¿Para que sirve la estadística descriptiva?

Estadística Descriptiva vs Inferencial

Estas 2 ramas de la estadística se diferencian en:

¿Puedes mentir con estadística?

¿Por qué aprender estadística?

Plan del curso

Estadística descriptiva para analítica

Tipos de datos

Se comprenden en 2 categorías fundamentales:

import pandas as pd pd.options.display.float_format = '{:.2f}'.format df = pd.read_csv('../datasets/cars.csv') df

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 38531 entries, 0 to 38530
Data columns (total 30 columns):
 #   Column             Non-Null Count  Dtype  
---  ------             --------------  -----  
 0   manufacturer_name  38531 non-null  object 
 1   model_name         38531 non-null  object 
 2   transmission       38531 non-null  object 
 3   color              38531 non-null  object 
 4   odometer_value     38531 non-null  int64  
 5   year_produced      38531 non-null  int64  
 6   engine_fuel        38531 non-null  object 
 7   engine_has_gas     38531 non-null  bool   
 8   engine_type        38531 non-null  object 
 9   engine_capacity    38521 non-null  float64
 10  body_type          38531 non-null  object 
 11  has_warranty       38531 non-null  bool   
 12  state              38531 non-null  object 
 13  drivetrain         38531 non-null  object 
 14  price_usd          38531 non-null  float64
 15  is_exchangeable    38531 non-null  bool   
 16  location_region    38531 non-null  object 
 17  number_of_photos   38531 non-null  int64  
 18  up_counter         38531 non-null  int64  
 19  feature_0          38531 non-null  bool   
 20  feature_1          38531 non-null  bool   
 21  feature_2          38531 non-null  bool   
 22  feature_3          38531 non-null  bool   
 23  feature_4          38531 non-null  bool   
 24  feature_5          38531 non-null  bool   
 25  feature_6          38531 non-null  bool   
 26  feature_7          38531 non-null  bool   
 27  feature_8          38531 non-null  bool   
 28  feature_9          38531 non-null  bool   
 29  duration_listed    38531 non-null  int64  
dtypes: bool(13), float64(2), int64(5), object(10)
memory usage: 5.5+ MB

df.dtypes

df.describe()

df.describe(include='all')

Medidas de tendencia central

Metáfora de Bill Gates en un bar

Medidas de tendencia central en Python

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')

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')] Q7_df

sns.histplot(Q7_df, x = 'price_usd', hue = 'year_produced')