Python Para Analise De Dados - 3a Edicao Pdf -

Her journey into data analysis with Python had been enlightening. Ana realized that data analysis is not just about processing data but about extracting meaningful insights that can drive decisions. She continued to explore more advanced techniques and libraries in Python, always looking for better ways to analyze and interpret data.

# 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. Python Para Analise De Dados - 3a Edicao Pdf

# 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. Her journey into data analysis with Python had

# Handle missing values and convert data types data.fillna(data.mean(), inplace=True) data['age'] = pd.to_numeric(data['age'], errors='coerce') # Load the dataset data = pd

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.

from sklearn.model_selection import train_test_split from sklearn.ensemble import RandomForestRegressor from sklearn.metrics import mean_squared_error

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