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378 строки
26 KiB
Plaintext
378 строки
26 KiB
Plaintext
{
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"cells": [
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{
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"attachments": {},
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"cell_type": "markdown",
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"id": "d2c4d40d-8aca-4024-8d19-a65c4efe825d",
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"metadata": {},
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"source": [
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"# Facial Expression recognition with DeepFace"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"id": "51f8888b-d1a3-4b85-a596-95c0993fa192",
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"metadata": {},
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"source": [
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"Facial expressions can be detected using [DeepFace](https://github.com/serengil/deepface) and [RetinaFace](https://github.com/serengil/retinaface).\n",
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"\n",
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"The first cell is only run on google colab and installs the [ammico](https://github.com/ssciwr/AMMICO) package.\n",
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"\n",
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"After that, we can import `ammico` and read in the files given a folder path."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "50c1c1c7",
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"metadata": {
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"execution": {
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"iopub.execute_input": "2023-10-19T08:52:25.315547Z",
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"iopub.status.busy": "2023-10-19T08:52:25.314982Z",
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"iopub.status.idle": "2023-10-19T08:52:25.326267Z",
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"shell.execute_reply": "2023-10-19T08:52:25.325411Z"
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}
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},
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"outputs": [],
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"source": [
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"# if running on google colab\n",
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"# flake8-noqa-cell\n",
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"import os\n",
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"\n",
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"if \"google.colab\" in str(get_ipython()):\n",
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" # update python version\n",
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" # install setuptools\n",
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" # %pip install setuptools==61 -qqq\n",
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" # install ammico\n",
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" %pip install git+https://github.com/ssciwr/ammico.git -qqq\n",
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" # mount google drive for data and API key\n",
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" from google.colab import drive\n",
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"\n",
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" drive.mount(\"/content/drive\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "b21e52a5-d379-42db-aae6-f2ab9ed9a369",
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"metadata": {
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"execution": {
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"iopub.execute_input": "2023-10-19T08:52:25.330364Z",
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"iopub.status.busy": "2023-10-19T08:52:25.330042Z",
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"iopub.status.idle": "2023-10-19T08:52:39.412208Z",
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"shell.execute_reply": "2023-10-19T08:52:39.411190Z"
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}
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},
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"outputs": [],
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"source": [
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"import ammico\n",
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"from ammico import utils as mutils\n",
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"from ammico import display as mdisplay"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"id": "a2bd2153",
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"metadata": {},
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"source": [
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"We select a subset of image files to try facial expression detection on, see the `limit` keyword. The `find_files` function finds image files within a given directory:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "afe7e638-f09d-47e7-9295-1c374bd64c53",
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"metadata": {
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"execution": {
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"iopub.execute_input": "2023-10-19T08:52:39.416519Z",
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"iopub.status.busy": "2023-10-19T08:52:39.415825Z",
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"iopub.status.idle": "2023-10-19T08:52:39.428998Z",
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"shell.execute_reply": "2023-10-19T08:52:39.427879Z"
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}
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},
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"outputs": [],
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"source": [
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"# Here you need to provide the path to your google drive folder\n",
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"# or local folder containing the images\n",
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"images = mutils.find_files(\n",
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" path=\"data/\",\n",
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" limit=10,\n",
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")"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"id": "705e7328",
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"metadata": {},
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"source": [
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"We need to initialize the main dictionary that contains all information for the images and is updated through each subsequent analysis:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "b37c0c91",
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"metadata": {
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"execution": {
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"iopub.execute_input": "2023-10-19T08:52:39.432953Z",
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"iopub.status.busy": "2023-10-19T08:52:39.432665Z",
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"iopub.status.idle": "2023-10-19T08:52:39.438085Z",
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"shell.execute_reply": "2023-10-19T08:52:39.437260Z"
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}
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},
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"outputs": [],
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"source": [
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"mydict = mutils.initialize_dict(images)"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"id": "a9372561",
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"metadata": {},
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"source": [
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"To check the analysis, you can inspect the analyzed elements here. Loading the results takes a moment, so please be patient. If you are sure of what you are doing, you can skip this and directly export a csv file in the step below.\n",
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"Here, we display the face recognition results provided by the DeepFace and RetinaFace libraries. Click on the tabs to see the results in the right sidebar. You may need to increment the `port` number if you are already running several notebook instances on the same server."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "992499ed-33f1-4425-ad5d-738cf565d175",
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"metadata": {
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"execution": {
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"iopub.execute_input": "2023-10-19T08:52:39.441925Z",
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"iopub.status.busy": "2023-10-19T08:52:39.441612Z",
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"iopub.status.idle": "2023-10-19T08:52:40.411846Z",
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"shell.execute_reply": "2023-10-19T08:52:40.410948Z"
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}
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},
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"outputs": [
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{
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"ename": "TypeError",
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"evalue": "__init__() got an unexpected keyword argument 'identify'",
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"output_type": "error",
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"traceback": [
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
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"Cell \u001b[0;32mIn[5], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m analysis_explorer \u001b[38;5;241m=\u001b[39m \u001b[43mmdisplay\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mAnalysisExplorer\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmydict\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43midentify\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mfaces\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2\u001b[0m analysis_explorer\u001b[38;5;241m.\u001b[39mrun_server(port \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m8050\u001b[39m)\n",
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"\u001b[0;31mTypeError\u001b[0m: __init__() got an unexpected keyword argument 'identify'"
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]
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}
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],
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"source": [
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"analysis_explorer = mdisplay.AnalysisExplorer(mydict, identify=\"faces\")\n",
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"analysis_explorer.run_server(port = 8050)"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"id": "6f974341",
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"metadata": {},
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"source": [
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"Instead of inspecting each of the images, you can also directly carry out the analysis and export the result into a csv. This may take a while depending on how many images you have loaded."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"id": "6f97c7d0",
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"metadata": {
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"execution": {
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"iopub.execute_input": "2023-10-19T08:52:40.416345Z",
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"iopub.status.busy": "2023-10-19T08:52:40.415819Z",
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"iopub.status.idle": "2023-10-19T08:52:44.941423Z",
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"shell.execute_reply": "2023-10-19T08:52:44.940106Z"
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}
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},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"Downloading data from 'https://github.com/serengil/deepface_models/releases/download/v1.0/retinaface.h5' to file '/home/runner/.cache/pooch/3be32af6e4183fa0156bc33bda371147-retinaface.h5'.\n"
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]
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},
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{
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"ename": "ValueError",
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"evalue": "('Input image file path (', '102141_2_eng', ') does not exist.')",
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"output_type": "error",
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"traceback": [
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
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"Cell \u001b[0;32mIn[6], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m key \u001b[38;5;129;01min\u001b[39;00m mydict\u001b[38;5;241m.\u001b[39mkeys():\n\u001b[0;32m----> 2\u001b[0m mydict[key] \u001b[38;5;241m=\u001b[39m \u001b[43mammico\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfaces\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mEmotionDetector\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmydict\u001b[49m\u001b[43m[\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43manalyse_image\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
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"File \u001b[0;32m~/work/AMMICO/AMMICO/ammico/faces.py:139\u001b[0m, in \u001b[0;36mEmotionDetector.analyse_image\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 132\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21manalyse_image\u001b[39m(\u001b[38;5;28mself\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28mdict\u001b[39m:\n\u001b[1;32m 133\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 134\u001b[0m \u001b[38;5;124;03m Performs facial expression analysis on the image.\u001b[39;00m\n\u001b[1;32m 135\u001b[0m \n\u001b[1;32m 136\u001b[0m \u001b[38;5;124;03m Returns:\u001b[39;00m\n\u001b[1;32m 137\u001b[0m \u001b[38;5;124;03m dict: The updated subdict dictionary with analysis results.\u001b[39;00m\n\u001b[1;32m 138\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 139\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfacial_expression_analysis\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
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"File \u001b[0;32m~/work/AMMICO/AMMICO/ammico/faces.py:188\u001b[0m, in \u001b[0;36mEmotionDetector.facial_expression_analysis\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 185\u001b[0m \u001b[38;5;66;03m# Find (multiple) faces in the image and cut them\u001b[39;00m\n\u001b[1;32m 186\u001b[0m retinaface_model\u001b[38;5;241m.\u001b[39mget()\n\u001b[0;32m--> 188\u001b[0m faces \u001b[38;5;241m=\u001b[39m \u001b[43mRetinaFace\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mextract_faces\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msubdict\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mfilename\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 189\u001b[0m \u001b[38;5;66;03m# If no faces are found, we return empty keys\u001b[39;00m\n\u001b[1;32m 190\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(faces) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n",
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"File \u001b[0;32m/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/retinaface/RetinaFace.py:190\u001b[0m, in \u001b[0;36mextract_faces\u001b[0;34m(img_path, threshold, model, align, allow_upscaling)\u001b[0m\n\u001b[1;32m 186\u001b[0m resp \u001b[38;5;241m=\u001b[39m []\n\u001b[1;32m 188\u001b[0m \u001b[38;5;66;03m#---------------------------\u001b[39;00m\n\u001b[0;32m--> 190\u001b[0m img \u001b[38;5;241m=\u001b[39m \u001b[43mget_image\u001b[49m\u001b[43m(\u001b[49m\u001b[43mimg_path\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 192\u001b[0m \u001b[38;5;66;03m#---------------------------\u001b[39;00m\n\u001b[1;32m 194\u001b[0m obj \u001b[38;5;241m=\u001b[39m detect_faces(img_path \u001b[38;5;241m=\u001b[39m img, threshold \u001b[38;5;241m=\u001b[39m threshold, model \u001b[38;5;241m=\u001b[39m model, allow_upscaling \u001b[38;5;241m=\u001b[39m allow_upscaling)\n",
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"File \u001b[0;32m/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/retinaface/RetinaFace.py:43\u001b[0m, in \u001b[0;36mget_image\u001b[0;34m(img_path)\u001b[0m\n\u001b[1;32m 41\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mtype\u001b[39m(img_path) \u001b[38;5;241m==\u001b[39m \u001b[38;5;28mstr\u001b[39m: \u001b[38;5;66;03m# Load from file path\u001b[39;00m\n\u001b[1;32m 42\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39misfile(img_path):\n\u001b[0;32m---> 43\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mInput image file path (\u001b[39m\u001b[38;5;124m\"\u001b[39m, img_path, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m) does not exist.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 44\u001b[0m img \u001b[38;5;241m=\u001b[39m cv2\u001b[38;5;241m.\u001b[39mimread(img_path)\n\u001b[1;32m 46\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(img_path, np\u001b[38;5;241m.\u001b[39mndarray): \u001b[38;5;66;03m# Use given NumPy array\u001b[39;00m\n",
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"\u001b[0;31mValueError\u001b[0m: ('Input image file path (', '102141_2_eng', ') does not exist.')"
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]
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}
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],
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"source": [
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"for key in mydict.keys():\n",
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" mydict[key] = ammico.faces.EmotionDetector(mydict[key]).analyse_image()"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"id": "174357b1",
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"metadata": {},
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"source": [
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"These steps are required to convert the dictionary of dictionarys into a dictionary with lists, that can be converted into a pandas dataframe and exported to a csv file."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"id": "604bd257",
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"metadata": {
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"execution": {
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"iopub.execute_input": "2023-10-19T08:52:44.946307Z",
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"iopub.status.busy": "2023-10-19T08:52:44.945496Z",
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"iopub.status.idle": "2023-10-19T08:52:46.293099Z",
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"shell.execute_reply": "2023-10-19T08:52:46.291898Z"
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}
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},
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"outputs": [
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{
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"ename": "ValueError",
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"evalue": "All arrays must be of the same length",
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"output_type": "error",
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"traceback": [
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
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"Cell \u001b[0;32mIn[7], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m outdict \u001b[38;5;241m=\u001b[39m mutils\u001b[38;5;241m.\u001b[39mappend_data_to_dict(mydict)\n\u001b[0;32m----> 2\u001b[0m df \u001b[38;5;241m=\u001b[39m \u001b[43mmutils\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdump_df\u001b[49m\u001b[43m(\u001b[49m\u001b[43moutdict\u001b[49m\u001b[43m)\u001b[49m\n",
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"File \u001b[0;32m~/work/AMMICO/AMMICO/ammico/utils.py:222\u001b[0m, in \u001b[0;36mdump_df\u001b[0;34m(mydict)\u001b[0m\n\u001b[1;32m 220\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdump_df\u001b[39m(mydict: \u001b[38;5;28mdict\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m DataFrame:\n\u001b[1;32m 221\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Utility to dump the dictionary into a dataframe.\"\"\"\u001b[39;00m\n\u001b[0;32m--> 222\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mDataFrame\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfrom_dict\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmydict\u001b[49m\u001b[43m)\u001b[49m\n",
|
|
"File \u001b[0;32m/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/pandas/core/frame.py:1816\u001b[0m, in \u001b[0;36mDataFrame.from_dict\u001b[0;34m(cls, data, orient, dtype, columns)\u001b[0m\n\u001b[1;32m 1810\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 1811\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mExpected \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mindex\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m, \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcolumns\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m or \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtight\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m for orient parameter. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 1812\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mGot \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00morient\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m instead\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 1813\u001b[0m )\n\u001b[1;32m 1815\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m orient \u001b[38;5;241m!=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtight\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m-> 1816\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mcls\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mindex\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mindex\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcolumns\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcolumns\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1817\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 1818\u001b[0m realdata \u001b[38;5;241m=\u001b[39m data[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdata\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n",
|
|
"File \u001b[0;32m/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/pandas/core/frame.py:736\u001b[0m, in \u001b[0;36mDataFrame.__init__\u001b[0;34m(self, data, index, columns, dtype, copy)\u001b[0m\n\u001b[1;32m 730\u001b[0m mgr \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_init_mgr(\n\u001b[1;32m 731\u001b[0m data, axes\u001b[38;5;241m=\u001b[39m{\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mindex\u001b[39m\u001b[38;5;124m\"\u001b[39m: index, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcolumns\u001b[39m\u001b[38;5;124m\"\u001b[39m: columns}, dtype\u001b[38;5;241m=\u001b[39mdtype, copy\u001b[38;5;241m=\u001b[39mcopy\n\u001b[1;32m 732\u001b[0m )\n\u001b[1;32m 734\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(data, \u001b[38;5;28mdict\u001b[39m):\n\u001b[1;32m 735\u001b[0m \u001b[38;5;66;03m# GH#38939 de facto copy defaults to False only in non-dict cases\u001b[39;00m\n\u001b[0;32m--> 736\u001b[0m mgr \u001b[38;5;241m=\u001b[39m \u001b[43mdict_to_mgr\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mindex\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcolumns\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtyp\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmanager\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 737\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(data, ma\u001b[38;5;241m.\u001b[39mMaskedArray):\n\u001b[1;32m 738\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mma\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m mrecords\n",
|
|
"File \u001b[0;32m/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/pandas/core/internals/construction.py:503\u001b[0m, in \u001b[0;36mdict_to_mgr\u001b[0;34m(data, index, columns, dtype, typ, copy)\u001b[0m\n\u001b[1;32m 499\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 500\u001b[0m \u001b[38;5;66;03m# dtype check to exclude e.g. range objects, scalars\u001b[39;00m\n\u001b[1;32m 501\u001b[0m arrays \u001b[38;5;241m=\u001b[39m [x\u001b[38;5;241m.\u001b[39mcopy() \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(x, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdtype\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;28;01melse\u001b[39;00m x \u001b[38;5;28;01mfor\u001b[39;00m x \u001b[38;5;129;01min\u001b[39;00m arrays]\n\u001b[0;32m--> 503\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43marrays_to_mgr\u001b[49m\u001b[43m(\u001b[49m\u001b[43marrays\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcolumns\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mindex\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtyp\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtyp\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconsolidate\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m)\u001b[49m\n",
|
|
"File \u001b[0;32m/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/pandas/core/internals/construction.py:114\u001b[0m, in \u001b[0;36marrays_to_mgr\u001b[0;34m(arrays, columns, index, dtype, verify_integrity, typ, consolidate)\u001b[0m\n\u001b[1;32m 111\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m verify_integrity:\n\u001b[1;32m 112\u001b[0m \u001b[38;5;66;03m# figure out the index, if necessary\u001b[39;00m\n\u001b[1;32m 113\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m index \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 114\u001b[0m index \u001b[38;5;241m=\u001b[39m \u001b[43m_extract_index\u001b[49m\u001b[43m(\u001b[49m\u001b[43marrays\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 115\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 116\u001b[0m index \u001b[38;5;241m=\u001b[39m ensure_index(index)\n",
|
|
"File \u001b[0;32m/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/pandas/core/internals/construction.py:677\u001b[0m, in \u001b[0;36m_extract_index\u001b[0;34m(data)\u001b[0m\n\u001b[1;32m 675\u001b[0m lengths \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(\u001b[38;5;28mset\u001b[39m(raw_lengths))\n\u001b[1;32m 676\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(lengths) \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[0;32m--> 677\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAll arrays must be of the same length\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 679\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m have_dicts:\n\u001b[1;32m 680\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 681\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMixing dicts with non-Series may lead to ambiguous ordering.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 682\u001b[0m )\n",
|
|
"\u001b[0;31mValueError\u001b[0m: All arrays must be of the same length"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"outdict = mutils.append_data_to_dict(mydict)\n",
|
|
"df = mutils.dump_df(outdict)"
|
|
]
|
|
},
|
|
{
|
|
"attachments": {},
|
|
"cell_type": "markdown",
|
|
"id": "8373d9f8",
|
|
"metadata": {},
|
|
"source": [
|
|
"Check the dataframe:"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 8,
|
|
"id": "aa4b518a",
|
|
"metadata": {
|
|
"execution": {
|
|
"iopub.execute_input": "2023-10-19T08:52:46.297186Z",
|
|
"iopub.status.busy": "2023-10-19T08:52:46.296918Z",
|
|
"iopub.status.idle": "2023-10-19T08:52:46.335436Z",
|
|
"shell.execute_reply": "2023-10-19T08:52:46.334561Z"
|
|
}
|
|
},
|
|
"outputs": [
|
|
{
|
|
"ename": "NameError",
|
|
"evalue": "name 'df' is not defined",
|
|
"output_type": "error",
|
|
"traceback": [
|
|
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
|
|
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
|
|
"Cell \u001b[0;32mIn[8], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mdf\u001b[49m\u001b[38;5;241m.\u001b[39mhead(\u001b[38;5;241m10\u001b[39m)\n",
|
|
"\u001b[0;31mNameError\u001b[0m: name 'df' is not defined"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"df.head(10)"
|
|
]
|
|
},
|
|
{
|
|
"attachments": {},
|
|
"cell_type": "markdown",
|
|
"id": "579cd59f",
|
|
"metadata": {},
|
|
"source": [
|
|
"Write the csv file - here you should provide a file path and file name for the csv file to be written."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 9,
|
|
"id": "4618decb",
|
|
"metadata": {
|
|
"execution": {
|
|
"iopub.execute_input": "2023-10-19T08:52:46.339920Z",
|
|
"iopub.status.busy": "2023-10-19T08:52:46.339657Z",
|
|
"iopub.status.idle": "2023-10-19T08:52:46.387838Z",
|
|
"shell.execute_reply": "2023-10-19T08:52:46.386742Z"
|
|
}
|
|
},
|
|
"outputs": [
|
|
{
|
|
"ename": "NameError",
|
|
"evalue": "name 'df' is not defined",
|
|
"output_type": "error",
|
|
"traceback": [
|
|
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
|
|
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
|
|
"Cell \u001b[0;32mIn[9], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mdf\u001b[49m\u001b[38;5;241m.\u001b[39mto_csv(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdata_out.csv\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
|
|
"\u001b[0;31mNameError\u001b[0m: name 'df' is not defined"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"df.to_csv(\"data_out.csv\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "b1a80023",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
}
|
|
],
|
|
"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": "3.9.18"
|
|
},
|
|
"vscode": {
|
|
"interpreter": {
|
|
"hash": "da98320027a74839c7141b42ef24e2d47d628ba1f51115c13da5d8b45a372ec2"
|
|
}
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 5
|
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}
|