diff --git a/notebook/problems.es.ipynb b/notebook/problems.es.ipynb index 9c5380c0..bcbcb807 100644 --- a/notebook/problems.es.ipynb +++ b/notebook/problems.es.ipynb @@ -20,7 +20,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "id": "ca3d2c0a", "metadata": {}, "outputs": [ @@ -201,7 +201,7 @@ "[979 rows x 6 columns]" ] }, - "execution_count": 3, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -221,62 +221,358 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "id": "61d39304", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "star_rating float64\n", + "title str\n", + "content_rating str\n", + "genre str\n", + "duration int64\n", + "actors_list str\n", + "title_length int64\n", + "dtype: object\n" + ] + }, + { + "data": { + "text/html": [ + "
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star_ratingtitlecontent_ratinggenredurationactors_listtitle_length
09.3The Shawshank RedemptionRCrime142[u'Tim Robbins', u'Morgan Freeman', u'Bob Gunt...24
19.2The GodfatherRCrime175[u'Marlon Brando', u'Al Pacino', u'James Caan']13
29.1The Godfather: Part IIRCrime200[u'Al Pacino', u'Robert De Niro', u'Robert Duv...22
39.0The Dark KnightPG-13Action152[u'Christian Bale', u'Heath Ledger', u'Aaron E...15
48.9Pulp FictionRCrime154[u'John Travolta', u'Uma Thurman', u'Samuel L....12
........................
9747.4TootsiePGComedy116[u'Dustin Hoffman', u'Jessica Lange', u'Teri G...7
9757.4Back to the Future Part IIIPGAdventure118[u'Michael J. Fox', u'Christopher Lloyd', u'Ma...27
9767.4Master and Commander: The Far Side of the WorldPG-13Action138[u'Russell Crowe', u'Paul Bettany', u'Billy Bo...47
9777.4PoltergeistPGHorror114[u'JoBeth Williams', u\"Heather O'Rourke\", u'Cr...11
9787.4Wall StreetRCrime126[u'Charlie Sheen', u'Michael Douglas', u'Tamar...11
\n", + "

979 rows × 7 columns

\n", + "
" + ], + "text/plain": [ + " star_rating title \\\n", + "0 9.3 The Shawshank Redemption \n", + "1 9.2 The Godfather \n", + "2 9.1 The Godfather: Part II \n", + "3 9.0 The Dark Knight \n", + "4 8.9 Pulp Fiction \n", + ".. ... ... \n", + "974 7.4 Tootsie \n", + "975 7.4 Back to the Future Part III \n", + "976 7.4 Master and Commander: The Far Side of the World \n", + "977 7.4 Poltergeist \n", + "978 7.4 Wall Street \n", + "\n", + " content_rating genre duration \\\n", + "0 R Crime 142 \n", + "1 R Crime 175 \n", + "2 R Crime 200 \n", + "3 PG-13 Action 152 \n", + "4 R Crime 154 \n", + ".. ... ... ... \n", + "974 PG Comedy 116 \n", + "975 PG Adventure 118 \n", + "976 PG-13 Action 138 \n", + "977 PG Horror 114 \n", + "978 R Crime 126 \n", + "\n", + " actors_list title_length \n", + "0 [u'Tim Robbins', u'Morgan Freeman', u'Bob Gunt... 24 \n", + "1 [u'Marlon Brando', u'Al Pacino', u'James Caan'] 13 \n", + "2 [u'Al Pacino', u'Robert De Niro', u'Robert Duv... 22 \n", + "3 [u'Christian Bale', u'Heath Ledger', u'Aaron E... 15 \n", + "4 [u'John Travolta', u'Uma Thurman', u'Samuel L.... 12 \n", + ".. ... ... \n", + "974 [u'Dustin Hoffman', u'Jessica Lange', u'Teri G... 7 \n", + "975 [u'Michael J. Fox', u'Christopher Lloyd', u'Ma... 27 \n", + "976 [u'Russell Crowe', u'Paul Bettany', u'Billy Bo... 47 \n", + "977 [u'JoBeth Williams', u\"Heather O'Rourke\", u'Cr... 11 \n", + "978 [u'Charlie Sheen', u'Michael Douglas', u'Tamar... 11 \n", + "\n", + "[979 rows x 7 columns]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# Crea una nueva columna llamada 'title_length' con la longitud (número de caracteres) de cada título" + "# Crea una nueva columna llamada 'title_length' con la longitud (número de caracteres) de cada título\n", + "df['title_length'] = df['title'].str.len()\n", + "print(df.dtypes)\n", + "df" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "id": "cae9c2e7", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Media: 15.48\n", + "Mediana: 14.0\n", + "Moda: 0 12\n", + "Name: title_length, dtype: int64\n" + ] + } + ], "source": [ - "# Calcula media, mediana y moda de la columna 'title_length'" + "# Calcula media, mediana y moda de la columna 'title_length'\n", + "media = round(df['title_length'].mean(),2)\n", + "print(f\"Media: {media}\")\n", + "\n", + "mediana = df['title_length'].median()\n", + "print(f\"Mediana: {mediana}\")\n", + "\n", + "moda = df['title_length'].mode()\n", + "print(f\"Moda: {moda}\")" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "id": "69664a9b", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Rango: 67\n", + "Varianza: 72.11\n", + "Desviación Estandar: 8.49\n" + ] + } + ], "source": [ - "# Calcula el rango, la varianza y la desviación estándar de 'title_length'" + "# Calcula el rango, la varianza y la desviación estándar de 'title_length'\n", + "rango = df['title_length'].max()-df['title_length'].min()\n", + "print(f\"Rango: {rango}\")\n", + "\n", + "varianza = round(df['title_length'].var(),2)\n", + "print(f\"Varianza: {varianza}\")\n", + "\n", + "desv_estandar = round(df['title_length'].std(),2)\n", + "print(f\"Desviación Estandar: {desv_estandar}\")\n", + "\n" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "id": "6b9a931c", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Asimetría: 1.53\n", + "Curtosis: 3.81\n" + ] + } + ], "source": [ - "# Calcula asimetría y curtosis de la columna 'title_length'" + "# Calcula asimetría y curtosis de la columna 'title_length'\n", + "asimetria = round(df['title_length'].skew(),2)\n", + "print(f\"Asimetría: {asimetria}\")\n", + "\n", + "curtosis = round(df['title_length'].kurt(),2)\n", + "print(f\"Curtosis: {curtosis}\")\n" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "id": "c0d09e68", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Dr. Strangelove or: How I Learned to Stop Worrying and Love the Bomb\n", + "M\n" + ] + } + ], "source": [ - "# Imprime el título más corto y el título más largo según su longitud" + "# Imprime el título más corto y el título más largo según su longitud\n", + "id_max = df['title_length'].idxmax()\n", + "titulo_largo = df.loc[id_max, 'title']\n", + "print(titulo_largo)\n", + "\n", + "id_min = df['title_length'].idxmin()\n", + "titulo_corto = df.loc[id_min, 'title']\n", + "print(titulo_corto)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "id": "e86a1ced", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "# grafica tus resultados" + "# grafica tus resultados\n", + "plt.figure(figsize=(10, 6))\n", + "df['title_length'].hist(bins=20)\n", + "plt.title('Histograma de la Distribución de title_length')\n", + "plt.xlabel('title_length')\n", + "plt.ylabel('Frecuencia')\n", + "plt.show()" ] }, { @@ -293,42 +589,85 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "id": "3005c0f9", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Lista de 5 valores reales de title_length: [24, 13, 22, 15, 12]\n" + ] + } + ], "source": [ - "# Crea una lista con 5 valores reales de df['title_length'], por ejemplo: [10, 13, 14, 18, 22]" + "# Crea una lista con 5 valores reales de df['title_length'], por ejemplo: [10, 13, 14, 18, 22]\n", + "title_lengths = df['title_length'].tolist()[:5]\n", + "print(f\"Lista de 5 valores reales de title_length: {title_lengths}\")" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 27, "id": "d96b771f", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Media de los 5 valores: 17.2\n" + ] + } + ], "source": [ - "# Calcula la media de los 5 valores" + "# Calcula la media de los 5 valores\n", + "media_list = sum(title_lengths)/len(title_lengths)\n", + "print(f\"Media de los 5 valores: {media_list}\")" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 28, "id": "346d0dc5", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Diferencias al cuadrado: 118.8\n" + ] + } + ], "source": [ - "# Calcula las diferencias al cuadrado con respecto a la media" + "# Calcula las diferencias al cuadrado con respecto a la media\n", + "sum_diferencias = round(sum([(x - media_list) ** 2 for x in title_lengths]),2)\n", + "print(f\"Diferencias al cuadrado: {sum_diferencias}\")" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 31, "id": "f56517ff", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Varianza de los 5 valores: 23.76\n", + "Desviación estándar de los 5 valores: 4.87\n" + ] + } + ], "source": [ - "# Calcula la varianza (promedio de las diferencias al cuadrado) y su raíz cuadrada para obtener la desviación" + "# Calcula la varianza (promedio de las diferencias al cuadrado) y su raíz cuadrada para obtener la desviación\n", + "varianza_list = round((sum_diferencias / len(title_lengths)),2)\n", + "desv_estandar = round(varianza_list ** 0.5,2)\n", + "print(f\"Varianza de los 5 valores: {varianza_list}\")\n", + "print(f\"Desviación estándar de los 5 valores: {desv_estandar}\")" ] } ],