206 lines
7.1 KiB
Plaintext
206 lines
7.1 KiB
Plaintext
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{
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"cells": [
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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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"# Données manquantes\n",
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"\n",
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"Montrons quelques méthodes pratiques pour traiter les données manquantes dans pandas :"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import pandas as pd"
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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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"df = pd.DataFrame({'A':[1,2,np.nan],\n",
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" 'B':[5,np.nan,np.nan],\n",
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" 'C':[1,2,3]})"
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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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{
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"output_type": "execute_result",
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"data": {
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"text/plain": [
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" A B C\n",
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"0 1.0 5.0 1\n",
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"1 2.0 NaN 2\n",
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"2 NaN NaN 3"
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],
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"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>A</th>\n <th>B</th>\n <th>C</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>1.0</td>\n <td>5.0</td>\n <td>1</td>\n </tr>\n <tr>\n <th>1</th>\n <td>2.0</td>\n <td>NaN</td>\n <td>2</td>\n </tr>\n <tr>\n <th>2</th>\n <td>NaN</td>\n <td>NaN</td>\n <td>3</td>\n </tr>\n </tbody>\n</table>\n</div>"
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},
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"metadata": {},
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"execution_count": 3
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}
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],
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"source": [
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"df"
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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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"output_type": "execute_result",
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"data": {
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"text/plain": [
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" A B C\n",
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"0 1.0 5.0 1"
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],
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"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>A</th>\n <th>B</th>\n <th>C</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>1.0</td>\n <td>5.0</td>\n <td>1</td>\n </tr>\n </tbody>\n</table>\n</div>"
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},
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"metadata": {},
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"execution_count": 4
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}
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],
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"source": [
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"df.dropna()"
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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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{
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"output_type": "execute_result",
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"data": {
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"text/plain": [
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" C\n",
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"0 1\n",
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"1 2\n",
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"2 3"
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],
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"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>C</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>1</td>\n </tr>\n <tr>\n <th>1</th>\n <td>2</td>\n </tr>\n <tr>\n <th>2</th>\n <td>3</td>\n </tr>\n </tbody>\n</table>\n</div>"
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},
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"metadata": {},
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"execution_count": 5
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}
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],
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"source": [
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"df.dropna(axis=1)"
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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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{
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"output_type": "execute_result",
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"data": {
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"text/plain": [
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" A B C\n",
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"0 1.0 5.0 1\n",
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"1 2.0 NaN 2"
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],
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"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>A</th>\n <th>B</th>\n <th>C</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>1.0</td>\n <td>5.0</td>\n <td>1</td>\n </tr>\n <tr>\n <th>1</th>\n <td>2.0</td>\n <td>NaN</td>\n <td>2</td>\n </tr>\n </tbody>\n</table>\n</div>"
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},
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"metadata": {},
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"execution_count": 6
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}
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],
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"source": [
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"df.dropna(thresh=2)"
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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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{
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"output_type": "execute_result",
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"data": {
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"text/plain": [
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" A B C\n",
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"0 1 5 1\n",
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"1 2 Valeur de remplacement 2\n",
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"2 Valeur de remplacement Valeur de remplacement 3"
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],
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"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>A</th>\n <th>B</th>\n <th>C</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>1</td>\n <td>5</td>\n <td>1</td>\n </tr>\n <tr>\n <th>1</th>\n <td>2</td>\n <td>Valeur de remplacement</td>\n <td>2</td>\n </tr>\n <tr>\n <th>2</th>\n <td>Valeur de remplacement</td>\n <td>Valeur de remplacement</td>\n <td>3</td>\n </tr>\n </tbody>\n</table>\n</div>"
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},
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"metadata": {},
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"execution_count": 7
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}
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],
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"source": [
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"df.fillna(value='Valeur de remplacement')"
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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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{
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"output_type": "execute_result",
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"data": {
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"text/plain": [
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"0 1.0\n",
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"1 2.0\n",
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"2 1.5\n",
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"Name: A, dtype: float64"
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]
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},
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"metadata": {},
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"execution_count": 8
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}
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],
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"source": [
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"df['A'].fillna(value=df['A'].mean())"
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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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"# Bon travail!"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"name": "python3",
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"display_name": "Python 3.7.9 64-bit ('pyfinance': conda)",
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"metadata": {
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"interpreter": {
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"hash": "e89404a230d8800c54ad520c7b67d1bd9bb833a07b37dd3e521a178a3dc34904"
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}
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}
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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": "3.7.9-final"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 1
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}
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