{"id":2592,"date":"2025-10-25T19:07:33","date_gmt":"2025-10-25T17:07:33","guid":{"rendered":"https:\/\/bioinfo2.ugr.es\/biocomputacion\/?page_id=2592"},"modified":"2025-10-25T19:28:44","modified_gmt":"2025-10-25T17:28:44","slug":"graficos","status":"publish","type":"page","link":"https:\/\/bioinfo2.ugr.es\/biocomputacion\/graficos\/","title":{"rendered":"Gr\u00e1ficos"},"content":{"rendered":"

Los DataFrames juegan un papel importante a la hora de generar gr\u00e1ficos. Tienen un m\u00e9todo plot() para generar gr\u00e1ficos directamente<\/p>\n

df.plot(<\/span>kind=<\/span> argument<\/span>)<\/span><\/p>\n

Tipos<\/span><\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n
Kind<\/b><\/td>\nFunction<\/b><\/td>\nTypical Use<\/b><\/td>\n<\/tr>\n
‘line’<\/span><\/td>\ndf.plot.line()<\/span><\/td>\nTrends, time series<\/span><\/td>\n<\/tr>\n
‘bar’<\/span><\/td>\ndf.plot.bar()<\/span><\/td>\nCategorical comparisons<\/span><\/td>\n<\/tr>\n
‘barh’<\/span><\/td>\ndf.plot.barh()<\/span><\/td>\nHorizontal bar chart<\/span><\/td>\n<\/tr>\n
‘hist’<\/span><\/td>\ndf.plot.hist()<\/span><\/td>\nDistribution of values<\/span><\/td>\n<\/tr>\n
‘box’<\/span><\/td>\ndf.plot.box()<\/span><\/td>\nStatistical summary (min, Q1, median, etc.)<\/span><\/td>\n<\/tr>\n
‘kde’<\/span> \/ <\/span>‘density’<\/span><\/td>\ndf.plot.kde()<\/span><\/td>\nSmoothed density curve<\/span><\/td>\n<\/tr>\n
‘area’<\/span><\/td>\ndf.plot.area()<\/span><\/td>\nCumulative values or composition<\/span><\/td>\n<\/tr>\n
‘pie’<\/span><\/td>\ndf.plot.pie()<\/span><\/td>\nProportions of categories<\/span><\/td>\n<\/tr>\n
‘scatter’<\/span><\/td>\ndf.plot.scatter(x=’col1′, y=’col2′)<\/span><\/td>\nRelationship between two variables<\/span><\/td>\n<\/tr>\n
‘hexbin’<\/span><\/td>\ndf.plot.hexbin(x=’col1′, y=’col2′, gridsize=25)<\/span><\/td>\nDensity of 2D points<\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

 <\/p>\n

Visualizar las frecuencias de codones:<\/h4>\n

Gr\u00e1fico de baras:<\/p>\n

import<\/span> pandas as pd<\/span>\r\nimport<\/span> matplotlib.pyplot <\/span>as<\/span> plt<\/span>\r\n\r\ninfile='\/opt\/datos\/plotting_25\/thermophilus_codon.tsv'\r\ndf = pd.read_csv(infile,sep='\\t') # df es un DataFrame\r\ndf.plot(kind='bar',x='Codon',y='Frequency',xlabel='Codon',ylabel='Frequency (per 1k)',title=\"Codon Frequency of Thermus thermophilus \")\r\nplt.show()\r\n\r\n<\/pre>\n
Para guardar el gr\u00e1fico\r\ndf.plot()<\/span> \r\nax = df.plot()<\/span>\r\nplt.savefig(\"plot1.png\")<\/span>\r\nplt.close()<\/span><\/pre>\n
 <\/p>\n
\u00bfComo podemos graficar la frecuencia para cada amino\u00e1cido?<\/h4>\n
Soluci\u00f3n: los m\u00e9todos groupby() y agg()<\/p>\n
\n
<\/div>\n
grouped_df = df.groupby('AA',as_index=False).agg(Frequency=('Frequency',\"sum\"))\r\n\r\nprint(grouped_df.head())\r\n\r\ngrouped_df['sample']='thermo'<\/pre>\n<\/div>\n
Usar plotly<\/h4>\n
\n
\n
\n
import plotly.express as px<\/pre>\n<\/div>\n
fig = px.bar(grouped_df,x='AA',y='Frequency')\r\nfig.show()\r\nexit()<\/pre>\n<\/div>\n<\/div>\n
 <\/p>\n
Comparar datos<\/h4>\n
Tarea1:<\/h5>\n
Tenemos otra salida del cusp: \/opt\/datos\/plotting_25\/NC_007633_codon.tsv<\/p>\n
Generar un gr\u00e1fico comparando las frecuencias de los dos ficheros<\/p>\n","protected":false},"excerpt":{"rendered":"
Los DataFrames juegan un papel importante a la hora de generar gr\u00e1ficos. Tienen un m\u00e9todo plot() para generar gr\u00e1ficos directamente df.plot(kind= argument) Tipos Kind Function Typical Use ‘line’ df.plot.line() Trends, time series ‘bar’ df.plot.bar() Categorical comparisons ‘barh’ df.plot.barh() Horizontal bar Read More …<\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":[],"_links":{"self":[{"href":"https:\/\/bioinfo2.ugr.es\/biocomputacion\/wp-json\/wp\/v2\/pages\/2592"}],"collection":[{"href":"https:\/\/bioinfo2.ugr.es\/biocomputacion\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/bioinfo2.ugr.es\/biocomputacion\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/bioinfo2.ugr.es\/biocomputacion\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/bioinfo2.ugr.es\/biocomputacion\/wp-json\/wp\/v2\/comments?post=2592"}],"version-history":[{"count":7,"href":"https:\/\/bioinfo2.ugr.es\/biocomputacion\/wp-json\/wp\/v2\/pages\/2592\/revisions"}],"predecessor-version":[{"id":2599,"href":"https:\/\/bioinfo2.ugr.es\/biocomputacion\/wp-json\/wp\/v2\/pages\/2592\/revisions\/2599"}],"wp:attachment":[{"href":"https:\/\/bioinfo2.ugr.es\/biocomputacion\/wp-json\/wp\/v2\/media?parent=2592"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}