# Monitors and observes running processes with their command line details.
## Tested against Windows 10 / Python 3.11 / Anaconda
### pip install cmdlineobserver
### updated XML
```xml
This function continuously collects information about running processes, specifically their
command line details, and stores them in a Pandas DataFrame. It can be configured with a
custom breaking condition to stop data collection and save the results to a CSV file.
Parameters:
- breakcondition (callable, optional): A function that takes a DataFrame as input and
returns a boolean indicating whether to stop data collection. If not provided or set
to False, data collection continues indefinitely.
- save_path (str, optional): If specified, the collected data will be saved to a CSV
file at the specified path when the breaking condition is met.
Returns:
- pandas.DataFrame: A DataFrame containing information about running processes, including
columns such as 'CommandLine', 'ProcessId', and more.
Example:
# columns for possible conditions:
# CommandLine,CSName,Description,ExecutablePath,ExecutionState,Handle,HandleCount,InstallDate,KernelModeTime,
# MaximumWorkingSetSize,MinimumWorkingSetSize,Name,OSName,OtherOperationCount,OtherTransferCount,PageFaults,
# PageFileUsage,ParentProcessId,PeakPageFileUsage,PeakVirtualSize,PeakWorkingSetSize,Priority,PrivatePageCount,
# ProcessId,QuotaNonPagedPoolUsage,QuotaPagedPoolUsage,QuotaPeakNonPagedPoolUsage,QuotaPeakPagedPoolUsage,
# ReadOperationCount,ReadTransferCount,SessionId,Status,TerminationDate,ThreadCount,UserModeTime,
# VirtualSize,WindowsVersion,WorkingSetSize,WriteOperationCount,WriteTransferCount,procid
Example:
from cmdlineobserver import observe_cmdline
df = observe_cmdline(
#breakcondition=lambda df: not df.loc[df.CommandLine.str.contains("cmd.exe")].empty,
save_path="c:\\cmdlineobserver.csv",
)
print(df)
Note:
- The DataFrame columns correspond to various process attributes, and you can customize
the conditions to break the observation loop based on specific criteria.
```
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"description": "\r\n# Monitors and observes running processes with their command line details.\r\n\r\n## Tested against Windows 10 / Python 3.11 / Anaconda\r\n\r\n### pip install cmdlineobserver\r\n\r\n\r\n### updated XML\r\n\r\n```xml \r\n\r\nThis function continuously collects information about running processes, specifically their\r\ncommand line details, and stores them in a Pandas DataFrame. It can be configured with a\r\ncustom breaking condition to stop data collection and save the results to a CSV file.\r\n\r\nParameters:\r\n- breakcondition (callable, optional): A function that takes a DataFrame as input and\r\n returns a boolean indicating whether to stop data collection. If not provided or set\r\n to False, data collection continues indefinitely.\r\n- save_path (str, optional): If specified, the collected data will be saved to a CSV\r\n file at the specified path when the breaking condition is met.\r\n\r\nReturns:\r\n- pandas.DataFrame: A DataFrame containing information about running processes, including\r\n columns such as 'CommandLine', 'ProcessId', and more.\r\n\r\nExample:\r\n\t# columns for possible conditions:\r\n\t# CommandLine,CSName,Description,ExecutablePath,ExecutionState,Handle,HandleCount,InstallDate,KernelModeTime,\r\n\t# MaximumWorkingSetSize,MinimumWorkingSetSize,Name,OSName,OtherOperationCount,OtherTransferCount,PageFaults,\r\n\t# PageFileUsage,ParentProcessId,PeakPageFileUsage,PeakVirtualSize,PeakWorkingSetSize,Priority,PrivatePageCount,\r\n\t# ProcessId,QuotaNonPagedPoolUsage,QuotaPagedPoolUsage,QuotaPeakNonPagedPoolUsage,QuotaPeakPagedPoolUsage,\r\n\t# ReadOperationCount,ReadTransferCount,SessionId,Status,TerminationDate,ThreadCount,UserModeTime,\r\n\t# VirtualSize,WindowsVersion,WorkingSetSize,WriteOperationCount,WriteTransferCount,procid\r\n\r\n\tExample:\r\n\t\tfrom cmdlineobserver import observe_cmdline\r\n\t\tdf = observe_cmdline(\r\n\t\t\t#breakcondition=lambda df: not df.loc[df.CommandLine.str.contains(\"cmd.exe\")].empty,\r\n\t\t\tsave_path=\"c:\\\\cmdlineobserver.csv\",\r\n\t\t)\r\n\t\tprint(df)\r\n\r\n\r\nNote:\r\n- The DataFrame columns correspond to various process attributes, and you can customize\r\n the conditions to break the observation loop based on specific criteria.\r\n\r\n\r\n```\r\n",
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