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Merge pull request #5 from calculquebec/nouveau-chap4
Nouveau chapitre 4 - Graphiques de courbes temporelles
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‎src/04-mark_line.ipynb‎

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"* Create different types of plots."
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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": "7886f873-0800-4a2f-afa7-9f57f6cdb3fa",
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"metadata": {
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"lang": "fr"
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},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"\n",
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"# Charger les données nettoyées\n",
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"surveys_complet = pd.read_csv('../data/surveys_0_NA.csv')\n",
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"surveys_complet"
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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": "ace4ab7d-b2d7-42d2-bc7b-d29646f2fd81",
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"metadata": {
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"lang": "en"
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},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"\n",
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"# Load the cleaned data\n",
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"surveys_complete = pd.read_csv('../data/surveys_0_NA.csv')\n",
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"surveys_complete"
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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": "dcaa744e-e541-4e99-b385-43820d64bbb1",
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"metadata": {
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"lang": "en,fr"
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},
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"outputs": [],
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"source": [
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"import altair as alt\n",
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"alt.data_transformers.disable_max_rows()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "29c791ed-c9c1-4d28-8a8d-12f43fbc1b7e",
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"metadata": {
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"lang": "fr"
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},
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"source": [
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"## Visualiser des données selon le temps\n",
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"* Nombre d'enregistrements par type d'espèce pour chaque année :"
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]
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},
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{
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"cell_type": "markdown",
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"id": "edb24b16-97a3-4b47-8146-41cc9a9336b5",
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"metadata": {
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"lang": "en"
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},
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"source": [
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"## Plotting time series data\n",
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"* Let’s visualize the number of records per year for each species"
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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": "273a8a39-c161-4f74-94f0-50334927174a",
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"metadata": {
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"lang": "fr"
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},
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"outputs": [],
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"source": [
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"alt.Chart(surveys_complet).mark_line().encode(\n",
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" x=alt.X('year').type('ordinal'),\n",
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" y=alt.Y('count()').scale(type='log', base=2),\n",
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" color=alt.Color('species_id'),\n",
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")"
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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": "c34f9aa4-ed84-4c02-85e8-dca890636433",
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"metadata": {
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"lang": "en"
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},
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"outputs": [],
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"source": [
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"alt.Chart(surveys_complete).mark_line().encode(\n",
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" x=alt.X('year').type('ordinal'),\n",
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" y=alt.Y('count()').scale(type='log', base=2),\n",
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" color=alt.Color('species_id'),\n",
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")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "8bc40c68-66ae-408e-8d9c-bcd16e8a9924",
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"metadata": {
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"lang": "fr"
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},
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"source": [
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"* Poids médian par type d'espèce pour chaque mois :"
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]
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},
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{
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"cell_type": "markdown",
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"id": "d0f084a6-c69d-4896-8a7e-796ff77be155",
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"metadata": {
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"lang": "en"
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},
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"source": [
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"* And now, the median weight per month for each species"
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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": "fef50d4d-305a-4b5d-b8b9-4895f4ad9d50",
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"metadata": {
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"lang": "fr"
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},
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"outputs": [],
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"source": [
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"alt.Chart(surveys_complet).mark_line().encode(\n",
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" x=alt.X('month').type('ordinal'),\n",
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" y=alt.Y('weight').aggregate('median'),\n",
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" color=alt.Color('species_id'),\n",
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" tooltip=['species_id'],\n",
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")"
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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": "b95897bc-7434-4405-8b48-557e38af53c8",
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"metadata": {
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"lang": "en"
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},
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"outputs": [],
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"source": [
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"alt.Chart(surveys_complete).mark_line().encode(\n",
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" x=alt.X('month').type('ordinal'),\n",
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" y=alt.Y('weight').aggregate('median'),\n",
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" color=alt.Color('species_id'),\n",
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" tooltip=['species_id'],\n",
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")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "ccbd4df4-8152-4810-85bd-260180dbc34c",
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"metadata": {
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"lang": "fr"
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},
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"source": [
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"### Exercice - Visualisation selon le temps\n",
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"`1`. Utilisez la fonction `pd.to_datetime()` pour générer une colonne\n",
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" de dates à partir des colonnes `year`, `month` et `day`. (3 min.)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "fec3f053-3465-449d-81dd-dd2dbd5a8c69",
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"metadata": {
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"lang": "en"
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},
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"source": [
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"### Exercise - Plotting time series data\n",
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"`1`. Use the `pd.to_datetime()` function to generate a new\n",
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"`date` column from the columns `year`, `month` and `day`. (3 min.)"
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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": "54217ca2-08d7-4c56-8063-a168387c2a9b",
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"metadata": {
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"lang": "fr",
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"tags": [
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"soln"
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]
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},
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"outputs": [],
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"source": [
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"# Décennie 1990 - pour éviter les 31 avril et 31 septembre 2000\n",
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"dec_1990 = surveys_complet[\n",
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" surveys_complet['year'].isin(range(1990, 2000))].copy()\n",
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"\n",
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"dec_1990['date'] = pd.to_datetime(dec_1990[['year', 'month', 'day']])\n",
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"dec_1990['date']"
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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": "eae426f2-07be-4495-9cb1-a3c6fe6a6af7",
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"metadata": {
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"lang": "fr",
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"tags": [
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"exer"
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]
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},
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"outputs": [],
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"source": [
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"# Décennie 1990 - pour éviter les 31 avril et 31 septembre 2000\n",
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"dec_1990 = surveys_complet[\n",
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" surveys_complet['year'].isin(range(1990, 2000))].copy()\n",
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"\n",
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"dec_1990['date'] = ###['year', 'month', 'day']###\n",
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"dec_1990['date']"
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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": "253efd92-63aa-44dd-9364-afb0afd46655",
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"metadata": {
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"lang": "en",
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"tags": [
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"soln"
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]
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},
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"outputs": [],
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"source": [
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"# Decade 1990 - avoid invalid dates April 31 and September 31, 2000\n",
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"dec_1990 = surveys_complete[\n",
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" surveys_complete['year'].isin(range(1990, 2000))].copy()\n",
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"\n",
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"dec_1990['date'] = pd.to_datetime(dec_1990[['year', 'month', 'day']])\n",
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"dec_1990['date']"
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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": "348236bf-aaa5-4f3b-8058-0b024e150bec",
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"metadata": {
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"lang": "en",
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"tags": [
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"exer"
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]
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},
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"outputs": [],
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"source": [
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"# Decade 1990 - avoid invalid dates April 31 and September 31, 2000\n",
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"dec_1990 = surveys_complete[\n",
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" surveys_complete['year'].isin(range(1990, 2000))].copy()\n",
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"\n",
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"dec_1990['date'] = ###['year', 'month', 'day']###\n",
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"dec_1990['date']"
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]
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},
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{
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"cell_type": "markdown",
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"id": "9de12028-9813-44b6-bc36-a85d83bc106f",
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"metadata": {
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"lang": "fr"
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},
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"source": [
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"`2`. Affichez le poids médian de chaque espèce selon la `date`.\n",
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"(3 min.)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "1d77e0e8-0663-4f73-a2a3-10f32ea3337f",
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"metadata": {
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"lang": "en"
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},
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"source": [
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"`2`. Visualize the median weight of each species by the `date`.\n",
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"(3 min.)"
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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": "e67bd045-bf33-4a54-aa4b-8a001509d9fc",
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"metadata": {
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"lang": "en,fr",
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"tags": [
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"soln"
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]
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},
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"outputs": [],
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"source": [
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"alt.Chart(dec_1990).mark_line().encode(\n",
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" x=alt.X('date'),\n",
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" y=alt.Y('weight').aggregate('median'),\n",
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" color=alt.Color('species_id'),\n",
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" tooltip=['species_id', 'date'],\n",
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")"
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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": "2e3acb45-f733-4c70-94ef-14771a7f50ef",
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"metadata": {
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"lang": "en,fr",
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"tags": [
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"exer"
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]
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},
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"outputs": [],
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"source": [
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"alt.Chart(###).mark_line().encode(\n",
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" x=alt.X(###),\n",
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" y=alt.Y('weight').###('median'),\n",
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" color=alt.Color('species_id'),\n",
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" tooltip=['species_id', 'date'],\n",
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")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "8b198859-0736-4b92-b56c-67cb3e102156",
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"metadata": {
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"lang": "fr"
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},
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"source": [
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"## Résumé technique\n",
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"* **Création d'une colonne de dates**\n",
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" * `df['date'] = pd.to_datetime(df[['year', 'month', 'day']])`\n",
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"* **Choix du type de marqueurs** à afficher\n",
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" * `graphique.mark_line()`\n",
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"* **Assigner des variables** à des canaux du graphique\n",
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" * `graphique.encode(...)`\n",
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" * Différents canaux :\n",
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" * `y=alt.Y('count()')`\n",
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" * `y=alt.Y('varY').aggregate('stat')`,\n",
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" avec les statistiques `'mean'`, `'median'`, etc."
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]
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},
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{
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"cell_type": "markdown",
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"id": "d9cf858b-cf70-44f3-962e-016144764737",
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"metadata": {
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"lang": "en"
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},
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"source": [
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"## Key points\n",
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"* **Creating a column of dates**\n",
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" * `df['date'] = pd.to_datetime(df[['year', 'month', 'day']])`\n",
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"* **Choosing a type of chart**\n",
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" * `chart.mark_line()`\n",
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"* **Assigning data fields to encoding channels**:\n",
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" * `chart.encode(...)`\n",
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" * Encoding channels:\n",
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" * `y=alt.Y('count()')`\n",
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" * `y=alt.Y('field_for_Y').aggregate(...)`,\n",
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" with either `'mean'`, `'median'`, etc."
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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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