v0.2.1

.gitignore

@@ -10,6 +10,7 @@ log
 tmp
 venv
 venv.coverage
+*.csv
 *.db
 *.doctrees
 *.env
@@ -20,6 +21,7 @@ venv.coverage
 *.pyd
 *.pyo
 *.swp
+*.txt
 *.egg-info
 _build
 _version.py

.theia/launch.json

@@ -0,0 +1,17 @@
+{
+  // Use IntelliSense to learn about possible attributes.
+  // Hover to view descriptions of existing attributes.
+  "version": "0.2.0",
+  "configurations": [
+  
+    {
+      "name": "Python: Current File",
+      "type": "python",
+      "request": "launch",
+      "program": "${file}",
+      "console": "integratedTerminal",
+      "justMyCode": true
+    }
+  ]
+}
+

CHANGELOG.txt

@@ -1 +1,4 @@
+v0.2.1         Script to extract useful fields from CSV.
+v0.2.0         Functions for finding useful fields in CSV.
+v0.1.0         Setup scripts.
 v0.0.1         Hubspot Parse.

README.md

@@ -1,2 +1,33 @@
 # Hubspot Parse
 Scripts for parsing Hubspot data with a goal towards migrations.
+
+# Install
+Thusly.
+
+```
+git clone https://code.libre.is/libre/hsparse
+cd hsparse/
+python -m venv venv
+source venv/bin/activate
+pip install poetry
+poetry install
+```
+
+# Usage
+```
+$ hsparse-csv-contacts -h
+usage: hsparse-csv-contacts [-h] [-d] [-e] [-f] [-n] csv_file
+
+Parse Hubspot Contacts CSV Export
+
+positional arguments:
+  csv_file         Contacts CSV File
+
+options:
+  -h, --help       show this help message and exit
+  -d, --dump       Dump CSV contents
+  -e, --empty      List empty columns
+  -f, --fields     Fields from CSV header
+  -n, --non_empty  List number of non-empty values for each column
+```
+

hsparse/extract_columns_to_csv.py

@@ -0,0 +1,71 @@
+# MIT License
+# Copyright (c) 2024 Jeff Moe
+""" Read CSV and extract selected columns and write to new CVS"""
+
+import csv
+import argparse
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description="Extract CSV Columns, Output CSV")
+
+    parser.add_argument("headers_file", help="Headers File", type=str)
+    parser.add_argument("input_csv", help="Input CSV File", type=str)
+    parser.add_argument("output_csv", help="Output CSV File", type=str)
+
+    args = parser.parse_args()
+    return args
+
+
+def read_good_headers(filename):
+    """Reads and returns the list of 'good' headers from a given file."""
+    with open(filename, "r") as file:
+        return [line.strip() for line in file.readlines()]
+
+
+def filter_csv(input_csv, output_csv, good_headers):
+    """Filters an input CSV based on the provided good headers and writes to output CSV."""
+
+    # Read the original CSV
+    with open(input_csv, mode="r", newline="", encoding="utf-8") as infile:
+        reader = csv.DictReader(infile)
+
+        # Get only the required fieldnames ('good' headers)
+        filtered_fieldnames = [
+            field for field in reader.fieldnames if field in good_headers
+        ]
+
+        # Write to output CSV
+        with open(output_csv, mode="w", newline="", encoding="utf-8") as outfile:
+            writer = csv.DictWriter(outfile, fieldnames=filtered_fieldnames)
+
+            # Write the header line (column names) first
+            writer.writeheader()
+
+            for row in reader:
+                filtered_row = {
+                    key: value
+                    for key, value in row.items()
+                    if key in filtered_fieldnames
+                }
+                writer.writerow(filtered_row)
+
+
+def main():
+    args = parse_args()
+
+    headers_file = args.headers_file
+    input_csv = args.input_csv
+    output_csv = args.output_csv
+
+    # Step 1: Read the list of good headers
+    good_headers = read_good_headers(headers_file)
+
+    # Step 2: Filter the CSV based on these headers and write to a new file
+    filter_csv(input_csv, output_csv, good_headers)
+
+    print(f"Filtered CSV has been written to {output_csv}")
+
+
+if __name__ == "__main__":
+    main()

hsparse/parse_csv_contacts.py

@@ -1,14 +1,110 @@
-#!/usr/bin/env python3
+# MIT License
-''' Read CSV contacts file exported from hubspot.'''
+# Copyright (c) 2024 Jeff Moe
 
+""" Read CSV contacts file exported from hubspot."""
+
+import argparse
 import csv
+import pandas as pd
 
-CSV="all-contacts.csv"
 
-print("Parsing" + CSV)
+def parse_args():
+    parser = argparse.ArgumentParser(description="Parse Hubspot Contacts CSV Export")
 
-with open(CSV, newline='') as csvfile:
+    parser.add_argument("csv_file", help="Contacts CSV File", type=str)
-    contactreader = csv.reader(csvfile, delimiter=',', quotechar='"')
-    for row in contactreader:
-        print(', '.join(row))
 
+    parser.add_argument(
+        "-d",
+        "--dump",
+        help="Dump CSV contents",
+        action="store_true",
+    )
+
+    parser.add_argument(
+        "-e",
+        "--empty",
+        help="List empty columns",
+        action="store_true",
+    )
+
+    parser.add_argument(
+        "-f",
+        "--fields",
+        help="Fields from CSV header",
+        action="store_true",
+    )
+
+    parser.add_argument(
+        "-n",
+        "--non_empty",
+        help="List number of non-empty values for each column",
+        action="store_true",
+    )
+
+    args = parser.parse_args()
+    return args
+
+
+def csv_dump(CSV):
+    df = pd.read_csv(CSV, low_memory=False, chunksize=1000)
+    for chunk in df:
+        print(chunk.to_string())
+
+
+def csv_empty(CSV):
+    df = pd.read_csv(CSV, low_memory=False, header=0)
+    empty_columns = [col for col in df.columns if df[col].isnull().all()]
+    if empty_columns:
+        print("Empty columns:")
+        print("\n".join(empty_columns))
+    else:
+        print("No empty columns found.")
+
+
+def csv_fields(CSV):
+    df = pd.read_csv(CSV, low_memory=False, header=0)
+    print("\n".join([col for col in df.columns]))
+
+
+def csv_non_empty(CSV):
+    df = pd.read_csv(CSV, low_memory=False, header=0)
+    non_empty_columns = {
+        col: df[col].count() for col in df.columns if not df[col].isnull().all()
+    }
+
+    unique_counts = {col: df[col].nunique() for col in non_empty_columns.keys()}
+    sorted_columns = sorted(
+        unique_counts.items(),
+        key=lambda x: (unique_counts[x[0]], non_empty_columns[x[0]]),
+        reverse=True,
+    )
+
+    print("Column\tNon-empty values\tUnique values")
+
+    if sorted_columns:
+        for col, unique_count in sorted_columns:
+            count = non_empty_columns[col]
+            print(f"{col}\t{count}\t{unique_count}")
+    else:
+        print("No non-empty values found.")
+
+
+def main():
+    args = parse_args()
+    CSV = args.csv_file
+
+    if args.dump:
+        csv_dump(CSV)
+
+    if args.empty:
+        csv_empty(CSV)
+
+    if args.fields:
+        csv_fields(CSV)
+
+    if args.non_empty:
+        csv_non_empty(CSV)
+
+
+if __name__ == "__main__":
+    main()

pyproject.toml

@@ -22,20 +22,20 @@ packages = [
     { include = "hsparse" },
 ]
 readme = "README.md"
-version = "0.0.1"
+version = "0.2.1"
-
 
 [tool.poetry.dependencies]
 python = "^3.10"
 setuptools_scm = "*"
-
+pandas = "^2.2.2"
 
 [build-system]
 requires = ["poetry-core", "setuptools_scm"]
 build-backend = "poetry.core.masonry.api"
 
 [tool.poetry.scripts]
-hsparse = "hsparse.main:parse_csv_contacts"
+hsparse-csv-contacts = "hsparse.parse_csv_contacts:main"
+hsparse-extract-columns = "hsparse.extract_columns_to_csv:main"
 
 [tool.poetry.urls]
 homepage = "https://libre.is/libre/hsparse"