import pandas as pd
df = pd.read_csv('data_test.csv')
df
df
mean = df['price'].mean()
mean
mediana = df['price'].median()
moda = df['year'].mode()
moda
Ahora usaremos un data set grande sobre _coches_
df_car = pd.read_csv('cars.csv')
df_car.dtypes
mean_prices_cars = round(df_car['price_usd'].mean(),2)
mean_prices_cars
media_prices_cars = df_car['price_usd'].median()
media_prices_cars
df_car['price_usd'].plot.hist(bins = 30)
import seaborn as sns
sns.displot(df_car, x = 'price_usd', hue="manufacturer_name")
df_car.groupby('transmission').count()
Obtención de datos específicos
cars_audi_less_12k = df_car[(df_car['manufacturer_name'] == 'Audi')&(df_car['price_usd'] < 12000)]
sns.histplot(cars_audi_less_12k,x = 'year_produced' ,hue = 'model_name')