Showing posts from August, 2023

Mapping Values in DataFrames: An Introduction to Pandas' map Method

Mapping Values in DataFrames: An In-depth Guide to Pandas' map Method Mapping Values in DataFrames: An Introduction to Pandas' map Method Introduction Data transformation is a common task in data analysis and manipulation. One of the frequent requirements is to replace or map values in a series or DataFrame based on a given relationship or logic. The Pandas library in Python offers a powerful method for this task, known as map . Understanding the map Method The map method allows us to substitute each value in a Series with another value. This can be achieved using a function, a Series, or a dictionary that contains the mapping relationships. Example: Mapping Cities to Regions Consider the following DataFrame containing information about cities in North Carolina, their respective states, attendance figures, and coordinates. import pandas as pd data = { 'City': ['Charlotte', '


Handling Missing Data with Pandas: A Comprehensive Guide Handling Missing Data with Pandas: A Comprehensive Guide Dealing with missing data is an essential part of the data cleaning process in Python programming. The Pandas library provides various methods to fill or drop missing values, depending on the nature of the data and the desired outcome. In this guide, we'll explore different techniques to handle missing data, including hard-coded values, filling specific columns, forward fill, and using rolling averages. 1. Filling All Missing Values with a Fixed Number You can simply use the fillna() function to replace all NaNs with a specific value: import pandas as pd df = pd.DataFrame({'A': [1, 2, None], 'B': [None, 5, 6]}) df.fillna(0) 2. Filling Specific Columns with Different Values df.fillna({1: 0.5, 2: 0}) 3. Forward Fill Method df.fillna(method='ff


Working with Missing Data in Pandas Working with Missing Data in Pandas In this blog post, we will explore various techniques for handling missing data using the Pandas library in Python. Specifically, we will focus on removing rows or columns with NaN or None values using different methods provided by Pandas. Setup We begin by importing the required libraries and reading the CSV file containing the movie data. import pandas as pd import numpy as np loc = '' m = pd.read_csv(loc) Creating a Subset and Introducing Missing Values We create a subset of the data, keeping only the columns we want to work with. Additionally, we introduce NaN values in specific rows and columns. m1 = m[['movie_title','director_name','actor_1_name']][0:5] m1.loc[0:1,'director_name'] = np.nan m1.loc[0:2,'actor_1_name

Fun Function Friday!!!

My Random Function: Count and Percent The other day I was working on a project to simply count and give me the total percent count. First I started with the data.  The Data: import pandas as pd from itertools import combinations data = { 'id': list(range(1, 21)), 'course': ['Math', 'Math', 'Bio', 'Chem', 'Bio', 'Math', 'Chem', 'Bio', 'Chem', 'Math', 'Bio', 'Chem', 'Math', 'Math', 'Bio', 'Chem', 'Bio', 'Math', 'Chem', 'Math'], 'building': ['A', 'B', 'A', 'B', 'B', 'A', 'A', 'B', 'B', 'A', 'A', 'B', 'A', 'B', 'A', 'B', 'B', 'A', 'A', 'B'], 'room': [101, 102, 101, 102, 103, 101, 104, 103, 102, 101, 105, 106, 107, 108, 109