b[39m, in \u001b[36m_get_filepath_or_buffer\u001b[39m\u001b[34m(filepath_or_buffer, encoding, compression, mode, storage_options)\u001b[39m\n\u001b[32m 468\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\n\u001b[32m 469\u001b[39m \u001b[38;5;28mhasattr\u001b[39m(filepath_or_buffer, \u001b[33m\"\u001b[39m\u001b[33mread\u001b[39m\u001b[33m\"\u001b[39m) \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(filepath_or_buffer, \u001b[33m\"\u001b[39m\u001b[33mwrite\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m 470\u001b[39m ):\n\u001b[32m 471\u001b[39m msg = \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mInvalid file path or buffer object type: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mtype\u001b[39m(filepath_or_buffer)\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m\n\u001b[32m--> \u001b[39m\u001b[32m472\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(msg)\n\u001b[32m 474\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m IOArgs(\n\u001b[32m 475\u001b[39m filepath_or_buffer=filepath_or_buffer,\n\u001b[32m 476\u001b[39m encoding=encoding,\n\u001b[32m (...)\u001b[39m\u001b[32m 479\u001b[39m mode=mode,\n\u001b[32m 480\u001b[39m )\n", + "\u001b[31mValueError\u001b[39m: Invalid file path or buffer object type: " + ] + } + ], + "source": [ + "worc = load_and_clean(worc)\n", + "worc\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { +<<<<<<< HEAD + "data": { + "text/html": [ + "
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Auto IdFull NameEmailEnrollmentIdCompany NameJob TitleStart DateProgram: Program NameMailing CityMailing Zip/Postal CodeATP Placement TypeSalaryGenderRaceKY Region
20202410-17818name namename@gmail.comEnrollment-11698CentratelRemote Telephone Service Representative2025-04-21Code Kentucky 24-25Sandy Hook41171First ATP Placement - New to Tech23.42MaleWhiteSOAR
21202306-12312name namename@gmail.comEnrollment-9608Richard Harris, Inc.Logistics Technology & Training Specialist2025-05-03Code Kentucky 24-25Busy41723First ATP Placement - Promotion15.00MaleWhiteSOAR
22202412-19753name namename@gmail.comEnrollment-11586Delta Gas CompanyOffice Manager2025-05-19Code Kentucky 24-25Pikeville41501First ATP Placement - New to Tech18.00FemaleWhiteSOAR
23202406-15679name namename@gmail.comEnrollment-9552BurbioGenerative AI Developer2025-05-21Code Kentucky 24-25Loyall40854First ATP Placement - New to Tech28.84MaleWhiteSOAR
24202412-19675name namename@gmail.comEnrollment-11718Mountain AssociationTechnology Associate2025-06-01Code Kentucky 24-25Brodhead40409First ATP Placement - Already in Tech23.83FemaleWhiteSOAR
\n", + "
" + ], + "text/plain": [ + " Auto Id Full Name Email EnrollmentId \\\n", + "20 202410-17818 name name name@gmail.com Enrollment-11698 \n", + "21 202306-12312 name name name@gmail.com Enrollment-9608 \n", + "22 202412-19753 name name name@gmail.com Enrollment-11586 \n", + "23 202406-15679 name name name@gmail.com Enrollment-9552 \n", + "24 202412-19675 name name name@gmail.com Enrollment-11718 \n", + "\n", + " Company Name Job Title \\\n", + "20 Centratel Remote Telephone Service Representative \n", + "21 Richard Harris, Inc. Logistics Technology & Training Specialist \n", + "22 Delta Gas Company Office Manager \n", + "23 Burbio Generative AI Developer \n", + "24 Mountain Association Technology Associate \n", + "\n", + " Start Date Program: Program Name Mailing City Mailing Zip/Postal Code \\\n", + "20 2025-04-21 Code Kentucky 24-25 Sandy Hook 41171 \n", + "21 2025-05-03 Code Kentucky 24-25 Busy 41723 \n", + "22 2025-05-19 Code Kentucky 24-25 Pikeville 41501 \n", + "23 2025-05-21 Code Kentucky 24-25 Loyall 40854 \n", + "24 2025-06-01 Code Kentucky 24-25 Brodhead 40409 \n", + "\n", + " ATP Placement Type Salary Gender Race KY Region \n", + "20 First ATP Placement - New to Tech 23.42 Male White SOAR \n", + "21 First ATP Placement - Promotion 15.00 Male White SOAR \n", + "22 First ATP Placement - New to Tech 18.00 Female White SOAR \n", + "23 First ATP Placement - New to Tech 28.84 Male White SOAR \n", + "24 First ATP Placement - Already in Tech 23.83 Female White SOAR " + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" +======= + "ename": "NameError", + "evalue": "name 'worc_clean' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mNameError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[3]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[43mworc_clean\u001b[49m.tail()\n", + "\u001b[31mNameError\u001b[39m: name 'worc_clean' is not defined" + ] +>>>>>>> origin/main + } + ], + "source": [ + "worc_clean.tail()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plots\n", + "1. Average Salary by Region, Gender, Race\n", + "\n", + "2. Placement Type Distribution\n", + "\n", + "3. Program Participation Trends\n", + "\n", + "4. Start Dates Timeline\n", + "\n", + "5. Salary Distributions\n", + "\n", + "6. Job Titles or Companies by Region" + ] + }, + { + "cell_type": "code", +<<<<<<< HEAD + "execution_count": 4, +======= + "execution_count": null, +>>>>>>> origin/main + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", +<<<<<<< HEAD + "execution_count": 5, +======= + "execution_count": null, +>>>>>>> origin/main + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Auto Id Full Name Email EnrollmentId \\\n", + "count 25 25 25 25 \n", + "unique 25 1 1 25 \n", + "top 202203-7853 name name name@gmail.com Enrollment-6442 \n", + "freq 1 25 25 1 \n", + "mean NaN NaN NaN NaN \n", + "min NaN NaN NaN NaN \n", + "25% NaN NaN NaN NaN \n", + "50% NaN NaN NaN NaN \n", + "75% NaN NaN NaN NaN \n", + "max NaN NaN NaN NaN \n", + "std NaN NaN NaN NaN \n", + "\n", + " Company Name Job Title \\\n", + "count 25 25 \n", + "unique 25 23 \n", + "top Appalachian Regional Healthcare Customer Service Representative \n", + "freq 1 3 \n", + "mean NaN NaN \n", + "min NaN NaN \n", + "25% NaN NaN \n", + "50% NaN NaN \n", + "75% NaN NaN \n", + "max NaN NaN \n", + "std NaN NaN \n", + "\n", + " Start Date Program: Program Name Mailing City \\\n", + "count 25 25 25 \n", + "unique NaN 3 23 \n", + "top NaN Code Kentucky 23-24 Richmond \n", + "freq NaN 15 2 \n", + "mean 2024-10-05 04:48:00 NaN NaN \n", + "min 2023-10-09 00:00:00 NaN NaN \n", + "25% 2024-06-01 00:00:00 NaN NaN \n", + "50% 2024-09-09 00:00:00 NaN NaN \n", + "75% 2025-03-24 00:00:00 NaN NaN \n", + "max 2025-06-01 00:00:00 NaN NaN \n", + "std NaN NaN NaN \n", + "\n", + " Mailing Zip/Postal Code ATP Placement Type Salary \\\n", + "count 25.000000 25 22.000000 \n", + "unique NaN 3 NaN \n", + "top NaN First ATP Placement - New to Tech NaN \n", + "freq NaN 18 NaN \n", + "mean 41252.200000 NaN 21.221364 \n", + "min 40391.000000 NaN 13.500000 \n", + "25% 40741.000000 NaN 16.500000 \n", + "50% 41179.000000 NaN 21.500000 \n", + "75% 41701.000000 NaN 25.360000 \n", + "max 42633.000000 NaN 32.210000 \n", + "std 723.973008 NaN 5.433627 \n", + "\n", + " Gender Race KY Region \n", + "count 25 25 25 \n", + "unique 2 4 1 \n", + "top Male White SOAR \n", + "freq 13 22 25 \n", + "mean NaN NaN NaN \n", + "min NaN NaN NaN \n", + "25% NaN NaN NaN \n", + "50% NaN NaN NaN \n", + "75% NaN NaN NaN \n", + "max NaN NaN NaN \n", + "std NaN NaN NaN \n", + "ATP Placement Type\n", + "First ATP Placement - New to Tech 18\n", + "First ATP Placement - Promotion 4\n", + "First ATP Placement - Already in Tech 3\n", + "Name: count, dtype: int64\n" + ] + } + ], + "source": [ + "print(worc_clean.describe(include='all'))\n", + "print(worc_clean['ATP Placement Type'].value_counts())\n" + ] + }, + { + "cell_type": "code", +<<<<<<< HEAD + "execution_count": 6, +======= + "execution_count": null, +>>>>>>> origin/main + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Salary by Gender\n", + "\n", + "plt.figure(figsize=(8, 5))\n", + "sns.boxplot(data=worc_clean, x='Gender', y='Salary')\n", + "plt.title(\"Salary Distribution by Gender\")\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", +<<<<<<< HEAD + "execution_count": 7, +======= + "execution_count": null, +>>>>>>> origin/main + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Average Salary by City\n", + "region_salary = worc_clean.groupby('Mailing City')['Salary'].mean().sort_values()\n", + "region_salary.plot(kind='barh', figsize=(8, 5), title=\"Average Salary by KY Region\")\n", + "plt.xlabel(\"Average Salary\")\n", + "plt.show()\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Blanks in Average Salary by KY Region\n", + "The following cities Williamsburg, Somerset, and Annville had blanks in the Salary column " + ] + }, + { + "cell_type": "code", +<<<<<<< HEAD + "execution_count": 8, +======= + "execution_count": null, +>>>>>>> origin/main + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/sw/mf1x4fnn1jg2jq5n72k6mkm80000gn/T/ipykernel_4812/1675383775.py:2: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n", + " worc_clean.set_index('Start Date').resample('M').size().plot(kind='line', marker='o', figsize=(10, 4))\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Placements over time\n", + "worc_clean.set_index('Start Date').resample('M').size().plot(kind='line', marker='o', figsize=(10, 4))\n", + "plt.title(\"Number of Placements Over Time\")\n", + "plt.ylabel(\"Placements\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", +<<<<<<< HEAD + "execution_count": 9, +======= + "execution_count": null, +>>>>>>> origin/main + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Count of placement type by region\n", + "plt.figure(figsize=(10, 6))\n", + "sns.countplot(data=worc_clean, x='ATP Placement Type', hue='Program: Program Name')\n", + "plt.xticks(rotation=45)\n", + "plt.title(\"Placement Type by Program\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", +<<<<<<< HEAD + "execution_count": 10, +======= + "execution_count": null, +>>>>>>> origin/main + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Count of Participants by City\n", + "city_counts = worc_clean['Mailing City'].value_counts().head(10)\n", + "city_counts.plot(kind='bar', title='Top Cities by Participant Count', figsize=(8, 4))\n", + "plt.ylabel(\"Count\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", +<<<<<<< HEAD + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Gender\n", + "Male 13\n", + "Female 12\n", + "Name: count, dtype: int64" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Count of Gender\n", + "worc_clean['Gender'].value_counts()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ATP Placement Type Gender\n", + "First ATP Placement - Already in Tech Female 2\n", + " Male 1\n", + "First ATP Placement - New to Tech Female 9\n", + " Male 9\n", + "First ATP Placement - Promotion Female 1\n", + " Male 3\n", + "dtype: int64\n" + ] + } + ], + "source": [ + " # Count of gender by ATP Placement Type\n", + "grouped = worc_clean.groupby(['ATP Placement Type', 'Gender']).size()\n", + "\n", + "print(grouped)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, +======= + "execution_count": null, +>>>>>>> origin/main + "metadata": {}, + "outputs": [], + "source": [ + "worc_clean.to_excel(\"worc_employment_clean.xlsx\", index=False)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "venv (3.12.2)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}