Data Analytics With Python

Column Selection Techniques in Pandas Column Selection Techniques in Pandas This blog post aims to explore different methods for selecting columns in Pandas DataFrames, inspired by Matt Harrison's book, Effective Pandas . The Python library Pandas provides multiple flexible and efficient ways to manipulate, analyze, and visualize data. One of the most common tasks in data wrangling is column selection. Let's examine some effective techniques for this. Initial Setup import pandas as pd loc = 'https://raw.githubusercontent.com/aew5044/Python---Public/main/movie.csv' m = pd.read_csv(loc) m.head() Basic Column Selection You can start by selecting columns directly by their names. ma = m[['actor_1_name', 'actor_2_name', 'actor_3_name', 'director_name', 'movie_title','gross']] ma.head() Using Regex for Column Selection Regular expressions (Regex) can be po

A Beginner's Guide to Data Binning and Categorization in Pandas A Beginner's Guide to Data Binning and Categorization in Pandas Introduction When you're new to Python and data analysis, terms like "data binning" or "categorization" might seem daunting. But don't worry! This guide will walk you through these concepts using simple examples. Generating Random Data import random import pandas as pd def generate_random_series(): return pd.Series([random.randint(0, 100) for x in range(15)]) We start by importing the necessary modules and then define a function that generates a Pandas Series of 15 random integers between 0 and 100. Binning with Custom Ranges ages = generate_random_series() bins = [10, 20, 30, 40, 50, 60, 70, 80, 90, 100] age_bins = pd.cut(ages, bins) age_bins.value_counts() The pd.cut() function bins the random ages into custom age groups defined by the list bins . The value_counts() method shows the f

Mastering Descriptive Tables in Python: A Nod to SAS's PROC FREQ Mastering Descriptive Tables in Python: A Nod to SAS's PROC FREQ The art of data analysis often begins with understanding the landscape of your dataset. In SAS, PROC FREQ has long been the de facto tool for generating descriptive tables. Python, via its Pandas library, offers comparable functionalities albeit with a different approach. This post aims to bridge the gap between SAS's PROC FREQ and Python's Pandas, focusing on generating descriptive tables that are replete with counts, row percentages, column percentages, and overall percentages. A Quick Dive into SAS's PROC FREQ Before we dive into Python, let's understand what PROC FREQ in SAS is capable of. This procedure is immensely powerful for categorical data analysis. It provides easy ways to calculate counts, percentages, and additional statistics with simple syntax. For instance, one might execute: PROC FREQ

Mastering DataFrame Merge in Pandas: Options and Pitfalls Mastering DataFrame Merge in Pandas: Options and Pitfalls The Pandas library's DataFrame.merge() function is a powerful tool for merging DataFrames, yet its numerous options can sometimes lead to unexpected results. In this blog, we will explore each option in detail and highlight scenarios that could produce inaccurate data. Function Signature DataFrame.merge(right, how='inner', on=None, left_on=None, right_on=None, left_index=False, right_index=False, sort=False, suffixes=('_x', '_y'), copy=None, indicator=False, validate=None) Options 1. right The DataFrame you want to merge with. Always required. 2. how Specifies the type of join to execute. The default is 'inner', but other options include 'left', 'right', and 'outer'. 3.