0Pricing
Pandas & NumPy Academy · Lección

Análisis bivariante y de correlación

Explore las relaciones entre pares de columnas mediante gráficos de dispersión, gráficos de cajas y un mapa de calor de correlación.

Análisis bivariante y de correlación es una lección gratuita de Pandas & NumPy Academy en CoddyKit. Esta es la lección 3 de 4. Puedes leer la lección completa abajo gratuitamente — luego la practicas en el navegador con un editor de código integrado y un tutor de IA 24/7. Forma parte de la ruta de aprendizaje de Pandas & NumPy Academy, y tu progreso se sincroniza en la web y la app de CoddyKit. El curso de Pandas & NumPy Academy incluye 4 lecciones en total.

Partes de esta lección aún no han sido traducidas y se muestran en inglés.

From One Variable to Two

Bivariate analysis examines the relationship between exactly two columns at a time. After profiling each column individually (univariate analysis), you ask: how do these two variables relate? The analysis method depends on the combination of variable types: numeric vs. numeric (scatter plot + correlation), categorical vs. numeric (box/violin plot), or categorical vs. categorical (crosstab + chi-squared). Each combination requires a different technique.

import pandas as pd
import seaborn as sns

df = sns.load_dataset('titanic')

# Relationship types present in the dataset
print('Numeric columns:', df.select_dtypes('number').columns.tolist())
print('Categorical columns:', df.select_dtypes('object').columns.tolist())
print('Boolean columns:', df.select_dtypes('bool').columns.tolist())

Numeric vs. Numeric: Scatter Plot

For two numeric variables, a scatter plot is the primary bivariate tool. It shows each observation as a point at (x, y) coordinates, revealing the direction, strength, and form of the relationship. Always look at the plot before computing a correlation coefficient — a correlation of 0 can still show a strong non-linear (curved) relationship that the coefficient misses.

import seaborn as sns
import matplotlib.pyplot as plt

df = sns.load_dataset('titanic')

sns.scatterplot(data=df, x='age', y='fare', alpha=0.4)
plt.title('Age vs. Fare — Is There a Linear Relationship?')
plt.xlabel('Age')
plt.ylabel('Fare ($)')
plt.show()

Pearson Correlation Coefficient

The Pearson r quantifies the strength and direction of the linear relationship between two numeric variables. Compute it with df[['col1', 'col2']].corr() or scipy.stats.pearsonr(x, y) which also returns a p-value for significance testing. Always pair r with a scatter plot — high r can be driven by a few outliers, and the same r can describe very different scatter shapes (Anscombe's Quartet famously illustrates this).

import pandas as pd
import seaborn as sns
from scipy import stats

df = sns.load_dataset('titanic').dropna(subset=['age', 'fare'])

# Pearson correlation
r, p = stats.pearsonr(df['age'], df['fare'])
print(f'Pearson r = {r:.3f}, p-value = {p:.4f}')

# Spearman for robustness check
rho, p_sp = stats.spearmanr(df['age'], df['fare'])
print(f'Spearman rho = {rho:.3f}, p-value = {p_sp:.4f}')

Categorical vs. Numeric: Box and Violin Plots

When one variable is categorical and the other is numeric, the question is: does the numeric distribution differ across categories? Use a box plot or violin plot to compare. For example, does survival status affect the fare paid? Does the passenger class affect age? These plots immediately reveal whether category membership is associated with higher or lower values of the numeric variable.

import seaborn as sns
import matplotlib.pyplot as plt

df = sns.load_dataset('titanic')

fig, axes = plt.subplots(1, 2, figsize=(12, 5))

sns.boxplot(data=df, x='class', y='fare',
            order=['First', 'Second', 'Third'], ax=axes[0])
axes[0].set_title('Fare by Passenger Class')

sns.violinplot(data=df, x='survived', y='age',
               inner='quartile', ax=axes[1])
axes[1].set_title('Age Distribution by Survival')

plt.tight_layout()
plt.show()

GroupBy Statistics for Categorical vs. Numeric

Complement the visual comparison with numeric group statistics using groupby. Compute the mean, median, and count for each category to quantify the differences seen in plots. Look for categories where the mean and median diverge (indicating outliers within a group), and check whether group sizes are balanced — very small groups (<5 observations) make comparisons unreliable.

import pandas as pd
import seaborn as sns

df = sns.load_dataset('titanic')

stats = df.groupby('class')['fare'].agg(['mean', 'median', 'std', 'count'])
stats.columns = ['Mean Fare', 'Median Fare', 'Std Fare', 'Count']
print(stats.round(2))

print('\nSurvival rate by class:')
print(df.groupby('class')['survived'].mean().round(3))

Categorical vs. Categorical: Crosstabs

For two categorical variables, use pd.crosstab(df['var1'], df['var2']) to create a frequency table showing how many observations fall in each combination of categories. Normalise by row or column with normalize='index' or normalize='columns' to get proportions instead of counts. This is the foundation for studying whether two categorical variables are independent or associated.

import pandas as pd
import seaborn as sns

df = sns.load_dataset('titanic')

# Frequency table
counts = pd.crosstab(df['class'], df['survived'])
counts.columns = ['Died', 'Survived']
print('Counts:')
print(counts)

# Row-normalised proportions (survival rate per class)
props = pd.crosstab(df['class'], df['survived'], normalize='index')
props.columns = ['Died', 'Survived']
print('\nSurvival Rate per Class:')
print(props.round(3))

Visualising Crosstabs as Heatmaps

Turn a crosstab into a heatmap for instant visual comparison of cell frequencies. This is more scannable than a printed table when there are many category combinations. Annotate each cell with its value using annot=True and choose a sequential colour map (like 'YlOrRd') since all values are non-negative. Heatmaps of row-normalised proportions effectively show the conditional distribution of one category given another.

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

df = sns.load_dataset('titanic')
props = pd.crosstab(df['class'], df['sex'], normalize='index')

sns.heatmap(props, annot=True, fmt='.2f', cmap='YlOrRd')
plt.title('Gender Mix by Passenger Class (Row %)')
plt.xlabel('Sex')
plt.ylabel('Class')
plt.show()

Chi-Squared Test for Independence

The chi-squared test of independence checks whether two categorical variables are statistically independent. scipy.stats.chi2_contingency(crosstab) returns the chi-squared statistic, p-value, degrees of freedom, and expected frequencies. A small p-value (typically < 0.05) means there is a statistically significant association between the two variables — the distribution of one varies systematically with the other.

import pandas as pd
import seaborn as sns
from scipy.stats import chi2_contingency

df = sns.load_dataset('titanic')

# Test if passenger class and survival are independent
crosstab = pd.crosstab(df['class'], df['survived'])
chi2, p, dof, expected = chi2_contingency(crosstab)

print(f'Chi-squared: {chi2:.2f}')
print(f'P-value: {p:.6f}')
print(f'Degrees of freedom: {dof}')
print(f'\nConclusion: {"Significant association" if p < 0.05 else "No significant association"}')

Full Correlation Matrix for Numeric Columns

Rather than examining pairs one at a time, compute the full correlation matrix for all numeric columns and visualise it as a heatmap. This reveals which pairs of variables are strongly correlated in one glance. High correlations between independent features (multicollinearity) can cause problems in regression models — knowing about them early allows you to remove or combine redundant features before modelling.

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np

df = sns.load_dataset('titanic')
corr = df.select_dtypes('number').corr()
mask = np.triu(np.ones_like(corr, dtype=bool))

sns.heatmap(corr, mask=mask, annot=True, fmt='.2f',
            cmap='coolwarm', vmin=-1, vmax=1,
            linewidths=0.5, square=True)
plt.title('Correlation Matrix — Titanic Numeric Columns')
plt.tight_layout()
plt.show()

Interactions Across Subgroups

Sometimes a relationship between two variables differs dramatically across subgroups — a phenomenon known as Simpson's Paradox. For example, the correlation between age and fare might be positive for first-class passengers but negative for third-class. Always segment your bivariate analysis by key categorical variables (using hue in Seaborn or separate groupby aggregations) to check whether the relationship is consistent or reverses across subgroups.

import seaborn as sns
import matplotlib.pyplot as plt

df = sns.load_dataset('titanic')

# Scatter coloured by passenger class
sns.scatterplot(
    data=df.dropna(subset=['age', 'fare']),
    x='age',
    y='fare',
    hue='class',
    alpha=0.5,
    palette='deep'
)
plt.title('Age vs Fare — Does Class Change the Relationship?')
plt.legend(title='Class')
plt.yscale('log')  # log scale due to fare skewness
plt.show()

Documenting Bivariate Findings

After completing bivariate analysis, document key findings: which pairs of features are correlated (and how strongly), whether categorical variables show meaningful group differences in numeric outcomes, and any subgroup interactions or paradoxes found. These insights should drive feature selection decisions — highly correlated feature pairs may need one dropped, and strong categorical predictors of the target variable should be flagged as high-priority inputs for modelling.

Quick Check

Test your understanding of bivariate and correlation analysis from this lesson.

Lesson Recap

In this lesson you learned: scatter plots and Pearson r analyse numeric-numeric relationships, box/violin plots and groupby compare numeric variables across categories, and crosstabs with chi-squared tests measure association between categorical variables. Next up we cover how to compile EDA findings into a structured summary report.

Preguntas frecuentes

¿La lección «Análisis bivariante y de correlación» es gratis?

Sí — el texto completo de «Análisis bivariante y de correlación» es gratis para leer aquí en la web. Para practicarla de forma interactiva (editor de código integrado y tutor de IA 24/7) y desbloquear el resto del curso de Pandas & NumPy Academy, actualiza a CoddyKit PRO. El curso de Pandas & NumPy Academy incluye 4 lecciones en total.

¿Qué aprenderé en «Análisis bivariante y de correlación»?

Explore las relaciones entre pares de columnas mediante gráficos de dispersión, gráficos de cajas y un mapa de calor de correlación. Practicas Pandas & NumPy Academy con código real que ejecutas directamente en el navegador, y un tutor de IA 24/7 responde tus preguntas mientras trabajas en la lección.

¿Necesito experiencia previa para empezar Pandas & NumPy Academy?

No se requiere experiencia previa. Pandas & NumPy Academy en CoddyKit está estructurado para principiantes hasta estudiantes avanzados, así que puedes empezar aquí o desde el inicio y avanzar a tu ritmo. Esta es la lección 3 de 4.

¿Cuánto tiempo toma la lección «Análisis bivariante y de correlación»?

La mayoría de las lecciones de CoddyKit toman alrededor de 5–10 minutos. Cada una es compacta e interactiva, así que avanzas constantemente y retomas exactamente por donde dejaste en la web y la app.

¿Puedo escribir y ejecutar código en esta lección de Pandas & NumPy Academy?

Sí. Cada lección de Pandas & NumPy Academy incluye un editor de código integrado, así que escribes y ejecutas código real directamente en tu navegador y obtienes retroalimentación instantánea de IA — sin configuración local necesaria.

Todas las lecciones de este curso

  1. Lista de comprobación para perfilar un dataset
  2. Análisis univariante
  3. Análisis bivariante y de correlación
  4. Resumir conclusiones en un informe
← Volver a Pandas & NumPy Academy