Multi formats input
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180
parseIntuneSettingsCSV.py
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180
parseIntuneSettingsCSV.py
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import os
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import io
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import argparse
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import pandas as pd
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# --- Parsing helpers -------------------------------------------------------
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def _read_csv_flexible(text: str) -> pd.DataFrame:
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"""Try parsing CSV text with common delimiters and forgiving options.
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Returns an empty DataFrame if nothing works.
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"""
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for sep in (';', ','):
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try:
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df = pd.read_csv(io.StringIO(text), sep=sep, dtype=str, engine='python', on_bad_lines='skip')
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if not df.empty:
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return df.fillna('')
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except Exception:
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continue
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return pd.DataFrame()
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def process_csv(file_path: str) -> pd.DataFrame:
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"""Process a single CSV file.
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Supports two formats:
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1) "Older" sectioned exports that contain headers like `# Basic info` and `# Settings`.
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- We split by the `# Settings` marker and parse each block as CSV.
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- Adds a `Profile Name` column using the "Basics" table's Name/Value.
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2) "New" simple CSV exports (typically semicolon-delimited) with two columns (Name;Value).
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- We read the whole file and normalize to columns `Name`, `Value`.
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- Adds a `Profile Name` column derived from the filename.
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"""
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try:
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with open(file_path, 'r', encoding='utf-8', errors='ignore') as f:
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raw_text = f.read()
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except Exception as e:
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print(f"Skipping {file_path}: cannot read file ({e})")
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return pd.DataFrame()
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# Detect sectioned format by presence of the '# Settings' marker
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if '\n# Settings' in raw_text or raw_text.startswith('# Settings'):
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# --- Sectioned format ---
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basic_info_lines = []
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settings_lines = []
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settings_section = False
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for line in raw_text.splitlines(True): # keep line endings
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if line.startswith('# Settings'):
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settings_section = True
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continue
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if settings_section:
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settings_lines.append(line)
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else:
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basic_info_lines.append(line)
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# Parse basics
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basic_df = _read_csv_flexible(''.join(basic_info_lines))
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if basic_df.empty:
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print(f"Warning: could not parse Basics in {os.path.basename(file_path)}")
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profile_name = os.path.splitext(os.path.basename(file_path))[0]
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else:
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# Try to locate Name/Value pair where Name == 'Name' and Category == 'Basics'
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cols = {c.lower().strip(): c for c in basic_df.columns}
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name_col = cols.get('name')
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value_col = cols.get('value')
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category_col = cols.get('category')
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profile_name = os.path.splitext(os.path.basename(file_path))[0]
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try:
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if name_col and value_col:
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basics = basic_df
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if category_col:
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basics = basics[ (basics[name_col] == 'Name') & (basics[category_col] == 'Basics') ]
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else:
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basics = basics[ basics[name_col] == 'Name' ]
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if not basics.empty:
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profile_name = basics.iloc[0][value_col]
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except Exception:
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pass
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# Parse settings
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settings_df = _read_csv_flexible(''.join(settings_lines))
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if settings_df.empty:
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print(f"Warning: could not parse Settings in {os.path.basename(file_path)}")
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return pd.DataFrame()
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settings_df['Profile Name'] = profile_name
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return settings_df.fillna('')
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# --- New simple CSV format ---
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df = _read_csv_flexible(raw_text)
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if df.empty:
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print(f"Warning: empty or unrecognized CSV in {os.path.basename(file_path)}")
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return pd.DataFrame()
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# Normalize probable two-column shape to Name/Value
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norm_map = {c: c.strip().strip('"').lower() for c in df.columns}
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df.columns = [norm_map[c] for c in df.columns]
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name_col = next((c for c in df.columns if c == 'name'), None)
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value_col = next((c for c in df.columns if c == 'value'), None)
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if name_col and value_col:
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tidy = df[[name_col, value_col]].copy()
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tidy.rename(columns={name_col: 'Name', value_col: 'Value'}, inplace=True)
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else:
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# If it doesn't look like the new two-column format, skip gracefully
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return pd.DataFrame()
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tidy['Profile Name'] = os.path.splitext(os.path.basename(file_path))[0]
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return tidy[['Profile Name', 'Name', 'Value']].fillna('')
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# --- CLI & orchestration ---------------------------------------------------
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def consolidate_folder(input_dir: str, output_file: str) -> int:
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files = [
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os.path.join(input_dir, f)
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for f in os.listdir(input_dir)
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if f.lower().endswith('.csv') and os.path.isfile(os.path.join(input_dir, f))
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]
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files.sort()
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frames = []
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for path in files:
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part = process_csv(path)
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if not part.empty:
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# Ensure consistently named columns for consolidation
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if 'Name' in part.columns and 'Value' in part.columns:
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part = part.rename(columns={'Name': 'Setting', 'Value': 'Value'})
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frames.append(part)
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if not frames:
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print("No usable CSVs found to consolidate.")
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return 0
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result = pd.concat(frames, ignore_index=True).fillna('')
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# Light dedupe within the consolidated output (by Profile/Setting/Value if present)
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key_cols = [c for c in ('Profile Name', 'Setting', 'Value') if c in result.columns]
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if key_cols:
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before = len(result)
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result = result.drop_duplicates(subset=key_cols)
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after = len(result)
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print(f"Deduplicated {before - after} rows (by {', '.join(key_cols)}).")
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# Choose writer based on extension
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ext = os.path.splitext(output_file)[1].lower()
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if ext in ('.xlsx', '.xlsm'):
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result.to_excel(output_file, index=False)
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else:
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# default to CSV with LF endings and UTF-8
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result.to_csv(output_file, index=False, encoding='utf-8', line_terminator='\n')
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print(f"Wrote {len(result)} rows from {len(frames)} files to {output_file}")
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return len(result)
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def main():
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parser = argparse.ArgumentParser(
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description=(
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"Consolidate Intune settings from a folder of CSV exports (supports both sectioned "
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"and simple Name/Value formats)."
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)
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)
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parser.add_argument(
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'input_dir', nargs='?', default='.',
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help='Directory containing CSV files to process (default: current directory)'
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)
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parser.add_argument(
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'-o', '--output', default='consolidated_policies.xlsx',
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help='Output file path (CSV or XLSX). Default: consolidated_policies.xlsx'
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)
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args = parser.parse_args()
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consolidate_folder(args.input_dir, args.output)
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if __name__ == '__main__':
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main()
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86
parseIntuneSettingsText.py
Normal file
86
parseIntuneSettingsText.py
Normal file
@@ -0,0 +1,86 @@
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import pandas as pd
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import io
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import csv
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def parse_section(section):
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lines = section.strip().split('\n')
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if len(lines) < 2:
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return pd.DataFrame() # Return empty DataFrame if there's no data
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data_lines = lines[1:]
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data = list(csv.reader(data_lines))
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if len(data) <= 1:
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return pd.DataFrame() # Return empty DataFrame if there's no data
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df = pd.DataFrame(data[1:], columns=data[0])
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return df
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def process_txt(file_path):
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with open(file_path, 'r', encoding='utf-8') as file:
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content = file.read()
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objects = content.split('#########################################################')
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all_settings = []
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for obj in objects:
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if obj.strip() == "":
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continue
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sections = obj.split('# ')
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if len(sections) < 8:
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continue
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obj_name = sections[1].strip().split('\n')[0].replace("Object: ", "").strip()
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print(f"Processing object: {obj_name}")
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try:
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basic_info = sections[3].strip()
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settings = sections[5].strip()
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assignments = sections[7].strip()
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print(f"\nRaw Basic Info for {obj_name}:\n{basic_info}")
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print(f"\nRaw Settings for {obj_name}:\n{settings}")
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print(f"\nRaw Assignments for {obj_name}:\n{assignments}")
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basic_info_df = parse_section(basic_info)
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settings_df = parse_section(settings)
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assignments_df = parse_section(assignments)
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print(f"\nBasic Info DataFrame for {obj_name}:")
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print(basic_info_df)
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print(f"\nSettings DataFrame for {obj_name}:")
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print(settings_df)
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print(f"\nAssignments DataFrame for {obj_name}:")
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print(assignments_df)
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if not settings_df.empty:
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settings_df['Profile Name'] = obj_name
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all_settings.append(settings_df)
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else:
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print(f"Settings DataFrame is empty for {obj_name}")
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except Exception as e:
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print(f"Error processing object {obj_name}: {e}")
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continue
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if all_settings:
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all_settings_df = pd.concat(all_settings, ignore_index=True)
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return all_settings_df
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else:
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print("No settings were processed successfully.")
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return pd.DataFrame()
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# Path to the settings.txt file
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file_path = 'settings.txt'
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# Process the file and consolidate the settings
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consolidated_settings_df = process_txt(file_path)
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if not consolidated_settings_df.empty:
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# Export to Excel
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output_file = 'consolidated_policies_from_txt.xlsx'
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consolidated_settings_df.to_excel(output_file, index=False)
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print(f"Consolidated policies have been exported to {output_file}")
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else:
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print("No data to export.")
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