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 rango = df['price_usd'].max() - df['price_usd'].min() rango

# Quartiles median = df['price_usd'].median() print(f'Media: {median}') Q1 = df['price_usd'].quantile(q=0.25) print(f'Q1: {Q1}') Q3 = df['price_usd'].quantile(q=0.75) print(f'Q3: {Q3}') # Los cuartiles y percentiles son casos especificos de uso del Quantil min_val = df['price_usd'].quantile(q=0) print(f'Valor minimo: {min_val}') max_val = df['price_usd'].quantile(q=1.0) print(f'Valor maximo: {max_val}')

iqr = Q3 - Q1 print(f"Rango Intercuartil: {iqr}")

minlimit = Q1 - 1.5*iqr maxlimit = Q3 + 1.5*iqr print('rango para detección de outliers: {}, {}'.format(minlimit, maxlimit))

sns.set(rc={'figure.figsize':(11.7,8.27)}) # f, (ax_hist, ax_box) = plt.subplots(2, sharex=True, gridspec_kw={"height_ratios": (.6, .4)}) sns.histplot(df['price_usd'])

sns.boxplot(df['price_usd'])

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

sns.set(rc={'figure.figsize':(11.7,8.27)}) f, (ax_hist, ax_box) = plt.subplots(2, sharex=True, gridspec_kw={"height_ratios": (.6, .4)}) sns.histplot(df['price_usd'], ax=ax_hist) sns.boxplot(df['price_usd'], ax=ax_box) ax_hist.set(xlabel='')

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