pandas :如何将多个数据帧作为HTML表格引用和打印 [英] Pandas: How to reference and print multiple dataframes as HTML tables
问题描述
我正在尝试从 groupby
拆分单个数据帧,将它们打印为pandas HTML表格。我需要将它们作为表单独引用和呈现,以便我可以截取它们进行演示。
I'm trying split out individual dataframes from a groupby
to print them as pandas HTML tables. I need to reference and render them individually as tables so I can screenshot them for a presentation.
这是我当前的代码:
import pandas as pd
df = pd.DataFrame(
{'area': [5, 42, 20, 20, 43, 78, 89, 30, 46, 78],
'cost': [52300, 52000, 25000, 61600, 43000, 23400, 52300, 62000, 62000, 73000],
'grade': [1, 3, 2, 1, 2, 2, 2, 4, 1, 2], 'size': [1045, 957, 1099, 1400, 1592, 1006, 987, 849, 973, 1005],
'team': ['man utd', 'chelsea', 'arsenal', 'man utd', 'man utd', 'arsenal', 'man utd', 'chelsea', 'arsenal', 'arsenal']})
result = df.groupby(['team', 'grade']).agg({'cost':'mean', 'area':'mean', 'size':'sum'}).rename(columns={'cost':'mean_cost', 'area':'mean_area'})
dfs = {team:grp.drop('team', axis=1)
for team, grp in result.reset_index().groupby('team')}
for team, grp in dfs.items():
print('{}:\n{}\n'.format(team, gap))
哪些打印(作为非HTML表格):
Which prints (as non HTML tables):
chelsea:
grade mean_cost mean_area size
2 3 52000 42 957
3 4 62000 30 849
arsenal:
grade mean_cost mean_area size
0 1 62000.000000 46.000000 973
1 2 40466.666667 58.666667 3110
man utd:
grade mean_cost mean_area size
4 1 56950 12.5 2445
5 2 47650 66.0 2579
是否可以获得这些数据帧一个接一个地作为HTML表?为避免疑问,我不需要迭代方法将它们全部作为HTML表格返回 - 我很乐意单独引用每个表格。
Is it possible to get these dataframes one by one as HTML tables? For the avoidance of doubt, I don't need an iterative method to return them all as HTML tables in one go - am happy to reference each one individually.
推荐答案
作为 Thomas K指出,你可以使用 IPython.core.display.display
在IPython笔记本中包含DataFrames和print语句的显示:
As Thomas K points out, you could use IPython.core.display.display
to incorporate the display of DataFrames along with print statements in an IPython notebook:
import pandas as pd
from IPython.core import display as ICD
df = pd.DataFrame(
{'area': [5, 42, 20, 20, 43, 78, 89, 30, 46, 78],
'cost': [52300, 52000, 25000, 61600, 43000, 23400, 52300, 62000, 62000, 73000],
'grade': [1, 3, 2, 1, 2, 2, 2, 4, 1, 2], 'size': [1045, 957, 1099, 1400, 1592, 1006, 987, 849, 973, 1005],
'team': ['man utd', 'chelsea', 'arsenal', 'man utd', 'man utd', 'arsenal', 'man utd', 'chelsea', 'arsenal', 'arsenal']})
result = df.groupby(['team', 'grade']).agg({'cost':'mean', 'area':'mean', 'size':'sum'}).rename(columns={'cost':'mean_cost', 'area':'mean_area'})
dfs = {team:grp.drop('team', axis=1)
for team, grp in result.reset_index().groupby('team')}
for team, grp in dfs.items():
print(team)
ICD.display(grp)
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