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oldoc63 merged 1 commit into from
Oct 27, 2021
Merged

Summarizing a nominal var with frequencies and proportions #209 #210

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83 changes: 83 additions & 0 deletions summAutoEval/.ipynb_checkpoints/autoEval-checkpoint.ipynb
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@@ -0,0 +1,83 @@
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "22d044d3",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "07497e4d",
"metadata": {},
"outputs": [],
"source": [
"car_eval = pd.read_csv('car_eval_dataset.csv')"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "0be19bea",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" buying_cost maintenance_cost doors capacity luggage safety acceptability \\\n",
"0 vhigh low 4 4 small med unacc \n",
"1 vhigh med 3 4 small high acc \n",
"2 med high 3 2 med high unacc \n",
"3 low med 4 more big low unacc \n",
"4 low high 2 more med high acc \n",
"\n",
" manufacturer_country \n",
"0 China \n",
"1 France \n",
"2 United States \n",
"3 United States \n",
"4 South Korea \n"
]
}
],
"source": [
"print(car_eval.head())"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "37008d81",
"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": "3.9.7"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
170 changes: 170 additions & 0 deletions summAutoEval/autoEval.ipynb
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@@ -0,0 +1,170 @@
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "f961e22b",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "eec8cecf",
"metadata": {},
"outputs": [],
"source": [
"car_eval = pd.read_csv('car_eval_dataset.csv')"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "4d1c9e0c",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" buying_cost maintenance_cost doors capacity luggage safety acceptability \\\n",
"0 vhigh low 4 4 small med unacc \n",
"1 vhigh med 3 4 small high acc \n",
"2 med high 3 2 med high unacc \n",
"3 low med 4 more big low unacc \n",
"4 low high 2 more med high acc \n",
"\n",
" manufacturer_country \n",
"0 China \n",
"1 France \n",
"2 United States \n",
"3 United States \n",
"4 South Korea \n"
]
}
],
"source": [
"print(car_eval.head())"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "da5fed0d",
"metadata": {},
"outputs": [],
"source": [
"manufacturer_country_counts = car_eval.manufacturer_country.value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "319136c5",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Japan 228\n",
"Germany 218\n",
"South Korea 159\n",
"United States 138\n",
"Italy 97\n",
"France 87\n",
"China 73\n",
"Name: manufacturer_country, dtype: int64\n"
]
}
],
"source": [
"print(manufacturer_country_counts)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "496ac1d4",
"metadata": {},
"outputs": [],
"source": [
"fourth_manufacturer_country = manufacturer_country_counts.index[3]"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "03b74363",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"United States\n"
]
}
],
"source": [
"print(fourth_manufacturer_country)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "57684edc",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Japan 0.228\n",
"Germany 0.218\n",
"South Korea 0.159\n",
"United States 0.138\n",
"Italy 0.097\n",
"Name: manufacturer_country, dtype: float64\n"
]
}
],
"source": [
"manufacturer_country_proportions = car_eval.manufacturer_country.value_counts(normalize=True)\n",
"print(manufacturer_country_proportions.head())"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "af9a3594",
"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": "3.9.7"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
9 changes: 9 additions & 0 deletions summAutoEval/script.py
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Expand Up @@ -2,3 +2,12 @@

car_eval = pd.read_csv('car_eval_dataset.csv')
print(car_eval.head())

manufacturer_country_counts = car_eval.manufacturer_country.value_counts()
print(manufacturer_country_counts)

fourth_manufacturer_country = manufacturer_country_counts.index[3]
print(fourth_manufacturer_country)

manufacturer_country_proportions = car_eval.manufacturer_country.value_counts(normalize=True)
print(manufacturer_country_proportions.head())