Introduction to Pandas & Creating a DataFrame

Simple explanation of Pandas, key features, and how to create a DataFrame from list and dictionary data.

1. What is Pandas?

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Pandas is a Python library used for data analysis and data manipulation.

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It works mainly with tabular data (rows & columns) similar to Excel tables or SQL tables.

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Core objects are Series (1D) and DataFrame (2D table).

Feature	Explanation
Easy Data Loading	Read data from CSV, Excel, JSON, SQL, Parquet, HTML, etc. into a DataFrame with a single function call.
Data Cleaning	Handle missing values, remove duplicates, convert data types, and fix invalid data.
Data Filtering	Filter rows with conditions (for example: `age > 21`, `city == "Hyderabad"`).
Data Transformation	Add new columns, apply custom functions, merge/join multiple tables, group and aggregate data.
Time-Series Support	Strong support for date/time operations like resampling, rolling windows, and date-based indexing.
Fast Operations	Internally uses NumPy arrays, so operations are vectorized and much faster than normal Python loops.
Easy Export	Export cleaned/processed data back to CSV, Excel, JSON, SQL, etc. using `to_*` functions.

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Use when data is available as a list of rows, each row as a list or tuple.

data = [
    ["John", 25],
    ["Anna", 30],
    ["Peter", 35]
]

import pandas as pd

df_from_list = pd.DataFrame(data, columns=["Name", "Age"])
print(df_from_list)

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Use when data is available as a dictionary of columns (each key is a column name).

data_dict = {
    "Name": ["John", "Anna", "Peter"],
    "Age":  [25, 30, 35]
}

import pandas as pd

df_from_dict = pd.DataFrame(data_dict)
print(df_from_dict)

Pandas is used for fast, easy data analysis and manipulation.

Key features: loading, cleaning, filtering, transforming, time-series, and exporting data.

You can create a DataFrame from list data or dictionary data in just one line using pd.DataFrame().