396 lines
12 KiB
Text
396 lines
12 KiB
Text
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "c09834e2-1f4d-4b97-b5bb-50f8940bfcab",
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"metadata": {},
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"outputs": [],
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"source": [
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"import sys\n",
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"import csv\n",
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"import matplotlib\n",
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"import numpy as np\n",
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"import pandas as pd\n",
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"import seaborn as sns\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": "markdown",
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"id": "67f7914b-a7a7-4d96-b92f-9cb6c788f063",
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"metadata": {},
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"source": [
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"# 1 - Oil production\n",
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"\n",
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"Relative oil production per country between 1900 and 2023"
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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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"id": "6f8dcf94-577f-4b44-a658-1e9fd07469d4",
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"metadata": {},
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"outputs": [],
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"source": [
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"# load data, set index, and workout total production in given unit\n",
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"oil = pd.read_csv(\"version_2/input/oilproductionrelative_v2.csv\")\n",
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"oil.set_index(\"Year\", inplace=True)\n",
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"oil['Total'] = oil.sum(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": null,
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"id": "19d81163-1d42-407b-bf34-2fe194593285",
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"metadata": {},
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"outputs": [],
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"source": [
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"# relative percentage of production into \"country_percent\" columns\n",
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"for column in oil:\n",
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" oil[column + \"_percent\"] = oil[column] / oil.Total"
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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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"id": "49ba427d-7d99-4b11-a121-1bfe1c9aec42",
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"metadata": {},
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"outputs": [],
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"source": [
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"# grey palette for black and white printing\n",
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"sns.set_palette(\"Greys\")\n",
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"\n",
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"# decent size and light grey background\n",
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"fig, ax = plt.subplots(figsize = (17,8))\n",
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"ax.set_facecolor('#fdfdfd')\n",
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"\n",
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"# actual plot\n",
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"ax.stackplot(\n",
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" oil.index,\n",
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" oil.Argentina_percent,\n",
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" oil.Brazil_percent,\n",
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" oil.Colombia_percent,\n",
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" oil.Ecuador_percent,\n",
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" oil.Mexico_percent,\n",
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" oil.Venezuela_percent,\n",
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" oil.Others_percent\n",
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")\n",
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"\n",
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"# minimalistic x axis with fancy labels\n",
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"plt.xticks(np.arange(1900, 2023, 10))\n",
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"ax.get_yaxis().set_ticks([])\n",
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"ax.tick_params(length=4, color=\"white\")\n",
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"ax.tick_params(rotation=35)\n",
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"plt.xticks(ha='left')\n",
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"\n",
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"# no border, clean layout, no non-sense\n",
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"ax.spines['top'].set_visible(False)\n",
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"ax.spines['right'].set_visible(False)\n",
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"ax.spines['bottom'].set_visible(False)\n",
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"ax.spines['left'].set_visible(False)\n",
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"ax.set_ymargin(0.005)\n",
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"ax.set_xmargin(0)\n",
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"\n",
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"# legend placement is manual (and tricky)\n",
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"labels_legend = [\n",
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" 'Argentina',\n",
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" 'Brazil',\n",
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" 'Colombia',\n",
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" 'Ecuador',\n",
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" 'Mexico',\n",
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" 'Venezuela',\n",
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" 'Others']\n",
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"ax.legend(labels=labels_legend, loc='right', bbox_to_anchor=(0.558, 0.5, 0.545, 0.77), edgecolor=\"white\", reverse=True)\n",
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"\n",
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"# output to file\n",
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"plt.savefig(\"version_2/output/1_oil.png\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "6d1ead40-e7ae-4dc3-826e-3c437108080d",
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"metadata": {},
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"source": [
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"# 2 - Selected\n",
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"\n",
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"Subset of 3 countries with raw amounts\n",
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"\n",
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"__Important__: match the colors to other graphics"
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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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"id": "afdca3f2-4605-4147-a0bb-f7a67af65341",
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"metadata": {},
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"outputs": [],
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"source": [
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"# load data, set index\n",
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"selected = pd.read_csv(\"final_sources/1-laselected.csv\")\n",
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"selected.set_index(\"Year\", inplace=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": null,
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"id": "f051778e-eed5-4288-8027-e62b1c68598d",
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"metadata": {},
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"outputs": [],
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"source": [
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"# custom palette to match country colors to first plot\n",
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"palette_selected = [\"#d1d1d1\", \"#5c5c5c\", \"#2b2b2b\"]\n",
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"sns.set_palette(sns.color_palette(palette_selected))\n",
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"\n",
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"# all plots with same lightest grey background\n",
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"fig, ax = plt.subplots(figsize = (17,8))\n",
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"ax.set_facecolor('#fdfdfd')\n",
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"\n",
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"# actual plot\n",
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"ax.stackplot(\n",
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" selected.index,\n",
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" selected.Brazil,\n",
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" selected.Mexico,\n",
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" selected.Venezuela\n",
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")\n",
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"\n",
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"# minimalistic x axis with fancy labels\n",
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"plt.xticks(np.arange(1900, 2023, 10))\n",
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"ax.get_yaxis().set_ticks([])\n",
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"ax.tick_params(length=4, color=\"white\")\n",
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"ax.tick_params(rotation=35)\n",
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"plt.xticks(ha='left')\n",
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"\n",
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"# no border, clean layout, no non-sense\n",
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"ax.spines['top'].set_visible(False)\n",
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"ax.spines['right'].set_visible(False)\n",
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"ax.spines['bottom'].set_visible(False)\n",
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"ax.spines['left'].set_visible(False)\n",
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"ax.set_ymargin(0.005)\n",
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"ax.set_xmargin(0)\n",
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"\n",
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"# legend placement is manual (and tricky)\n",
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"labels_legend = [\n",
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" 'Brazil',\n",
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" 'Mexico',\n",
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" 'Venezuela',\n",
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"]\n",
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"ax.legend(labels=labels_legend, loc='right', bbox_to_anchor=(0.558, 0.5, 0.544, 0.91), edgecolor=\"white\", reverse=True)\n",
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"\n",
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"# output to file\n",
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"plt.savefig(\"version_2/output/2_selected.png\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "d950448c-503b-4c77-a48a-a4ffcb686d14",
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"metadata": {},
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"source": [
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"# 3 - Raffinage\n",
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"Raw data of oil refinment by country of shorter period of time (1940-2023)"
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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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"id": "863148f6-5ad4-456a-81ac-ccbbc347a9da",
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"metadata": {},
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"outputs": [],
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"source": [
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"# load data, set index\n",
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"raffinage = pd.read_csv(\"version_2/input/2-refining.csv\")\n",
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"raffinage.set_index(\"Year\", inplace=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": null,
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"id": "44fd6c05-2fd9-4a71-8ff3-28e35eb2a4fb",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Same palette as first (oil) plot but Ecuador is now Peru\n",
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"sns.set_palette(\"Greys\")\n",
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"\n",
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"# same size and background color\n",
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"fig, ax = plt.subplots(figsize = (17,8))\n",
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"ax.set_facecolor('#fdfdfd')\n",
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"\n",
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"# actual plot\n",
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"ax.stackplot(\n",
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" raffinage.index,\n",
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" raffinage.Argentina,\n",
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" raffinage.Brazil,\n",
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" raffinage.Colombia,\n",
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" raffinage.Peru,\n",
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" raffinage.Mexico,\n",
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" raffinage.Venezuela\n",
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")\n",
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"\n",
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"# same minimalist look, update timespan\n",
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"plt.xticks(np.arange(1940, 2023, 10))\n",
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"ax.get_yaxis().set_ticks([])\n",
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"ax.tick_params(length=4, color=\"white\")\n",
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"ax.tick_params(rotation=35)\n",
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"plt.xticks(ha='left')\n",
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"\n",
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"# same minimalist look\n",
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"ax.spines['top'].set_visible(False)\n",
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"ax.spines['right'].set_visible(False)\n",
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"ax.spines['bottom'].set_visible(False)\n",
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"ax.spines['left'].set_visible(False)\n",
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"ax.set_ymargin(0.005)\n",
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"ax.set_xmargin(0)\n",
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"\n",
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"# legend\n",
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"labels_legend = [\n",
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" 'Argentina',\n",
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" 'Brazil',\n",
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" 'Colombia',\n",
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" 'Peru',\n",
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" 'Mexico',\n",
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" 'Venezuela',\n",
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"]\n",
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"ax.legend(labels=labels_legend, loc='right', bbox_to_anchor=(0.558, 0.5, 0.545, 0.80), edgecolor=\"white\", reverse=True)\n",
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"\n",
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"# output to file\n",
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"plt.savefig(\"version_2/output/3_raffinage.png\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "0d8dd45e-5b6d-4e1d-a283-10e05265b599",
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"metadata": {},
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"source": [
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"# 4 - Energy mix\n",
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"General proportion of energy production source from 1800 to 2000"
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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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"id": "85b04a56-62a4-46db-9b92-499b09177afb",
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"metadata": {},
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"outputs": [],
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"source": [
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"# load data, set index, and workout total production in given unit\n",
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"mix = pd.read_csv(\"version_2/input/3-energymix.csv\")\n",
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"mix.set_index(\"Year\", inplace=True)\n",
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"mix['Total'] = mix.sum(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": null,
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"id": "d05090cd-2c40-4920-88df-97b8d2aec519",
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"metadata": {},
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"outputs": [],
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"source": [
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"# workout percentages in their columns\n",
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"for column in mix:\n",
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" mix[column + \"_percent\"] = mix[column] / mix.Total"
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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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"id": "0a446974-6469-44ac-b406-88ab0247bbd1",
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"metadata": {},
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"outputs": [],
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"source": [
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"# custom palette to prevent dark grey \"biomass\" to empty printer black ink\n",
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"paletto = [\n",
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" \"#ededed\",\n",
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" \"#d1d1d1\",\n",
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" \"#adadad\",\n",
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" \"#828282\",\n",
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" \"#5c5c5c\"\n",
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"]\n",
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"\n",
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"# same settings as before\n",
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"fig, ax = plt.subplots(figsize = (17,8))\n",
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"ax.set_facecolor('#fdfdfd')\n",
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"\n",
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"# actual plot\n",
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"ax.stackplot(\n",
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" mix.index,\n",
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" mix.Coal_percent,\n",
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" mix.Oil_percent,\n",
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" mix[\"Natural gas_percent\"],\n",
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" mix.Electricity_percent,\n",
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" mix.Biomass_percent,\n",
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" colors = paletto[::-1]\n",
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")\n",
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"\n",
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"# changed x axis labels anchor\n",
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"plt.xticks(np.arange(1800, 2001, 10))\n",
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"ax.get_yaxis().set_ticks([])\n",
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"ax.tick_params(length=4, color=\"white\")\n",
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"ax.tick_params(rotation=35)\n",
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"#plt.xticks(ha='left')\n",
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"\n",
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"# same clean look\n",
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"ax.spines['top'].set_visible(False)\n",
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"ax.spines['right'].set_visible(False)\n",
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"ax.spines['bottom'].set_visible(False)\n",
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"ax.spines['left'].set_visible(False)\n",
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"ax.set_ymargin(0.005)\n",
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"ax.set_xmargin(0)\n",
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"\n",
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"# legend\n",
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"labels_legend = [\n",
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" 'Coal',\n",
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" 'Oil',\n",
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" 'Natural gas',\n",
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" 'Electricity',\n",
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" 'Biomass'\n",
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"]\n",
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"ax.legend(labels=labels_legend, loc='right', bbox_to_anchor=(0.558, 0.5, 0.55, 0.832), edgecolor=\"white\", reverse=True)\n",
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"\n",
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"# output to file\n",
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"plt.savefig(\"version_2/output/4_mix.png\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "4cb24807-ee27-4f07-a850-a3d86b8ac20f",
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"metadata": {},
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"source": [
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"__Note regarding x axis labels placement__\n",
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"\n",
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"In most of the plots (oil, raffinage, selected), labels on the x axis are \"anchored left of their ticks\". It means that they are shifted slightly to the right relative to the actual date. Their respective individual tick, placed on the x axis at the precise date, are aligned with the tip of the first character of the label, not the middle, as is the case with the above plot (mix).\n",
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"This was done to prevent the first and last label to be visualy misaligned with the frame of the plot. The first label would be halfway out of the frame, and the last (2020) would be entirely inside the frame, as the source data have yearly datapoints until 2023.\n",
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"Label have been shifted to visualy mark this imprecision. Conveying a sense of temporal blur, it underlines the function of these visual representations: these are not precise analytical tools, but allow the reader to perceive historical trends in the data."
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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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"id": "eac58e10-4305-4175-b01c-3744a319e014",
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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 (ipykernel)",
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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": "3.12.9"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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