254 lines
5.0 KiB
Plaintext
254 lines
5.0 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 pandas as pd\n",
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"import numpy as np\n",
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"from pathlib import Path\n",
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"import seaborn as sns\n",
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"import matplotlib.pyplot as plt\n",
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"import numpy as np\n",
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"from sklearn.linear_model import LinearRegression"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## TRENING"
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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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"NAMES = [\"Price\",\"Mileage\",\"Year\",\"Brand\",\"EngineType\",\"EngineCapacity\"]\n",
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"TRAIN_BASE = pd.read_csv(\"train/train.tsv\", sep ='\\t', names=NAMES)"
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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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"Y_TRAIN = np.array(TRAIN_BASE[\"Price\"])\n",
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"X_TRAIN = np.array(TRAIN_BASE[[\"Mileage\",\"Year\",\"EngineCapacity\"]])"
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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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"source": [
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"MODEL = LinearRegression().fit(X_TRAIN,Y_TRAIN)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## DEV-0"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"NAMES = [\"Mileage\",\"Year\",\"Brand\",\"EngineType\",\"EngineCapacity\"]\n",
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"FILE_BASE = pd.read_csv(\"dev-0/in.tsv\", sep ='\\t', names=NAMES)"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"X_TEST = np.array(FILE_BASE[[\"Mileage\",\"Year\",\"EngineCapacity\"]])"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"Y_TEST = MODEL.predict(X_TEST)"
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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": 8,
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"metadata": {},
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"outputs": [],
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"source": [
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"VALUES = np.array2string(Y_TEST, precision=5, separator='\\n',suppress_small=True)"
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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": 9,
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"metadata": {},
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"outputs": [],
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"source": [
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"VALUES = VALUES.split(\".\\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": 10,
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"metadata": {},
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"outputs": [],
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"source": [
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"OUTFILE = open(\"dev-0/out.tsv\", \"w\")"
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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": 11,
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"metadata": {
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"scrolled": false
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},
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"outputs": [],
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"source": [
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"for x in VALUES:\n",
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" RESULT = x.replace(\" \",\"\")\n",
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" RESULT = RESULT.replace(\"[\",\"\")\n",
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" RESULT = RESULT.replace(\"]\",\"\")\n",
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" OUTFILE.write(str(RESULT))"
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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": 12,
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"metadata": {},
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"outputs": [],
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"source": [
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"OUTFILE.close()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## TEST A"
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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": 13,
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"metadata": {},
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"outputs": [],
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"source": [
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"NAMES = [\"Mileage\",\"Year\",\"Brand\",\"EngineType\",\"EngineCapacity\"]\n",
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"FILE_BASE = pd.read_csv(\"test-A/in.tsv\", sep ='\\t', names=NAMES)"
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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": 14,
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"metadata": {},
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"outputs": [],
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"source": [
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"X_TEST = np.array(FILE_BASE[[\"Mileage\",\"Year\",\"EngineCapacity\"]])"
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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": 15,
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"metadata": {},
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"outputs": [],
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"source": [
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"Y_TEST = MODEL.predict(X_TEST)"
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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": 16,
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"metadata": {},
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"outputs": [],
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"source": [
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"VALUES = np.array2string(Y_TEST, precision=5, separator='\\n',suppress_small=True)"
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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": 17,
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"metadata": {},
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"outputs": [],
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"source": [
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"VALUES = VALUES.split(\".\\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": 18,
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"metadata": {},
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"outputs": [],
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"source": [
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"OUTFILE = open(\"test-A/out.tsv\", \"w\")"
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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": 19,
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"metadata": {},
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"outputs": [],
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"source": [
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"for x in VALUES:\n",
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" RESULT = x.replace(\" \",\"\")\n",
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" RESULT = RESULT.replace(\"[\",\"\")\n",
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" RESULT = RESULT.replace(\"]\",\"\")\n",
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" OUTFILE.write(str(RESULT) )"
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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": 20,
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"metadata": {},
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"outputs": [],
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"source": [
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"OUTFILE.close()"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.5"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}
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