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Obisidian vault auto-backup: 27-02-2025 09:15:38 on constellation. 5 files edited

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Félix MARQUET
2025-02-27 09:15:39 +01:00
parent c8d56f6c0e
commit 56b223a9ed
5 changed files with 76 additions and 5 deletions
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11
.obsidian/workspace.json vendored
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@@ -106,7 +106,7 @@
"state": {
"type": "markdown",
"state": {
"file": "ISEN/Daily/25-02-2025.md",
"file": "ISEN/Daily/27-02-2025.md",
"mode": "source",
"source": false,
"backlinks": true,
@@ -121,7 +121,7 @@
}
},
"icon": "lucide-file",
"title": "25-02-2025"
"title": "27-02-2025"
}
}
],
@@ -244,13 +244,13 @@
"state": {
"type": "outline",
"state": {
"file": "ISEN/Daily/25-02-2025.md",
"file": "ISEN/Daily/27-02-2025.md",
"followCursor": false,
"showSearch": false,
"searchQuery": ""
},
"icon": "lucide-list",
"title": "Plan de 25-02-2025"
"title": "Plan de 27-02-2025"
}
},
{
@@ -298,8 +298,9 @@
},
"active": "9ef6255ed11ce32c",
"lastOpenFiles": [
"ISEN/Daily/30-01-2025.pdf",
"ISEN/Daily/25-02-2025.md",
"ISEN/Daily/27-02-2025.md",
"ISEN/Daily/30-01-2025.pdf",
"ISEN/Daily/24-02-2025.md",
"ISEN/Daily/03-02-2025.md",
"ISEN/Daily/30-01-2025.md",

7
ISEN/Daily/27-02-2025.md Normal file
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@@ -0,0 +1,7 @@
#Daily
Cours du 2025-02-27
---
© Félix MARQUET

2
ISEN/Stat/CIPA3/.gitignore vendored Normal file
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@@ -0,0 +1,2 @@
.idea
.idea/*

31
ISEN/Stat/CIPA3/donnees_cours1.csv Normal file
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@@ -0,0 +1,31 @@
Note classe A maths;Note classe A phys;Note classe B maths;Note classe B phys
9.26;14.01;11.06;10.89
10.49;17.16;11.36;12.23
7.55;15.24;13.16;9.95
9.56;16.31;15.5;12.74
12.7;8.95;12.45;11.47
9.28;12.01;13.6;9.17
10.53;13.03;12.68;11.32
12.18;16.39;14.56;11.02
7.36;13.17;10.42;11.95
6.06;16.68;15.43;10.92
10.61;11.69;10.28;11.4
10.03;4.99;13.35;12.88
7.23;13.95;14.22;9.52
11.78;9.48;11.74;11.43
10.72;11.29;14.74;9.49
9.01;11.14;12.39;10.73
9.94;14.84;13.0;10.82
9.64;17.14;12.0;9.88
8.84;5.07;14.11;12.04
7.63;16.48;13.58;10.97
8.28;14.96;14.61;11.15
10.85;14.73;12.54;12.5
12.16;13.81;15.11;9.66
10.87;15.99;13.65;10.82
3.31;13.36;14.38;10.6
11.71;15.22;13.2;11.12
10.9;11.54;10.11;10.53
10.73;11.51;17.71;10.19
9.9;16.76;13.71;12.64
13.88;10.13;13.25;12.61
1 Note classe A maths Note classe A phys Note classe B maths Note classe B phys
2 9.26 14.01 11.06 10.89
3 10.49 17.16 11.36 12.23
4 7.55 15.24 13.16 9.95
5 9.56 16.31 15.5 12.74
6 12.7 8.95 12.45 11.47
7 9.28 12.01 13.6 9.17
8 10.53 13.03 12.68 11.32
9 12.18 16.39 14.56 11.02
10 7.36 13.17 10.42 11.95
11 6.06 16.68 15.43 10.92
12 10.61 11.69 10.28 11.4
13 10.03 4.99 13.35 12.88
14 7.23 13.95 14.22 9.52
15 11.78 9.48 11.74 11.43
16 10.72 11.29 14.74 9.49
17 9.01 11.14 12.39 10.73
18 9.94 14.84 13.0 10.82
19 9.64 17.14 12.0 9.88
20 8.84 5.07 14.11 12.04
21 7.63 16.48 13.58 10.97
22 8.28 14.96 14.61 11.15
23 10.85 14.73 12.54 12.5
24 12.16 13.81 15.11 9.66
25 10.87 15.99 13.65 10.82
26 3.31 13.36 14.38 10.6
27 11.71 15.22 13.2 11.12
28 10.9 11.54 10.11 10.53
29 10.73 11.51 17.71 10.19
30 9.9 16.76 13.71 12.64
31 13.88 10.13 13.25 12.61

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ISEN/Stat/CIPA3/script.py Normal file
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import pandas as pd
import matplotlib.pyplot as plt
# Lire le fichier CSV
df = pd.read_csv('donnees_cours1.csv', sep=';')
# Calculer la moyenne de chaque colonne
moyennes = df.mean()
# Afficher les moyennes
print("================== Moyennes ==================")
print(moyennes)
# Calculer la mediane de chaque colonne
median = df.median()
print("================== Medianes ==================")
print(median)
# Calculer l'écart-type de chaque colonne
ecart_type = df.std()
print("================== Ecart-types ==================")
print(ecart_type)
# Construire un histogramme de chaque colonne (arrondie à l'entier le plus proche)
for col in df.columns:
plt.hist(df[col].round(), bins=10)
plt.title(col)
plt.show()