Análisis de datos con Python

Esta notebook fue creada originalmente como un blog post por Raúl E. López Briega en Matemáticas, análisis de datos y python. El contenido esta bajo la licencia BSD.


In [2]:
# <!-- collapse=True -->
# importando modulos necesarios
import matplotlib.pyplot as plt
import numpy as np 
from scipy import stats 
import pandas as pd 
import seaborn as sns 
from pydataset import data
from pivottablejs import pivot_ui
from pandas_profiling import ProfileReport
from pandasql import sqldf, load_meat, load_births

def pysqldf(q):
    return sqldf(q, globals())

%matplotlib inline

In [3]:
meat = load_meat()
births = load_births()

In [5]:
ProfileReport(births)


Out[5]:

Overview

Dataset info

Number of variables 2
Number of observations 408
Total Missing (%) 0.0%
Total size in memory 6.5 KiB
Average record size in memory 16.2 B

Variables types

Numeric 1
Categorical 0
Date 1
Text (Unique) 0
Rejected 0

Warnings

    Variables

    births
    Numeric

    Distinct count 393
    Unique (%) 96.3%
    Missing (%) 0.0%
    Missing (n) 0
    Infinite (%) 0.0%
    Infinite (n) 0
    Mean 320590
    Minimum 236551
    Maximum 387798
    Zeros (%) 0.0%

    Quantile statistics

    Minimum 236551
    5-th percentile 261190
    Q1 303460
    Median 325630
    Q3 342050
    95-th percentile 361000
    Maximum 387798
    Range 151247
    Interquartile range 38590

    Descriptive statistics

    Standard deviation 29449
    Coef of variation 0.09186
    Kurtosis -0.086406
    Mean 320590
    MAD 23530
    Skewness -0.61121
    Sum 130798885
    Variance 867240000
    Memory size 3.3 KiB

    date
    Date

    Distinct count 300
    Unique (%) 73.5%
    Missing (%) 0.0%
    Missing (n) 0
    Infinite (%) 0.0%
    Infinite (n) 0
    Minimum 1975-01-01 00:00:00
    Maximum 2012-12-01 00:00:00

    Sample

    date births
    0 1975-01-01 265775
    1 1975-02-01 241045
    2 1975-03-01 268849
    3 1975-04-01 247455
    4 1975-05-01 254545
    
    
    In [6]:
    pivot_ui(meat)
    
    
    
    
    Out[6]:
    
    
    In [ ]: