Python Para Analise De Dados - 3a Edicao Pdf Review

# 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.

# Handle missing values and convert data types data.fillna(data.mean(), inplace=True) data['age'] = pd.to_numeric(data['age'], errors='coerce') Python Para Analise De Dados - 3a Edicao Pdf

Ana's first project involved analyzing a dataset of user engagement on a popular social media platform. The dataset included user demographics, the type of content they engaged with, and the frequency of their engagement. Ana's goal was to identify patterns in user behavior that could help the platform improve its content recommendation algorithm. # Filter out irrelevant data data = data[data['engagement']

# Load the dataset data = pd.read_csv('social_media_engagement.csv') The dataset was massive, with millions of rows, and Ana needed to clean and preprocess it before analysis. She handled missing values, converted data types where necessary, and filtered out irrelevant data. The dataset included user demographics, the type of