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94383ad073
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{
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"cells": [],
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"metadata": {},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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243
ctrl_2/ej_01/ejercicio_01.ipynb
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243
ctrl_2/ej_01/ejercicio_01.ipynb
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{
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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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18
ctrl_2/ej_01/test_00.py
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18
ctrl_2/ej_01/test_00.py
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import pandas as pd
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# import numpy as np
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df = pd.read_csv('./venta_verduras_y_frutas.csv')
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print(df.describe(include='all'))
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print(df.head(10))
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print(type(df))
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frutas = df[df['Category'] == 'Fruit']
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categorias = df.groupby(['Category'])
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print(frutas)
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print(type(frutas))
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pt = df.pivot(columns='Category')
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print(pt)
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214
ctrl_2/ej_01/venta_verduras_y_frutas.csv
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214
ctrl_2/ej_01/venta_verduras_y_frutas.csv
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Order_ID,Product,Category,Amount,Date,Country
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1,Carrots,Vegetables,$4'270,06-01-16,United States
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2,Broccoli,Vegetables,$8'239,07-01-16,United Kingdom
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3,Banana,Fruit,$617,08-01-16,United States
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4,Banana,Fruit,$8'384,10-01-16,Canada
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5,Beans,Vegetables,$2'626,10-01-16,Germany
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6,Orange,Fruit,$3'610,11-01-16,United States
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7,Broccoli,Vegetables,$9'062,11-01-16,Australia
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8,Banana,Fruit,$6'906,16-01-16,New Zealand
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9,Apple,Fruit,$2'417,16-01-16,France
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10,Apple,Fruit,$7'431,16-01-16,Canada
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11,Banana,Fruit,$8'250,16-01-16,Germany
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12,Broccoli,Vegetables,$7'012,18-01-16,United States
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13,Carrots,Vegetables,$1'903,20-01-16,Germany
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14,Broccoli,Vegetables,$2'824,22-01-16,Canada
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15,Apple,Fruit,$6'946,24-01-16,France
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16,Banana,Fruit,$2'320,27-01-16,United Kingdom
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17,Banana,Fruit,$2'116,28-01-16,United States
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18,Banana,Fruit,$1'135,30-01-16,United Kingdom
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19,Broccoli,Vegetables,$3'595,30-01-16,United Kingdom
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20,Apple,Fruit,$1'161,02-02-16,United States
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21,Orange,Fruit,$2'256,04-02-16,France
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22,Banana,Fruit,$1'004,11-02-16,New Zealand
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23,Banana,Fruit,$3'642,14-02-16,Canada
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24,Banana,Fruit,$4'582,17-02-16,United States
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25,Beans,Vegetables,$3'559,17-02-16,United Kingdom
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26,Carrots,Vegetables,$5'154,17-02-16,Australia
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27,Mango,Fruit,$7'388,18-02-16,France
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28,Beans,Vegetables,$7'163,18-02-16,United States
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29,Beans,Vegetables,$5'101,20-02-16,Germany
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30,Apple,Fruit,$7'602,21-02-16,France
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31,Mango,Fruit,$1'641,22-02-16,United States
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32,Apple,Fruit,$8'892,23-02-16,Australia
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33,Apple,Fruit,$2'060,29-02-16,France
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34,Broccoli,Vegetables,$1'557,29-02-16,Germany
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35,Apple,Fruit,$6'509,01-03-16,France
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36,Apple,Fruit,$5'718,04-03-16,Australia
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37,Apple,Fruit,$7'655,05-03-16,United States
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38,Carrots,Vegetables,$9'116,05-03-16,United Kingdom
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39,Banana,Fruit,$2'795,15-03-16,United States
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40,Banana,Fruit,$5'084,15-03-16,United States
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41,Carrots,Vegetables,$8'941,15-03-16,United Kingdom
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42,Broccoli,Vegetables,$5'341,16-03-16,France
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43,Banana,Fruit,$135,19-03-16,Canada
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44,Banana,Fruit,$9'400,19-03-16,Australia
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45,Beans,Vegetables,$6'045,21-03-16,Germany
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46,Apple,Fruit,$5'820,22-03-16,New Zealand
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47,Orange,Fruit,$8'887,23-03-16,Germany
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48,Orange,Fruit,$6'982,24-03-16,United States
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49,Banana,Fruit,$4'029,26-03-16,Australia
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50,Carrots,Vegetables,$3'665,26-03-16,Germany
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51,Banana,Fruit,$4'781,29-03-16,France
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52,Mango,Fruit,$3'663,30-03-16,Australia
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53,Apple,Fruit,$6'331,01-04-16,France
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54,Apple,Fruit,$4'364,01-04-16,Canada
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55,Carrots,Vegetables,$607,03-04-16,United Kingdom
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56,Banana,Fruit,$1'054,06-04-16,New Zealand
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57,Carrots,Vegetables,$7'659,06-04-16,United States
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58,Broccoli,Vegetables,$277,12-04-16,Germany
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59,Banana,Fruit,$235,17-04-16,United States
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60,Orange,Fruit,$1'113,18-04-16,Australia
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61,Apple,Fruit,$1'128,21-04-16,United States
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62,Broccoli,Vegetables,$9'231,22-04-16,Canada
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63,Banana,Fruit,$4'387,23-04-16,United States
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64,Apple,Fruit,$2'763,25-04-16,Canada
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65,Banana,Fruit,$7'898,27-04-16,United Kingdom
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66,Banana,Fruit,$2'427,30-04-16,France
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67,Banana,Fruit,$8'663,01-05-16,New Zealand
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68,Carrots,Vegetables,$2'789,01-05-16,Germany
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69,Banana,Fruit,$4'054,02-05-16,United States
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70,Mango,Fruit,$2'262,02-05-16,United States
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71,Mango,Fruit,$5'600,02-05-16,United Kingdom
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72,Banana,Fruit,$5'787,03-05-16,United States
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73,Orange,Fruit,$6'295,03-05-16,Canada
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74,Banana,Fruit,$474,05-05-16,Germany
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75,Apple,Fruit,$4'325,05-05-16,France
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76,Banana,Fruit,$592,06-05-16,United States
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||||
77,Orange,Fruit,$4'330,08-05-16,United States
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||||
78,Banana,Fruit,$9'405,08-05-16,United Kingdom
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79,Apple,Fruit,$7'671,08-05-16,France
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80,Carrots,Vegetables,$5'791,08-05-16,United Kingdom
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||||
81,Banana,Fruit,$6'007,12-05-16,Canada
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82,Banana,Fruit,$5'030,14-05-16,Germany
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83,Carrots,Vegetables,$6'763,14-05-16,United Kingdom
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84,Banana,Fruit,$4'248,15-05-16,Australia
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85,Banana,Fruit,$9'543,16-05-16,France
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86,Broccoli,Vegetables,$2'054,16-05-16,United Kingdom
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87,Beans,Vegetables,$7'094,16-05-16,Germany
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88,Carrots,Vegetables,$6'087,18-05-16,United States
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89,Apple,Fruit,$4'264,19-05-16,Australia
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90,Mango,Fruit,$9'333,20-05-16,United States
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91,Mango,Fruit,$8'775,22-05-16,Germany
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92,Broccoli,Vegetables,$2'011,23-05-16,United Kingdom
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93,Banana,Fruit,$5'632,25-05-16,United States
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94,Banana,Fruit,$4'904,25-05-16,New Zealand
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||||
95,Beans,Vegetables,$1'002,25-05-16,Australia
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||||
96,Orange,Fruit,$8'141,26-05-16,United Kingdom
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||||
97,Orange,Fruit,$3'644,26-05-16,Canada
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||||
98,Orange,Fruit,$1'380,26-05-16,Australia
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99,Broccoli,Vegetables,$8'354,26-05-16,Germany
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||||
100,Banana,Fruit,$5'182,27-05-16,United States
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101,Apple,Fruit,$2'193,27-05-16,France
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102,Mango,Fruit,$3'647,28-05-16,United States
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||||
103,Apple,Fruit,$4'104,28-05-16,United States
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104,Carrots,Vegetables,$7'457,28-05-16,United States
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105,Mango,Fruit,$3'767,29-05-16,Canada
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106,Broccoli,Vegetables,$4'685,30-05-16,Germany
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||||
107,Banana,Fruit,$3'917,04-06-16,United States
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108,Apple,Fruit,$521,04-06-16,Canada
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109,Apple,Fruit,$5'605,10-06-16,France
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110,Broccoli,Vegetables,$9'630,11-06-16,Germany
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111,Banana,Fruit,$6'941,20-06-16,Canada
|
||||
112,Broccoli,Vegetables,$7'231,20-06-16,United Kingdom
|
||||
113,Broccoli,Vegetables,$8'891,23-06-16,Australia
|
||||
114,Banana,Fruit,$107,25-06-16,France
|
||||
115,Banana,Fruit,$4'243,26-06-16,United States
|
||||
116,Orange,Fruit,$4'514,27-06-16,United States
|
||||
117,Mango,Fruit,$5'480,02-07-16,United States
|
||||
118,Banana,Fruit,$5'002,02-07-16,France
|
||||
119,Banana,Fruit,$8'530,05-07-16,Canada
|
||||
120,Orange,Fruit,$4'819,07-07-16,New Zealand
|
||||
121,Broccoli,Vegetables,$6'343,11-07-16,United Kingdom
|
||||
122,Orange,Fruit,$2'318,13-07-16,United Kingdom
|
||||
123,Orange,Fruit,$220,20-07-16,United Kingdom
|
||||
124,Orange,Fruit,$6'341,20-07-16,New Zealand
|
||||
125,Apple,Fruit,$330,20-07-16,Germany
|
||||
126,Broccoli,Vegetables,$3'027,20-07-16,United Kingdom
|
||||
127,Orange,Fruit,$850,22-07-16,New Zealand
|
||||
128,Banana,Fruit,$8'986,23-07-16,United Kingdom
|
||||
129,Broccoli,Vegetables,$3'800,25-07-16,United States
|
||||
130,Carrots,Vegetables,$5'751,28-07-16,United Kingdom
|
||||
131,Apple,Fruit,$1'704,29-07-16,United Kingdom
|
||||
132,Banana,Fruit,$7'966,30-07-16,Australia
|
||||
133,Banana,Fruit,$852,31-07-16,United States
|
||||
134,Beans,Vegetables,$8'416,31-07-16,Australia
|
||||
135,Banana,Fruit,$7'144,01-08-16,France
|
||||
136,Broccoli,Vegetables,$7'854,01-08-16,United States
|
||||
137,Orange,Fruit,$859,03-08-16,United States
|
||||
138,Broccoli,Vegetables,$8'049,12-08-16,United States
|
||||
139,Banana,Fruit,$2'836,13-08-16,Germany
|
||||
140,Carrots,Vegetables,$1'743,19-08-16,United States
|
||||
141,Apple,Fruit,$3'844,23-08-16,France
|
||||
142,Apple,Fruit,$7'490,24-08-16,France
|
||||
143,Broccoli,Vegetables,$4'483,25-08-16,Germany
|
||||
144,Apple,Fruit,$7'333,27-08-16,Canada
|
||||
145,Carrots,Vegetables,$7'654,28-08-16,United States
|
||||
146,Apple,Fruit,$3'944,29-08-16,United Kingdom
|
||||
147,Beans,Vegetables,$5'761,29-08-16,Germany
|
||||
148,Banana,Fruit,$6'864,01-09-16,New Zealand
|
||||
149,Banana,Fruit,$4'016,01-09-16,Germany
|
||||
150,Banana,Fruit,$1'841,02-09-16,United States
|
||||
151,Banana,Fruit,$424,05-09-16,Australia
|
||||
152,Banana,Fruit,$8'765,07-09-16,United Kingdom
|
||||
153,Banana,Fruit,$5'583,08-09-16,United States
|
||||
154,Broccoli,Vegetables,$4'390,09-09-16,New Zealand
|
||||
155,Broccoli,Vegetables,$352,09-09-16,Canada
|
||||
156,Apple,Fruit,$8'489,11-09-16,United States
|
||||
157,Banana,Fruit,$7'090,11-09-16,France
|
||||
158,Banana,Fruit,$7'880,15-09-16,United States
|
||||
159,Orange,Fruit,$3'861,18-09-16,United States
|
||||
160,Broccoli,Vegetables,$7'927,19-09-16,Germany
|
||||
161,Banana,Fruit,$6'162,20-09-16,United States
|
||||
162,Mango,Fruit,$5'523,25-09-16,Australia
|
||||
163,Broccoli,Vegetables,$5'936,25-09-16,United Kingdom
|
||||
164,Carrots,Vegetables,$7'251,26-09-16,Germany
|
||||
165,Orange,Fruit,$6'187,27-09-16,Australia
|
||||
166,Banana,Fruit,$3'210,29-09-16,Germany
|
||||
167,Carrots,Vegetables,$682,29-09-16,Germany
|
||||
168,Banana,Fruit,$793,03-10-16,Australia
|
||||
169,Carrots,Vegetables,$5'346,04-10-16,Germany
|
||||
170,Banana,Fruit,$7'103,07-10-16,New Zealand
|
||||
171,Carrots,Vegetables,$4'603,10-10-16,United States
|
||||
172,Apple,Fruit,$8'160,16-10-16,France
|
||||
173,Apple,Fruit,$7'171,23-10-16,United Kingdom
|
||||
174,Banana,Fruit,$3'552,23-10-16,New Zealand
|
||||
175,Banana,Fruit,$7'273,25-10-16,Australia
|
||||
176,Banana,Fruit,$2'402,26-10-16,Germany
|
||||
177,Banana,Fruit,$1'197,26-10-16,Australia
|
||||
178,Beans,Vegetables,$5'015,26-10-16,Australia
|
||||
179,Orange,Fruit,$5'818,02-11-16,United States
|
||||
180,Banana,Fruit,$4'399,03-11-16,United Kingdom
|
||||
181,Carrots,Vegetables,$3'011,03-11-16,United States
|
||||
182,Apple,Fruit,$4'715,09-11-16,United Kingdom
|
||||
183,Apple,Fruit,$5'321,12-11-16,France
|
||||
184,Banana,Fruit,$8'894,15-11-16,United States
|
||||
185,Carrots,Vegetables,$4'846,25-11-16,United Kingdom
|
||||
186,Broccoli,Vegetables,$284,25-11-16,Germany
|
||||
187,Orange,Fruit,$8'283,26-11-16,United Kingdom
|
||||
188,Orange,Fruit,$9'990,28-11-16,Canada
|
||||
189,Banana,Fruit,$9'014,28-11-16,Australia
|
||||
190,Apple,Fruit,$1'942,29-11-16,France
|
||||
191,Banana,Fruit,$7'223,30-11-16,United States
|
||||
192,Carrots,Vegetables,$4'673,02-12-16,United States
|
||||
193,Carrots,Vegetables,$9'104,04-12-16,France
|
||||
194,Apple,Fruit,$6'078,05-12-16,United States
|
||||
195,Beans,Vegetables,$3'278,06-12-16,Germany
|
||||
196,Banana,Fruit,$136,12-12-16,Canada
|
||||
197,Banana,Fruit,$8'377,12-12-16,Australia
|
||||
198,Banana,Fruit,$2'382,12-12-16,United States
|
||||
199,Banana,Fruit,$8'702,15-12-16,Germany
|
||||
200,Banana,Fruit,$5'021,16-12-16,United States
|
||||
201,Apple,Fruit,$1'760,16-12-16,Australia
|
||||
202,Banana,Fruit,$4'766,18-12-16,Germany
|
||||
203,Beans,Vegetables,$1'541,19-12-16,United Kingdom
|
||||
204,Orange,Fruit,$2'782,20-12-16,United Kingdom
|
||||
205,Apple,Fruit,$2'455,20-12-16,Canada
|
||||
206,Apple,Fruit,$4'512,22-12-16,New Zealand
|
||||
207,Apple,Fruit,$8'752,22-12-16,Germany
|
||||
208,Carrots,Vegetables,$9'127,25-12-16,United States
|
||||
209,Apple,Fruit,$1'777,28-12-16,France
|
||||
210,Beans,Vegetables,$680,28-12-16,France
|
||||
211,Orange,Fruit,$958,29-12-16,United States
|
||||
212,Carrots,Vegetables,$2'613,29-12-16,Australia
|
||||
213,Carrots,Vegetables,$339,30-12-16,Australia
|
|
Loading…
Reference in New Issue
Block a user