what is Pandas how to use?

Question

What is pandas

Lokendra Singh · Answer

Pandas is a Python library for data analysis and manipulation. Works with structured data (like Excel tables) Main structures: DataFrame (2D table) and Series (1D array) Used for data cleaning, analysis, and file handling (CSV, Excel etc..). Install : pip install pandas

Vijay Kumari · Answer

Pandas is a powerful Python library used for data manipulation and analysis. It provides tools to work with structured data like tables (DataFrames). You can load, clean, transform, and analyze data efficiently using Pandas. For a complete guide, visit: A Complete Pandas Tutorial

Sandhiya Priya · Answer

What is Pandas? Pandas stands for “Python Data Analysis Library” . It’s one of the most powerful and popular Python libraries for data manipulation and analysis . In simple terms: Pandas helps you work with data in tables — like an Excel sheet — directly in Python. Why Use Pandas? Pandas makes it easy to load, clean, transform, and analyze data . You can: Read and write Excel, CSV, SQL, JSON files easily Handle missing data Perform filtering, sorting, and grouping Generate statistics and summaries quickly 1. Installing Pandas In your terminal or command prompt, run: pip install pandas 2. Importing Pandas In Python: import pandas as pd ( pd is a common alias used by developers.) 3. Creating a Simple DataFrame A DataFrame is the main data structure in Pandas — like an Excel sheet (rows and columns). import pandas as pd data = { "Name": ["Sandhiya", "Priya", "Kumar"], "Age": [25, 28, 22], "City": ["Chennai", "Bangalore", "Delhi"] } df = pd.DataFrame(data) print(df) Output: Name Age City 0 Sandhiya 25 Chennai 1 Priya 28 Bangalore 2 Kumar 22 Delhi 4. Reading Data from Files Read CSV file: df = pd.read_csv("data.csv") Read Excel file: df = pd.read_excel("data.xlsx") Read SQL table: import sqlite3 conn = sqlite3.connect("mydb.db") df = pd.read_sql("SELECT * FROM Employees", conn) 5. Viewing Data df.head() # Show first 5 rows df.tail() # Show last 5 rows df.info() # Summary of data types and nulls df.describe() # Statistical summary 6. Selecting Data df["Name"] # Select one column df[["Name", "City"]] # Select multiple columns df.iloc[0] # Select first row df.loc[1, "Age"] # Select specific cell (row 1, Age) 7. Filtering Rows df[df["Age"] > 23] # Filter rows where age > 23 df[df["City"] == "Chennai"] # Filter by city 8. Modifying Data df["Country"] = "India" # Add new column df["Age"] = df["Age"] + 1 # Update values df.drop("City", axis=1) # Remove column 9. Grouping and Aggregation grouped = df.groupby("City")["Age"].mean() print(grouped) 10. Saving Data df.to_csv("output.csv", index=False) df.to_excel("output.xlsx", index=False) Summary Table Feature Pandas Command Import library import pandas as pd Create DataFrame pd.DataFrame(data) Read CSV pd.read_csv("file.csv") View data df.head() Filter df[df["Age"] > 20] Add column df["New"] = value Group data df.groupby("Col").mean() Export df.to_csv("file.csv") Real-Life Uses Analyze sales reports Clean Excel data before uploading Generate reports automatically Combine data from multiple sources Power machine learning pipelines

Rajanikant Hawaldar · Answer

https://www.c-sharpcorner.com/article/understanding-pandas-with-examples/ https://www.c-sharpcorner.com/article/a-complete-pandas-tutorial/

Sangeetha S · Answer

Pandas is a powerful and easy-to-use data analysis and manipulation library for Python. It provides data structures like: Series – a one-dimensional labeled array DataFrame – a two-dimensional labeled table (like an Excel sheet or SQL table) Pandas is widely used in data science, machine learning, finance, and analytics .

Quinn Lillian · Answer

Handling data efficiently with Pandas is similar to how you have to be quick and precise in Geometry Arrow – each row of data is a target, and you need to hit it if you want to go further.

what is Pandas how to use?

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