Python: Performing basic Statistical Analysis in Google Colab using Excel file in Google Drive

In this Python tutorial, we will explore how to access an Excel file stored in Google Drive using Google Colab and perform basic statistical analysis on the data.
To begin, we will open the Excel file in Google Sheets and then import it into Python for analysis. First, navigate to "My Drive" and locate the "Colab Notebooks" folder, which contains all your Python notebooks. If you don't have a notebook yet, create a new one by going to the "New" menu, selecting "More," and then choosing "Google Colaboratory." This will create a new Python notebook in your Google Drive.
Next, we will rename the notebook to "Basic Statistics" and save it to our Google Drive. If desired, you can easily move the file to the "Colab Notebooks" folder using the drag and drop feature.
To access Google Drive from our Python notebook, we need to import the Google Drive library. By importing the "drive" module, we gain access to functions like mounting and unmounting Google Drive, as well as working with files and directories.
After importing the "drive" module, we can mount the Google Drive and connect to it from our Python notebook. When running the code, Google Colab will request permission to connect to your Google Drive folder. Choose your account and click the "Allow" button to grant permission. Once connected, Colab will confirm that the drive has been successfully mounted.
Since we only need to mount the Google Drive once, we can add another code block to include additional Python code. In this case, we will import the pandas library, which will be used to read the Excel file.
We will read the Excel file from Google Drive into a Pandas DataFrame by specifying the path to the Excel file. To ensure that the code is functioning correctly, we can print the DataFrame and verify that the Excel file has been successfully loaded.
Performing basic statistical analysis on the DataFrame is straightforward using the Pandas library. The "describe" function provides summary statistics that give an overview of the data distribution, including measures of central tendency, dispersion, and shape. Running this code will display the results of our statistical analysis.
To further analyze our dataset, we can perform correlation analysis using the correlation function provided by the Pandas library. Correlation measures the relationship or connection between two or more variables. We can create a heat map of correlations using the seaborn library to visualize the relationships between different variables in our dataset.
Using the seaborn library's heatmap function, we can convert the correlations into a heatmap for better understanding. The resulting heatmap reveals that sales are more affected by social media marketing.
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Chapters:
00:30 - Creating a Python Notebook
01:34 - Mounting Google Drive
02:38 - Reading Excel Files using Pandas Library
03:58 - Performing Basic Statistics on a Dataframe
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