Data Analytics with Python - Lecture 2: Python Fundamentals

Python Installation

• Installation Process:
  • Step 1: Type https://www.anaconda.com in the web browser's address bar.
  • Step 2: Click the download button.
  • Step 3: Download Python 3.7 version for Windows OS.
  • Step 4: Double click the downloaded file to run the application.
  • Step 5: Follow the on-screen instructions to complete the installation process.
• Anaconda Navigator is a GUI that comes with the Anaconda distribution, providing a user-friendly interface to launch applications like Jupyter Notebook, Spyder, etc.

Jupyter Notebook

• Why use Jupyter Notebook?
  • Edit code in a web browser.
  • Facilitates easy documentation.
  • Aids in easy demonstration.
  • User-friendly interface.
• Python and Jupyter Interaction:
  • Jupyter application is a software package that contains both Python and the Jupyter application.
• About Jupyter Notebook Interface:
  • The interface operates through a web browser, typically accessed via http://localhost:8888/notebooks/About_Jupyter_Notebook.ipynb.
  • Input Field Indicators:
    • Green color indicates "edit mode."
    • Blue color indicates "command mode."
• Markdown Cells:
  • Contain documentation.
  • Text within Markdown cells is not executed as code.
• Command Mode:
  • Allows editing of the notebook as a whole.
  • To exit edit mode, press the Escape key.
• Execution Methods:
  • Ctrl + Enter: Executes the current cell, and the output field cannot be modified.
  • Shift + Enter: Executes the current cell, and the output field can be modified.
  • Run button: Use the Run button on the Jupyter interface to execute a cell.
• Comments:
  • A comment line is written preceding with the # symbol.
• Important Shortcut Keys:
  • A: Create a cell above the current cell.
  • B: Create a cell below the current cell.
  • D + D: Delete the current cell.
  • M: Convert the current cell to a Markdown cell.
  • Y: Convert the current cell to a code cell.

Fundamentals of Python Data Analysis with Pandas

• Objectives:
  • Loading a simple delimited data file.
  • Counting the number of rows and columns loaded.
  • Determining the data type of each column.
  • Looking at different parts of the data by subsetting rows and columns.
• Importing Libraries:
  • Import the Pandas library using the alias pd: import pandas as pd.
  • Import the NumPy library using the alias np: import numpy as np.
  • Import the Matplotlib library using the alias plt: import matplotlib.pyplot as plt.
• Loading Data:
  • Use the read_csv function in Pandas to load a CSV file into a DataFrame.
  • Example: df = pandas.read_csv('F: /2019-20/NPTEL/2 Introduction to Python/data/gapminder-Five YearData.csv').
• Displaying Data:
  • To show the entire DataFrame: df.
  • To display the first 5 rows, use the .head() method: df.head().
• Inspecting Data:
  • Get the number of rows and columns using .shape: df.shape (Outputs a tuple, e.g., (1704, 6)).
  • Get the column names using .columns: df.columns (Outputs an Index object).
  • Get the data type of each column using .dtypes: df.dtypes.
• Pandas Data Types vs. Python Data Types:
  • object: Typically represents strings.
  • int64: Represents integer numbers.
  • float64: Represents floating-point numbers (numbers with decimals).
  • datetime64: Represents dates and times (found in Python's datetime library).
• Getting More Information:
  • Use .info() to get a summary of the DataFrame, including data types, non-null values, and memory usage: df.info().
• Accessing Columns:
  • To get a specific column and save it as a Series (one-dimensional array): country_df = df['country'].
  • Display the first 5 observations: country_df.head().
  • Display the last 5 observations: country_df.tail().
• Subsetting Data:
  • To select multiple columns, pass a list of column names: subset = df[['country', 'continent', 'year']].
  • Display the first few rows of the subset: subset.head().
  • Display the last few rows of the subset: subset.tail().