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Laboratorio de testeo de hipotesis #225

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Laboratorio de testeo de hipotesis #225

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132 changes: 109 additions & 23 deletions lab-hypothesis-testing.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -38,7 +38,7 @@
},
{
"cell_type": "code",
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"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -51,7 +51,7 @@
},
{
"cell_type": "code",
"execution_count": 3,
"execution_count": 10,
"metadata": {},
"outputs": [
{
Expand Down Expand Up @@ -278,14 +278,14 @@
"[800 rows x 11 columns]"
]
},
"execution_count": 3,
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.read_csv(\"https://raw.githubusercontent.com/data-bootcamp-v4/data/main/pokemon.csv\")\n",
"df"
"pokemon = pd.read_csv(\"https://raw.githubusercontent.com/data-bootcamp-v4/data/main/pokemon.csv\")\n",
"pokemon"
]
},
{
Expand All @@ -297,11 +297,34 @@
},
{
"cell_type": "code",
"execution_count": 1,
"execution_count": 18,
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"El valor del test es 3.590444254130357 y el valor de p es 0.0002567969150153481\n",
"Se rechaza la hipotesis al tener el tipo dragon un Hp mayor al tipo planta.\n"
]
}
],
"source": [
"#code here"
"# Filtramos:\n",
"t_dragon = pokemon[pokemon['Type 1'] == 'Dragon']['HP']\n",
"t_cesped = pokemon[pokemon['Type 1'] == 'Grass']['HP']\n",
"\n",
"# Hacemos T Student:\n",
"t_stat, p_value = st.ttest_ind(t_dragon, t_cesped, alternative='greater')\n",
"\n",
"# Obtenemos los resultados: \n",
"print(f\"El valor del test es {t_stat} y el valor de p es {p_value}\")\n",
"\n",
"# Evaluamos el valor p: \n",
"if p_value < 0.5:\n",
" print(\"Se rechaza la hipotesis al tener el tipo dragon un Hp mayor al tipo planta.\")\n",
"else:\n",
" print(\"No se rechaza la hipotesis, pero por falta de datos.\")"
]
},
{
Expand All @@ -313,11 +336,52 @@
},
{
"cell_type": "code",
"execution_count": 18,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#code here"
"pokemon.info()"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Comparacion para ['HP', 'Attack', 'Defense', 'Sp. Atk', 'Sp. Def', 'Speed'], el valor de t_student es 11.47504444631443, para p es 1.0490163118824507e-18\n",
"Se rechaza la hipotesis debido que hay una diferencia significativa entre ['HP', 'Attack', 'Defense', 'Sp. Atk', 'Sp. Def', 'Speed'] de los legendarios y los no legendarios.\n"
]
}
],
"source": [
"# Filtrado: \n",
"t_legendario = pokemon[pokemon['Legendary'] == True]\n",
"n_t_lengendario = pokemon[pokemon['Legendary'] == False]\n",
"\n",
"# Atributos a comparar: \n",
"atributos = ['HP', 'Attack', 'Defense', 'Sp. Atk', 'Sp. Def', 'Speed']\n",
"\n",
"# Haceos t_student para cada atributo:\n",
"ajustes = 0.05 / len(atributos)\n",
"for atributo in atributos: \n",
" stadist_legendario = t_legendario[atributo]\n",
" stadist_no_legendario = n_t_lengendario[atributo]\n",
"\n",
"# Hacemos t-student:\n",
" t_stat, p_value = st.ttest_ind(stadist_legendario, stadist_no_legendario, equal_var=False) \n",
"\n",
"# Revisamos valores:\n",
"print(f\"Comparacion para {atributos}, el valor de t_student es {t_stat}, para p es {p_value}\")\n",
"\n",
"# Veamos si aceptamos o rechazmos la hipotesis: \n",
"if p_value < ajustes:\n",
" print(f\"Se rechaza la hipotesis debido que hay una diferencia significativa entre {atributos} de los legendarios y los no legendarios.\")\n",
"else: \n",
" print(\"No se rechaza la hipotesis al no haber una diferencia signficativa entre categorías.\")"
]
},
{
Expand All @@ -337,7 +401,7 @@
},
{
"cell_type": "code",
"execution_count": 5,
"execution_count": 34,
"metadata": {},
"outputs": [
{
Expand Down Expand Up @@ -453,14 +517,14 @@
"4 624.0 262.0 1.9250 65500.0 "
]
},
"execution_count": 5,
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.read_csv(\"https://raw.githubusercontent.com/data-bootcamp-v4/data/main/california_housing.csv\")\n",
"df.head()"
"distancias = pd.read_csv(\"https://raw.githubusercontent.com/data-bootcamp-v4/data/main/california_housing.csv\")\n",
"distancias.head()"
]
},
{
Expand All @@ -486,14 +550,36 @@
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
"source": [
"# Se declaran las distancias:\n",
"coor_escuela = (-118, 34)\n",
"coor_hosp = (-122, 37)\n",
"\n",
"# Se crea la función para calcular la distancia euclidea:\n",
"def dist_eu (x1, y1, x2, y2):\n",
" return np.sqrt((x2 -x1)**2 + (y2 - y1)**2)\n",
"\n",
"# Calculamos las distancias de cada casa con el hospital y el colegio: \n",
"distancias['d_hospital'] = distancias.apply(lambda row: dist_eu(row['X'], row['Y'], coor_hosp[0], coor_hosp[1]), axis=1)\n",
"distancias['d_hospital'] = distancias.apply(lambda row: dist_eu(row['X'], row['Y'], coor_escuela[0], coor_escuela[1]), axis=1)\n",
"\n",
"# Clasificamos las casas como \"lejos\" o \"cerca\":\n",
"distancias['cerca'] = ((distancias['d_hospital'] < 0.5) | (distancias['d_escuela'] < 0.5))\n",
"\n",
"# Dividimos el dataset en dos: \n",
"cerca = distancias[distancias['Cerca'] == True]['Price']\n",
"lejos = distancias[distancias['Lejos'] == True]['Price']\n",
"\n",
"# ##\n",
"t_stat, p_value = st.ttest_ind(cerca, lejos, equal_var=False)\n",
"print(f\"Valor de t {t_stat}, valor de p {p_value}\")\n",
"\n",
"if p_value < 0.5:\n",
" print(\"Se rechaza la hipotesis nula.\")\n",
"else: \n",
" print(\"No se rechaza la hipotesis.\")\n",
"\n"
]
}
],
"metadata": {
Expand All @@ -512,7 +598,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.9"
"version": "3.12.5"
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"nbformat": 4,
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