Import Necessary Libraries and Data

Firstly, we need to import all necessary modules, upload the data, and visually examine it.

Modules and methods description

numpy (np): Essential for numerical computing and array operations;
pandas (pd):
- read_csv(): Facilitates reading CSV files into DataFrame for data analysis;
- DataFrame.head(): Offers a quick glimpse of the initial rows in the DataFrame.
matplotlib.pyplot (plt): Enables creating various types of plots for data visualization;
seaborn (sns): Enhances statistical data visualization based on matplotlib.

Task

Import numpy, pandas, matplotlib.pyplot and seaborn (as np, pd, plt, and sns, respectively).
Read the "Live.csv" file.
Show the first 5 rows of the dataset.

Task

Import numpy, pandas, matplotlib.pyplot and seaborn (as np, pd, plt, and sns, respectively).
Read the "Live.csv" file.
Show the first 5 rows of the dataset.

Mark tasks as Completed

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Everything was clear?

Firstly, we need to import all necessary modules, upload the data, and visually examine it.

numpy (np): Essential for numerical computing and array operations;
pandas (pd):
- read_csv(): Facilitates reading CSV files into DataFrame for data analysis;
- DataFrame.head(): Offers a quick glimpse of the initial rows in the DataFrame.
matplotlib.pyplot (plt): Enables creating various types of plots for data visualization;
seaborn (sns): Enhances statistical data visualization based on matplotlib.

Task

Import numpy, pandas, matplotlib.pyplot and seaborn (as np, pd, plt, and sns, respectively).
Read the "Live.csv" file.
Show the first 5 rows of the dataset.

Mark tasks as Completed

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Section 1. Chapter 2

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