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Work with labeled 1D and 2D data structures for intuitive manipulation and analysis.
Handle missing values, duplicates, and type conversions with built-in methods.
Summarize data using split-apply-combine logic for fast statistical insights.
Read/write CSV, Excel, JSON, Parquet, and SQL with simple one-liners.
Use `import pandas as pd` to access all core functionality.
Read CSV, Excel, or JSON files into a DataFrame using `pd.read_csv()` or similar.
Use methods like `.dropna()`, `.fillna()`, `.astype()`, and `.apply()` to prepare data.
Use `.groupby()`, `.agg()`, and `.plot()` to extract insights and trends.
Save results to CSV, Excel, or database using `.to_csv()` or `.to_sql()`.
# Pandas GroupBy Example
import pandas as pd
df = pd.read_csv("sales.csv")
summary = df.groupby("Region")["Revenue"].agg(["sum", "mean"])
print(summary)Clean and transform raw data before feeding into ML models or dashboards.
Summarize distributions, correlations, and outliers in structured datasets.
Resample, interpolate, and analyze trends over time using datetime indexing.
Generate clean outputs for BI tools, Excel reports, or downstream APIs.
Explore Pandas’s ecosystem and find the tools, platforms, and docs to accelerate your workflow.
Common questions about Pandas’s capabilities, usage, and ecosystem.