{ "cells": [ { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "import os\n", "import pandas as pd\n", "import glob\n", "import re" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [], "source": [ "def combine_excel_files(directory_path, output_csv='combined_mcas_data.csv', output_parquet='combined_mcas_data.parquet'):\n", " \"\"\"\n", " Combine all Excel files in a directory into a single dataset with proper headers.\n", " Converts to both CSV and Parquet formats.\n", " Handles data types: numbers for most fields, strings for District name, subject, district code and year.\n", " \n", " Parameters:\n", " directory_path (str): Path to the directory containing Excel files\n", " output_csv (str): Name of the output CSV file\n", " output_parquet (str): Name of the output Parquet file\n", " \"\"\"\n", " # Get list of all Excel files in the directory\n", " all_files = glob.glob(os.path.join(directory_path, \"*.xlsx\"))\n", " \n", " # Create an empty list to store DataFrames\n", " dfs = []\n", " all_columns_sets = []\n", " \n", " # First pass - examine structure of the first file to identify headers\n", " if all_files:\n", " # Read the first few rows of the first file to inspect\n", " sample_df = pd.read_excel(all_files[0], header=None, nrows=10)\n", " print(f\"Preview of first file ({all_files[0]}):\")\n", " print(sample_df.head(10))\n", " \n", " # Find the actual header row by looking for rows where many columns have values\n", " non_empty_counts = sample_df.notna().sum(axis=1)\n", " potential_header_rows = non_empty_counts[non_empty_counts > 5].index.tolist()\n", " \n", " if potential_header_rows:\n", " header_row = potential_header_rows[1] if len(potential_header_rows) > 1 else potential_header_rows[0]\n", " # Use row index where we detect MCAS data pattern\n", " for i in potential_header_rows:\n", " if 'DISTRICT NAME' in str(sample_df.iloc[i].values).upper() or 'SCHOOL NAME' in str(sample_df.iloc[i].values).upper():\n", " header_row = i\n", " break\n", " print(f\"Detected header row at index {header_row}: {sample_df.iloc[header_row].tolist()}\")\n", " else:\n", " header_row = 0\n", " print(\"Could not detect header row, using first row\")\n", " \n", " # Get the first file's column order as a reference\n", " first_file_columns = None\n", " \n", " # Loop through each Excel file\n", " for file in all_files:\n", " try:\n", " # Extract year from filename\n", " year_match = re.search(r'NextGenMCAS_(\\d{4})\\.xlsx', os.path.basename(file))\n", " if year_match:\n", " year = year_match.group(1)\n", " else:\n", " year = \"Unknown\"\n", " \n", " # Define columns that should be treated as strings\n", " string_cols = []\n", " \n", " # First check what columns exist in the file\n", " temp_df = pd.read_excel(file, header=header_row, nrows=0)\n", " for col in temp_df.columns:\n", " col_str = str(col).upper()\n", " if ('DISTRICT' in col_str and ('CODE' in col_str or 'ID' in col_str or 'NUMBER' in col_str)) or \\\n", " ('DISTRICT NAME' in col_str) or ('SUBJECT' in col_str):\n", " string_cols.append(col)\n", " \n", " # Read the Excel file\n", " df = pd.read_excel(\n", " file, \n", " header=header_row, \n", " dtype={col: str for col in string_cols} # Convert specified columns to string\n", " )\n", " \n", " # Clean column names\n", " df.columns = [col.strip().replace('\\n', ' ') if isinstance(col, str) else str(col) for col in df.columns]\n", " \n", " # Store the first file's column order\n", " if first_file_columns is None:\n", " first_file_columns = df.columns.tolist()\n", " \n", " # Add year column as string\n", " df['Year'] = str(year)\n", " \n", " # Store the columns from this file\n", " all_columns_sets.append(set(df.columns))\n", " \n", " # Append to list\n", " dfs.append(df)\n", " print(f\"Successfully processed: {file} (Year: {year})\")\n", " except Exception as e:\n", " print(f\"Error processing {file}: {e}\")\n", " \n", " # Find common columns across all files (intersection of all column sets)\n", " if all_columns_sets:\n", " common_columns = set.intersection(*all_columns_sets)\n", " print(f\"Common columns across all files: {common_columns}\")\n", " \n", " # Ensure 'Year' is in common columns\n", " if 'Year' not in common_columns:\n", " common_columns.add('Year')\n", " \n", " # Keep only common columns in each DataFrame\n", " for i in range(len(dfs)):\n", " dfs[i] = dfs[i][list(common_columns)]\n", " \n", " # Combine all DataFrames\n", " if dfs:\n", " # Combine DataFrames with only the common columns\n", " combined_df = pd.concat(dfs, ignore_index=True)\n", " \n", " # Remove rows that are likely headers from other files\n", " possible_header_rows = []\n", " for col in common_columns:\n", " if col != 'Year': # Skip checking Year column\n", " for i, row in combined_df.iterrows():\n", " for val in row:\n", " if isinstance(val, str) and col.lower() in str(val).lower():\n", " possible_header_rows.append(i)\n", " \n", " # Remove duplicate header rows\n", " possible_header_rows = list(set(possible_header_rows))\n", " print(f\"Removing {len(possible_header_rows)} possible header rows\")\n", " combined_df = combined_df.drop(possible_header_rows, errors='ignore')\n", " \n", " # Identify string columns and numeric columns\n", " string_columns = []\n", " district_col = None\n", " district_code_col = None\n", " subject_col = None\n", " \n", " for col in combined_df.columns:\n", " col_upper = col.upper()\n", " if 'DISTRICT NAME' in col_upper:\n", " district_col = col\n", " string_columns.append(col)\n", " elif ('DISTRICT' in col_upper and ('CODE' in col_upper or 'ID' in col_upper or 'NUMBER' in col_upper)):\n", " district_code_col = col\n", " string_columns.append(col)\n", " elif 'SUBJECT' in col_upper:\n", " subject_col = col\n", " string_columns.append(col)\n", " \n", " # Add Year to string columns\n", " string_columns.append('Year')\n", " \n", " # Convert all other columns to numeric\n", " for col in combined_df.columns:\n", " if col not in string_columns:\n", " # Try to convert to numeric, replace errors with NaN\n", " combined_df[col] = pd.to_numeric(combined_df[col], errors='coerce')\n", " \n", " # Reorder columns to put DISTRICT NAME and Year first\n", " if district_col:\n", " # Create new column order with district_col and Year first, then others in original order\n", " remaining_cols = [col for col in combined_df.columns \n", " if col != district_col and col != 'Year']\n", " \n", " # Sort remaining columns based on their order in the first file\n", " if first_file_columns:\n", " # Get the indices of each column in the original order\n", " col_indices = {}\n", " for i, col in enumerate(first_file_columns):\n", " if col in remaining_cols:\n", " col_indices[col] = i\n", " \n", " # Sort remaining columns based on their original indices\n", " remaining_cols.sort(key=lambda col: col_indices.get(col, 999))\n", " \n", " new_column_order = [district_col, 'Year'] + remaining_cols\n", " combined_df = combined_df[new_column_order]\n", " \n", " # Export to CSV\n", " csv_path = os.path.join(directory_path, output_csv)\n", " combined_df.to_csv(csv_path, index=False)\n", " print(f\"Successfully created CSV: {csv_path}\")\n", " \n", " # Export to Parquet\n", " parquet_path = os.path.join(directory_path, output_parquet)\n", " combined_df.to_parquet(parquet_path, index=False)\n", " print(f\"Successfully created Parquet: {parquet_path}\")\n", " \n", " print(f\"Combined {len(dfs)} Excel files with {len(combined_df)} total rows\")\n", " print(f\"Final columns: {', '.join(combined_df.columns)}\")\n", " \n", " # Print data types for verification\n", " print(\"\\nData types in final dataset:\")\n", " for col, dtype in combined_df.dtypes.items():\n", " print(f\"{col}: {dtype}\")\n", " else:\n", " print(\"No Excel files were successfully processed\")" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Preview of first file (/fsx/avijit/projects/datacommonsMA/mcas_results/NextGenMCAS_2018.xlsx):\n", " 0 1 2 \\\n", "0 2018 Next Generation MCAS Achievement Results NaN NaN \n", "1 District Name District Code Subject \n", "2 Abby Kelley Foster Charter Public (District) 04450000 ELA \n", "3 Abby Kelley Foster Charter Public (District) 04450000 MATH \n", "4 Abington 00010000 ELA \n", "5 Abington 00010000 MATH \n", "6 Academy Of the Pacific Rim Charter Public (Dis... 04120000 ELA \n", "7 Academy Of the Pacific Rim Charter Public (Dis... 04120000 MATH \n", "8 Acton-Boxborough 06000000 ELA \n", "9 Acton-Boxborough 06000000 MATH \n", "\n", " 3 4 5 6 7 8 9 \\\n", "0 NaN NaN NaN NaN NaN NaN NaN \n", "1 M+E # M+E % E # E % M # M % PM # \n", "2 313 44 31 4 282 39 347 \n", "3 266 37 20 3 246 34 390 \n", "4 554 55 90 9 464 46 374 \n", "5 482 48 46 5 436 44 440 \n", "6 138 44 15 5 123 39 151 \n", "7 103 33 10 3 93 30 175 \n", "8 1,908 73 522 20 1,386 53 622 \n", "9 1,882 72 574 22 1,308 50 619 \n", "\n", " 10 11 12 13 14 \\\n", "0 NaN NaN NaN NaN NaN \n", "1 PM % NM # NM % No. of Students Included Avg. Scaled Score \n", "2 48 56 8 716 497.3 \n", "3 54 61 9 717 494.6 \n", "4 37 75 7 1,003 502.3 \n", "5 44 79 8 1,001 498.7 \n", "6 48 26 8 315 497.3 \n", "7 56 37 12 315 492.3 \n", "8 24 92 4 2,622 513.3 \n", "9 24 118 5 2,619 512.7 \n", "\n", " 15 16 \n", "0 NaN NaN \n", "1 SGP Included In SGP \n", "2 48.7 586 \n", "3 52.9 587 \n", "4 50.2 784 \n", "5 47.3 785 \n", "6 57.7 289 \n", "7 52.9 289 \n", "8 58.9 2,069 \n", "9 58.8 2,073 \n", "Detected header row at index 1: ['District Name', 'District Code', 'Subject', 'M+E #', 'M+E %', 'E #', 'E %', 'M #', 'M %', 'PM #', 'PM %', 'NM #', 'NM %', 'No. of Students Included', 'Avg. Scaled Score', 'SGP', 'Included In SGP']\n", "Successfully processed: /fsx/avijit/projects/datacommonsMA/mcas_results/NextGenMCAS_2018.xlsx (Year: 2018)\n", "Successfully processed: /fsx/avijit/projects/datacommonsMA/mcas_results/NextGenMCAS_2017.xlsx (Year: 2017)\n", "Successfully processed: /fsx/avijit/projects/datacommonsMA/mcas_results/NextGenMCAS_2020.xlsx (Year: 2020)\n", "Successfully processed: /fsx/avijit/projects/datacommonsMA/mcas_results/NextGenMCAS_2024.xlsx (Year: 2024)\n", "Successfully processed: /fsx/avijit/projects/datacommonsMA/mcas_results/NextGenMCAS_2021.xlsx (Year: 2021)\n", "Successfully processed: /fsx/avijit/projects/datacommonsMA/mcas_results/NextGenMCAS_2022.xlsx (Year: 2022)\n", "Successfully processed: /fsx/avijit/projects/datacommonsMA/mcas_results/NextGenMCAS_2023.xlsx (Year: 2023)\n", "Successfully processed: /fsx/avijit/projects/datacommonsMA/mcas_results/NextGenMCAS_2019.xlsx (Year: 2019)\n", "Common columns across all files: {'E %', 'M+E #', 'PM #', 'SGP', 'No. of Students Included', 'NM #', 'District Code', 'Avg. Scaled Score', 'M #', 'Year', 'Included In SGP', 'PM %', 'E #', 'NM %', 'M+E %', 'Subject', 'M %', 'District Name'}\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2144425/3133257492.py:108: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", " combined_df = pd.concat(dfs, ignore_index=True)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Removing 0 possible header rows\n", "Successfully created CSV: /fsx/avijit/projects/datacommonsMA/mcas_results/combined_mcas_data.csv\n", "Successfully created Parquet: /fsx/avijit/projects/datacommonsMA/mcas_results/combined_mcas_data.parquet\n", "Combined 8 Excel files with 6741 total rows\n", "Final columns: District Name, Year, District Code, Subject, M+E #, M+E %, E #, E %, M #, M %, PM #, PM %, NM #, NM %, No. of Students Included, Avg. Scaled Score, SGP, Included In SGP\n", "\n", "Data types in final dataset:\n", "District Name: object\n", "Year: object\n", "District Code: object\n", "Subject: object\n", "M+E #: float64\n", "M+E %: int64\n", "E #: float64\n", "E %: int64\n", "M #: float64\n", "M %: int64\n", "PM #: float64\n", "PM %: int64\n", "NM #: float64\n", "NM %: int64\n", "No. of Students Included: float64\n", "Avg. Scaled Score: float64\n", "SGP: float64\n", "Included In SGP: float64\n" ] } ], "source": [ "data_folder = \"/fsx/avijit/projects/datacommonsMA/mcas_results\"\n", "combine_excel_files(data_folder)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "py312", "language": "python", "name": "py312" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.9" } }, "nbformat": 4, "nbformat_minor": 2 }