Data Visualization Made Easy: Plotting Pandas DataFrames in Python with Google Colab

In Google Colab, you can use the Pandas library to create and manipulate DataFrames and the Matplotlib library to create plots and visualizations from your data.

Here's step-by-step guide on how to create another basic plot from a Pandas DataFrame in Google Colab:

Import the required libraries:

python
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import pandas as pd
Create a Pandas DataFrame:

You can either load data from a file or create a DataFrame from scratch. For demonstration purposes, let's create a simple DataFrame:

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data = {'Year': [2010, 2011, 2012, 2013, 2014],
'Sales': [500, 600, 750, 900, 1000]}

df = pd.DataFrame(data)
Plot the data:

You can use Matplotlib to create various types of plots. For a basic line plot of the data, you can do the following:

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This code will create a simple line plot showing how sales have changed over the years. You can customize the plot by adjusting labels, titles, colors, and other parameters as needed.

Display the plot in the Colab notebook:

When you run the code in a Colab notebook cell, the plot will be displayed directly below the code cell.

Here's the complete code:

python
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import pandas as pd

data = {'Year': [2010, 2011, 2012, 2013, 2014],
'Sales': [500, 600, 750, 900, 1000]}

df = pd.DataFrame(data)

This code will create a basic line plot, but you can explore Matplotlib's documentation for more advanced plotting options and customization.