Getting Started with Pandas
Pandas is a fast, flexible, and easy-to-use Python library for data analysis and manipulation. It gives you tools to clean, transform, and analyze datasets quickly without writing endless loops.
Why Pandas?
- Expressive syntax: filter, group, pivot, and aggregate in just a few keystrokes;
- Integration: seamlessly plugs into the broader Python ecosystem (NumPy, Matplotlib, SciPy, scikit-learn);
- Performance: vectorized operations under the hood make many tasks faster than plain Python;
- Versatility: read/write data from CSV, Excel, SQL databases, JSON, HTML tables, and more.
Pandas vs Other Tools
Data analysts often juggle between Excel, SQL, and now pandas. So, when is pandas the right choice?
It is best suited for:
- Small- to medium-scale data that fits in memory;
- Advanced or custom transformations that Excel formulas can't handle;
- Python-based projects where pandas integrates directly with other libraries.
Think of it this way: Excel is great for a few thousand rows, SQL is built for billions, and pandas sits comfortably in the middle.
AI in Action
Throughout this course, you'll often come across prompts you can give to the AI. They're written in natural language - the way you'd normally ask for help. Right after each prompt, you'll see an example response so you know what the code might look like.
For example, you might say:
And AI could reply with something like:
12345678910import pandas as pd data = { "Login": ["user1", "user2", "user3"], "Name": ["Alice", "Bob", "Charlie"], "Age": [24, 30, 27] } df = pd.DataFrame(data) print(df)
Notice two important things here:
- The line
import pandas as pdbrings pandas into your code. Thepdalias is a community standard - almost every example you'll see in tutorials, documentation, and real-world code uses it to keep things concise; - The object
dfis a DataFrame - pandas' main data structure. Think of it as a table inside Python: rows and columns, but with much more flexibility.
If you use Jupyter Notebook, then writing a single variable name on the last line will print it. So, print(df) can be replaced with df.
Tack för dina kommentarer!
Fråga AI
Fråga AI
Fråga vad du vill eller prova någon av de föreslagna frågorna för att starta vårt samtal
Awesome!
Completion rate improved to 10
Getting Started with Pandas
Svep för att visa menyn
Pandas is a fast, flexible, and easy-to-use Python library for data analysis and manipulation. It gives you tools to clean, transform, and analyze datasets quickly without writing endless loops.
Why Pandas?
- Expressive syntax: filter, group, pivot, and aggregate in just a few keystrokes;
- Integration: seamlessly plugs into the broader Python ecosystem (NumPy, Matplotlib, SciPy, scikit-learn);
- Performance: vectorized operations under the hood make many tasks faster than plain Python;
- Versatility: read/write data from CSV, Excel, SQL databases, JSON, HTML tables, and more.
Pandas vs Other Tools
Data analysts often juggle between Excel, SQL, and now pandas. So, when is pandas the right choice?
It is best suited for:
- Small- to medium-scale data that fits in memory;
- Advanced or custom transformations that Excel formulas can't handle;
- Python-based projects where pandas integrates directly with other libraries.
Think of it this way: Excel is great for a few thousand rows, SQL is built for billions, and pandas sits comfortably in the middle.
AI in Action
Throughout this course, you'll often come across prompts you can give to the AI. They're written in natural language - the way you'd normally ask for help. Right after each prompt, you'll see an example response so you know what the code might look like.
For example, you might say:
And AI could reply with something like:
12345678910import pandas as pd data = { "Login": ["user1", "user2", "user3"], "Name": ["Alice", "Bob", "Charlie"], "Age": [24, 30, 27] } df = pd.DataFrame(data) print(df)
Notice two important things here:
- The line
import pandas as pdbrings pandas into your code. Thepdalias is a community standard - almost every example you'll see in tutorials, documentation, and real-world code uses it to keep things concise; - The object
dfis a DataFrame - pandas' main data structure. Think of it as a table inside Python: rows and columns, but with much more flexibility.
If you use Jupyter Notebook, then writing a single variable name on the last line will print it. So, print(df) can be replaced with df.
Tack för dina kommentarer!