Q&A 1 What are summary statistics and how do you compute them?

1.1 Explanation

Summary statistics provide a quick overview of the distribution and central tendency of a dataset. They include measures like mean, median, mode, standard deviation, min, max, and quantiles.

1.2 Python Code

import pandas as pd

# Load sample data
df = pd.read_csv("data/iris.csv")

# Summary statistics
summary = df.describe()
print(summary)

       sepal_length  sepal_width  petal_length  petal_width
count    150.000000   150.000000    150.000000   150.000000
mean       5.843333     3.057333      3.758000     1.199333
std        0.828066     0.435866      1.765298     0.762238
min        4.300000     2.000000      1.000000     0.100000
25%        5.100000     2.800000      1.600000     0.300000
50%        5.800000     3.000000      4.350000     1.300000
75%        6.400000     3.300000      5.100000     1.800000
max        7.900000     4.400000      6.900000     2.500000

1.3 R Code

library(readr)
library(dplyr)

# Load sample data
df <- read_csv("data/iris.csv")

# Summary statistics
summary(df)

  sepal_length    sepal_width     petal_length    petal_width   
 Min.   :4.300   Min.   :2.000   Min.   :1.000   Min.   :0.100  
 1st Qu.:5.100   1st Qu.:2.800   1st Qu.:1.600   1st Qu.:0.300  
 Median :5.800   Median :3.000   Median :4.350   Median :1.300  
 Mean   :5.843   Mean   :3.057   Mean   :3.758   Mean   :1.199  
 3rd Qu.:6.400   3rd Qu.:3.300   3rd Qu.:5.100   3rd Qu.:1.800  
 Max.   :7.900   Max.   :4.400   Max.   :6.900   Max.   :2.500  
   species         
 Length:150        
 Class :character  
 Mode  :character