{
"cells": [
{
"cell_type": "markdown",
"id": "56f0b3b1",
"metadata": {},
"source": [
"# 04 Reading bar graphs movies"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "d9ccfeb7-6cfc-441b-9730-2a3be6a99146",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%%html\n",
""
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "46fcd448-258a-403c-ba08-dee0609935e5",
"metadata": {},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import pandas as pd\n",
"import plotly.graph_objects as go\n",
"import seaborn as sns"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "d18a40bb-feab-4ea2-a09c-79aa6dce3b80",
"metadata": {},
"outputs": [],
"source": [
"import findspark\n",
"\n",
"findspark.init()\n",
"from pyspark.context import SparkContext\n",
"from pyspark.sql.session import SparkSession\n",
"\n",
"spark = SparkSession.builder.appName(\"statistics\").master(\"local\").getOrCreate()"
]
},
{
"cell_type": "markdown",
"id": "cb217f5f-8009-492b-a9c4-be45734e0a1c",
"metadata": {},
"source": [
"[khanacademy](https://www.khanacademy.org/math/ap-statistics/analyzing-categorical-ap/xfb5d8e68:categorical-variable-graphs/v/more-solving-problems-with-bar-graphs?modal=1)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "58523461-f4f5-4133-ab89-aab91dc7f2ec",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"id": "7aa3cefa-0fea-4f84-a87b-05eba89a5766",
"metadata": {},
"source": [
"\n",
"\n",
"\n",
"\n",
"\n",
""
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "b9d75e99-a212-48b1-af73-578f187f2824",
"metadata": {},
"outputs": [],
"source": [
"dataset = {\n",
" 'Favorite type of movie': ['Comdey', 'Scary', 'Adenvture', 'Cartoon', 'Mystery'],\n",
" 'Number of people' : [20, 6, 10, 10, 16],\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "8b52a0cb-80d5-4735-a6f2-aa46266027c7",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" Number of people | \n",
"
\n",
" \n",
" Favorite type of movie | \n",
" | \n",
"
\n",
" \n",
" \n",
" \n",
" Comdey | \n",
" 20 | \n",
"
\n",
" \n",
" Scary | \n",
" 6 | \n",
"
\n",
" \n",
" Adenvture | \n",
" 10 | \n",
"
\n",
" \n",
" Cartoon | \n",
" 10 | \n",
"
\n",
" \n",
" Mystery | \n",
" 16 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" Number of people\n",
"Favorite type of movie \n",
"Comdey 20\n",
"Scary 6\n",
"Adenvture 10\n",
"Cartoon 10\n",
"Mystery 16"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.DataFrame(dataset).set_index('Favorite type of movie')\n",
"df"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "1c3556de-d582-4836-98e0-691c5f85e0d9",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"+----------------------+----------------+\n",
"|Favorite type of movie|Number of people|\n",
"+----------------------+----------------+\n",
"| Comdey| 20|\n",
"| Scary| 6|\n",
"| Adenvture| 10|\n",
"| Cartoon| 10|\n",
"| Mystery| 16|\n",
"+----------------------+----------------+\n",
"\n"
]
}
],
"source": [
"sdf = spark.createDataFrame(zip(*dataset.values()), list(dataset.keys()))\n",
"sdf.show()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "3957996d-47bd-4744-b971-fa94589e3841",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" Number of people | \n",
"
\n",
" \n",
" Favorite type of movie | \n",
" | \n",
"
\n",
" \n",
" \n",
" \n",
" Scary | \n",
" 6 | \n",
"
\n",
" \n",
" Adenvture | \n",
" 10 | \n",
"
\n",
" \n",
" Cartoon | \n",
" 10 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" Number of people\n",
"Favorite type of movie \n",
"Scary 6\n",
"Adenvture 10\n",
"Cartoon 10"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[df['Number of people'] < 14]"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "dbe50d47-c6a2-4951-8906-6cae2afe0ad1",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"+----------------------+----------------+\n",
"|Favorite type of movie|Number of people|\n",
"+----------------------+----------------+\n",
"| Scary| 6|\n",
"| Adenvture| 10|\n",
"| Cartoon| 10|\n",
"+----------------------+----------------+\n",
"\n"
]
}
],
"source": [
"sdf[sdf['Number of people'] < 14].show()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "2d4dd3a0-d390-42f0-8c3f-1f78a661ffa2",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"df.plot(kind=\"bar\")"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "54aab67a-0a4d-4b41-8f7a-f10c53f4f09b",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.bar(x=dataset['Favorite type of movie'], height=dataset['Number of people'])\n",
"plt.xlabel('Favorite type of movie')\n",
"plt.ylabel('Number of people')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "12b8f0c0-2913-43c4-a90c-40373e1ad814",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"sns.barplot(x=dataset['Favorite type of movie'], y=dataset['Number of people'])\n",
"plt.xlabel('Favorite type of movie')\n",
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",
"text/html": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"data = [go.Bar(x=dataset['Favorite type of movie'], y=dataset['Number of people'])]\n",
"layout = go.Layout(\n",
" xaxis=dict(title='Favorite type of movie'),\n",
" yaxis=dict(title='Number of people')\n",
")\n",
"fig = go.Figure(data, layout)\n",
"fig.show()"
]
},
{
"cell_type": "code",
"execution_count": 70,
"id": "666ec232-e661-4d2b-9f30-44e713b351ae",
"metadata": {},
"outputs": [],
"source": [
"dataset = {\n",
" 'Day':['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday'],\n",
" 'Dogs': [80, 160, 80, 140, 180]\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": 71,
"id": "c4143642-751d-4279-80c4-2200f6a3a1eb",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" Dogs | \n",
"
\n",
" \n",
" Day | \n",
" | \n",
"
\n",
" \n",
" \n",
" \n",
" Monday | \n",
" 80 | \n",
"
\n",
" \n",
" Tuesday | \n",
" 160 | \n",
"
\n",
" \n",
" Wednesday | \n",
" 80 | \n",
"
\n",
" \n",
" Thursday | \n",
" 140 | \n",
"
\n",
" \n",
" Friday | \n",
" 180 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" Dogs\n",
"Day \n",
"Monday 80\n",
"Tuesday 160\n",
"Wednesday 80\n",
"Thursday 140\n",
"Friday 180"
]
},
"execution_count": 71,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.DataFrame(dataset).set_index('Day')\n",
"df"
]
},
{
"cell_type": "code",
"execution_count": 72,
"id": "72247e1c-26c1-43fe-935d-76315b79addf",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"+---------+----+\n",
"| Day|Dogs|\n",
"+---------+----+\n",
"| Monday| 80|\n",
"| Tuesday| 160|\n",
"|Wednesday| 80|\n",
"| Thursday| 140|\n",
"| Friday| 180|\n",
"+---------+----+\n",
"\n"
]
}
],
"source": [
"sdf = spark.createDataFrame(zip(*dataset.values()), list(dataset.keys()))\n",
"sdf.show()"
]
},
{
"cell_type": "code",
"execution_count": 73,
"id": "08d7c2c9-14d2-40a4-a758-2a3d9ebce259",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" Dogs | \n",
"
\n",
" \n",
" Day | \n",
" | \n",
"
\n",
" \n",
" \n",
" \n",
" Tuesday | \n",
" 160 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" Dogs\n",
"Day \n",
"Tuesday 160"
]
},
"execution_count": 73,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sum_of_monday_and_wednesday = df[(df.index == 'Monday') | (df.index == 'Wednesday')]['Dogs'].sum()\n",
"df[df['Dogs'] == sum_of_monday_and_wednesday]"
]
},
{
"cell_type": "code",
"execution_count": 74,
"id": "70ba514e-8ae4-4c5a-b98e-d5614e4ca343",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"+-------+----+\n",
"| Day|Dogs|\n",
"+-------+----+\n",
"|Tuesday| 160|\n",
"+-------+----+\n",
"\n"
]
}
],
"source": [
"sum_of_monday_and_wednesday = sdf[(sdf['Day'] == 'Monday') | (sdf['Day'] == 'Wednesday')].groupby('Dogs').sum().collect()[0]['sum(Dogs)']\n",
"sdf[sdf['Dogs'] == sum_of_monday_and_wednesday].show()"
]
},
{
"cell_type": "code",
"execution_count": 75,
"id": "83685587-8d75-401b-854d-f4e57c5fb8de",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 75,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"df.plot(kind='bar')"
]
},
{
"cell_type": "code",
"execution_count": 76,
"id": "85ac95f7-ea74-4188-a1e4-614a23a42163",
"metadata": {},
"outputs": [],
"source": [
"dataset = {\n",
" 'Ice cream flavor': ['Vanilla', 'Chocolate', 'Strawberry', 'Cookie dough'],\n",
" 'Number of customers': [175, 225, 75, 200]\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": 77,
"id": "c08336a7-851e-41ea-abaf-d811282b7b67",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" Number of customers | \n",
"
\n",
" \n",
" Ice cream flavor | \n",
" | \n",
"
\n",
" \n",
" \n",
" \n",
" Vanilla | \n",
" 175 | \n",
"
\n",
" \n",
" Chocolate | \n",
" 225 | \n",
"
\n",
" \n",
" Strawberry | \n",
" 75 | \n",
"
\n",
" \n",
" Cookie dough | \n",
" 200 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" Number of customers\n",
"Ice cream flavor \n",
"Vanilla 175\n",
"Chocolate 225\n",
"Strawberry 75\n",
"Cookie dough 200"
]
},
"execution_count": 77,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.DataFrame(dataset).set_index('Ice cream flavor')\n",
"df"
]
},
{
"cell_type": "code",
"execution_count": 93,
"id": "ce41afe2-6abb-4964-b8ac-3e1f3dc5c18f",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"150"
]
},
"execution_count": 93,
"metadata": {},
"output_type": "execute_result"
}
],
"source": []
},
{
"cell_type": "code",
"execution_count": 78,
"id": "853e9f56-29fe-4722-9e1f-f951b9ab6604",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"+----------------+-------------------+\n",
"|Ice cream flavor|Number of customers|\n",
"+----------------+-------------------+\n",
"| Vanilla| 175|\n",
"| Chocolate| 225|\n",
"| Strawberry| 75|\n",
"| Cookie dough| 200|\n",
"+----------------+-------------------+\n",
"\n"
]
}
],
"source": [
"sdf = spark.createDataFrame(zip(*dataset.values()), list(dataset.keys()))\n",
"sdf.show()"
]
},
{
"cell_type": "code",
"execution_count": 94,
"id": "c3ba8e5b-2594-4ab0-bd43-b564a4af98de",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"150"
]
},
"execution_count": 94,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df['Number of customers'].max() - df['Number of customers'].min()"
]
},
{
"cell_type": "code",
"execution_count": 106,
"id": "9003ecb5-6459-402d-a998-391365c2080f",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"150"
]
},
"execution_count": 106,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sdf.groupby().max('Number of customers').collect()[0]['max(Number of customers)'] - sdf.groupby().min('Number of customers').collect()[0]['min(Number of customers)'] "
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "09eddf8b-5521-4fcb-a439-defe4e6c623b",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "342e6f50-0319-4b10-8be9-e5331627e208",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.12"
}
},
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