Python Script for Data Cleaning & Analysis

This script is designed to clean & analyze data from multiple files in a directory. It performs various operations such as removing newline & tab characters, checking for null values & more.

Dependencies

The script requires the following Python libraries:

• json
• os
• pandas
• tqdm

Directory Structure

The script expects a directory named 'parsed_files' on the Desktop of a user named 'narayansajeev' on a machine running macOS. The path to this directory is /Users/narayansajeev/Desktop/MIT/parsed_files.

Functions

The script contains several functions that perform specific tasks:

• loop_fnames(prov): Loops through the files in the directory & returns a list of files that have been parsed.
• get_df(prov, fname): Reads in the first file from the list using pandas.
• get_known_cols(): Checks column classifier.
• clean(col_headers): Cleans up the headers by removing newline, carriage return, non-breaking space & space characters.
• substr_check(substr_sets, k): Checks for substrings in the column headers.
• substring(df, known_cols): Lists column headers.
• drop_columns(df, col_headers): Drops specified columns from the dataframe.
• newline(df, prov), tab(df, prov), adltrnt_msrmnt(df, prov), adltrnt_none(df, prov), headers(df, prov), none(df, prov), test_legal_none(df, prov) & test_concat_legal_none(df, prov): These functions check for specific conditions in the dataframe & update global dictionaries accordingly.

Execution

The script loops through all files in the specified directory. For each file, it performs a series of operations to clean & analyze the data. The results are stored in global dictionaries.

Output

At the end of execution, the script prints out the results stored in the global dictionaries. This includes information about rows affected by certain conditions, files containing these rows & sources of these files.