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Introduction to Pandas Library

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In this video, we will understand what the Pandas Library is and its importance in data analysis.

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Identify and describe Pandas' key data structures.

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Learn how to load, inspect, filter, sort, and group data using Pandas.

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Apply basic Pandas' operations to analyze and summarize real-world data sets.

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Have you ever known how in programming, computers organize and handle huge amounts of data?

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That's where Pandas comes in.

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Pandas is a Python library that helps us manipulate and understand data easily.

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Now, let's understand Pandas step-by-step, starting with installing Python.

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Go to your web browser and search python.org.

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Open the official Python website.

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Download the latest version and run the setup file.

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Once the installation is complete, open your command prompt type.

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Python-ELPIP install--upgrade-PIP and press enter.

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This updates PIP, which helps install other Python packages smoothly.

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Next, install Pandas.

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Type Python-EMPIP install-pandas- and hit enter.

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Wait for the installation to complete.

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You'll see a requirement-satisfied message.

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Now, install Jupyter Notebook.

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Type Python-EMPIP install notebook- and press enter.

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This will download and set up Jupyter on your computer.

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Once it's done, start Jupyter Notebook by typing.

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Python-EM notebook.

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The browser will automatically open the Jupyter interface.

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Now, let's upload a CSV file containing a large data set.

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In Jupyter Notebook, we'll use pandas to load and organize this data easily.

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After uploading your CSV file, click on new at the top right corner and select Python 3.

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A new blank notebook will open where we can start writing Python code to load and analyze our data.

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First, import pandas using import pandas as PD.

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Then, read your CSV file with dash-df equals PD.read-csv-open-quote-example.csv-close-quote.

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Finally, type df.head to see the first few rows of your data set.

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Pandas displays a neat table so you can quickly inspect and understand your data.

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Now that we've loaded the data set, let's filter and sort the data.

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First, we'll filter the rows where the age is greater than 30.

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In Python-type, filtered equals df.open-bracket,

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f.close-bracket, open-bracket, df.close-bracket, open-bracket, age.close-bracket,

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greater than 30.close-bracket, and then print open parenthesis filtered, close parenthesis.

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Pandas will show only the rows where age is above 30.

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Next, we can sort the entire data set by the sales column.

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Type, sorted, all equals df.sort.values.

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Sales, ascending equals false, and then print sorted all.

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This displays all rows sorted from highest to lowest sales, making it easier to analyze top performers.

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Now, let's explore grouping and aggregation in our data set to get meaningful insights.

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First, we'll group the data by product and calculate the total sales.

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Pandas will display the total sales for each product.

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Next, we'll calculate the average sales per product.

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This gives a clear summary of how each product is performing on average.

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Finally, we can count the number of sales entries per product.

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Now, we can see how many sales records exist for each product helping us understand the distribution of our sales data.

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Let's merge two data sets in pandas.

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First, load the sales data, then load regional targets.

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Merge them using merged all-duty df.

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Finally, print the merged data frame.

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Now, all sales data and regional targets are combined in one organized table.