244 lines
8.0 KiB
Plaintext
244 lines
8.0 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "22f35792-a38d-49bf-8e02-c299271055cb",
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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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" Order_ID Product Category Amount Date Country\n",
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"count 213.000000 213 213 213 213 213\n",
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"unique NaN 7 2 213 150 7\n",
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"top NaN Banana Fruit $4'270 16-01-16 United States\n",
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"freq NaN 71 146 1 4 57\n",
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"mean 107.000000 NaN NaN NaN NaN NaN\n",
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"std 61.631972 NaN NaN NaN NaN NaN\n",
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"min 1.000000 NaN NaN NaN NaN NaN\n",
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"25% 54.000000 NaN NaN NaN NaN NaN\n",
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"50% 107.000000 NaN NaN NaN NaN NaN\n",
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"75% 160.000000 NaN NaN NaN NaN NaN\n",
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"max 213.000000 NaN NaN NaN NaN NaN\n"
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]
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}
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],
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"source": [
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"import pandas as pd\n",
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"\n",
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"df = pd.read_csv('venta_verduras_y_frutas.csv')\n",
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"\n",
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"print(df.describe(include='all'))"
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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": "ec8937dc-0605-4dcc-9f7c-faeac54c05d9",
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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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" Order_ID Product Category Amount Date Country\n",
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"0 1 Carrots Vegetables $4'270 06-01-16 United States\n",
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"1 2 Broccoli Vegetables $8'239 07-01-16 United Kingdom\n",
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"2 3 Banana Fruit $617 08-01-16 United States\n",
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"3 4 Banana Fruit $8'384 10-01-16 Canada\n",
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"4 5 Beans Vegetables $2'626 10-01-16 Germany\n",
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"5 6 Orange Fruit $3'610 11-01-16 United States\n",
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"6 7 Broccoli Vegetables $9'062 11-01-16 Australia\n",
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"7 8 Banana Fruit $6'906 16-01-16 New Zealand\n",
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"8 9 Apple Fruit $2'417 16-01-16 France\n",
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"9 10 Apple Fruit $7'431 16-01-16 Canada\n"
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]
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}
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],
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"source": [
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"print(df.head(10))"
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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": 4,
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"id": "598245c7-d0c7-4173-aaf8-e7511d4b10a9",
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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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" Order_ID Product Category Amount Date Country\n",
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"203 204 Orange Fruit $2'782 20-12-16 United Kingdom\n",
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"204 205 Apple Fruit $2'455 20-12-16 Canada\n",
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"205 206 Apple Fruit $4'512 22-12-16 New Zealand\n",
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"206 207 Apple Fruit $8'752 22-12-16 Germany\n",
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"207 208 Carrots Vegetables $9'127 25-12-16 United States\n",
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"208 209 Apple Fruit $1'777 28-12-16 France\n",
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"209 210 Beans Vegetables $680 28-12-16 France\n",
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"210 211 Orange Fruit $958 29-12-16 United States\n",
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"211 212 Carrots Vegetables $2'613 29-12-16 Australia\n",
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"212 213 Carrots Vegetables $339 30-12-16 Australia\n"
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]
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}
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],
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"source": [
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"print(df.tail(10))"
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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": "64be6624-d703-4ade-9eb6-c55f9e379250",
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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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" Category Amount\n",
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"0 Vegetables $4'270\n",
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"1 Vegetables $8'239\n",
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"2 Fruit $617\n",
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"3 Fruit $8'384\n",
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"4 Vegetables $2'626\n",
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".. ... ...\n",
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"208 Fruit $1'777\n",
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"209 Vegetables $680\n",
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"210 Fruit $958\n",
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"211 Vegetables $2'613\n",
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"212 Vegetables $339\n",
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"\n",
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"[213 rows x 2 columns]\n"
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]
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}
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],
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"source": [
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"parcial_data = df[['Category','Amount']]\n",
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"print(parcial_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": 7,
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"id": "1511d979-3831-4f84-ac77-73d0f0958497",
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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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"Index(['Order_ID', 'Product', 'Category', 'Amount', 'Date', 'Country'], dtype='object')\n"
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]
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}
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],
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"source": [
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"columnas = df.columns\n",
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"print(columnas)"
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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": "6a97d112-05f8-44f2-8ca9-0bded4b73283",
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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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" Order_ID Product Category Amount Date Country\n",
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"2 3 Banana Fruit $617 08-01-16 United States\n",
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"3 4 Banana Fruit $8'384 10-01-16 Canada\n",
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"5 6 Orange Fruit $3'610 11-01-16 United States\n",
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"7 8 Banana Fruit $6'906 16-01-16 New Zealand\n",
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"8 9 Apple Fruit $2'417 16-01-16 France\n",
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".. ... ... ... ... ... ...\n",
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"204 205 Apple Fruit $2'455 20-12-16 Canada\n",
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"205 206 Apple Fruit $4'512 22-12-16 New Zealand\n",
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"206 207 Apple Fruit $8'752 22-12-16 Germany\n",
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"208 209 Apple Fruit $1'777 28-12-16 France\n",
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"210 211 Orange Fruit $958 29-12-16 United States\n",
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"\n",
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"[146 rows x 6 columns]\n"
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]
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}
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],
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"source": [
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"frutas = df[df['Category']=='Fruit']\n",
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"print(frutas)"
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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": "e49afb79-1749-421a-bd63-919bc1c74bcf",
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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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" Order_ID \n",
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" count mean std min 25% 50% 75% max\n",
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"Category \n",
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"Fruit 146.0 107.239726 60.488734 3.0 56.75 106.0 158.75 211.0\n",
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"Vegetables 67.0 106.477612 64.516467 1.0 47.50 110.0 161.50 213.0\n"
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]
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}
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],
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"source": [
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"grupos = df.groupby('Category')\n",
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"print(grupos.describe())"
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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": "afe9d318-f0f9-4214-8935-9f6c6214f5e8",
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"metadata": {},
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"outputs": [
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{
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"ename": "NameError",
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"evalue": "name 'inspect' is not defined",
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"output_type": "error",
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"traceback": [
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
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"Cell \u001b[0;32mIn[20], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m#frutas = df.filter()\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m help(\u001b[43minspect\u001b[49m)\n",
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"\u001b[0;31mNameError\u001b[0m: name 'inspect' is not defined"
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]
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}
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],
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"source": [
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"#frutas = df.filter()\n",
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"help(inspect)"
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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": "3.12.4"
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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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