# Filter out irrelevant data data = data[data['engagement'] > 0] With her data cleaned and preprocessed, Ana moved on to exploratory data analysis (EDA) to understand the distribution of variables and relationships between them. She used histograms, scatter plots, and correlation matrices to gain insights.
import pandas as pd import numpy as np import matplotlib.pyplot as plt Python Para Analise De Dados - 3a Edicao Pdf
# Plot histograms for user demographics data.hist(bins=50, figsize=(20,15)) plt.show() # Filter out irrelevant data data = data[data['engagement']
She began by importing the necessary libraries and loading the dataset into a Pandas DataFrame. Python Para Analise De Dados - 3a Edicao Pdf
And so, Ana's story became a testament to the power of Python in data analysis, a tool that has democratized access to data insights and continues to shape various industries.