python - without - ¿Cómo guardar los datos del cuadro de datos/series de Pandas como una figura?
pandas to_csv separator (4)
Aquí hay una solución algo hackish pero hace el trabajo bien.
import numpy as np
import pandas as pd
from matplotlib.backends.backend_pdf import PdfPages
import matplotlib.pyplot as plt
from PySide.QtGui import QImage
from PySide.QtGui import QPainter
from PySide.QtCore import QSize
from PySide.QtWebKit import QWebPage
arrays = [np.hstack([ [''one'']*3, [''two'']*3]), [''Dog'', ''Bird'', ''Cat'']*2]
columns = pd.MultiIndex.from_arrays(arrays, names=[''foo'', ''bar''])
df =pd.DataFrame(np.zeros((3,6)),columns=columns,index=pd.date_range(''20000103'',periods=3))
h = "<!DOCTYPE html> <html> <body> <p> " + df.to_html() + " </p> </body> </html>";
page = QWebPage()
page.setViewportSize(QSize(5000,5000))
frame = page.mainFrame()
frame.setHtml(h, "text/html")
img = QImage(1000,700, QImage.Format(5))
painter = QPainter(img)
frame.render(painter)
painter.end()
a = img.save("html.png")
Suena un tanto extraño, pero necesito guardar la cadena de salida de la consola Pandas en png pics. Por ejemplo:
>>> df
sales net_pft ROE ROIC
STK_ID RPT_Date
600809 20120331 22.1401 4.9253 0.1651 0.6656
20120630 38.1565 7.8684 0.2567 1.0385
20120930 52.5098 12.4338 0.3587 1.2867
20121231 64.7876 13.2731 0.3736 1.2205
20130331 27.9517 7.5182 0.1745 0.3723
20130630 40.6460 9.8572 0.2560 0.4290
20130930 53.0501 11.8605 0.2927 0.4369
¿Hay alguna forma como df.output_as_png(filename=''df_data.png'') para generar un archivo de imagen que solo muestre el contenido anterior?
Me interesaba guardar mi dataframe como una tabla para un apéndice para un informe. Encontré esta es la solución más simple:
import pandas as pd
from matplotlib.backends.backend_pdf import PdfPages
import matplotlib.pyplot as plt
# Assuming that you have a dataframe, df
pp = PdfPages(''Appendix_A.pdf'')
total_rows, total_cols = df.shape; #There were 3 columns in my df
rows_per_page = 40; # Assign a page cut off length
rows_printed = 0
page_number = 1;
while (total_rows >0):
#put the table on a correctly sized figure
fig=plt.figure(figsize=(8.5, 11))
plt.gca().axis(''off'')
matplotlib_tab = pd.tools.plotting.table(plt.gca(),df.iloc[rows_printed:rows_printed+rows_per_page],
loc=''upper center'', colWidths=[0.2, 0.2, 0.2])
# Give you cells some styling
table_props=matplotlib_tab.properties()
table_cells=table_props[''child_artists''] # I have no clue why child_artists works
for cell in table_cells:
cell.set_height(0.024)
cell.set_fontsize(12)
# Add a header and footer with page number
fig.text(4.25/8.5, 10.5/11., "Appendix A", ha=''center'', fontsize=12)
fig.text(4.25/8.5, 0.5/11., ''A''+str(page_number), ha=''center'', fontsize=12)
pp.savefig()
plt.close()
#Update variables
rows_printed += rows_per_page;
total_rows -= rows_per_page;
page_number+=1;
pp.close()
Utilizaría la funcionalidad de la tabla matplotlib, con un poco de estilo adicional:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import six
df = pd.DataFrame()
df[''date''] = [''2016-04-01'', ''2016-04-02'', ''2016-04-03'']
df[''calories''] = [2200, 2100, 1500]
df[''sleep hours''] = [2200, 2100, 1500]
df[''gym''] = [True, False, False]
def render_mpl_table(data, col_width=3.0, row_height=0.625, font_size=14,
header_color=''#40466e'', row_colors=[''#f1f1f2'', ''w''], edge_color=''w'',
bbox=[0, 0, 1, 1], header_columns=0,
ax=None, **kwargs):
if ax is None:
size = (np.array(data.shape[::-1]) + np.array([0, 1])) * np.array([col_width, row_height])
fig, ax = plt.subplots(figsize=size)
ax.axis(''off'')
mpl_table = ax.table(cellText=data.values, bbox=bbox, colLabels=data.columns, **kwargs)
mpl_table.auto_set_font_size(False)
mpl_table.set_fontsize(font_size)
for k, cell in six.iteritems(mpl_table._cells):
cell.set_edgecolor(edge_color)
if k[0] == 0 or k[1] < header_columns:
cell.set_text_props(weight=''bold'', color=''w'')
cell.set_facecolor(header_color)
else:
cell.set_facecolor(row_colors[k[0]%len(row_colors) ])
return ax
render_mpl_table(df, header_columns=0, col_width=2.0)
DataFrame.plot() usar la figura devuelta por el DataFrame.plot() :
ax = df.plot()
fig = ax.get_figure()
fig.savefig(''asdf.png'')