Q3: Descriptive Statistics - Measures of Central Tendency and Variability Perform the following operations on any open source dataset (e.g., data.csv) 1. Provide summary statistics (mean, median, minimum, maximum, standard deviation) for a dataset (age, income etc.) with numeric variables grouped by one of the qualitative (categorical) variable. For example, if your categorical variable is age groups and quantitative variable is income, then provide summary statistics of income grouped by the age groups. Create a list that contains a numeric value for each response to the categorical variable. 2. Write a Python program to display some basic statistical details like percentile, mean, standard deviation etc. of the species of 'Iris-setosa', 'Iris-versicolor' and 'Iris-versicolor' of iris.csv dataset. Provide the code with outputs and explain everything that you do in this step.

import pandas as pd df = pd.read_csv("data3.csv") print("Dataset:\n", df) # Group by categorical variable (Gender) grouped = df.groupby("Gender")["Income"] # Calculate statistics summary = grouped.agg(['mean', 'median', 'min', 'max', 'std']) print("Summary Statistics (Income grouped by Gender):\n") print(summary) # Create list for each category income_list = grouped.apply(list) print("\nIncome values grouped by Gender:\n") print(income_list)

import pandas as pd iris = pd.read_csv("iris.csv") print(iris.head()) # Group by species grouped = iris.groupby("species") # Mean values print("Mean values for each species:\n") print(grouped.mean()) # Standard deviation print("Standard deviation for each species:\n") print(grouped.std()) # Percentiles for each species print("Percentiles for each species:\n") percentiles = grouped.quantile([0.25, 0.5, 0.75]) print(percentiles)

sepal_length,sepal_width,petal_length,petal_width,species 5.1,3.5,1.4,0.2,setosa 4.9,3.0,1.4,0.2,setosa 4.7,3.2,1.3,0.2,setosa 4.6,3.1,1.5,0.2,setosa 5.0,3.6,1.4,0.2,setosa 5.4,3.9,1.7,0.4,setosa 4.6,3.4,1.4,0.3,setosa 5.0,3.4,1.5,0.2,setosa 4.4,2.9,1.4,0.2,setosa 4.9,3.1,1.5,0.1,setosa 5.4,3.7,1.5,0.2,setosa 4.8,3.4,1.6,0.2,setosa 4.8,3.0,1.4,0.1,setosa 4.3,3.0,1.1,0.1,setosa 5.8,4.0,1.2,0.2,setosa 5.7,4.4,1.5,0.4,setosa 5.4,3.9,1.3,0.4,setosa 5.1,3.5,1.4,0.3,setosa 5.7,3.8,1.7,0.3,setosa 5.1,3.8,1.5,0.3,setosa 5.4,3.4,1.7,0.2,setosa 5.1,3.7,1.5,0.4,setosa 4.6,3.6,1.0,0.2,setosa 5.1,3.3,1.7,0.5,setosa 4.8,3.4,1.9,0.2,setosa 5.0,3.0,1.6,0.2,setosa 5.0,3.4,1.6,0.4,setosa 5.2,3.5,1.5,0.2,setosa 5.2,3.4,1.4,0.2,setosa 4.7,3.2,1.6,0.2,setosa 4.8,3.1,1.6,0.2,setosa 5.4,3.4,1.5,0.4,setosa 5.2,4.1,1.5,0.1,setosa 5.5,4.2,1.4,0.2,setosa 4.9,3.1,1.5,0.1,setosa 5.0,3.2,1.2,0.2,setosa 5.5,3.5,1.3,0.2,setosa 4.9,3.1,1.5,0.1,setosa 4.4,3.0,1.3,0.2,setosa 5.1,3.4,1.5,0.2,setosa 5.0,3.5,1.3,0.3,setosa 4.5,2.3,1.3,0.3,setosa 4.4,3.2,1.3,0.2,setosa 5.0,3.5,1.6,0.6,setosa 5.1,3.8,1.9,0.4,setosa 4.8,3.0,1.4,0.3,setosa 5.1,3.8,1.6,0.2,setosa 4.6,3.2,1.4,0.2,setosa 5.3,3.7,1.5,0.2,setosa 5.0,3.3,1.4,0.2,setosa 7.0,3.2,4.7,1.4,versicolor 6.4,3.2,4.5,1.5,versicolor 6.9,3.1,4.9,1.5,versicolor 5.5,2.3,4.0,1.3,versicolor 6.5,2.8,4.6,1.5,versicolor 5.7,2.8,4.5,1.3,versicolor 6.3,3.3,4.7,1.6,versicolor 4.9,2.4,3.3,1.0,versicolor 6.6,2.9,4.6,1.3,versicolor 5.2,2.7,3.9,1.4,versicolor 5.0,2.0,3.5,1.0,versicolor 5.9,3.0,4.2,1.5,versicolor 6.0,2.2,4.0,1.0,versicolor 6.1,2.9,4.7,1.4,versicolor 5.6,2.9,3.6,1.3,versicolor 6.7,3.1,4.4,1.4,versicolor 5.6,3.0,4.5,1.5,versicolor 5.8,2.7,4.1,1.0,versicolor 6.2,2.2,4.5,1.5,versicolor 5.6,2.5,3.9,1.1,versicolor 5.9,3.2,4.8,1.8,versicolor 6.1,2.8,4.0,1.3,versicolor 6.3,2.5,4.9,1.5,versicolor 6.1,2.8,4.7,1.2,versicolor 6.4,2.9,4.3,1.3,versicolor 6.6,3.0,4.4,1.4,versicolor 6.8,2.8,4.8,1.4,versicolor 6.7,3.0,5.0,1.7,versicolor 6.0,2.9,4.5,1.5,versicolor 5.7,2.6,3.5,1.0,versicolor 5.5,2.4,3.8,1.1,versicolor 5.5,2.4,3.7,1.0,versicolor 5.8,2.7,3.9,1.2,versicolor 6.0,2.7,5.1,1.6,versicolor 5.4,3.0,4.5,1.5,versicolor 6.0,3.4,4.5,1.6,versicolor 6.7,3.1,4.7,1.5,versicolor 6.3,2.3,4.4,1.3,versicolor 5.6,3.0,4.1,1.3,versicolor 5.5,2.5,4.0,1.3,versicolor 5.5,2.6,4.4,1.2,versicolor 6.1,3.0,4.6,1.4,versicolor 5.8,2.6,4.0,1.2,versicolor 5.0,2.3,3.3,1.0,versicolor 5.6,2.7,4.2,1.3,versicolor 5.7,3.0,4.2,1.2,versicolor 5.7,2.9,4.2,1.3,versicolor 6.2,2.9,4.3,1.3,versicolor 5.1,2.5,3.0,1.1,versicolor 5.7,2.8,4.1,1.3,versicolor 6.3,3.3,6.0,2.5,virginica 5.8,2.7,5.1,1.9,virginica 7.1,3.0,5.9,2.1,virginica 6.3,2.9,5.6,1.8,virginica 6.5,3.0,5.8,2.2,virginica 7.6,3.0,6.6,2.1,virginica 4.9,2.5,4.5,1.7,virginica 7.3,2.9,6.3,1.8,virginica 6.7,2.5,5.8,1.8,virginica 7.2,3.6,6.1,2.5,virginica 6.5,3.2,5.1,2.0,virginica 6.4,2.7,5.3,1.9,virginica 6.8,3.0,5.5,2.1,virginica 5.7,2.5,5.0,2.0,virginica 5.8,2.8,5.1,2.4,virginica 6.4,3.2,5.3,2.3,virginica 6.5,3.0,5.5,1.8,virginica 7.7,3.8,6.7,2.2,virginica 7.7,2.6,6.9,2.3,virginica 6.0,2.2,5.0,1.5,virginica 6.9,3.2,5.7,2.3,virginica 5.6,2.8,4.9,2.0,virginica 7.7,2.8,6.7,2.0,virginica 6.3,2.7,4.9,1.8,virginica 6.7,3.3,5.7,2.1,virginica 7.2,3.2,6.0,1.8,virginica 6.2,2.8,4.8,1.8,virginica 6.1,3.0,4.9,1.8,virginica 6.4,2.8,5.6,2.1,virginica 7.2,3.0,5.8,1.6,virginica 7.4,2.8,6.1,1.9,virginica 7.9,3.8,6.4,2.0,virginica 6.4,2.8,5.6,2.2,virginica 6.3,2.8,5.1,1.5,virginica 6.1,2.6,5.6,1.4,virginica 7.7,3.0,6.1,2.3,virginica 6.3,3.4,5.6,2.4,virginica 6.4,3.1,5.5,1.8,virginica 6.0,3.0,4.8,1.8,virginica 6.9,3.1,5.4,2.1,virginica 6.7,3.1,5.6,2.4,virginica 6.9,3.1,5.1,2.3,virginica 5.8,2.7,5.1,1.9,virginica 6.8,3.2,5.9,2.3,virginica 6.7,3.3,5.7,2.5,virginica 6.7,3.0,5.2,2.3,virginica 6.3,2.5,5.0,1.9,virginica 6.5,3.0,5.2,2.0,virginica 6.2,3.4,5.4,2.3,virginica 5.9,3.0,5.1,1.8,virginica

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Descriptive Statistics - Measures of Central Tendency and Variability

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