Curso de Matemáticas para Data Science: Estadística Descriptiva

import pandas as pd df = pd.read_csv('cars.csv') df.head()

df.columns

df.dtypes

df.describe()

import pandas as pd df = pd.read_csv('cars.csv')

df['price_usd'].mean()

df['price_usd'].median()

df['price_usd'].plot.hist(bins=20)

import seaborn as sns sns.displot(df, x='price_usd', hue='manufacturer_name')

sns.displot(df, x='price_usd', hue='engine_type', multiple='stack')

df.groupby('engine_type').count()

Q7_df = df[(df['manufacturer_name'] == 'Audi') & (df['model_name'] == 'Q7')] sns.histplot(Q7_df, x='price_usd', hue='year_produced')

df.groupby('transmission').count()

focus_df = df[(df['manufacturer_name'] == 'Ford') & (df['transmission'] == 'mechanical') & (df['model_name'] == 'Focus')] focus_df

sns.histplot(focus_df, x='price_usd', hue='year_produced')

sns.histplot(focus_df, x='price_usd', hue='color')

sns.histplot(focus_df, x='price_usd', hue='engine_type')

import pandas as pd import matplotlib.pyplot as plt import seaborn as sns df = pd.read_csv('cars.csv')

# Desviación estandar df['price_usd'].std()

# Rango = valor max - valor min df['price_usd'].max() - df['price_usd'].min()

# Cuartiles median = df['price_usd'].median() q1 = df['price_usd'].quantile(q=0.25) q3 = df['price_usd'].quantile(q=0.75) min_val = df['price_usd'].quantile(q=0) max_val = df['price_usd'].quantile(q=1.0) print("Min=", min_val, "Q1=", q1, "Q2=", median, "Q3=", q3, "Max=", max_val)

Min= 1.0 Q1= 2100.0 Q2= 4800.0 Q3= 8990.0 Max= 50000.0

# Rango intercuartil iqr = q3 - q1 iqr

min_limit = q1 - (1.5 * iqr) max_limit = q3 + (1.5 * iqr) print("Valor mínimo =", min_limit, "| Valor máximo =", max_limit)

Valor mínimo = -8235.0 | Valor máximo = 19325.0

sns.histplot(df['price_usd'])

sns.boxplot(df['price_usd'])

/shared-libs/python3.9/py/lib/python3.9/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
  warnings.warn(

sns.boxplot(data=df, x='engine_fuel', y='price_usd')

sns.boxplot(data=df, x='color', y='price_usd')

import pandas as pd import seaborn as sns iris = sns.load_dataset('iris') iris.head()

iris.dtypes

iris.describe()

# scatterplot sns.scatterplot(data=iris, x='sepal_length', y='petal_length', hue='species')

# jointplot sns.jointplot(data=iris, x='sepal_length', y='petal_length', hue='species')

# boxplot sns.boxplot(data=iris, x='species', y='sepal_length')

# barplot sns.barplot(data=iris, y='species', x='petal_length')

import timeit import numpy as np import pandas as pd import matplotlib.pyplot as plt from sklearn import datasets, linear_model X, y = datasets.load_diabetes(return_X_y=True) raw = X[:, None, 2]

# reglas de escalamiento max_raw = max(raw) min_raw = min(raw) scaled = (2*raw - max_raw - min_raw) / (max_raw - min_raw) fig, axs = plt.subplots(2, 1, sharex=True) axs[0].hist(raw) axs[1].hist(scaled)

# Modelo de entrenamiento def train_raw(): linear_model.LinearRegression().fit(raw, y) def train_scaled(): linear_model.LinearRegression().fit(scaled, y)

raw_time = timeit.timeit(train_raw, number=100) scaled_time = timeit.timeit(train_scaled, number=100) print("train raw: {}".format(raw_time)) print("train scaled: {}".format(scaled_time))

train raw: 0.03855987700080732
train scaled: 0.03810651000094367

# Escalamiento Z-Score import numpy as np mean_raw = np.mean(raw) stdev_raw = np.std(raw) scaled_zscore = (raw - mean_raw) / stdev_raw fig, axs = plt.subplots(2, 1, sharex=True) axs[0].hist(raw) axs[1].hist(scaled_zscore)

# Modelo de entrenamiento def train_raw(): linear_model.LinearRegression().fit(raw, y) def train_scaled(): linear_model.LinearRegression().fit(scaled_zscore, y)

raw_time = timeit.timeit(train_raw, number=1) scaled_time = timeit.timeit(train_scaled, number=100) print("train raw: {}".format(raw_time)) print("train scaled: {}".format(scaled_time))

train raw: 0.000728926999727264
train scaled: 0.03307844200026011

df = pd.read_csv('cars.csv')

df.price_usd.hist()

p = 10_000 df.price_usd.apply(lambda x: np.tanh(x/p)).hist()

import pandas as pd df = pd.read_csv("cars.csv")

pd.get_dummies(df['engine_type'])

import sklearn.preprocessing as preprocessing encoder = preprocessing.OneHotEncoder(handle_unknown='ignore')

encoder.fit(df[['engine_type']].values)

encoder.transform([['gasoline'], ['diesel'], ['aceite']]).toarray()

encoder.fit(df[['year_produced']].values)

encoder.transform([[2016], [2009], [2031]]).toarray()

import numpy as np import matplotlib.pyplot as plt import seaborn as sns from sklearn.preprocessing import StandardScaler iris = sns.load_dataset('iris')

sns.pairplot(iris, hue='species')

iris.columns

scaler = StandardScaler() scaled = scaler.fit_transform( iris[['sepal_length', 'sepal_width', 'petal_length', 'petal_width']] ) scaled.T

covariance_matrix = np.cov(scaled.T) covariance_matrix

plt.figure(figsize=(10, 10)) sns.set(font_scale=1.5) hm = sns.heatmap(covariance_matrix, cbar=True, annot=True, square=True, fmt='.2f', annot_kws={'size': 12}, yticklabels=['sepal_length', 'sepal_width', 'petal_length', 'petal_width'], xticklabels=['sepal_length', 'sepal_width', 'petal_length', 'petal_width']) # sns.heatmap(iris.corr(), annot=True)

# En python el producto de matrices se calcula fácil usando la librería numpy: import numpy as np A = np.array([[2, 4], [-1, 2]]) B = np.array([[2, 3], [-4, 1]]) np.matmul(A, B)

# El cálculo de matriz inversa se hace rápido usando NumPy nuevamente así: A = np.array([[2, 4], [-1, 2]]) Ainversa = np.linalg.inv(A) Ainversa

# Para comprobar que esto está bien, puedes multiplicar ambas matrices para ver que da lo correcto: np.matmul(A, Ainversa)

# Aquí entonces debemos encontrar las combinaciones de x, y e que satisfacen el sistema de ecuaciones A = np.array([[1, 2], [1, 0]]) values, vectors = np.linalg.eig(A)

print(values, "\n") print(vectors)

[ 2. -1.] 

[[ 0.89442719 -0.70710678]
 [ 0.4472136   0.70710678]]

# Puedes verificar que cada vector y su respectivo valor propio cumplen la ecuación original ejecutando cada parte así: np.matmul(A, vectors.T[1])

#Mientras que por otro lado calculando, Resulta en lo mismo: values[1]*vectors.T[1]

import numpy as np import pandas as pd import matplotlib.pyplot as plt import seaborn as sns from sklearn.preprocessing import StandardScaler iris = sns.load_dataset('iris') scaler = StandardScaler() scaled = scaler.fit_transform( iris[['sepal_length', 'sepal_width', 'petal_length', 'petal_width']].values ) covariance_matrix = np.cov(scaled.T) covariance_matrix

sns.pairplot(iris)

sns.jointplot(x=iris['petal_length'], y=iris['petal_width']) sns.jointplot(x=scaled[:, 2], y=scaled[:, 3])

eigen_values, eigen_vectors = np.linalg.eig(covariance_matrix)

eigen_values

eigen_vectors

variance_explained = [] for i in eigen_values: variance_explained.append((i/sum(eigen_values))*100) print(variance_explained)

[72.9624454132999, 22.850761786701725, 3.6689218892828612, 0.5178709107154993]

from sklearn.decomposition import PCA pca = PCA(n_components=2) pca.fit(scaled)

pca.explained_variance_ratio_

reduced_scaled = pca.transform(scaled)

reduced_scaled

scaled

iris['pca_1'] = reduced_scaled[:, 0] iris['pca_2'] = reduced_scaled[:, 1] iris

sns.jointplot(iris['pca_1'], iris['pca_2'], hue=iris['species'])

/shared-libs/python3.9/py/lib/python3.9/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
  warnings.warn(