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Pandas Data Analysis And Manipulations / of Pandas DataFrame Creation

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).

2. Key Features of Pandas

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.

3. Creating a DataFrame from a List

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

Step 1: Sample list data

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

Step 2: Create DataFrame

import pandas as pd

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

Output Table

Name Age
John 25
Anna 30
Peter 35

4. Creating a DataFrame from a Dictionary

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

Step 1: Sample dictionary data

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

Step 2: Create DataFrame

import pandas as pd

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

Output Table

Name Age
John 25
Anna 30
Peter 35

5. Visual – List vs Dictionary DataFrame

6. Quick Summary

1.
Pandas is used for fast, easy data analysis and manipulation.
2.
Key features: loading, cleaning, filtering, transforming, time-series, and exporting data.
3.
You can create a DataFrame from list data or dictionary data in just one line using pd.DataFrame().