94 lines
12 KiB
Plaintext
94 lines
12 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import json\n",
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"import pandas as pd\n",
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"import numpy as np\n",
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"import matplotlib.pyplot as plt"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"# Load the data\n",
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"with open('data/pun_anno/het_dataset.json') as f:\n",
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" data = json.load(f)\n",
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"\n"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"\n",
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"# Create a DataFrame\n",
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"df = pd.DataFrame(data)\n",
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"# df switch columns to rows\n",
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"df = df.T"
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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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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"<Axes: >"
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]
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},
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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},
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{
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"data": {
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"image/png": 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",
|
|
"text/plain": [
|
|
"<Figure size 640x480 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"# hist 'human_rating'\n",
|
|
"df['human_rating'].hist()"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Python 3",
|
|
"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"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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