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+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Append UUID to SHA256 CSV"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%reload_ext autoreload\n",
+ "%autoreload 2\n",
+ "\n",
+ "import os\n",
+ "from os.path import join\n",
+ "import math\n",
+ "from glob import glob\n",
+ "from random import randint\n",
+ "from pathlib import Path\n",
+ "\n",
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "%matplotlib inline\n",
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "import sys\n",
+ "sys.path.append('/work/megapixels_dev/megapixels/')\n",
+ "from app.utils import file_utils"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 30,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "DATA_STORE = '/data_store_ssd/'\n",
+ "dir_dataset = join(DATA_STORE, 'apps/megapixels/datasets/lfw')\n",
+ "fp_shas = join(dir_dataset, 'records.csv')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>ext</th>\n",
+ " <th>fn</th>\n",
+ " <th>sha256</th>\n",
+ " <th>subdir</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>jpg</td>\n",
+ " <td>AJ_Cook_0001</td>\n",
+ " <td>550937b71b9af36b6083fa1ce7c76e97e3254c439614a6...</td>\n",
+ " <td>AJ_Cook</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>jpg</td>\n",
+ " <td>AJ_Lamas_0001</td>\n",
+ " <td>46d7ddeec9b00add61ade2f89277d74e8264a2b6cec193...</td>\n",
+ " <td>AJ_Lamas</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>jpg</td>\n",
+ " <td>Aaron_Eckhart_0001</td>\n",
+ " <td>b68ed8d50ba85209d826b962987077bc8e1826f7f2f325...</td>\n",
+ " <td>Aaron_Eckhart</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>jpg</td>\n",
+ " <td>Aaron_Guiel_0001</td>\n",
+ " <td>156f5428fad30c420ef01d9b0a3ab73e98aa6a1e5a2f0b...</td>\n",
+ " <td>Aaron_Guiel</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>jpg</td>\n",
+ " <td>Aaron_Patterson_0001</td>\n",
+ " <td>34dfe798220b53aac910e5e39705770d212cdfbe4be8a4...</td>\n",
+ " <td>Aaron_Patterson</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " ext fn \\\n",
+ "0 jpg AJ_Cook_0001 \n",
+ "1 jpg AJ_Lamas_0001 \n",
+ "2 jpg Aaron_Eckhart_0001 \n",
+ "3 jpg Aaron_Guiel_0001 \n",
+ "4 jpg Aaron_Patterson_0001 \n",
+ "\n",
+ " sha256 subdir \n",
+ "0 550937b71b9af36b6083fa1ce7c76e97e3254c439614a6... AJ_Cook \n",
+ "1 46d7ddeec9b00add61ade2f89277d74e8264a2b6cec193... AJ_Lamas \n",
+ "2 b68ed8d50ba85209d826b962987077bc8e1826f7f2f325... Aaron_Eckhart \n",
+ "3 156f5428fad30c420ef01d9b0a3ab73e98aa6a1e5a2f0b... Aaron_Guiel \n",
+ "4 34dfe798220b53aac910e5e39705770d212cdfbe4be8a4... Aaron_Patterson "
+ ]
+ },
+ "execution_count": 18,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# load names\n",
+ "df_records = pd.read_csv(fp_shas)\n",
+ "records = df_records.to_dict('index')\n",
+ "df_records.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import uuid\n",
+ "import base64\n",
+ "\n",
+ "# get a UUID - URL safe, Base64\n",
+ "def b64uuid():\n",
+ " r_uuid = base64.urlsafe_b64encode(uuid.uuid4().bytes)\n",
+ " print(r_uuid)\n",
+ " return r_uuid.replace('=', '')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "UUID('05ba06b3-875e-429a-ac39-02b129b77d71')"
+ ]
+ },
+ "execution_count": 25,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "uuid.uuid4()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 32,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# append a UUID to every entry\n",
+ "for idx, item in records.items():\n",
+ " records[idx]['uuid'] = uuid.uuid4()\n",
+ "# save to csv\n",
+ "fp_sha_uuid = join(dir_dataset, 'records_uuid.csv')\n",
+ "df_uuid = pd.DataFrame.from_dict(list(records.values())) # ignore the indices\n",
+ "df_uuid.to_csv(fp_sha_uuid, index=False)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 170,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import time\n",
+ "from tqdm import tqdm"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 171,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "fp = '/data_store_ssd/apps/megapixels/datasets/lfw/embeddings_arr_test.csv'"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 172,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df = pd.read_csv(fp)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 180,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "['a', 'a', 'a', 'a', 'a']"
+ ]
+ },
+ "execution_count": 180,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "['a'] * 5"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 184,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>ext</th>\n",
+ " <th>fn</th>\n",
+ " <th>h</th>\n",
+ " <th>image_height</th>\n",
+ " <th>image_width</th>\n",
+ " <th>subdir</th>\n",
+ " <th>vec</th>\n",
+ " <th>w</th>\n",
+ " <th>x</th>\n",
+ " <th>y</th>\n",
+ " <th>newcol</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>jpg</td>\n",
+ " <td>AJ_Cook_0001</td>\n",
+ " <td>0.330000</td>\n",
+ " <td>250</td>\n",
+ " <td>250</td>\n",
+ " <td>AJ_Cook</td>\n",
+ " <td>-0.07324773073196411, 0.150736004114151, 0.006...</td>\n",
+ " <td>0.330000</td>\n",
+ " <td>0.336667</td>\n",
+ " <td>0.350000</td>\n",
+ " <td>10</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>jpg</td>\n",
+ " <td>AJ_Lamas_0001</td>\n",
+ " <td>0.393333</td>\n",
+ " <td>250</td>\n",
+ " <td>250</td>\n",
+ " <td>AJ_Lamas</td>\n",
+ " <td>-0.12234891951084137, 0.06931854784488678, 0.0...</td>\n",
+ " <td>0.393333</td>\n",
+ " <td>0.286667</td>\n",
+ " <td>0.313333</td>\n",
+ " <td></td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " ext fn h image_height image_width subdir \\\n",
+ "0 jpg AJ_Cook_0001 0.330000 250 250 AJ_Cook \n",
+ "1 jpg AJ_Lamas_0001 0.393333 250 250 AJ_Lamas \n",
+ "\n",
+ " vec w x \\\n",
+ "0 -0.07324773073196411, 0.150736004114151, 0.006... 0.330000 0.336667 \n",
+ "1 -0.12234891951084137, 0.06931854784488678, 0.0... 0.393333 0.286667 \n",
+ "\n",
+ " y newcol \n",
+ "0 0.350000 10 \n",
+ "1 0.313333 "
+ ]
+ },
+ "execution_count": 184,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 185,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "<class 'list'>\n",
+ "128\n"
+ ]
+ }
+ ],
+ "source": [
+ "for idx, row in df.iterrows():\n",
+ " vec = row['vec'].split(',')\n",
+ " print(type(vec))\n",
+ " print(len(vec))\n",
+ " break"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 146,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "fp_out = '/data_store_ssd/apps/megapixels/datasets/lfw/embeddings_arr_test_idx.csv'\n",
+ "df.to_csv(fp_out)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 188,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "fp_in = '/data_store_ssd/apps/megapixels/datasets/lfw/records.csv'\n",
+ "fp_out = '/data_store_ssd/apps/megapixels/datasets/lfw/records_idx.csv'\n",
+ "df = pd.read_csv(fp_in)\n",
+ "df.to_csv(fp_out, index=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 192,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df.loc[(df['fn'] == 'AJ_Cook_0001') & (df['subdir'] == 'AJ_Cook'), 'ext'] = 'wow'"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 208,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>ext</th>\n",
+ " <th>fn</th>\n",
+ " <th>sha256</th>\n",
+ " <th>subdir</th>\n",
+ " <th>uuid</th>\n",
+ " <th>newcol</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>wow</td>\n",
+ " <td>AJ_Cook_0001</td>\n",
+ " <td>550937b71b9af36b6083fa1ce7c76e97e3254c439614a6...</td>\n",
+ " <td>AJ_Cook</td>\n",
+ " <td>f03fd921-2d56-4e83-8115-f658d6a72287</td>\n",
+ " <td>10</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>jpg</td>\n",
+ " <td>AJ_Lamas_0001</td>\n",
+ " <td>46d7ddeec9b00add61ade2f89277d74e8264a2b6cec193...</td>\n",
+ " <td>AJ_Lamas</td>\n",
+ " <td>0c96c5bb-dbd1-4584-bd68-af11664b98bb</td>\n",
+ " <td>x</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>jpg</td>\n",
+ " <td>Aaron_Eckhart_0001</td>\n",
+ " <td>b68ed8d50ba85209d826b962987077bc8e1826f7f2f325...</td>\n",
+ " <td>Aaron_Eckhart</td>\n",
+ " <td>8221e75c-9537-4a4f-9693-483b445244b4</td>\n",
+ " <td>x</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>jpg</td>\n",
+ " <td>Aaron_Guiel_0001</td>\n",
+ " <td>156f5428fad30c420ef01d9b0a3ab73e98aa6a1e5a2f0b...</td>\n",
+ " <td>Aaron_Guiel</td>\n",
+ " <td>a2955610-ed5e-433c-bdd4-e3a72ff44736</td>\n",
+ " <td>x</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>jpg</td>\n",
+ " <td>Aaron_Patterson_0001</td>\n",
+ " <td>34dfe798220b53aac910e5e39705770d212cdfbe4be8a4...</td>\n",
+ " <td>Aaron_Patterson</td>\n",
+ " <td>1d0782e9-ed16-4550-b1e9-d9c03eef6181</td>\n",
+ " <td>x</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " ext fn \\\n",
+ "0 wow AJ_Cook_0001 \n",
+ "1 jpg AJ_Lamas_0001 \n",
+ "2 jpg Aaron_Eckhart_0001 \n",
+ "3 jpg Aaron_Guiel_0001 \n",
+ "4 jpg Aaron_Patterson_0001 \n",
+ "\n",
+ " sha256 subdir \\\n",
+ "0 550937b71b9af36b6083fa1ce7c76e97e3254c439614a6... AJ_Cook \n",
+ "1 46d7ddeec9b00add61ade2f89277d74e8264a2b6cec193... AJ_Lamas \n",
+ "2 b68ed8d50ba85209d826b962987077bc8e1826f7f2f325... Aaron_Eckhart \n",
+ "3 156f5428fad30c420ef01d9b0a3ab73e98aa6a1e5a2f0b... Aaron_Guiel \n",
+ "4 34dfe798220b53aac910e5e39705770d212cdfbe4be8a4... Aaron_Patterson \n",
+ "\n",
+ " uuid newcol \n",
+ "0 f03fd921-2d56-4e83-8115-f658d6a72287 10 \n",
+ "1 0c96c5bb-dbd1-4584-bd68-af11664b98bb x \n",
+ "2 8221e75c-9537-4a4f-9693-483b445244b4 x \n",
+ "3 a2955610-ed5e-433c-bdd4-e3a72ff44736 x \n",
+ "4 1d0782e9-ed16-4550-b1e9-d9c03eef6181 x "
+ ]
+ },
+ "execution_count": 208,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df['newcol'] = ['x'] * len(df)\n",
+ "df.at[0, 'newcol'] = '10'\n",
+ "df.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 214,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
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+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>ext</th>\n",
+ " <th>fn</th>\n",
+ " <th>sha256</th>\n",
+ " <th>subdir</th>\n",
+ " <th>uuid</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>wow</td>\n",
+ " <td>AJ_Cook_0001</td>\n",
+ " <td>550937b71b9af36b6083fa1ce7c76e97e3254c439614a6...</td>\n",
+ " <td>AJ_Cook</td>\n",
+ " <td>f03fd921-2d56-4e83-8115-f658d6a72287</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>jpg</td>\n",
+ " <td>AJ_Lamas_0001</td>\n",
+ " <td>46d7ddeec9b00add61ade2f89277d74e8264a2b6cec193...</td>\n",
+ " <td>AJ_Lamas</td>\n",
+ " <td>0c96c5bb-dbd1-4584-bd68-af11664b98bb</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>jpg</td>\n",
+ " <td>Aaron_Eckhart_0001</td>\n",
+ " <td>b68ed8d50ba85209d826b962987077bc8e1826f7f2f325...</td>\n",
+ " <td>Aaron_Eckhart</td>\n",
+ " <td>8221e75c-9537-4a4f-9693-483b445244b4</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>jpg</td>\n",
+ " <td>Aaron_Guiel_0001</td>\n",
+ " <td>156f5428fad30c420ef01d9b0a3ab73e98aa6a1e5a2f0b...</td>\n",
+ " <td>Aaron_Guiel</td>\n",
+ " <td>a2955610-ed5e-433c-bdd4-e3a72ff44736</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>jpg</td>\n",
+ " <td>Aaron_Patterson_0001</td>\n",
+ " <td>34dfe798220b53aac910e5e39705770d212cdfbe4be8a4...</td>\n",
+ " <td>Aaron_Patterson</td>\n",
+ " <td>1d0782e9-ed16-4550-b1e9-d9c03eef6181</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " ext fn \\\n",
+ "0 wow AJ_Cook_0001 \n",
+ "1 jpg AJ_Lamas_0001 \n",
+ "2 jpg Aaron_Eckhart_0001 \n",
+ "3 jpg Aaron_Guiel_0001 \n",
+ "4 jpg Aaron_Patterson_0001 \n",
+ "\n",
+ " sha256 subdir \\\n",
+ "0 550937b71b9af36b6083fa1ce7c76e97e3254c439614a6... AJ_Cook \n",
+ "1 46d7ddeec9b00add61ade2f89277d74e8264a2b6cec193... AJ_Lamas \n",
+ "2 b68ed8d50ba85209d826b962987077bc8e1826f7f2f325... Aaron_Eckhart \n",
+ "3 156f5428fad30c420ef01d9b0a3ab73e98aa6a1e5a2f0b... Aaron_Guiel \n",
+ "4 34dfe798220b53aac910e5e39705770d212cdfbe4be8a4... Aaron_Patterson \n",
+ "\n",
+ " uuid \n",
+ "0 f03fd921-2d56-4e83-8115-f658d6a72287 \n",
+ "1 0c96c5bb-dbd1-4584-bd68-af11664b98bb \n",
+ "2 8221e75c-9537-4a4f-9693-483b445244b4 \n",
+ "3 a2955610-ed5e-433c-bdd4-e3a72ff44736 \n",
+ "4 1d0782e9-ed16-4550-b1e9-d9c03eef6181 "
+ ]
+ },
+ "execution_count": 214,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df = df.drop('newcol', axis=1, errors='ignore')\n",
+ "df.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 218,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "a = [1,2,3,4]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 220,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "['1.00', '2.00', '3.00', '4.00']"
+ ]
+ },
+ "execution_count": 220,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "['{:.2f}'.format(x) for x in a]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 221,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from pathlib import Path"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 222,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "a = Path('/path/to/file.mp3')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 235,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "bc7e9ccf-49ba-4672-b1d8-6880d6b7e251\n"
+ ]
+ }
+ ],
+ "source": [
+ "import pandas as pd\n",
+ "id_to_lookup = 13000\n",
+ "fp_records = '/data_store_ssd/apps/megapixels/datasets/lfw/records.csv'\n",
+ "df = pd.read_csv(fp_records)\n",
+ "row = df.iloc[id_to_lookup]\n",
+ "print(row['uuid'])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python [conda env:megapixels]",
+ "language": "python",
+ "name": "conda-env-megapixels-py"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.6"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
generated by cgit v1.2.3-70-g09d2 (git 2.51.0) at 2025-12-10 14:12:11 +0000