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7 Tricks That’ll Make You Feel Like a Data Magician!
After a long time, I’m finally writing on Medium again, and it feels great to be back! Today, I’m sharing seven Pandas tricks that have saved me tons of time on my projects.
If you’re a data scientist or data analyst, you probably use the Pandas library daily. But have you ever wondered if your code is memory-efficient and flexible? Optimizing these aspects is crucial for a smooth data workflow.
Let’s explore these powerful tricks — you’ll want to start using them right away!
1. Use dtype to Avoid Memory Overload
How many times have you loaded a massive CSV file? Almost every time, right? But did you know that inefficient loading can eat up all your system memory? The solution? Specify dtype when reading your CSV file:
import pandas as pd
df = pd.read_csv("large_file.csv", dtype={"column1": "int32", "column2": "float32"})This ensures Pandas assigns appropriate data types to each column, reducing memory usage and improving loading speed. Try it — you’ll thank me later!
2. Chaining Operations for Cleaner Code
Ever heard of method chaining? Probably. But have you ever tried it in Pandas? Instead of writing separate steps for modifying, filtering, and selecting data, you can chain them together for cleaner, more efficient code:
df_cleaned = (df.dropna().query("column1 > 10").assign(new_column=lambda x: x["column2"] * 2))This approach reduces errors and allows you to execute everything in a single streamlined process.
3. Use .query() for Simple Filtering
If you frequently filter data using multiple conditions, your code can get long and difficult to follow. Make it easy to read by using .query() for simple filtering:
Conclusion
These seven tricks will make you feel like a data magician, and you can start using them right away! With dtype, method chaining, and .query(), you can optimize your Pandas code for better performance, cleaner code, and reduced errors.
