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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Prepare Flickr API Batch CSV"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"%reload_ext autoreload\n",
"%autoreload 2\n",
"\n",
"import os\n",
"from os.path import join\n",
"from glob import glob, iglob\n",
"from pathlib import Path\n",
"from tqdm import tqdm_notebook as tqdm\n",
"\n",
"import pandas as pd"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Create CSV for API"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"| photo_id |\n",
"|:---|\n",
"| 12234 |"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"# flickr api data\n",
"fp_in_flickr_meta = '/data_store_hdd/datasets/people/ibm_dif/research/flickr_api_query_dump.csv'\n",
"# ibm count data\n",
"fp_in_ibm_meta = '/data_store_hdd/datasets/people/ibm_dif/research/ibm_dif_metadata.csv'\n",
"# output\n",
"fp_out = '/data_store_hdd/datasets/people/ibm_dif/research/ibm_dif_metadata.csv'"
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {},
"outputs": [],
"source": [
"# load ibm data and create count lookup with photoid\n",
"df_ibm_meta = pd.read_csv(fp_in_ibm_meta)\n",
"ibm_meta_records = df_ibm_meta.to_dict('records')\n",
"count_lookup = {}\n",
"for ibm_meta_record in ibm_meta_records:\n",
" photo_id = int(Path(ibm_meta_record['url']).stem.split('_')[0])\n",
" count_lookup[photo_id] = ibm_meta_record['count']"
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"100438"
]
},
"execution_count": 69,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(count_lookup)"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {},
"outputs": [],
"source": [
"results = []"
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {},
"outputs": [],
"source": [
"df_flickr_meta = pd.read_csv(fp_in_flickr_meta, dtype={'count': int, 'username': str, 'sha256': str}).fillna('')\n",
"flickr_meta_records = df_flickr_meta.to_dict('records')"
]
},
{
"cell_type": "code",
"execution_count": 74,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Error: invalid literal for int() with base 10: '', {'country': '', 'description': 'Haircut Next...', 'lat': '', 'lon': '', 'nsid': '', 'owner_location': '', 'path_alias': '', 'photo_id': '', 'place': '', 'place_id': '', 'posted': '', 'realname': '', 'taken': '', 'username': '', 'woeid': ''}\n",
"Error: invalid literal for int() with base 10: '', {'country': '', 'description': '', 'lat': '86085317@N00', 'lon': 'New York', 'nsid': 'anonymousthomas', 'owner_location': '4975598', 'path_alias': '', 'photo_id': '', 'place': '1108685469', 'place_id': 'Thomas', 'posted': '2005-02-18 00:11:09', 'realname': 'anonymousthomas', 'taken': '', 'username': '', 'woeid': ''}\n"
]
}
],
"source": [
"# load flickr data\n",
"for flickr_meta_record in flickr_meta_records:\n",
" try:\n",
" nsid = flickr_meta_record['nsid']\n",
" photo_id = int(flickr_meta_record['photo_id'])\n",
" count = count_lookup[photo_id]\n",
" except Exception as e:\n",
" print(f'Error: {e}, {flickr_meta_record}')\n",
" continue\n",
" obj = {\n",
" 'photo_id': photo_id,\n",
" 'nsid': nsid,\n",
" 'count': count \n",
" }\n",
" results.append(obj)"
]
},
{
"cell_type": "code",
"execution_count": 75,
"metadata": {},
"outputs": [],
"source": [
"df_out = pd.DataFrame.from_dict(results)\n",
"df_out.to_csv(fp_out, index=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Create meta count file"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"fp_flickr_api_dump = '/data_store_hdd/datasets/people/ibm_dif/research/flickr_api_query_dump.csv'\n",
"fp_out_meta = '/data_store_hdd/datasets/people/ibm_dif/research/ibm_dif_flickr_meta.csv'"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/adam/anaconda3/envs/megapixels/lib/python3.6/site-packages/IPython/core/interactiveshell.py:3020: DtypeWarning: Columns (2,3,10) have mixed types. Specify dtype option on import or set low_memory=False.\n",
" interactivity=interactivity, compiler=compiler, result=result)\n"
]
}
],
"source": [
"df = pd.read_csv(fp_flickr_api_dump)\n",
"groups = df.groupby('nsid')"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"results = []\n",
"for nsid, group in groups:\n",
" obj = {\n",
" 'nsid': nsid,\n",
" 'count': len(group)\n",
" }\n",
" results.append(obj)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"pd.DataFrame.from_dict(results).to_csv(fp_out_meta, index=False)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "megapixels",
"language": "python",
"name": "megapixels"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.8"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
|
