Added codes, datasets and Jupyter notebooks directory.

2025-06-11 13:48:53 +05:30
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+# A3 - Descriptive Statistics
+
+✅ Tested and working as intended.
+
+---
+
+## Pre-requisites
+
+- Install required libraries: `pandas`
+
+```shell
+pip install pandas
+```
+
+---
+
+## Code blocks
+
+### Problem Statement - Part 1 (data.csv)
+
+1. Import library
+
+```python3
+import pandas as pd
+```
+
+2. Generate data and load into DataFrame:
+
+```python3
+# Generate data
+data = {
+    'age': [25, 30, 22, 40, 55, 60, 33, 28, 45, 50],
+    'income': [50000, 60000, 45000, 70000, 80000, 90000, 65000, 62000, 75000, 85000],
+    'age_group': ['20-30', '30-40', '20-30', '40-50', '50-60', '50-60', '30-40', '20-30', '40-50', '50-60']
+}
+
+# Define data in DataFrame
+df = pd.DataFrame(data)
+```
+
+3. Group data by `age_group`, compute statistics for `income` + print:
+
+```python3
+# Group the data by age_group and compute summary statistics for 'income'
+summary_stats = df.groupby('age_group')['income'].describe()
+
+# Print summary
+print(summary_stats)
+```
+
+4. Group the data by `age_group`; Select `income` column for each of the groups created; Calculate median for `income`:
+
+```python3
+# Group the data by age_group; Select income column for each of the groups created; Calculate median for income
+median_income = df.groupby('age_group')['income'].median()
+
+# Print dat median
+print("Median Income by Age Group:")
+print(median_income)
+```
+
+5. Print column names:
+
+```python3
+print("Column Names:", df.columns)
+```
+
+6. Modified dataset with repeated values; define in DataFrame:
+
+```python3
+# Modified dataset with repeated values
+data = {
+    'age': [25, 30, 25, 40, 55, 60, 33, 28, 45, 50, 25, 30, 28, 30, 25],
+    'income': [50000, 60000, 50000, 70000, 80000, 90000, 65000, 62000, 75000, 85000, 50000, 60000, 62000, 70000, 75000],
+    'age_group': ['20-30', '30-40', '20-30', '40-50', '50-60', '50-60', '30-40', '20-30', '40-50', '50-60', '20-30', '30-40', '20-30', '30-40', '20-30']
+}
+
+# Define data in DataFrame
+df = pd.DataFrame(data)
+```
+
+7. Calculate mode:
+
+```python3
+# Calculate the mode for each column
+mode_age = df['age'].mode()
+mode_income = df['income'].mode()
+print(f"Mode of Age: {mode_age.values}")
+print(f"Mode of Income: {mode_income.values}")
+```
+
+---
+
+### Problem Statement - Part 2 (iris.csv)
+
+- Save the dataset [iris.csv](https://git.kska.io/sppu-te-comp-content/DataScienceAndBigDataAnalytics/src/branch/main/Datasets/iris.csv) in the same directory as this Jupyter notebook.
+
+1. Load dataset and print first 5 rows:
+
+```python3
+# Load iris.csv in the DataFrame
+df = pd.read_csv('iris.csv')
+
+print(df.head()) # Print first 5 columns
+```
+
+2. Group data; Compute percentiles; Display:
+
+```python3
+# Group the data by species and display summary statistics
+summary_stats_species = df.groupby('variety').describe()
+
+# Compute specific percentiles and statistics
+percentiles = df.groupby('variety').quantile([0.25, 0.5, 0.75])
+
+# Display summary statistics and percentiles
+summary_stats_species = df.groupby('variety').describe()
+
+print("\nPercentiles by Species:")
+print(percentiles)
+```
+
+3. Group the data by variety; Select sepal.width column for each of the groups created; Display summary statistics:
+
+```python3
+# Group the data by variety; Select sepal.width column for each of the groups created; Display summary statistics
+summary_stats_species = df.groupby('variety')['sepal.width'].describe()
+
+print("\nSummary Statistics by Species for Sepal Width:")
+print(summary_stats_species)
+```
+
+4. Group by variety and compute the median for numeric columns:
+
+```python3
+# Group by variety and compute the median for numeric columns
+median_values = df.groupby('variety').median()
+
+print("Median Values by Species:")
+print(median_values)
+```
+
+5. Group the data by variety; Select sepal.width column for each of the groups created; Display median:
+
+```python3
+# Group the data by variety; Select sepal.width column for each of the groups created; Display median
+median_sepal_length = df.groupby('variety')['sepal.length'].median()
+print("Median Sepal Length by Species:")
+print(median_sepal_length)
+```
+
+6. Calculate & print mode for sepal.width:
+
+```python3
+# Calculate & print mode for sepal.width
+mode_width = df['sepal.width'].mode()
+print(f"Mode of Width: {mode_width.values}")
+```
+
+---