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.

# Plot histograms for user demographics data.hist(bins=50, figsize=(20,15)) plt.show()

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'] > 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

# Calculate and display the correlation matrix corr = data.corr() plt.figure(figsize=(10,8)) sns.heatmap(corr, annot=True, cmap='coolwarm', square=True) plt.show() Ana's EDA revealed interesting patterns, such as a strong correlation between age and engagement frequency, and a preference for video content among younger users. These insights were crucial for informing the social media platform's content strategy.

To further refine her analysis, Ana decided to build a simple predictive model using scikit-learn, a machine learning library for Python. She aimed to predict user engagement based on demographics and content preferences.