PANDAS Full Course with PRACTICAL (2024) | Learn Python Pandas in 1 Hour

PANDAS Full Course with PRACTICAL (2024) | Learn Python Pandas in 1 Hour - Notes

**Channel:** WsCube Tech

**Video Length:** 1 hour 28 minutes

1. Summary

This video provides a comprehensive introduction to the Python Pandas library, covering its core concepts and practical applications for data analysis. It starts with an overview of Pandas and its data structures (Series and DataFrames), followed by practical demonstrations of creating, exploring, manipulating, and summarizing data using Pandas. The course also addresses common data cleaning tasks like handling duplicates and missing data and concludes with advanced features like GroupBy, merging, concatenating, and pivoting dataframes.

2. Key Takeaways

* Pandas is a powerful Python library for data analysis, manipulation, cleaning, and visualization.

* It offers two primary data structures: Series (one-dimensional labeled arrays) and DataFrames (two-dimensional, tabular data).

* Key functions for data exploration: `head()`, `tail()`, `info()`, `describe()`, `isnull().sum()`.

* Essential data manipulation techniques: handling duplicates (`duplicated()`, `drop_duplicates()`), dealing with missing values (`fillna()`, `dropna()`), column transformation.

* Data summarization is achieved through GroupBy.

* Pandas provides functions for merging, concatenating, and joining DataFrames.

* Techniques for comparing dataframes for differences.

* Pivoting and melting are crucial for data reshaping.

3. Detailed Notes

**00:00:00 - 00:00:17:080 Introduction**

* Overview of Pandas as a powerful library for data analysis and visualization.

* Used for data cleaning, preprocessing, inspection, and summarization.

**00:00:17:080 - 00:01:11:600 Introduction to Pandas**

* Pandas full form is "Panel Data" or "Python Data Analysis."

* Created in 2008 by Wes McKinney.

* Applications include missing data handling, data mutability (adding/deleting columns), data alignment, and powerful GroupBy functionality.

* Core Data Structures: Series and DataFrames.

* **Series:** One-dimensional labeled array (like a single column in Excel).

* **DataFrames:** Two-dimensional data structure (like an Excel sheet with rows and columns).

**00:01:11:600 - 00:07:28 Creation of Data Frames**

* Installation using `pip install pandas` in the command prompt.

* Import using `import pandas as pd`.

* Creating DataFrames from dictionaries:

* Example: Dictionary of lists representing columns. Keys become column names.

* Creating DataFrames from CSV and Excel files using `pd.read_csv()` and `pd.read_excel()`.

* Need to install `pip install openpyxl` for excel files.

* File path considerations (relative vs. absolute paths).

* Using `.head()` and `.tail()` to view the first and last few rows of a DataFrame.

* Default value is 5 rows.

**00:07:28 - 00:15:34 Exploring Data in Pandas**

* Data Exploration methods:

* `.info()`: Provides information about the DataFrame (column names, data types, non-null counts, memory usage).

* `.describe()`: Generates descriptive statistics (count, mean, standard deviation, min, max, quartiles) for numerical columns.

* `.isnull().sum()`: Counts the number of missing values (NaNs) in each column.

**00:15:34 - 00:24:16 Dealing with Duplicate values in Pandas**

* Identifying duplicate rows using `.duplicated()`: Returns True for duplicate rows based on all columns.

* Use `.sum()` to get total number of duplicated rows.

* Dropping duplicate rows using `.drop_duplicates()`:

* Specify the `subset` argument (e.g., `subset=['column_name']`) to check for duplicates within a specific column.

**00:24:16 - 00:30:40 Working with Missing Data in Pandas**

* Identifying missing values: The `isnull()` method.

* Handling missing data:

* `.dropna()`: Removes rows with missing values (can be used selectively).

* `.fillna()`: Fills missing values with a specified value (e.g., 0, mean, median, mode).

* `fill_na()`

* You can fill the missing data with a specific number.

* You can fill the missing data using `Bfill()` or `Ffill()` methods.

**00:30:40 - 00:43:14 Column Transformation in Pandas (Part-1)**

* Performing operations on columns using arithmetic operations (e.g., creating a new column based on existing columns).

* Using the `.loc` (location) attribute to modify values based on conditions (e.g., assigning values based on boolean conditions on a column).

**00:43:14 - 00:52:27 Column Transformation in Pandas (Part-2)**

* Creating a new column based on an existing column and functions

* Using `.apply()` can perform more complex operations (such as string operations).

**00:52:27 - 00:58:22 Python Pandas - GroupBy**

* Grouping data using `.groupby()`: Groups rows based on one or more columns (e.g., `df.groupby('column_name')`).

* Using `.agg()` (aggregate) with `groupby()` to perform calculations on grouped data (e.g., `count()`, `mean()`, `max()`, `min()`, etc.).

* Using multi level groupby (group by multiple columns)

**00:58:22 - 01:06:31 Merge, concatenate and join - Pandas**

* Concatenating dataframes: `pd.concat()`: Combines DataFrames vertically (rows) or horizontally (columns).

* Merging dataframes:

* Using `pd.merge()`: Combines DataFrames based on common columns (like SQL joins).

* Different types of merges: `inner`, `left`, `right`.

* Define the `on` argument to specify the column(s) to merge on.

**01:06:31 - 01:17:55 Pandas-Compare DataFrames**

* Using `.compare()` to compare dataframes:

* Compares values between two DataFrames at the same index.

* Key parameters: `align_axis` , `keep_shape`

**01:17:55 - 01:28:18 Pivoting and Melting Dataframes**

* * Pivoting dataframes: The `.pivot()` method: Reshapes a DataFrame to a new structure.

* Arguments: `index` (column to use as index), `columns` (column to use for new columns), `values` (column to populate the values).

* Melting dataframes: Converts a wide DataFrame to a long format.

* Creates a new column for variables and a column for values.

* Used for reshaping data for analysis.