{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import plotly.figure_factory as ff\n",
"\n",
"df = pd.read_csv(\"https://corona-open-data.ckan.de/dataset/afc2e212-b32c-4340-92fe-a278b677abc2/resource/8661d664-56bf-40ca-845d-ebd280e3ce28/download/_airbyte_raw_data.csv\")\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"table = ff.create_table(df)\n",
"table.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def myprint(x):\n",
" global i\n",
" i=i+1\n",
" pprint.pprint((i,x))\n",
" return x"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import json\n",
"import pprint\n",
"import plotly.figure_factory as ff\n",
"\n",
"def extract_airbyte(a,key):\n",
" a = a.apply(json.loads).apply(lambda x: x['data'][key])\n",
" return a\n",
"\n",
"new_df = df.copy()\n",
"new_df[[\"value\"]] = new_df[[\"_airbyte_data\"]].apply(extract_airbyte,key=\"value\")\n",
"new_df[[\"timestamp\"]] = new_df[[\"_airbyte_data\"]].apply(extract_airbyte,key= \"timestamp\")\n",
"new_df[[\"time\"]] = new_df[[\"_airbyte_data\"]].apply(extract_airbyte,key= \"time\")\n",
"del new_df[\"_airbyte_data\"]\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"table = ff.create_table(new_df)\n",
"table.show()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.3"
}
},
"nbformat": 4,
"nbformat_minor": 4
}