为什么 Excel 样式在 Pandas 中不起作用？
Why does Excel styling not work in Pandas?
import pandas as pd
import xlsxwriter
from datetime import datetime
import sys
path = sys.argv[1]
xl = pd.ExcelFile(path)
df = xl.parse("Sheet1")
df.columns = ['Nume', 'Tip de', 'Unit', 'Speciale Price', 'Suma de', 'Suma']
def highlight_max(x):
return ['background-color: yellow' if v == x.max() else ''
for v in x]
df.style.apply(highlight_max)
df.loc[-1] = ['Totul', '', '', '', df['Suma de'].sum(), df['Suma'].sum()]
我尝试将 highlight_max 应用于列
如果您的目标是获得带有样式单元格的 Excel sheet，那么您仍然需要 explicitly export the dataframe to an Excel file.
尝试将 df.style.apply(highlight_max) 替换为 df.style.apply(highlight_max).to_excel('styled.xlsx')。
所以我假设您想以 python 设计的方式将您的样式更改应用到您的数据框，并在您在控制台中打印数据框时显示颜色修改。问题是（我假设）您没有使用 IDE Jupyter Notebook（或任何其他基于网络的 IDE），它旨在被利用。请参阅下面的比较。
来源：Pandas Styling Documentation
样式对象在 html 中呈现，其中 Jupyter Notebook 等基于网络的 IDE 可以读入并显示修改。
这是 运行 从 Spyder IDE 文档中提取的简化。注意输出是一个样式对象。呈现后，您可以在第二个输出中看到 Spyder IDE 无法解释的 html/css。
import pandas as pd
data1 = {'Nume': [1,-2,-3],
'Tip de':[4,-5,-6],
'Unit':[-7,-8,9],
'Speciale Price': [10,11,12],
'Suma de': [13,14,15],
'Suma':[16,17,18]}
df1 = pd.DataFrame(data1)
print(df1)
def color_negative_red(val):
"""
Takes a scalar and returns a string with
the css property `'color: red'` for negative
strings, black otherwise.
"""
color = 'red' if val < 0 else 'black'
return 'color: %s' % color
s = df1.style.applymap(color_negative_red)
s
Output[] = <pandas.io.formats.style.Styler at 0x18aaa2a6c50>
s.render()
Output[] = '<style type="text/css" >\n #T_db7ac00c_6244_11e9_887c_74d4355eed37row0_col0 {\n color: black;\n color: black;\n } #T_db7ac00c_6244_11e9_887c_74d4355eed37row0_col1 {\n color: black;\n color: black;\n } #T_db7ac00c_6244_11e9_887c_74d4355eed37row0_col2 {\n color: black;\n color: black;\n } #T_db7ac00c_6244_11e9_887c_74d4355eed37row0_col3 {\n color: black;\n color: black;\n } #T_db7ac00c_6244_11e9_887c_74d4355eed37row0_col4 {\n color: black;\n color: black;\n } #T_db7ac00c_6244_11e9_887c_74d4355eed37row0_col5 {\n color: red;\n color: red;\n } #T_db7ac00c_6244_11e9_887c_74d4355eed37row1_col0 {\n color: red;\n color: red;\n } #T_db7ac00c_6244_11e9_887c_74d4355eed37row1_col1 {\n color: black;\n color: black;\n } #T_db7ac00c_6244_11e9_887c_74d4355eed37row1_col2 {\n color: black;\n color: black;\n } #T_db7ac00c_6244_11e9_887c_74d4355eed37row1_col3 {\n color: black;\n color: black;\n } #T_db7ac00c_6244_11e9_887c_74d4355eed37row1_col4 {\n color: red;\n color: red;\n } #T_db7ac00c_6244_11e9_887c_74d4355eed37row1_col5 {\n color: red;\n color: red;\n } #T_db7ac00c_6244_11e9_887c_74d4355eed37row2_col0 {\n color: red;\n color: red;\n } #T_db7ac00c_6244_11e9_887c_74d4355eed37row2_col1 {\n color: black;\n color: black;\n } #T_db7ac00c_6244_11e9_887c_74d4355eed37row2_col2 {\n color: black;\n color: black;\n } #T_db7ac00c_6244_11e9_887c_74d4355eed37row2_col3 {\n color: black;\n color: black;\n } #T_db7ac00c_6244_11e9_887c_74d4355eed37row2_col4 {\n color: red;\n color: red;\n } #T_db7ac00c_6244_11e9_887c_74d4355eed37row2_col5 {\n color: black;\n color: black;\n }</style> \n<table id="T_db7ac00c_6244_11e9_887c_74d4355eed37" > \n<thead> <tr> \n <th class="blank level0" ></th> \n <th class="col_heading level0 col0" >Nume</th> \n <th class="col_heading level0 col1" >Speciale Price</th> \n <th class="col_heading level0 col2" >Suma</th> \n <th class="col_heading level0 col3" >Suma de</th> \n <th class="col_heading level0 col4" >Tip de</th> \n <th class="col_heading level0 col5" >Unit</th> \n </tr></thead> \n<tbody> <tr> \n <th id="T_db7ac00c_6244_11e9_887c_74d4355eed37level0_row0" class="row_heading level0 row0" >0</th> \n <td id="T_db7ac00c_6244_11e9_887c_74d4355eed37row0_col0" class="data row0 col0" >1</td> \n <td id="T_db7ac00c_6244_11e9_887c_74d4355eed37row0_col1" class="data row0 col1" >10</td> \n <td id="T_db7ac00c_6244_11e9_887c_74d4355eed37row0_col2" class="data row0 col2" >16</td> \n <td id="T_db7ac00c_6244_11e9_887c_74d4355eed37row0_col3" class="data row0 col3" >13</td> \n <td id="T_db7ac00c_6244_11e9_887c_74d4355eed37row0_col4" class="data row0 col4" >4</td> \n <td id="T_db7ac00c_6244_11e9_887c_74d4355eed37row0_col5" class="data row0 col5" >-7</td> \n </tr> <tr> \n <th id="T_db7ac00c_6244_11e9_887c_74d4355eed37level0_row1" class="row_heading level0 row1" >1</th> \n <td id="T_db7ac00c_6244_11e9_887c_74d4355eed37row1_col0" class="data row1 col0" >-2</td> \n <td id="T_db7ac00c_6244_11e9_887c_74d4355eed37row1_col1" class="data row1 col1" >11</td> \n <td id="T_db7ac00c_6244_11e9_887c_74d4355eed37row1_col2" class="data row1 col2" >17</td> \n <td id="T_db7ac00c_6244_11e9_887c_74d4355eed37row1_col3" class="data row1 col3" >14</td> \n <td id="T_db7ac00c_6244_11e9_887c_74d4355eed37row1_col4" class="data row1 col4" >-5</td> \n <td id="T_db7ac00c_6244_11e9_887c_74d4355eed37row1_col5" class="data row1 col5" >-8</td> \n </tr> <tr> \n <th id="T_db7ac00c_6244_11e9_887c_74d4355eed37level0_row2" class="row_heading level0 row2" >2</th> \n <td id="T_db7ac00c_6244_11e9_887c_74d4355eed37row2_col0" class="data row2 col0" >-3</td> \n <td id="T_db7ac00c_6244_11e9_887c_74d4355eed37row2_col1" class="data row2 col1" >12</td> \n <td id="T_db7ac00c_6244_11e9_887c_74d4355eed37row2_col2" class="data row2 col2" >18</td> \n <td id="T_db7ac00c_6244_11e9_887c_74d4355eed37row2_col3" class="data row2 col3" >15</td> \n <td id="T_db7ac00c_6244_11e9_887c_74d4355eed37row2_col4" class="data row2 col4" >-6</td> \n <td id="T_db7ac00c_6244_11e9_887c_74d4355eed37row2_col5" class="data row2 col5" >9</td> \n </tr></tbody> \n</table> '
第二个示例是 运行 基于网络的 IDE Jupyter Notebooks 上完全相同的简化。请注意 s 的结果输出显示了所需的修改后的样式。
import pandas as pd
import xlsxwriter
from datetime import datetime
import sys
path = sys.argv[1]
xl = pd.ExcelFile(path)
df = xl.parse("Sheet1")
df.columns = ['Nume', 'Tip de', 'Unit', 'Speciale Price', 'Suma de', 'Suma']
def highlight_max(x):
return ['background-color: yellow' if v == x.max() else ''
for v in x]
df.style.apply(highlight_max)
df.loc[-1] = ['Totul', '', '', '', df['Suma de'].sum(), df['Suma'].sum()]
我尝试将 highlight_max 应用于列
如果您的目标是获得带有样式单元格的 Excel sheet，那么您仍然需要 explicitly export the dataframe to an Excel file.
尝试将 df.style.apply(highlight_max) 替换为 df.style.apply(highlight_max).to_excel('styled.xlsx')。
所以我假设您想以 python 设计的方式将您的样式更改应用到您的数据框，并在您在控制台中打印数据框时显示颜色修改。问题是（我假设）您没有使用 IDE Jupyter Notebook（或任何其他基于网络的 IDE），它旨在被利用。请参阅下面的比较。
来源：Pandas Styling Documentation
样式对象在 html 中呈现，其中 Jupyter Notebook 等基于网络的 IDE 可以读入并显示修改。
这是 运行 从 Spyder IDE 文档中提取的简化。注意输出是一个样式对象。呈现后，您可以在第二个输出中看到 Spyder IDE 无法解释的 html/css。
import pandas as pd
data1 = {'Nume': [1,-2,-3],
'Tip de':[4,-5,-6],
'Unit':[-7,-8,9],
'Speciale Price': [10,11,12],
'Suma de': [13,14,15],
'Suma':[16,17,18]}
df1 = pd.DataFrame(data1)
print(df1)
def color_negative_red(val):
"""
Takes a scalar and returns a string with
the css property `'color: red'` for negative
strings, black otherwise.
"""
color = 'red' if val < 0 else 'black'
return 'color: %s' % color
s = df1.style.applymap(color_negative_red)
s
Output[] = <pandas.io.formats.style.Styler at 0x18aaa2a6c50>
s.render()
Output[] = '<style type="text/css" >\n #T_db7ac00c_6244_11e9_887c_74d4355eed37row0_col0 {\n color: black;\n color: black;\n } #T_db7ac00c_6244_11e9_887c_74d4355eed37row0_col1 {\n color: black;\n color: black;\n } #T_db7ac00c_6244_11e9_887c_74d4355eed37row0_col2 {\n color: black;\n color: black;\n } #T_db7ac00c_6244_11e9_887c_74d4355eed37row0_col3 {\n color: black;\n color: black;\n } #T_db7ac00c_6244_11e9_887c_74d4355eed37row0_col4 {\n color: black;\n color: black;\n } #T_db7ac00c_6244_11e9_887c_74d4355eed37row0_col5 {\n color: red;\n color: red;\n } #T_db7ac00c_6244_11e9_887c_74d4355eed37row1_col0 {\n color: red;\n color: red;\n } #T_db7ac00c_6244_11e9_887c_74d4355eed37row1_col1 {\n color: black;\n color: black;\n } #T_db7ac00c_6244_11e9_887c_74d4355eed37row1_col2 {\n color: black;\n color: black;\n } #T_db7ac00c_6244_11e9_887c_74d4355eed37row1_col3 {\n color: black;\n color: black;\n } #T_db7ac00c_6244_11e9_887c_74d4355eed37row1_col4 {\n color: red;\n color: red;\n } #T_db7ac00c_6244_11e9_887c_74d4355eed37row1_col5 {\n color: red;\n color: red;\n } #T_db7ac00c_6244_11e9_887c_74d4355eed37row2_col0 {\n color: red;\n color: red;\n } #T_db7ac00c_6244_11e9_887c_74d4355eed37row2_col1 {\n color: black;\n color: black;\n } #T_db7ac00c_6244_11e9_887c_74d4355eed37row2_col2 {\n color: black;\n color: black;\n } #T_db7ac00c_6244_11e9_887c_74d4355eed37row2_col3 {\n color: black;\n color: black;\n } #T_db7ac00c_6244_11e9_887c_74d4355eed37row2_col4 {\n color: red;\n color: red;\n } #T_db7ac00c_6244_11e9_887c_74d4355eed37row2_col5 {\n color: black;\n color: black;\n }</style> \n<table id="T_db7ac00c_6244_11e9_887c_74d4355eed37" > \n<thead> <tr> \n <th class="blank level0" ></th> \n <th class="col_heading level0 col0" >Nume</th> \n <th class="col_heading level0 col1" >Speciale Price</th> \n <th class="col_heading level0 col2" >Suma</th> \n <th class="col_heading level0 col3" >Suma de</th> \n <th class="col_heading level0 col4" >Tip de</th> \n <th class="col_heading level0 col5" >Unit</th> \n </tr></thead> \n<tbody> <tr> \n <th id="T_db7ac00c_6244_11e9_887c_74d4355eed37level0_row0" class="row_heading level0 row0" >0</th> \n <td id="T_db7ac00c_6244_11e9_887c_74d4355eed37row0_col0" class="data row0 col0" >1</td> \n <td id="T_db7ac00c_6244_11e9_887c_74d4355eed37row0_col1" class="data row0 col1" >10</td> \n <td id="T_db7ac00c_6244_11e9_887c_74d4355eed37row0_col2" class="data row0 col2" >16</td> \n <td id="T_db7ac00c_6244_11e9_887c_74d4355eed37row0_col3" class="data row0 col3" >13</td> \n <td id="T_db7ac00c_6244_11e9_887c_74d4355eed37row0_col4" class="data row0 col4" >4</td> \n <td id="T_db7ac00c_6244_11e9_887c_74d4355eed37row0_col5" class="data row0 col5" >-7</td> \n </tr> <tr> \n <th id="T_db7ac00c_6244_11e9_887c_74d4355eed37level0_row1" class="row_heading level0 row1" >1</th> \n <td id="T_db7ac00c_6244_11e9_887c_74d4355eed37row1_col0" class="data row1 col0" >-2</td> \n <td id="T_db7ac00c_6244_11e9_887c_74d4355eed37row1_col1" class="data row1 col1" >11</td> \n <td id="T_db7ac00c_6244_11e9_887c_74d4355eed37row1_col2" class="data row1 col2" >17</td> \n <td id="T_db7ac00c_6244_11e9_887c_74d4355eed37row1_col3" class="data row1 col3" >14</td> \n <td id="T_db7ac00c_6244_11e9_887c_74d4355eed37row1_col4" class="data row1 col4" >-5</td> \n <td id="T_db7ac00c_6244_11e9_887c_74d4355eed37row1_col5" class="data row1 col5" >-8</td> \n </tr> <tr> \n <th id="T_db7ac00c_6244_11e9_887c_74d4355eed37level0_row2" class="row_heading level0 row2" >2</th> \n <td id="T_db7ac00c_6244_11e9_887c_74d4355eed37row2_col0" class="data row2 col0" >-3</td> \n <td id="T_db7ac00c_6244_11e9_887c_74d4355eed37row2_col1" class="data row2 col1" >12</td> \n <td id="T_db7ac00c_6244_11e9_887c_74d4355eed37row2_col2" class="data row2 col2" >18</td> \n <td id="T_db7ac00c_6244_11e9_887c_74d4355eed37row2_col3" class="data row2 col3" >15</td> \n <td id="T_db7ac00c_6244_11e9_887c_74d4355eed37row2_col4" class="data row2 col4" >-6</td> \n <td id="T_db7ac00c_6244_11e9_887c_74d4355eed37row2_col5" class="data row2 col5" >9</td> \n </tr></tbody> \n</table> '
第二个示例是 运行 基于网络的 IDE Jupyter Notebooks 上完全相同的简化。请注意 s 的结果输出显示了所需的修改后的样式。