首发公众号：pythonic生物人
平日看到的pandas.DataFrame数据是这个样子的~
平淡无奇，索然无味，读了本文后，可以这样子了~
这样子~
或者这样子~
目录
2 - apply着色符合条件的column-/row-/table-wise
4 - style.background_gradient设置背景填充色
以上个性化的设置主要用到pd.DataFrame.style的如下属性：
['apply', 'applymap', 'background_gradient', 'bar', 'caption', 'cell_ids', 'clear', 'columns', 'ctx', 'data', 'env', 'export', 'format', 'from_custom_template', 'hidden_columns', 'hidden_index', 'hide_columns', 'hide_index', 'highlight_max', 'highlight_min', 'highlight_null', 'index', 'loader', 'na_rep', 'pipe', 'precision', 'render', 'set_caption', 'set_na_rep', 'set_precision', 'set_properties', 'set_table_attributes', 'set_table_styles', 'set_uuid', 'table_attributes', 'table_styles', 'template', 'to_excel', 'use', 'uuid', 'where']
本文介绍部分~
1 - style.applymap着色符合条件的每个元素
-
import pandas
as pd
-
import numpy
as np
-
-
np.random.seed(
24)
-
df = pd.DataFrame({
'A': np.linspace(
1,
10,
10)})
-
df = pd.concat(
-
[df, pd.DataFrame(np.random.randn(
10,
4), columns=list(
'BCDE'))], axis=
1)
-
-
# 添加缺省值
-
df.iloc[
3,
3] = np.nan
-
df.iloc[
0,
2] = np.nan
-
-
# style.applymap着色符合条件的每个元素
-
def color_negative_red(val):
-
"""
-
小于0的元素上红色、反之上蓝色
-
"""
-
color =
'#c72e29'
if val <
0
else
'#01a2d9'
-
return
'color: %s' % color
-
-
s = df.style.applymap(color_negative_red)
-
s
2 - apply着色符合条件的column-/row-/table-wise
-
def highlight_max(s):
-
'''
-
对DataFrame的Seris中最大值上绿色
-
'''
-
is_max = s == s.max()
-
return [
'background-color: #74C476'
if v
else
''
for v
in is_max]
-
-
df.style.apply(highlight_max)
3 - style.applymap、apply联合使用
-
#.号连接即可
-
df.style.\
-
applymap(color_negative_red).\
-
apply(highlight_max)
4 - style.background_gradient设置背景填充色
-
import seaborn
as sns
-
-
s = df.style.background_gradient(cmap=
'Set2_r')
-
s
5 - style.bar绘制Series柱状图
df.style.bar(subset=['A', 'B'], align='mid', color=['#dc2624', '#649E7D'])
-
import pandas
as pd
-
from IPython.display
import HTML
-
-
# Test series
-
test1 = pd.Series([
-100,
-60,
-30,
-20], name=
'All Negative')
-
test2 = pd.Series([
10,
20,
50,
100], name=
'All Positive')
-
test3 = pd.Series([
-10,
-5,
0,
90], name=
'Both Pos and Neg')
-
-
head =
"""
-
<table>
-
<thead>
-
<th>Align</th>
-
<th>All Negative</th>
-
<th>All Positive</th>
-
<th>Both Neg and Pos</th>
-
</thead>
-
</tbody>
-
-
"""
-
-
aligns = [
'left',
'zero',
'mid']
-
for align
in aligns:
-
row =
"<tr><th>{}</th>".format(align)
-
for series
in [test1, test2, test3]:
-
s = series.copy()
-
s.name =
''
-
row +=
"<td>{}</td>".format(
-
s.to_frame().style.bar(align=align,
-
color=[
'#dc2624',
'#649E7D'],
-
width=
100).render())
#testn['width']
-
row +=
'</tr>'
-
head += row
-
-
head +=
"""
-
</tbody>
-
</table>"""
-
-
HTML(head)
6 - to_excel导出个性化结果到excel中
-
df.style.\
-
applymap(color_negative_red).\
-
apply(highlight_max).\
-
to_excel(
'styled.xlsx', engine=
'openpyxl')
7 - 更多设置
https://pandas.pydata.org/pandas-docs/stable/user_guide/style.html
首发公众号：pythonic生物人
转载：https://blog.csdn.net/qq_21478261/article/details/115773807
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