Pandas: Html Output With Conditional Formatting

I am trying to format a table, such that data in each column are formatted in a style depending on their values (similar to conditional formatting in spreadsheet programs). How can

Solution 1:

You can use the DataFrame to_html method, which comes with formatters argument.

An easier solution would be to surround by <span class="significant"> and </span>, (rather than *). Note: by default this will be escaped (i.e. < becomes <) so you will need to use the escape=False argument.

Solution 2:

Since pandas 0.17.1, it is easy to apply custom formatting to the data frame HTML representation using the styling api.

import pandas as pd

df = pd.DataFrame({
    'correlation':[0.5, 0.1,0.9],
    'p_value':[0.1,0.8,0.01]})
styled_df = df.style.apply(
    lambda x: ['font-weight: bold; background-color: yellow'if value <= 0.01else''for value in x])
styled_df

The output is rendered automatically in interfaces such as the Jupyter Notebook and the string representation of the HTML can be returned with the render() method.

print(styled_df.render())

<styletype="text/css" >#T_4e49b9da_8451_11e8_9166_605718a99a7frow2_col1 {
            font-weight:  bold;
             background-color:  yellow;
            font-weight:  bold;
             background-color:  yellow;
        }</style><tableid="T_4e49b9da_8451_11e8_9166_605718a99a7f" ><thead><tr><thclass="blank level0" ></th><thclass="col_heading level0 col0" >correlation</th><thclass="col_heading level0 col1" >p_value</th></tr></thead><tbody><tr><thid="T_4e49b9da_8451_11e8_9166_605718a99a7flevel0_row0"class="row_heading level0 row0" >0</th><tdid="T_4e49b9da_8451_11e8_9166_605718a99a7frow0_col0"class="data row0 col0" >0.5</td><tdid="T_4e49b9da_8451_11e8_9166_605718a99a7frow0_col1"class="data row0 col1" >0.1</td></tr><tr><thid="T_4e49b9da_8451_11e8_9166_605718a99a7flevel0_row1"class="row_heading level0 row1" >1</th><tdid="T_4e49b9da_8451_11e8_9166_605718a99a7frow1_col0"class="data row1 col0" >0.1</td><tdid="T_4e49b9da_8451_11e8_9166_605718a99a7frow1_col1"class="data row1 col1" >0.8</td></tr><tr><thid="T_4e49b9da_8451_11e8_9166_605718a99a7flevel0_row2"class="row_heading level0 row2" >2</th><tdid="T_4e49b9da_8451_11e8_9166_605718a99a7frow2_col0"class="data row2 col0" >0.9</td><tdid="T_4e49b9da_8451_11e8_9166_605718a99a7frow2_col1"class="data row2 col1" >0.01</td></tr></tbody></table>