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tomcistomcis
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🔥 Add notebook ipython memory usage
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‎notebooks/_ipython_memory_usage.ipynb‎

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{
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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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"execution": {
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"iopub.execute_input": "2020-09-02T20:37:09.340743Z",
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"iopub.status.busy": "2020-09-02T20:37:09.340488Z",
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"iopub.status.idle": "2020-09-02T20:37:09.469906Z",
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"shell.execute_reply": "2020-09-02T20:37:09.468326Z",
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"shell.execute_reply.started": "2020-09-02T20:37:09.340700Z"
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}
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},
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"outputs": [],
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"source": [
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"import ipython_memory_usage"
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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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"execution": {
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"iopub.execute_input": "2020-09-02T20:37:09.472168Z",
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"iopub.status.busy": "2020-09-02T20:37:09.471901Z",
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"iopub.status.idle": "2020-09-02T20:37:09.589613Z",
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"shell.execute_reply": "2020-09-02T20:37:09.588325Z",
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"shell.execute_reply.started": "2020-09-02T20:37:09.472122Z"
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}
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"'memory profile enabled'"
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]
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},
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"execution_count": 2,
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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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"name": "stdout",
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"output_type": "stream",
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"text": [
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"In [2] used 0.1211 MiB RAM in 0.12s, peaked 0.00 MiB above current, total RAM usage 44.46 MiB\n"
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]
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}
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],
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"source": [
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"%ipython_memory_usage_start"
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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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"execution": {
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"iopub.execute_input": "2020-09-02T20:37:09.591153Z",
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"iopub.status.busy": "2020-09-02T20:37:09.590927Z",
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"iopub.status.idle": "2020-09-02T20:37:09.861199Z",
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"shell.execute_reply": "2020-09-02T20:37:09.858674Z",
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"shell.execute_reply.started": "2020-09-02T20:37:09.591116Z"
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}
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"In [3] used 8.9805 MiB RAM in 0.26s, peaked 0.00 MiB above current, total RAM usage 53.44 MiB\n"
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]
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}
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],
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"source": [
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"import numpy as np"
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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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"execution": {
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"iopub.execute_input": "2020-09-02T20:37:09.862962Z",
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"iopub.status.busy": "2020-09-02T20:37:09.862720Z",
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"iopub.status.idle": "2020-09-02T20:37:10.011283Z",
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"shell.execute_reply": "2020-09-02T20:37:10.009740Z",
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"shell.execute_reply.started": "2020-09-02T20:37:09.862924Z"
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}
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"In [4] used 76.3555 MiB RAM in 0.14s, peaked 0.00 MiB above current, total RAM usage 129.80 MiB\n"
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]
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}
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],
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"source": [
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"a = np.ones(int(1e7))"
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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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"execution": {
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"iopub.execute_input": "2020-09-02T20:37:10.014996Z",
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"iopub.status.busy": "2020-09-02T20:37:10.014698Z",
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"iopub.status.idle": "2020-09-02T20:37:10.635785Z",
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"shell.execute_reply": "2020-09-02T20:37:10.634827Z",
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"shell.execute_reply.started": "2020-09-02T20:37:10.014912Z"
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}
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"In [5] used 30.2773 MiB RAM in 0.62s, peaked 0.00 MiB above current, total RAM usage 160.07 MiB\n"
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]
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}
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],
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"source": [
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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": 6,
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"metadata": {
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"execution": {
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"iopub.execute_input": "2020-09-02T20:37:10.638024Z",
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"iopub.status.busy": "2020-09-02T20:37:10.637746Z",
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"iopub.status.idle": "2020-09-02T20:37:10.748009Z",
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"shell.execute_reply": "2020-09-02T20:37:10.746856Z",
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"shell.execute_reply.started": "2020-09-02T20:37:10.637977Z"
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}
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"In [6] used 0.0977 MiB RAM in 0.10s, peaked 0.00 MiB above current, total RAM usage 160.17 MiB\n"
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]
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}
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],
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"source": [
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"df = pd.DataFrame(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": 7,
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"metadata": {
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"execution": {
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"iopub.execute_input": "2020-09-02T20:37:10.749908Z",
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"iopub.status.busy": "2020-09-02T20:37:10.749623Z",
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"iopub.status.idle": "2020-09-02T20:37:10.861548Z",
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"shell.execute_reply": "2020-09-02T20:37:10.859248Z",
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"shell.execute_reply.started": "2020-09-02T20:37:10.749870Z"
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}
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"Index 128\n",
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"0 80000000\n",
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"dtype: int64"
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]
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},
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"execution_count": 7,
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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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"name": "stdout",
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"output_type": "stream",
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"text": [
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"In [7] used 0.3008 MiB RAM in 0.11s, peaked 0.00 MiB above current, total RAM usage 160.47 MiB\n"
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]
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}
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],
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"source": [
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"df.memory_usage(deep=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": 8,
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"metadata": {
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"execution": {
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"iopub.execute_input": "2020-09-02T20:37:10.863013Z",
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"iopub.status.busy": "2020-09-02T20:37:10.862775Z",
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"iopub.status.idle": "2020-09-02T20:37:10.869681Z",
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"shell.execute_reply": "2020-09-02T20:37:10.868674Z",
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"shell.execute_reply.started": "2020-09-02T20:37:10.862973Z"
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}
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"'memory profile disabled'"
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]
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},
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"execution_count": 8,
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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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"source": [
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"%ipython_memory_usage_stop"
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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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"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": "3.7.7"
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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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