In this lecture you will learn how to improt dataset in pandas and run basic commands.
The Dataset used here in this section is already uploaded here.
The Link to Pandas library is here
Here are some of the key reasons why the Pandas library is important in Python:
📊 Data Manipulation: Pandas allows you to easily manipulate large datasets using its powerful DataFrame data structure. You can select, filter, group, pivot and transform your data efficiently.
📈 Data Analysis: Common data analysis tasks like cleaning, wrangling, aggregating, joining and reshaping data are built into Pandas fundamentally. This accelerates your analysis workflow.
📝 Data Visualization: While not as fully-featured as Matplotlib/Seaborn, Pandas makes basic data visualization very easy via plotting methods on DataFrames. Great for quick exploratory plots.
🗓 Time-Series Data: Pandas has excellent support for loading, working with and manipulating datetime data. This makes it very suitable for analysing financial, business or any temporal data.
📦 Data IO: Pandas simplifies data import/export from CSV, Excel, SQL, JSON and other formats using optimized read/write methods. Great for quick ETL tasks.
📡 APIs: Pandas works seamlessly with popular APIs like Google Sheets, SQL, REST APIs to provide more functionality. Expand your data science beyond local datasets.
⚡ Speed: Optimized data structures and algorithms ensure Pandas code runs very fast even on huge datasets compared to raw Python. Ideal for big data tasks.
In summary, Pandas empowers data scientists with an integrated toolkit for data wrangling, munging, cleaning, analysis and visualization in Python – making it an essential library for data manipulation and analysis tasks.
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