Data Analytics With Python

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. ...

 The goal of the article is to investigate the bar function through the style method of Pandas DataFrame.  So, when we work with DataFrames, we can create a visual within a DataFrame.  What does that mean? We can embed bar charts, sparklines, and mini bar charts in the DataFrame.  This can reduce the amount of cognitive load when reviewing a DataFrame.   Google Colab link with all the code To get started, we are going to import the data: import pandas as pd import numpy as np import pandas_profiling as pp loc = 'https://raw.githubusercontent.com/aew5044/Python---Public/main/movie.csv' m = pd.read_csv(loc) pd.set_option('display.max_columns',None) pd.options.display.min_rows = 10 Next, I want to create a new DataFrame that groups by the rating (e.g., "R" "PG-13"), then calculates the total sum, min, max, and total observations for gross.  content = ( m .groupby('content_rating') .agg({'gross':['sum','min...

Exploratory Data Analysis (EDA) Situation: I’m at a new analyst job, and I’m given access to a new database. What next? Do I wait for a data request?  Do I wait for a random task from my boss? Do I review established reports?  What do I do? I just started a new job in February of 2022. Every day I continually see new datasets, inherit established code, and update old code.  I like to ask open questions: What does each row represent? What are the main “filter” columns? What are the data types? How did the last analyst handle the data? Each list of questions and associated processes are unique to each person and the tasks at hand.  By now, this process is second nature. The purpose of this blog is to learn about Pandas. How  would I approach this in Pandas? Let's dig in! Assumptions, I have access to the table. In this example, m associated with movies as a DataFrame  First, I want to look at everything. The describe method is a method to quickly ...

  Goal: Filter DataFrames Filtering data is required for every data analysis project.  The majority of blogs I see only detail how to filter the DataFrame by the row index. It is rare I need to filter on the row index and I don't want to reset the index for every filter. For example, we only want data in the DataFrame where the budget is over $10,000,000, and the director's name is James Cameron.  Well, that is a very specific example, but you get the idea.   There are two general steps (example on Google Colab ) Define the filters/mask Reference the mask(s) between []  I always made the filtering process more difficult than reality.  After looking, and looking, and looking for ways to filter data in pandas, I found a method that meets my expectations.  It must be easy to remember, discuss, and explain to non-programmers.  Also, if another person not all that familiar we Python, they can update the filters as needed, add new ones, and continu...

Goal: Create a new column based on data from another column. Use Pandas.DataFrame and Numpy.select to create the new column.  You can see the full program at  Google Colaboratory There are three basic steps to accomplish the goal.   Define the conditions Define the values Use np.select to apply the conditions and values. First, create a list containing each "condition," then  create a list containing each return v alue.   The goal is to correlate each condition with each value.  What does that mean?  It means if the first condition in the "conditions" list evaluates as True, return the first value in the "values" list.   An error will occur when the lists have different index counts.  For example, if there are five conditions and 4 values.      This condition example below has two useful applications we would be remiss if we didn't take note:   The first is the use of .isnull() == True. The second is th...