csv-analyzer

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Analisar datasets CSV

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Análise de datasets CSV de benefícios sociais usando pandas.

Carregar CSV

import pandas as pd

# CSV padrão
df = pd.read_csv("arquivo.csv")

# CSV com encoding brasileiro
df = pd.read_csv("arquivo.csv", encoding="latin-1", sep=";")

# CSV grande (em chunks)
for chunk in pd.read_csv("arquivo.csv", chunksize=10000):
    processar(chunk)

Análises Comuns

Visão Geral

# Primeiras linhas
df.head()

# Informações
df.info()

# Estatísticas
df.describe()

# Valores únicos por coluna
df.nunique()

Contagens

# Por programa
df["programa"].value_counts()

# Por UF
df["uf"].value_counts()

# Por faixa de valor
pd.cut(df["valor"], bins=[0, 100, 300, 600, 1000]).value_counts()

Agregações

# Valor total por UF
df.groupby("uf")["valor"].sum()

# Média por programa
df.groupby("programa")["valor"].mean()

# Contagem por município
df.groupby(["uf", "municipio"]).size()

Datasets do Tá na Mão

Bolsa Família

df = pd.read_csv("backend/data/bolsa_familia.csv")
# Colunas esperadas: cpf, nis, valor, municipio_id, data_referencia

BPC/LOAS

df = pd.read_csv("backend/data/bpc.csv")
# Colunas: cpf, tipo (idoso/deficiente), valor, municipio_id

TSEE (Tarifa Social)

df = pd.read_csv("backend/data/tsee.csv")
# Colunas: cpf, distribuidora, desconto, municipio_id

Farmácia Popular

df = pd.read_csv("backend/data/farmacias.csv")
# Colunas: nome, endereco, lat, lng, municipio_id

Limpeza de Dados

# Remover duplicatas
df = df.drop_duplicates()

# Preencher nulos
df["valor"] = df["valor"].fillna(0)

# Converter tipos
df["cpf"] = df["cpf"].astype(str).str.zfill(11)
df["valor"] = pd.to_numeric(df["valor"], errors="coerce")

# Remover espaços
df["nome"] = df["nome"].str.strip()

Exportar Resultados

# CSV
df.to_csv("resultado.csv", index=False)

# JSON
df.to_json("resultado.json", orient="records", force_ascii=False)

# Resumo em markdown
resumo = df.groupby("uf")["valor"].sum().to_markdown()

Visualização Rápida

import matplotlib.pyplot as plt

# Barras por UF
df.groupby("uf")["valor"].sum().plot(kind="bar")
plt.title("Valor Total por UF")
plt.savefig("grafico.png")

Dicas de Performance

Datasets > 1GB: usar chunksize
Muitas colunas: selecionar apenas necessárias com usecols
Tipos conhecidos: especificar dtype para economizar memória