import pandas as pd
import numpy as np
import seaborn as sns
import scipy.stats as st
np.random.seed(20)
var1 = np.random.randint(0, 10, 10) # Días de vacaciones
var2 = var1+np.random.normal(0, 1, 10) # Dinero gastado
var1
var2
grafico = sns.regplot(var1, var2, ci=80)
/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(
# Unimos las variables var1 y var2 (estamos creando un tuple) a partir de zip
tuple = list(zip(var1, var2))
tuple
# Transformamos el tuple a un data frame a partir de DataFrame
tabla = pd.DataFrame(tuple,
columns = ['Días_vacaciones', 'Dinero_gastado'])
tabla
Días_vacacionesint64
0 - 9
Dinero_gastadofloat64
0.3234610099156434 - 9.559696289403918
0
3
1.915167413
1
9
9.559696289
2
4
4.93946935
3
6
5.021518958
4
7
7.50309684
5
2
2.406414469
6
0
0.3234610099
7
6
5.506589118
8
8
7.207983209
9
5
4.157632066
# Calcula los intervalos de confianza a un 95% para ambas variables
st.t.interval(alpha=0.95, df=len(tabla)-1, loc=np.mean(tabla), scale=st.sem(tabla))