{"id":5203,"date":"2020-06-19T12:14:39","date_gmt":"2020-06-19T12:14:39","guid":{"rendered":"https:\/\/geoinnova.cl\/?page_id=5203"},"modified":"2025-05-20T19:54:12","modified_gmt":"2025-05-20T19:54:12","slug":"intro-da-gc-python","status":"publish","type":"page","link":"https:\/\/geoinnova.cl\/en\/centro-id\/centro-aprendizaje\/intro-da-gc-python\/","title":{"rendered":"Intro Data Analytics en Geociencias con Python"},"content":{"rendered":"<p>[et_pb_section admin_label=&#8221;Secci\u00f3n&#8221; fullwidth=&#8221;off&#8221; specialty=&#8221;off&#8221; transparent_background=&#8221;off&#8221; background_color=&#8221;#ffffff&#8221; allow_player_pause=&#8221;off&#8221; inner_shadow=&#8221;off&#8221; parallax=&#8221;off&#8221; parallax_method=&#8221;off&#8221; padding_mobile=&#8221;off&#8221; make_fullwidth=&#8221;off&#8221; use_custom_width=&#8221;off&#8221; width_unit=&#8221;on&#8221; make_equal=&#8221;off&#8221; use_custom_gutter=&#8221;off&#8221; custom_css_before=&#8221;.tooltip {|| position: relative;|| display: inline-block;|| border-bottom: 1px dotted black;||}||||.tooltip .tooltiptext {|| visibility: hidden;|| width: 120px;|| background-color: black;|| color: #fff;|| text-align: center;|| border-radius: 6px;|| padding: 5px 0;|||| \/* Position the tooltip *\/|| position: absolute;|| z-index: 1;||}||||.tooltip:hover .tooltiptext {|| visibility: visible;||}&#8221;][et_pb_row admin_label=&#8221;Fila&#8221;][et_pb_column type=&#8221;4_4&#8243;][et_pb_text admin_label=&#8221;Texto&#8221; background_layout=&#8221;light&#8221; text_orientation=&#8221;center&#8221; use_border_color=&#8221;off&#8221; border_color=&#8221;#ffffff&#8221; border_style=&#8221;solid&#8221;]<\/p>\n<h1>Intro a Data Analytics en Geociencias con Python<\/h1>\n<p>[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row admin_label=&#8221;Fila&#8221; global_module=&#8221;5209&#8243; make_fullwidth=&#8221;on&#8221; use_custom_width=&#8221;off&#8221; width_unit=&#8221;on&#8221; use_custom_gutter=&#8221;off&#8221; padding_mobile=&#8221;off&#8221; allow_player_pause=&#8221;off&#8221; parallax=&#8221;off&#8221; parallax_method=&#8221;off&#8221; make_equal=&#8221;on&#8221; parallax_1=&#8221;off&#8221; parallax_method_1=&#8221;off&#8221; parallax_2=&#8221;off&#8221; parallax_method_2=&#8221;off&#8221; column_padding_mobile=&#8221;on&#8221; custom_css_main_element=&#8221;display: flex;||justify-content: center;||align-items: center;&#8221; custom_css_main_2=&#8221;border-left: 6px solid #00755E;||padding-left: 20px !important;&#8221;][et_pb_column type=&#8221;1_3&#8243;][et_pb_video global_parent=&#8221;5209&#8243; admin_label=&#8221;V\u00eddeo&#8221; src=&#8221;https:\/\/youtu.be\/rWGB3xUFFz4&#8243;]<\/p>\n<p>\u00a0<\/p>\n<p>[\/et_pb_video][\/et_pb_column][et_pb_column type=&#8221;2_3&#8243;][et_pb_text global_parent=&#8221;5209&#8243; admin_label=&#8221;Modulo0_IntroDAGS_Python&#8221; background_layout=&#8221;light&#8221; text_orientation=&#8221;left&#8221; use_border_color=&#8221;off&#8221; border_color=&#8221;#ffffff&#8221; border_style=&#8221;solid&#8221;]<\/p>\n<h4>M\u00f3dulo 0:&nbsp;Introducci\u00f3n a Python.<\/h4>\n<p>Con este m\u00f3dulo aprender\u00e1s a instalar y configurar un ambiente de desarrollo en Python, que te va a permitir ejecutar scripts, instalar librer\u00edas externas y generar tus propias aplicaciones f\u00e1cilmente. <\/p>\n<p>T\u00f3picos: <\/p>\n<ul>\n<li>Definici\u00f3n de Python<\/li>\n<li>Instalaci\u00f3n y configuraci\u00f3n del ambiente desarrollo Python<\/li>\n<li>Ejecuci\u00f3n Scripts en Python<\/li>\n<li>Principales estructuras de datos nativas en Python<\/li>\n<li>Principales librer\u00edas en la ciencia de datos<\/li>\n<li>Distribuci\u00f3n de aplicaciones<\/li>\n<\/ul>\n<p>Duraci\u00f3n : 6:35 min.<br \/>\nDuraci\u00f3n Pr\u00e1ctica : 30 min.<br \/>\nComplejidad :&nbsp;Baja<\/p>\n<p>[\/et_pb_text][et_pb_toggle global_parent=&#8221;5209&#8243; admin_label=&#8221;Conmutador&#8221; title=&#8221;Recursos&#8221; open=&#8221;off&#8221; use_border_color=&#8221;off&#8221; border_color=&#8221;#ffffff&#8221; border_style=&#8221;solid&#8221;]<\/p>\n<p><a href=\"https:\/\/geoinnova.cl\/wp-content\/uploads\/2020\/06\/M\u00f3dulo-0.-Introducci\u00f3n-a-Python.pdf\" target=\"_blank\" rel=\"noopener\">Diapositivas<\/a><\/p>\n<p>[\/et_pb_toggle][\/et_pb_column][\/et_pb_row][et_pb_row admin_label=&#8221;Fila&#8221; make_fullwidth=&#8221;on&#8221; use_custom_width=&#8221;off&#8221; width_unit=&#8221;on&#8221; use_custom_gutter=&#8221;off&#8221; padding_mobile=&#8221;off&#8221; allow_player_pause=&#8221;off&#8221; parallax=&#8221;off&#8221; parallax_method=&#8221;off&#8221; make_equal=&#8221;on&#8221; parallax_1=&#8221;off&#8221; parallax_method_1=&#8221;off&#8221; parallax_2=&#8221;off&#8221; parallax_method_2=&#8221;off&#8221; column_padding_mobile=&#8221;on&#8221; custom_css_main_element=&#8221;display: flex;||justify-content: center;||align-items: center;&#8221; custom_css_main_2=&#8221;border-left: 6px solid #00755E;||padding-left: 20px !important;&#8221;][et_pb_column type=&#8221;1_3&#8243;][et_pb_video admin_label=&#8221;V\u00eddeo&#8221; src=&#8221;https:\/\/youtu.be\/wv45Aj1Z6lo&#8221;]<\/p>\n<p>&nbsp;<\/p>\n<p>[\/et_pb_video][\/et_pb_column][et_pb_column type=&#8221;2_3&#8243;][et_pb_text admin_label=&#8221;Modulo1_IntroDAGS_Python&#8221; background_layout=&#8221;light&#8221; text_orientation=&#8221;left&#8221; use_border_color=&#8221;off&#8221; border_color=&#8221;#ffffff&#8221; border_style=&#8221;solid&#8221;]<\/p>\n<h4>M\u00f3dulo 1:&nbsp;Carga de datos en Python.<\/h4>\n<p>En este m\u00f3dulo aprender\u00e1s a cargar un archivo de muestras (csv o xlsx) en pandas a trav\u00e9s de su DataFrame.<\/p>\n<p>T\u00f3picos:<\/p>\n<ul>\n<li>Importaci\u00f3n librer\u00eda pandas.<\/li>\n<li>Estructura de Datos: DataFrame.<\/li>\n<li>Lectura de archivos con DataFrame: read_csv, read_excel.<\/li>\n<li>Exportaci\u00f3n DataFrame a archivo: to_csv, to_xlsx.<\/li>\n<\/ul>\n<p>Duraci\u00f3n : 3:30 min.<br \/>\nDuraci\u00f3n Pr\u00e1ctica : 20 min.<br \/>\nComplejidad :&nbsp;Baja<\/p>\n<p>[\/et_pb_text][et_pb_toggle admin_label=&#8221;Conmutador&#8221; title=&#8221;Recursos&#8221; open=&#8221;off&#8221; use_border_color=&#8221;off&#8221; border_color=&#8221;#ffffff&#8221; border_style=&#8221;solid&#8221;]<\/p>\n<p><a href=\"https:\/\/bitbucket.org\/geoinnova\/intro-a-data-analytics-en-geociencias-con-python\/src\/master\/Mod1\/\" target=\"_blank\" rel=\"noopener\">Repositorio<\/a><\/p>\n<p>[\/et_pb_toggle][\/et_pb_column][\/et_pb_row][et_pb_row admin_label=&#8221;Fila&#8221; make_fullwidth=&#8221;on&#8221; use_custom_width=&#8221;off&#8221; width_unit=&#8221;on&#8221; use_custom_gutter=&#8221;off&#8221; padding_mobile=&#8221;off&#8221; allow_player_pause=&#8221;off&#8221; parallax=&#8221;off&#8221; parallax_method=&#8221;off&#8221; make_equal=&#8221;on&#8221; parallax_1=&#8221;off&#8221; parallax_method_1=&#8221;off&#8221; parallax_2=&#8221;off&#8221; parallax_method_2=&#8221;off&#8221; column_padding_mobile=&#8221;on&#8221; custom_css_main_element=&#8221;display: flex;||justify-content: center;||align-items: center;&#8221; custom_css_main_2=&#8221;border-left: 6px solid #00755E;||padding-left: 20px !important;&#8221;][et_pb_column type=&#8221;1_3&#8243;][et_pb_video admin_label=&#8221;V\u00eddeo&#8221; src=&#8221;https:\/\/youtu.be\/xbxoVMBunfk&#8221;]<\/p>\n<p>&nbsp;<\/p>\n<p>[\/et_pb_video][\/et_pb_column][et_pb_column type=&#8221;2_3&#8243;][et_pb_text admin_label=&#8221;Modulo2_IntroDAGS_Python&#8221; background_layout=&#8221;light&#8221; text_orientation=&#8221;left&#8221; use_border_color=&#8221;off&#8221; border_color=&#8221;#ffffff&#8221; border_style=&#8221;solid&#8221;]<\/p>\n<h4>M\u00f3dulo 2: Despliegue de datos y estad\u00edsticas b\u00e1sicas.<\/h4>\n<p>En este m\u00f3dulo aprender\u00e1s a visualizar espacialmente variables continuas y\/o categ\u00f3ricas en Plotly a trav\u00e9s de un DataFrame en Pandas. Aprender\u00e1s a generar estad\u00edsticas b\u00e1sicas sobre tus datos de entrada, as\u00ed como tambi\u00e9n filtrar tus datos. Finalmente se exportar\u00e1n los resultados.<\/p>\n<p>T\u00f3picos:<\/p>\n<ul>\n<li>Importaci\u00f3n librer\u00edas: pandas, numpy y plotly.<\/li>\n<li>Descripci\u00f3n del contenido DataFrame al cargar muestras.<\/li>\n<li>Visualizaci\u00f3n espacial de variables continuas y categ\u00f3ricas.<\/li>\n<li>Estad\u00edsticas b\u00e1sicas.<\/li>\n<li>Generaci\u00f3n de operaciones l\u00f3gicas para filtros.<\/li>\n<li>Exportaci\u00f3n resultados.<\/li>\n<\/ul>\n<p>Duraci\u00f3n : 7:38 min.<br \/>\nDuraci\u00f3n Pr\u00e1ctica : 30 min.<br \/>\nComplejidad : Media<\/p>\n<p>[\/et_pb_text][et_pb_toggle admin_label=&#8221;Conmutador&#8221; title=&#8221;Recursos&#8221; open=&#8221;off&#8221; use_border_color=&#8221;off&#8221; border_color=&#8221;#ffffff&#8221; border_style=&#8221;solid&#8221;]<\/p>\n<p><a href=\"https:\/\/bitbucket.org\/geoinnova\/intro-a-data-analytics-en-geociencias-con-python\/src\/master\/Mod2\/\" target=\"_blank\" rel=\"noopener\">Repositorio<\/a><\/p>\n<p>[\/et_pb_toggle][\/et_pb_column][\/et_pb_row][et_pb_row admin_label=&#8221;Fila&#8221; make_fullwidth=&#8221;on&#8221; use_custom_width=&#8221;off&#8221; width_unit=&#8221;on&#8221; use_custom_gutter=&#8221;off&#8221; padding_mobile=&#8221;off&#8221; allow_player_pause=&#8221;off&#8221; parallax=&#8221;off&#8221; parallax_method=&#8221;off&#8221; make_equal=&#8221;on&#8221; parallax_1=&#8221;off&#8221; parallax_method_1=&#8221;off&#8221; parallax_2=&#8221;off&#8221; parallax_method_2=&#8221;off&#8221; column_padding_mobile=&#8221;on&#8221; custom_css_main_element=&#8221;display: flex;||justify-content: center;||align-items: center;&#8221; custom_css_main_2=&#8221;border-left: 6px solid #00755E;||padding-left: 20px !important;&#8221;][et_pb_column type=&#8221;1_3&#8243;][et_pb_video admin_label=&#8221;V\u00eddeo&#8221; src=&#8221;https:\/\/youtu.be\/_K96yB7LI-w&#8221;]<\/p>\n<p>&nbsp;<\/p>\n<p>[\/et_pb_video][\/et_pb_column][et_pb_column type=&#8221;2_3&#8243;][et_pb_text admin_label=&#8221;Modulo3_IntroDAGS_Python&#8221; background_layout=&#8221;light&#8221; text_orientation=&#8221;left&#8221; use_border_color=&#8221;off&#8221; border_color=&#8221;#ffffff&#8221; border_style=&#8221;solid&#8221;]<\/p>\n<h4>M\u00f3dulo 3: B\u00fasqueda de controles geol\u00f3gicos.<\/h4>\n<p>En este m\u00f3dulo aprender\u00e1s a determinar los atributos geol\u00f3gicos (Alteraci\u00f3n, Zona Mineral, Dominio) que tengan alg\u00fan control sobre la variable de inter\u00e9s (Bornita).<\/p>\n<p>T\u00f3picos:<\/p>\n<ul>\n<li>Importaci\u00f3n librer\u00edas: pandas, plotly, probscale, matplotlib.<\/li>\n<li>Estad\u00edsticas b\u00e1sicas (globales).<\/li>\n<li>Estad\u00edsticas b\u00e1sicas por categor\u00eda con groupby.<\/li>\n<li>Gr\u00e1ficos de distribuci\u00f3n acumulada por categor\u00eda (Probplot).<\/li>\n<li>Exportaci\u00f3n de gr\u00e1ficos 2D con matplotlib.<\/li>\n<li>Visualizaci\u00f3n espacial por poblaci\u00f3n de variables.<\/li>\n<\/ul>\n<p>Duraci\u00f3n : 10:55 min.<br \/>\nDuraci\u00f3n Pr\u00e1ctica : 30 min.<br \/>\nComplejidad : Media<\/p>\n<p>[\/et_pb_text][et_pb_toggle admin_label=&#8221;Conmutador&#8221; title=&#8221;Recursos&#8221; open=&#8221;off&#8221; use_border_color=&#8221;off&#8221; border_color=&#8221;#ffffff&#8221; border_style=&#8221;solid&#8221;]<\/p>\n<p><a href=\"https:\/\/bitbucket.org\/geoinnova\/intro-a-data-analytics-en-geociencias-con-python\/src\/master\/Mod3\/\" target=\"_blank\" rel=\"noopener\">Repositorio<\/a><\/p>\n<p>[\/et_pb_toggle][\/et_pb_column][\/et_pb_row][et_pb_row admin_label=&#8221;Fila&#8221; make_fullwidth=&#8221;on&#8221; use_custom_width=&#8221;off&#8221; width_unit=&#8221;on&#8221; use_custom_gutter=&#8221;off&#8221; padding_mobile=&#8221;off&#8221; allow_player_pause=&#8221;off&#8221; parallax=&#8221;off&#8221; parallax_method=&#8221;off&#8221; make_equal=&#8221;on&#8221; parallax_1=&#8221;off&#8221; parallax_method_1=&#8221;off&#8221; parallax_2=&#8221;off&#8221; parallax_method_2=&#8221;off&#8221; column_padding_mobile=&#8221;on&#8221; custom_css_main_element=&#8221;display: flex;||justify-content: center;||align-items: center;&#8221; custom_css_main_2=&#8221;border-left: 6px solid #00755E;||padding-left: 20px !important;&#8221;][et_pb_column type=&#8221;1_3&#8243;][et_pb_video admin_label=&#8221;V\u00eddeo&#8221; src=&#8221;https:\/\/youtu.be\/tt58pe9yh3U&#8221;]<\/p>\n<p>&nbsp;<\/p>\n<p>[\/et_pb_video][\/et_pb_column][et_pb_column type=&#8221;2_3&#8243;][et_pb_text admin_label=&#8221;Modulo4_IntroDAGS_Python&#8221; background_layout=&#8221;light&#8221; text_orientation=&#8221;left&#8221; use_border_color=&#8221;off&#8221; border_color=&#8221;#ffffff&#8221; border_style=&#8221;solid&#8221;]<\/p>\n<h4>M\u00f3dulo 4: Definici\u00f3n de unidad de estimaci\u00f3n<\/h4>\n<p>En este m\u00f3dulo aprender\u00e1s a crear una unidad de estimaci\u00f3n, a trav\u00e9s de la definici\u00f3n de una nueva variable (columna) en nuestro DataFrame (pandas). Posteriormente, mediante operaciones l\u00f3gicas, podr\u00e1s crear nuevos c\u00f3digos geol\u00f3gicos sujeto a las variables: alteraci\u00f3n, zona mineral y dominio.<\/p>\n<p>T\u00f3picos:<\/p>\n<ul>\n<li>Importaci\u00f3n librer\u00edas: pandas, plotly, probscale, matplotlib.<\/li>\n<li>Creaci\u00f3n de nuevas columnas en DataFrame.<\/li>\n<li>Iteraci\u00f3n sobre DataFrame mediante iterrows().<\/li>\n<li>Modificaci\u00f3n de valores en el DataFrame mediante loc[].<\/li>\n<li>Visualizaci\u00f3n espacial.<\/li>\n<\/ul>\n<p>Duraci\u00f3n : 08:56 min.<br \/>\nDuraci\u00f3n Pr\u00e1ctica : 45 min.<br \/>\nComplejidad : Alta<\/p>\n<p>[\/et_pb_text][et_pb_toggle admin_label=&#8221;Conmutador&#8221; title=&#8221;Recursos&#8221; open=&#8221;off&#8221; use_border_color=&#8221;off&#8221; border_color=&#8221;#ffffff&#8221; border_style=&#8221;solid&#8221;]<\/p>\n<p><a href=\"https:\/\/bitbucket.org\/geoinnova\/intro-a-data-analytics-en-geociencias-con-python\/src\/master\/Mod4\/\" target=\"_blank\" rel=\"noopener\">Repositorio<\/a><\/p>\n<p>[\/et_pb_toggle][\/et_pb_column][\/et_pb_row][et_pb_row admin_label=&#8221;Fila&#8221; make_fullwidth=&#8221;on&#8221; use_custom_width=&#8221;off&#8221; width_unit=&#8221;on&#8221; use_custom_gutter=&#8221;off&#8221; padding_mobile=&#8221;off&#8221; allow_player_pause=&#8221;off&#8221; parallax=&#8221;off&#8221; parallax_method=&#8221;off&#8221; make_equal=&#8221;on&#8221; parallax_1=&#8221;off&#8221; parallax_method_1=&#8221;off&#8221; parallax_2=&#8221;off&#8221; parallax_method_2=&#8221;off&#8221; column_padding_mobile=&#8221;on&#8221; custom_css_main_element=&#8221;display: flex;||justify-content: center;||align-items: center;&#8221; custom_css_main_2=&#8221;border-left: 6px solid #00755E;||padding-left: 20px !important;&#8221;][et_pb_column type=&#8221;1_3&#8243;][et_pb_video admin_label=&#8221;V\u00eddeo&#8221; src=&#8221;https:\/\/youtu.be\/2mlMVr4bZWI&#8221;]<\/p>\n<p>&nbsp;<\/p>\n<p>[\/et_pb_video][\/et_pb_column][et_pb_column type=&#8221;2_3&#8243;][et_pb_text admin_label=&#8221;Modulo5_IntroDAGS_Python&#8221; background_layout=&#8221;light&#8221; text_orientation=&#8221;left&#8221; use_border_color=&#8221;off&#8221; border_color=&#8221;#ffffff&#8221; border_style=&#8221;solid&#8221;]<\/p>\n<h4>M\u00f3dulo 5: Validaci\u00f3n de UG<\/h4>\n<p>En este m\u00f3dulo aprender\u00e1s a validar una definici\u00f3n de unidad geol\u00f3gica, a trav\u00e9s de la construcci\u00f3n de un conjunto de gr\u00e1ficos en Python, tales como: Histogramas, Boxplots y finalmente, un Scatter entre la Med\u00eda y Desviaci\u00f3n Est\u00e1ndar<\/p>\n<p>T\u00f3picos:<\/p>\n<ul>\n<li>Importaci\u00f3n librer\u00edas: pandas, plotly, probscale, matplotlib.<\/li>\n<li>Estad\u00edsticas b\u00e1sicas por UG con groupby.<\/li>\n<li>Gr\u00e1ficos de distribuci\u00f3n acumulada por categor\u00eda (Probplot).<\/li>\n<li>Visualizaci\u00f3n espacial.<\/li>\n<li>Histogramas por UG (pandas -> hist).<\/li>\n<li>Boxplot por UG (pandas -> boxplot).<\/li>\n<li>Scatter: Media v\/s STD.<\/li>\n<\/ul>\n<p>Duraci\u00f3n : 09:02 min.<br \/>\nTiempo aprox a emplear : 30 min.<br \/>\nComplejidad : Media<\/p>\n<p>[\/et_pb_text][et_pb_toggle admin_label=&#8221;Conmutador&#8221; title=&#8221;Recursos&#8221; open=&#8221;off&#8221; use_border_color=&#8221;off&#8221; border_color=&#8221;#ffffff&#8221; border_style=&#8221;solid&#8221;]<\/p>\n<p><a href=\"https:\/\/bitbucket.org\/geoinnova\/intro-a-data-analytics-en-geociencias-con-python\/src\/master\/Mod5\/\" target=\"_blank\" rel=\"noopener\">Repositorio<\/a><\/p>\n<p>[\/et_pb_toggle][\/et_pb_column][\/et_pb_row][et_pb_row admin_label=&#8221;Fila&#8221; make_fullwidth=&#8221;on&#8221; use_custom_width=&#8221;off&#8221; width_unit=&#8221;on&#8221; use_custom_gutter=&#8221;off&#8221; padding_mobile=&#8221;off&#8221; allow_player_pause=&#8221;off&#8221; parallax=&#8221;off&#8221; parallax_method=&#8221;off&#8221; make_equal=&#8221;on&#8221; parallax_1=&#8221;off&#8221; parallax_method_1=&#8221;off&#8221; parallax_2=&#8221;off&#8221; parallax_method_2=&#8221;off&#8221; column_padding_mobile=&#8221;on&#8221; custom_css_main_element=&#8221;display: flex;||justify-content: center;||align-items: center;&#8221; custom_css_main_2=&#8221;border-left: 6px solid #00755E;||padding-left: 20px !important;&#8221;][et_pb_column type=&#8221;1_3&#8243;][et_pb_video admin_label=&#8221;V\u00eddeo&#8221; src=&#8221;https:\/\/youtu.be\/eET3jU-S9GA&#8221;]<\/p>\n<p>&nbsp;<\/p>\n<p>[\/et_pb_video][\/et_pb_column][et_pb_column type=&#8221;2_3&#8243;][et_pb_text admin_label=&#8221;Modulo6_IntroDAGS_Python&#8221; background_layout=&#8221;light&#8221; text_orientation=&#8221;left&#8221; use_border_color=&#8221;off&#8221; border_color=&#8221;#ffffff&#8221; border_style=&#8221;solid&#8221;]<\/p>\n<h4>M\u00f3dulo 6: An\u00e1lisis Multivariable.<\/h4>\n<p>En este m\u00f3dulo aprender\u00e1s a realizar un an\u00e1lisis multivariable sobre las variables contin\u00faas, a trav\u00e9s de la construcci\u00f3n de un conjunto de gr\u00e1ficos en Python, tales como: Matriz de correlaci\u00f3n, Scatter y Media Condicional (o derivas).<\/p>\n<p>T\u00f3picos:<\/p>\n<ul>\n<li>Importaci\u00f3n librer\u00edas: pandas, seaborn, plotly, probscale, matplotlib.<\/li>\n<li>Matriz de correlaci\u00f3n a trav\u00e9s del m\u00e9todo corr (Pandas &#8211; DataFrame).<\/li>\n<li>HeatMap de la Matriz correlaci\u00f3n (seaborn).<\/li>\n<li>Scatter entre variables continuas agrupadas por la unidad geol\u00f3gica.<\/li>\n<li>Media Condicional (o Derivas) en la coordenada East.<\/li>\n<li>Visualizaci\u00f3n espacial.<\/li>\n<li>Gr\u00e1ficos de distribuci\u00f3n acumulada por categor\u00eda (Probplot).<\/li>\n<\/ul>\n<p>Duraci\u00f3n : 08:27 min.<br \/>\nTiempo aprox a emplear : 45 min.<br \/>\nComplejidad : Alta<\/p>\n<p>[\/et_pb_text][et_pb_toggle admin_label=&#8221;Conmutador&#8221; title=&#8221;Recursos&#8221; open=&#8221;off&#8221; use_border_color=&#8221;off&#8221; border_color=&#8221;#ffffff&#8221; border_style=&#8221;solid&#8221;]<\/p>\n<p><a href=\"https:\/\/bitbucket.org\/geoinnova\/intro-a-data-analytics-en-geociencias-con-python\/src\/master\/Mod6\/\" target=\"_blank\" rel=\"noopener\">Repositorio<\/a><\/p>\n<p>[\/et_pb_toggle][\/et_pb_column][\/et_pb_row][et_pb_row admin_label=&#8221;Fila&#8221; make_fullwidth=&#8221;on&#8221; use_custom_width=&#8221;off&#8221; width_unit=&#8221;on&#8221; use_custom_gutter=&#8221;off&#8221; padding_mobile=&#8221;off&#8221; allow_player_pause=&#8221;off&#8221; parallax=&#8221;off&#8221; parallax_method=&#8221;off&#8221; make_equal=&#8221;on&#8221; parallax_1=&#8221;off&#8221; parallax_method_1=&#8221;off&#8221; parallax_2=&#8221;off&#8221; parallax_method_2=&#8221;off&#8221; column_padding_mobile=&#8221;on&#8221; custom_css_main_element=&#8221;display: flex;||justify-content: center;||align-items: center;&#8221; custom_css_main_2=&#8221;border-left: 6px solid #00755E;||padding-left: 20px !important;&#8221;][et_pb_column type=&#8221;1_3&#8243;][et_pb_video admin_label=&#8221;V\u00eddeo&#8221; src=&#8221;https:\/\/youtu.be\/wRtwZevenHY&#8221;]<\/p>\n<p>&nbsp;<\/p>\n<p>[\/et_pb_video][\/et_pb_column][et_pb_column type=&#8221;2_3&#8243;][et_pb_text admin_label=&#8221;Modulo7_IntroDAGS_Python&#8221; background_layout=&#8221;light&#8221; text_orientation=&#8221;left&#8221; use_border_color=&#8221;off&#8221; border_color=&#8221;#ffffff&#8221; border_style=&#8221;solid&#8221;]<\/p>\n<h4>M\u00f3dulo 7: Exportaci\u00f3n y generaci\u00f3n de reporte.<\/h4>\n<p>En este m\u00f3dulo haremos un \u00e9nfasis a los m\u00e9todos en Pandas y Matplotlib que nos permiten exportar nuestros resultados.<\/p>\n<p>T\u00f3picos:<\/p>\n<ul>\n<li>Exportaci\u00f3n DataFrame a archivo: to_csv.<\/li>\n<li>Exportaci\u00f3n Gr\u00e1ficos a imagen (png o jpg): savefig.<\/li>\n<\/ul>\n<p>Duraci\u00f3n : 04:05 min.<br \/>\nTiempo aprox a emplear : 15 min.<br \/>\nComplejidad : baja<\/p>\n<p>[\/et_pb_text][et_pb_toggle admin_label=&#8221;Conmutador&#8221; title=&#8221;Recursos&#8221; open=&#8221;off&#8221; use_border_color=&#8221;off&#8221; border_color=&#8221;#ffffff&#8221; border_style=&#8221;solid&#8221;]<\/p>\n<p><a href=\"https:\/\/bitbucket.org\/geoinnova\/intro-a-data-analytics-en-geociencias-con-python\/src\/master\/Mod7\/\" target=\"_blank\" rel=\"noopener\">Repositorio<\/a><\/p>\n<p>[\/et_pb_toggle][\/et_pb_column][\/et_pb_row][\/et_pb_section]<\/p>\n","protected":false},"excerpt":{"rendered":"<p>[et_pb_section admin_label=&#8221;Secci\u00f3n&#8221; fullwidth=&#8221;off&#8221; specialty=&#8221;off&#8221; transparent_background=&#8221;off&#8221; background_color=&#8221;#ffffff&#8221; allow_player_pause=&#8221;off&#8221; inner_shadow=&#8221;off&#8221; parallax=&#8221;off&#8221; parallax_method=&#8221;off&#8221; padding_mobile=&#8221;off&#8221; make_fullwidth=&#8221;off&#8221; use_custom_width=&#8221;off&#8221; width_unit=&#8221;on&#8221; make_equal=&#8221;off&#8221; use_custom_gutter=&#8221;off&#8221; custom_css_before=&#8221;.tooltip {|| position: relative;|| [&hellip;]<\/p>\n","protected":false},"author":196,"featured_media":0,"parent":5173,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"footnotes":""},"class_list":["post-5203","page","type-page","status-publish","hentry"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.4 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Intro Data Analytics en Geociencias con Python - GeoInnova<\/title>\n<meta name=\"description\" content=\"GeoInnova es una empresa consultora de ingenier\u00eda y geociencias aplicadas a la miner\u00eda. 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