380 lines
11 KiB
Plaintext
380 lines
11 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "a46c141c-0657-4870-81d2-38d814c05877",
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"metadata": {},
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"source": [
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"# Notebook-A3 (Descriptive Statistics)\n",
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"\n",
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"- Measures of Central Tendency and variability\n",
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"- Dataset generated in this code"
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]
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},
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{
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"cell_type": "markdown",
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"id": "f7cf5345-dd87-41be-adb6-6fcec33f9255",
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"metadata": {},
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"source": [
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"## Problem Statement - Part 1 (data.csv)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "997254c8-a0c4-4cc6-a360-9a9534e89f4c",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Import library\n",
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"import pandas as pd"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "3326aae8-b9c4-448a-83f6-6060f84c7749",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Generate data\n",
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"data = {\n",
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" 'age': [25, 30, 22, 40, 55, 60, 33, 28, 45, 50],\n",
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" 'income': [50000, 60000, 45000, 70000, 80000, 90000, 65000, 62000, 75000, 85000],\n",
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" 'age_group': ['20-30', '30-40', '20-30', '40-50', '50-60', '50-60', '30-40', '20-30', '40-50', '50-60']\n",
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"}\n",
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"\n",
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"# Define data in DataFrame\n",
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"df = pd.DataFrame(data)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "31afc8b3-a7ef-492e-bebc-4a079cc6b402",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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" count mean std min 25% 50% \\\n",
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"age_group \n",
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"20-30 3.0 52333.333333 8736.894948 45000.0 47500.0 50000.0 \n",
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"30-40 2.0 62500.000000 3535.533906 60000.0 61250.0 62500.0 \n",
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"40-50 2.0 72500.000000 3535.533906 70000.0 71250.0 72500.0 \n",
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"50-60 3.0 85000.000000 5000.000000 80000.0 82500.0 85000.0 \n",
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"\n",
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" 75% max \n",
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"age_group \n",
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"20-30 56000.0 62000.0 \n",
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"30-40 63750.0 65000.0 \n",
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"40-50 73750.0 75000.0 \n",
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"50-60 87500.0 90000.0 \n"
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]
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}
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],
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"source": [
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"# Group the data by age_group and compute summary statistics for 'income'\n",
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"summary_stats = df.groupby('age_group')['income'].describe()\n",
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"\n",
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"# Print summary\n",
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"print(summary_stats)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "7bf97efc-1450-4289-a31f-15a7a9629743",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Median Income by Age Group:\n",
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"age_group\n",
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"20-30 50000.0\n",
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"30-40 62500.0\n",
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"40-50 72500.0\n",
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"50-60 85000.0\n",
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"Name: income, dtype: float64\n"
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]
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}
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],
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"source": [
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"# Group the data by age_group; Select income column for each of the groups created; Calculate median for income\n",
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"median_income = df.groupby('age_group')['income'].median()\n",
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"\n",
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"# Print dat median\n",
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"print(\"Median Income by Age Group:\")\n",
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"print(median_income)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"id": "3e69e1ef-9bdb-4ca9-8408-aa3cd09bcb64",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Column Names: Index(['age', 'income', 'age_group'], dtype='object')\n"
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]
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}
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],
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"source": [
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"# Print column names\n",
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"print(\"Column Names:\", df.columns)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"id": "86d9558e-7e50-4982-914a-9f409b67fa19",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Modified dataset with repeated values\n",
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"data = {\n",
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" 'age': [25, 30, 25, 40, 55, 60, 33, 28, 45, 50, 25, 30, 28, 30, 25],\n",
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" 'income': [50000, 60000, 50000, 70000, 80000, 90000, 65000, 62000, 75000, 85000, 50000, 60000, 62000, 70000, 75000],\n",
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" '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']\n",
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"}\n",
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"\n",
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"# Define data in DataFrame\n",
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"df = pd.DataFrame(data)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"id": "87006581-7ccd-4c4e-9db0-49448f651aea",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Mode of Age: [25]\n",
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"Mode of Income: [50000]\n"
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]
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}
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],
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"source": [
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"# Calculate the mode for each column\n",
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"mode_age = df['age'].mode()\n",
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"mode_income = df['income'].mode()\n",
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"print(f\"Mode of Age: {mode_age.values}\")\n",
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"print(f\"Mode of Income: {mode_income.values}\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "43a96895-7a33-40d9-bc8e-e0a3c7f140f5",
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"metadata": {},
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"source": [
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"---"
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]
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},
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{
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"cell_type": "markdown",
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"id": "7db076ac-079b-478a-b41c-c972ba2ca0b4",
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"metadata": {},
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"source": [
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"## Problem Statment - Part 2"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 20,
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"id": "5cd29330-9fcc-4023-a7c0-d791a19172eb",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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" sepal.length sepal.width petal.length petal.width variety\n",
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"0 5.1 3.5 1.4 0.2 Setosa\n",
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"1 4.9 3.0 1.4 0.2 Setosa\n",
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"2 4.7 3.2 1.3 0.2 Setosa\n",
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"3 4.6 3.1 1.5 0.2 Setosa\n",
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"4 5.0 3.6 1.4 0.2 Setosa\n"
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]
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}
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],
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"source": [
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"# Load iris.csv in the DataFrame\n",
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"df = pd.read_csv('iris.csv')\n",
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"\n",
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"print(df.head()) # Print first 5 rows"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 21,
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"id": "e6f74058-9c12-4f09-a376-29583480ac91",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\n",
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"Percentiles by Species:\n",
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" sepal.length sepal.width petal.length petal.width\n",
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"variety \n",
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"Setosa 0.25 4.800 3.200 1.400 0.2\n",
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" 0.50 5.000 3.400 1.500 0.2\n",
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" 0.75 5.200 3.675 1.575 0.3\n",
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"Versicolor 0.25 5.600 2.525 4.000 1.2\n",
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" 0.50 5.900 2.800 4.350 1.3\n",
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" 0.75 6.300 3.000 4.600 1.5\n",
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"Virginica 0.25 6.225 2.800 5.100 1.8\n",
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" 0.50 6.500 3.000 5.550 2.0\n",
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" 0.75 6.900 3.175 5.875 2.3\n"
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]
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}
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],
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"source": [
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"# Group the data by species and display summary statistics\n",
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"summary_stats_species = df.groupby('variety').describe()\n",
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"\n",
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"# Compute specific percentiles and statistics\n",
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"percentiles = df.groupby('variety').quantile([0.25, 0.5, 0.75])\n",
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"\n",
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"# Display summary statistics and percentiles\n",
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"summary_stats_species = df.groupby('variety').describe()\n",
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"\n",
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"print(\"\\nPercentiles by Species:\")\n",
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"print(percentiles)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"id": "2116e46c-a150-4df9-82b9-a4684007ba59",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\n",
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"Summary Statistics by Species for Sepal Width:\n",
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" count mean std min 25% 50% 75% max\n",
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"variety \n",
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"Setosa 50.0 3.428 0.379064 2.3 3.200 3.4 3.675 4.4\n",
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"Versicolor 50.0 2.770 0.313798 2.0 2.525 2.8 3.000 3.4\n",
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"Virginica 50.0 2.974 0.322497 2.2 2.800 3.0 3.175 3.8\n"
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]
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}
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],
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"source": [
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"# Group the data by variety; Select sepal.width column for each of the groups created; Display summary statistics\n",
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"summary_stats_species = df.groupby('variety')['sepal.width'].describe()\n",
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"\n",
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"print(\"\\nSummary Statistics by Species for Sepal Width:\")\n",
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"print(summary_stats_species)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 14,
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"id": "47148444-1790-46a9-93ce-dba152e58894",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Median Values by Species:\n",
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" sepal.length sepal.width petal.length petal.width\n",
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"variety \n",
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"Setosa 5.0 3.4 1.50 0.2\n",
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"Versicolor 5.9 2.8 4.35 1.3\n",
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"Virginica 6.5 3.0 5.55 2.0\n"
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]
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}
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],
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"source": [
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"# Group by variety and compute the median for numeric columns\n",
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"median_values = df.groupby('variety').median()\n",
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"\n",
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"print(\"Median Values by Species:\")\n",
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"print(median_values)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 15,
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"id": "3adfcaa8-d3e2-483d-9715-4d1a95cd2da9",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Median Sepal Length by Species:\n",
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"variety\n",
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"Setosa 5.0\n",
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"Versicolor 5.9\n",
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"Virginica 6.5\n",
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"Name: sepal.length, dtype: float64\n"
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]
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}
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],
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"source": [
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"# Group the data by variety; Select sepal.width column for each of the groups created; Display median\n",
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"median_sepal_length = df.groupby('variety')['sepal.length'].median()\n",
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"print(\"Median Sepal Length by Species:\")\n",
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"print(median_sepal_length)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 16,
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"id": "4a27f1b8-91d0-43a6-8593-983ddf5f9c58",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Mode of Width: [3.]\n"
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]
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}
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],
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"source": [
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"# Calculate & print mode for sepal.width\n",
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"mode_width = df['sepal.width'].mode()\n",
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"print(f\"Mode of Width: {mode_width.values}\")"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.20"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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